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One-Time Buyers: The Biggest Retention Problem in Retail Commerce

Executive Summary

While most brands actively invest in Customer Retention Strategies, the One-time buyers represent the largest overlooked opportunity for retail commerce marketers today. It has been that way as long as there have been retail sales.

Rather than focusing on the Trial Buyers (one-time purchasers), many organizations remain focused on acquisition as the avenue for growth.

Yet the stark reality is that the majority of those acquisitions lead to just a single purchase with no subsequent value.

Meanwhile, numerous published studies illustrate the cost of acquiring a new customer is at least 5 times the cost of maintaining an existing customer. This results in a significant waste of time, money, and resources that could be focused on cultivating repeat customers.

It’s important to understand that the bid-based digital media market has a near perfect structure to make customer acquisition the marketing problem it is today.

  • It has a massive number of participants – ie, “participant saturation”
  • It is effectively controlled by a duopoly – Facebook and Google
  • The duopoly has raised prices on average 10% per year, doubling costs in just 7 years
  • While household income and low inflation has kept retail pricing relatively static

The cost of acquisition has become onerous at best for most retailers. This whitepaper defines the one-time buyer opportunity in substantial detail. It will help you to weigh the impact one-time buyers have on your business, the opportunities it presents if addressed meaningfully, and help organizations break the “acquisition affliction” that plagues retail commerce.

Background

If you ask most retail and ecommerce businesses what their biggest problem is, they tend to consistently define it as one of the following:

  1. Customer acquisition
  2. Revenue growth
  3. Margin compression [discounting]

If you’re in retail / e-commerce, you may well be thinking “all of the above.”

The current wave of retail transformation (sometimes referred to as the “retailpocalypse”) suggests that many organizations simply aren’t solving these problems despite years of efforts, new websites, management and staff changes, and regularly “refreshing” the brand.

While each of these can have real value and may be necessary steps towards improving struggling direct-to-consumer businesses, our research and experience strongly suggests there is a more fundamental problem.

However, if this problem is addressed in a strategic and methodical manner, it does more than just produce a positive change. In fact, it can quite literally remake the character and profitability of a retail business.

In order to implement a change of such a magnitude, the first thing we have to learn is just what we’ve been missing – and one of the biggest misses of all time in retail commerce has quite consistently been the one-time buyers brands have now, and consistently add.

While it is reasonable to assume that a big miss like this has been solved before, many brands and retailers have described it not as a problem worthy of sustained focus, resources, and effort, but rather “just a part of the business.”

Our work suggests these are more expressions of frustration with one-time buyer problems than facts or some set of natural laws of human behavior.

Some brands deflect this problem by referring to these one-time buyers as “trial buyers” – and declare the mere feat of getting customers into trial as acquisition success. While we would wholeheartedly agree that a net new “trial” buyer is not inherently bad for business, it is often a critical starting point. Our evidence illustrates that questioning the value of trial buyers is a success facilitator that most retail commerce brands simply haven’t adopted to date.

Success is often defined by customer acquisition “count” alone. In some cases, this leads to what we refer to as an “Acquisition Addiction.”

Defining the One-Time Buyer Problem Clearly

Missed opportunities are essentially problems for your business, and the end result is that your cost structure is higher than it should be. So, why are one-time buyers such a big problem? Especially when the goal is to generate revenues and acquire customers now?

These short term goals are of course, the very beginnings of the one-time buyer problem. In reality, it goes much deeper.

“That which gets measured gets done”

Surely, revenue and acquisition are strong objectives that have been easily measurable for many years. In turn, it is not surprising that most retail / ecommerce organizations are doing a fine job at it. Why? Because “that which gets measured gets done.”

Retail has done a good job at measuring revenue for a long time — POS and ERP made that measurable in physical retail decades ago. Meanwhile, acquisition and its costs have become far more measurable on digital channels. The mass migration to efficient digital acquisition has also been enabled through superior measurement of acquisition and its costs.

While attribution is still a challenge, we won’t address it herein. Although it is a serious concern in justifying investments in a customer acquisition, it’s still not nearly as large of a problem as what happens after the customer enters “trial.”

Defining the Magnitude of The One-Time Buyer Problem in Your Company

There are multiple dimensions to how we determine the magnitude of a one-time buyer problem. The first and simplest is to just count the number of unduplicated individuals with only one transaction on their record.

It is worth noting once more how important it is to get a clean count by having a reliable mechanism to roll up the correct transactions under an individual buyer. Underestimating the complexity of the “roll up” and de-duplication process will add substantial noise to your data, and high fidelity is required to get a consistently effective solution to the one-time buyer problem (and other problems like it).

With that file in hand, we segment out the group of buyers with a single transaction. This is what is referred to as “the universe” of all one-time buyers.

The vast majority of retail / ecommerce organizations have a very large percentage of one-time buyers. For ecommerce-only businesses, the number tends to be a bit lower, but has still been steadily rising industry wide between 51-79%.

The values for the percentage of one-time buyers across all retail commerce organizations is not a normal distribution (the familiar “bell curve”). Instead, buyers stack up substantially around one purchase.

Is Your One-Time Buyer Problem Even a Problem Worth Solving?

For most retail / ecommerce organizations, a look at the percentage of buyers in their respective customer databases is usually sobering. However, the benefit is that it becomes quickly clear that this is a large enough problem to warrant focus and investment in solving.

There is good news and bad news in this top-level dimension of the problem. While large, these buyers are not homogenous (the same), they are typically quite heterogeneous (different) from one another. In many cases, the only thing your one-time buyers have in common is they are all in the trial stage of their relationship with your business and your brand. The longer trial buyers stay in the “trial” stage, the less likely they are to become profitable, and loyal customers.

Further segmenting your customers will allow you to tailor different types of communications to different types of individuals. Depending on the size of your customer database or file, we would segment it accordingly.

We will further segment this population in a later step. Your total universe likely contains a percentage of customers that are long gone and are overlapping with the “Inactive” segment. Therefore, they are not worth a substantial investment in time and money to pursue. These individuals may not have the value of a more recent one-time buyer, but they do serve to illustrate the value lost because they were not focused on earlier in their Buyer Lifecycle.

Defining The Dimensions of Your One-Time Buyer Problem

When solving any problem, the key is to break it down into its component parts, and solve it one piece at a time.

Predict the Timing of Your Second Purchase

First, we’ll want to know the median number of days between the 1st and 2nd purchases across all customers that have bought at least twice. This is known as the inter-order purchase time (we’ll refer to it as their “IPT”). It is a simple but effective way to start identifying the length of time (measured in days) before a typical repeat customer makes their second purchase. If a customer goes beyond the median number of days for a second purchase, it is a milestone for action.

What If We Miss an Expected Second Purchase?

Now that we have derived a good purchase window for the second sale, we’ll want to determine which buyers look more like they’ve missed that window.

In order to answer that question, you need to calculate the days since the first purchase for all one-time buyers.

Those who made their first purchase relatively recently are more likely to make a second purchase. Compare this to the population we just calculated (your IPT) and we’ll know how close or far we are from the norm for the second purchase.

Mean Reversion Can Serve You

This is effective, as data scientists have consistently found over time and across large datasets a powerful force known as “mean reversion.”

Mean reversion says – with all other factors held equal – that behaviors tend to revert to the norm even if there is variability over time. While the norm itself may change over longer periods of time, groups will tend to revert to that new norm.

With your data “de-duped” and “rolled up” to unique individuals with a clean purchase history, you now need to break those groups up by the time since their first purchase (the age of the customer relationship) from the “normal” time (aka the norm) to repeat purchase.

You must segment the lowest to highest by age (recency) to target with ever more aggressive offers and communications. Once they missed your calculated IPT window, it’s time to get more aggressive.

Why? Those below the norm are the most likely to convert to repeat purchase, while those above the norm are less likely. We’ll also define the population of those most likely to convert as those who are less between the norm (average) and your calculated IPT.

How To: Solve Your One-Time Buyer Problem

Solving your one-time buyer problem is within your reach by following this simple 5-step process.

Step One: Assemble Key Data for One-Time Buyers

First, we’ll share and discuss the “Simple Six” One-Time Buyer Data Points you can use to solve your one-time buyer problem. The good news is that most organizations either have them readily available, or can put them together with some help.

  1. Valid customer record & contact information
    You must have a deduplicated single record of the customer with all transactions rolled up under them in order to know who your one-time buyers are (and aren’t). You also need sufficient personally identifying information (PII) and a method of contacting them. This which includes their full name and a combination of postal/delivery address, phone, cell phone, and email. These are required to both complete a valid/merged customer record and provide a means to contact them with a personalized series of communications that are unique to their current state/situation and behavioral profile.
  2. First Transaction Information, Buyer Source and Offer
    You will need to know when that first transaction took place, what was purchased, if a special offer was tendered, and the source of the customer. The number of items and the SKU’s in that critical first “trial order” are generally good predictors of the probability of a second transaction.
  3. Transaction Amount
    The amount the buyer spent on the first order says more than the profitability (or lack thereof) on the first sale. It’s also indicative in many cases with the probability that they will order again in the future.
  4. What The Buyer Purchased
    In addition to knowing how many items were in the order basket, we also need to know what they bought – including the Category and SKU of the item.
  5. Date and Time of Transaction
    These are really two separate data points we split out and use for different purposes to solve the problem, but are usually captured in a single “time-date stamp” on the transaction. It tells us how much time has transpired since the first transaction so we can compare this buyer to all other buyers and determine whether or not they are more likely to buy again. Going beyond just “recency,” we can gauge one or more purchase windows for the individuals with a higher probability of a second transaction — or determine if they are just a lost opportunity.
  6. The Profile of The Buyer
    Who is this buyer? Demographics and lifestyle intelligence give us an extra advantage in understanding who our one-time buyers are. Are they affluent or of limited means? Do they have children at home? Are they skewed towards Millennials, Generation X, or Boomers? Merely using a well-worn story about your customer is an assumption or shortcut that has proven detrimental in cracking the one-time buyer problem. “Our customer is young, rich and beautiful” may be a true statement, but what about the 1x buyers who are Boomers? Will you be relevant or tone deaf? The answer to this question helps determine whether or not you will move a trial buyer into loyalty and an evergreen stream of profitability.These are very different customers, and our communications can be engineered in simple ways to spark them into a transaction and perform better when we speak in their voices and evidence our relevance to the customer. For example, personalizing an email’s subject line, hero shot, or the cellophane wrapper in a package can go a long way towards subsequent sales or more missed opportunities down the road.

Step Two: Rank Your One-Time Buyers by Aging

When we rank buyers by aging (a.k.a, recency), we’re determining how long it has been since they spent with us (note: this is not a ranking based on the age of the person). When we do this, we find out who has bought more recently and who hasn’t bought in a long time. This spectrum can be expressed as a distribution. In most data sets, you would expect to see a bell curve also called a normal distribution.

However, your frequency of purchases almost certainly does not follow a “normal” distribution. It most likely either has a huge spike around 1 purchase and then gradually falls to insignificance. Take a look at the example below. This visual is representative of the pattern we have observed across hundreds of brands when they first ingest their data into BuyerGenomics.

However, your frequency of purchases almost certainly does not follow a “normal” distribution. It most likely either has a huge spike around 1 purchase, or is negatively skewed.

A real example of distribution by purchase shows that one-time buyers skew well below the often imagined “norm” or center point in a normal distribution. This negative skewness illustrates that while it may seem “normal” that the typical customer buys a few times, we can clearly see in retail customer bases that most customers buy just one-time. This illustrates the need for addressing the problem proactively and early.

Notice also how the best spending customers that are the most loyal to the brand and have less time between their purchases, a requirement for a customer who spends a lot more. This is not to be confused with the fact that the majority of revenue is coming from the very large population of one-time buyers. Instead, it underscores the magnitude of the opportunity to sell again to your one-time buyers.

Step Three: Calculate the Window of Two-Time Buyer Purchases

Fortunately, we don’t have to solve the one-time buyer problem using only data about our one-time buyers. This is because all repeat buyers were once one-time buyers. Otherwise, it would be nearly impossible.

You almost certainly already have some two-time or more buyers. These individuals also have value in predicting when future purchases happen. The key is in the timing between the first and second purchase. Therefore, the goal is to understand both that behavior and its respective timing.

Step Four: Calculate The Inter-Order Purchase Time

We start identifying that window by looking at when historically our buyers made their second purchase. That’s measured as the difference between the date of the first purchase and the date of the second purchase in days. You’ll have to do this for every customer, next, compute the median number of days. We refer to this as the Golden Window for trial buyers.

Lastly, if we were to look at the distribution of the number of days between first and second purchase, we could determine if our repeat buyers might fall into different groups or clusters of behaviors that warrant further segmentation.

In the example below we have a distribution of customers by the days between purchase. In this particular example, there is a high probability opportunity to sell at about 114 days, and a second (even if its smaller) opportunity to sell at around twelve months.

The one year window is sometimes referred to as an anniversary purchase, and may coincide with a birthday or seasonal event (like travel for spring break). These cases in which more than one opportunity exist may indicate a dual universe with different types of customers, suggesting assignment to different communication groups to take full advantage of different buying behaviors.

Step Five: Campaign Execution

When we’re entering one of the spikes on the distribution, we see the probability to make the sale has increased based on the timing. This is an opportunity to sell, and we need to contact the customer and make a compelling offer. The conversion rate can be improved by leveraging the other data points we described in the “Simple Six” earlier, including the following:

The initial source and offer that led the customer to her first purchase offers key insights on those offers and discounts that will work in the future.

The Transaction Amount tells us if they are a high ticket buyer and if we should position more premium products. This also requires us to consider the number of items in the first cart. Many low cost items vs. a single high ticket item are indicators of different types of buyers. Your offer should distinguish between them.

Your buyer’s demographic and psychographic profile is also an opportunity to tailor your creative and messaging around him/her. If we know our one-time buyer is a Millennial, Generation Z, or a “Global,” we’ll need to communicate differently than if they are a Boomer.

Tailoring subject line, creative, message, and offer/call-to-action in either an email or the cover of a catalog or postcard has been shown to increase both response and revenue per campaign.

Some Customers Are Already “Lost”

There is a portion of your one-time buyers that stopped buying from your brand (or at least from you) long ago relative to those who bought a second time. These individuals have the lowest probability of buying again, yet should be the target of “reactivation campaigns.”

By personalizing a reactivation offer based on what we know of this one-time buyer, it increases the likelihood of obtaining a second purchase. If the data shows that the customer has a high potential value, we would be foolish not to try and engage them. Systematic testing of reactivation offers by segments provides retailers with the best opportunity to increase purchases from one-time buyers.

Conclusions

By definition, one-time buyers are retailer’s largest customer retention opportunity. Increasing customer retention rates is one of the most significant opportunities that retailers have to improve profitability, according to many published studies. Thus, the one-time buyer problem is one worth solving.

This white paper has identified a simple 5-step process to addressing the one-time buyer opportunity. While most retailers offer a welcome series of communications, the opportunity exists for many retailers to improve upon such series by applying the simple data elements and promotion timing insights mentioned in this white paper.

For more information about how you can address your one-time buyer opportunity, contact the author, Mike Ferranti at mferranti@buyergenomics.com.

By |2020-11-20T23:01:38+00:00November 20th, 2020|Uncategorized|0 Comments

3 Critical Priorities for New CMO’s

Priority #1 :
Shape Expectations with The Best Possible Data

Setting and managing expectations with the CEO and Board requires leveraging the best possible data and customer intelligence you can get –quickly. Too often expectations are unspoken, unclear or even unrealistic from the start. Sometimes historical data is of a quality or resolution that causes the current state to look either better or worse than it is.

CMO’s must have a clear-eyed view of the business and customer from clean, centralized, and intelligently enhanced data that exposes both new opportunities worth pursuing issues you’ll need to address quickly, and backs them up with hard evidence.

Priority #2 :
Know The Customer in High Resolution

The 2nd critical accomplishment is to have a superior fluency in who your customers really are ―and use your command over the data to tell a hard-evidence based story that describes the current, the true customer mix, and other current realities that the CEO and board likely does not have visibility into.  High resolution is much more than age, demos and historical spending –and most CMO’s are glad to have that.

While CMO’s often are charged with simplifying the complexity that hasn’t worked in the past –and complexity can only be justified through value created –remember what Albert Einstein said about simplicity vs complexity… “keep everything as simple as possible… but no simpler.”

Having a higher resolution view/understanding of the customer makes it markedly more likely you get all the resources you need to win. New CMO’s need to scrutinize narratives about the existing customer that the data either does or doesn’t support –in order to have clarity on “what must change.”

Priority #3:
Acquire The Right Customers

The 3rd critical accomplishment is to get growth at a pace that meets the expectations you’ve built using the best possible data –and the realities of the customer you have today. That growth will almost certainly require more effective customer acquisition than before –CFO’s are evolving and increasingly demand “the right customers.”

Truly understanding who your best customers are, based on hard evidence, puts you in the driver seat. Higher resolution still, is ID’ing and targeting the Optimal Customer for your unique brand, and to acquire them at the right cost. When you acquire the true optimal customer for your company, you meet and exceed expectations ―as the optimal customer will spend more and more often, producing higher returns, and more reliable growth.

A Complimentary CMO-Only Resource to Master Predictive Marketing

To come out on top in the critical first six months in your new role, CMO’s and CEO’s need to keep learning. In support of your success, Inevitable Success readers who are also CMO’s or CEO’s can now receive a complimentary read of the new and highly-rated strategy book, The Truth about Predictive Marketing Automation.

Whether Predictive Marketing Automation is on your radar or not doesn’t matter ―the concepts and tools in this book are hailed as “extremely valuable” by the many CMO’s that have read it.

To secure your complimentary copy of The Truth about Predictive Marketing Automation, simply fill out the form below, and please note, we currently only ship the hardcover edition to US addresses.

 
By |2020-12-04T17:22:29+00:00November 5th, 2020|Blog|0 Comments

8 Marketing Tasks That Can Be Automated (Save Time and Get Strategic with Marketing Automation)

 

Market automation can make or break a marketing campaign. After all, it changes a campaign into a more efficient and data-driven strategy. So, how should a company use automation?

Don’t worry; this guide dives deep into the best marketing tasks for automation. From scheduling social media posts to sending personalized emails, with these simple tips, a company can succeed.

Now, read on about marketing automation:

1. Automate Social Media Posts

The first step any company should take towards marking automation is automating their social media posts. By scheduling posts and automating them to post at certain times, a company doesn’t have to rely on a dedicated social media team.

Instead, all social media posts will automatically go live once a person hits publish. Social media automation also enables a company to schedule posts for reposting to maximize promotion and increase a brand’s reach.

2. Automate Signups For Newsletters

Rather than a common newsletter sign up, create a more direct plan to get people to register. To begin, create lead generation content for each area of your website. They can be tools such as in-depth guides, weekly podcasts, free downloads, or even online training. For example, at BuyerGenomics, you can get a free copy of our book “The Truth About Predictive Marketing Automation” in exchange for an email address. Whatever you choose, they must be tools and/or resources that are of interest to people.

Next, use a thought-provoking CTA to prompt people to sign up, and place these tools in blog posts and in areas of the website with the same topic. That way, people who are reading the topic are interested in it and are more likely to take advantage of all available offers.

Powerful CTA’s are a great automated way to generate leads and create a growing customer base.

3. Automated Email Personalization

Personalized emails are a great way to engage customers and compel them to use a certain product or service. By understanding their interests, industry, and salary, a company can market by people’s preferences and prospects.

With a Predictive Marketing Engine, you can nearly automate segmentation (along with the personalization required for that segment) with Machine Learning to do this at scale.

4. Automate Customer Re-Engagement

Sadly, sometimes customers lose engagement with a brand or product. It could happen when customers stop using a company’s products, or customers simply stop engaging with a company’s brand. However, the effect of this disengagement has a real effect.

In fact, $4.6 trillion is lost every year due to abandoned merchandise. That’s why it’s essential to have a system in place that can determine when there’s a decline in engagement and send an automated email or text to re-engage a customer. For example, often a company can offer discounts, a giveaway to engage the customer back to using a product or service.  In other cases, this can be accomplished simply by sending the right message at the right time (BEFORE IT’S TOO LATE).

For example, Buyergenomic’s AutoPilot technology is a strategic approach to increase revenue and even analyze the buyer lifecycle. With it, your company can boost customer loyalty and even pro-actively rescue those customers that might have lost interest without it.

5. Automated Customer Feedback

Customer feedback is essential as it displays what a company is doing right, wrong, and how it can improve. It even aids in making business decisions, boosts overall engagement, and proves that a brand cares about its consumers.

Automating customer feedback is easy, and there are numerous ways a company can gather information. For example, rather than give customers surveys, customers can give feedback as they interact and connect with the site. Companies can also ask why customers aren’t selecting their services or products and use that information to enhance their branding strategy.

6. Automate Customer Service Responses

Sometimes a customer needs help due to a technical problem or some related issue, the speed and clarity at which a customer service representative replies is crucial. However, thanks to automated email, representatives never have to worry about making customers wait.

Although all customer service emails should be constructed with the most up-to-date information that will help solve the issue at hand. Also, representatives should specify that someone will reach out to a customer urgently.

All customer representatives should aim to make the most of their automated responses and work to establish a friendly relationship with all customers. To do that, all representatives should address customers by their first names, talk in a conversational manner, and post relevant advice.

7. Implement Chatbots

Customer service response emails are great, but they undoubtedly tell the customer to wait for more information. However, an automated chatbot delivers an instant reply and help customers solve problems quickly and easily. Like a human, a chatbot engages in conversation and asks friendly questions to understand the problem at hand.

By having a chatbot, it allows the customer service team to work on more crucial problems. However, it displays a sense to actively work on each and every problem a customer has, even if an employee isn’t directly working on the issue.

8. A/B Testing

This marketing task is one where automation is steadily advancing in the years to come. Companies are already putting automated A/B testing systems in the market.

While this technology continues to evolve, there are numerous tools a company can use to automate a proportion of the A/B testing system.

For example, companies can include ad adaptions from Google ads (called Responsive Ads), which basically include different kinds of ads for the same marketing campaign. While the marketer still has to feed the system with ad components, the system is fairly effective at serving the best performing combination.

Automating Marketing Tasks Helps You Remain Strategic

Automation ensures that a company manages its time effectively. With a push of a button, a company can post to social media, respond to customer complaints, send personalized emails, and much more.

Such tasks, if done by hand, would take hours to complete. However, by automating them, a company can focus on more strategic projects that evolve and grow your brand.

If you are evaluating tools to automate some of your marketing tasks, we encourage you to request more information about how BuyerGenomics’ Predictive Marketing Automation Platform can put many of these tasks on AutoPilot. Get a demo today. We look forward to helping you and your organization succeed.

By |2020-11-20T23:24:00+00:00August 28th, 2020|Uncategorized|0 Comments

How to Build Trust with Customers and Increase Purchase Rate

Build Trust with Targeting & Relevancy

Damian Bergamaschi: Welcome to the Inevitable Success Podcast sponsored by BuyerGenomics, where our goal is to help you, the marketer, make success inevitable. Each episode will discuss the craft of data-driven marketing, helping you uncover new and profitable ideas. You will also learn what works and what doesn’t work from top marketing professionals and thought leaders. I’m your host Damian Bergamaschi and inevitable success starts here.

So, as normally happens, Stephen and I were discussing various experiences or just thinking about marketing in general and Stephen started to tell me about his experience with Waze and I thought it was so interesting. Waze, if you’re not familiar with it, is a navigation app which uses data, algorithms, and crowdsourcing to figure out how to get somebody from point A to Point B in as efficient a manner as possible. And the thing that was so interesting about it that ties back to marketing is that there is an experience of trust that had to happen where now Stephen uses it all the time when before he didn’t. So why don’t you tell us about your Waze experience?

Stephen: It came out and it was an intriguing product. Well, first of all, it was free. So why not? It was a good value proposition. So I downloaded it and I started comparing that with any other type of navigation tools. The one that came with the car or the Google and then I realized that, oh wait, this is a two-way communication now. I am, as a driver, I’m a contributor to this app. I can say, “Hey there’s a cop hiding behind a tree or there’s traffic, or how slow is it right now?” All those things are my input. So this is a really amazing crowdsourcing mechanism happening in real time, too. And it will help me to avoid traffic reported by somebody else. How noble an idea is that? But in the beginning, I had my doubts. I know I continue using it every day and I’m thinking, okay, Waze is making me taking a left turn and going to the Bronx. I’m going through midtown. Why is it telling me so? And the reason why I bought into this idea is that the cost of me trusting my gut feeling was so high that every time I didn’t listen to Waze I regretted it. It was mostly right and I was mostly wrong. So, therefore, I started trusting the data and the algorithm behind it.

Damian Bergamaschi: So that is basically when you said that, that started this thought process of what would it take for a brand to have that same kind of reaction with a customer, right? Where when a brand sends a message to a customer, this almost “I don’t need to think about it because I’ve already kind of pre-vetted this situation.” This utility is the value proposition. I know this is the best.

Stephen: That’s right. Right.

Damian Bergamaschi: So how do we get there? One of the ideas that we had when we were thinking about it was you have to build trust and ways to use data to really build trust — specifically by avoiding being wrong or avoiding sending out communication that would send me on an errant mission.

Stephen: Let’s reverse engineer what they do.

Damian Bergamaschi: Sure.

Stephen: First they collect the data. Traffic data come from people who point out, “hey the road you know is there already.” They’re not going to build new roads, but if I point out a road here it updates its database, which is fantastic. That starts with the data. Then there’s an algorithm. Now, an algorithm is complex. In other words you have to build a lot of algorithms so now I’m going from A to B, there are about 20 different options or if you’re going to, you know, go from Manhattan to New Jersey you could have a hundred different options. I mean, deciding whether I should cross Lincoln Tunnel or GW is a big decision isn’t it? Because you could end up being in traffic longer than if you’d have stayed on the GW path.

Damian: Right. And if you have a Tesla then you have to also have a route in a way to get the car charged.

Stephen: And by the way, they also have an option to find a gas station on the way.

Damian: Somehow we like to try to work in a Tesla reference to everything else but they’re not compensating us.

Stephen: The algorithm is important that they’re not out and also important and they have to present the way. In other words, it has been a useful form for the driver in the car that off he’s telling me that I’m going to make a left and a million ways without waste. And how do I get that message? They’ve got to have that. So these all these things come together really nicely and they even have a feedback routine. They know they’re measuring every move that I make. And when there’s traffic I’m going to report it, too. This is all a little bit scary for the future of humankind.

Imagine a situation where there’s really bad traffic on Road A. And Waze wants to send most people to Road B. If you do it too much then Road B gets congested. If the machine is smart enough they don’t send everybody to Road B. So you know what that means? The machine is making a decision for you. We’re literally forfeiting our ability to think at this point. One of the reasons we’re talking about this today is how can we do this for retail? How do we reverse engineer this process? The retailers can do the same thing. I mean, I read an article just this morning that AI is not far away. It’s not just reserved for, you know, Amazon and those guys. It should be for everybody because you know why? If you don’t do these things not only are you going to lose out you may go out of business in the world where everything is suggested by the machines. And let’s think about what the gut feelings are for the retailers now, not the buyer, but the retailers.

Why did you order a certain set of products to sell next season? Because your gut feeling told you so? Because your designer says so? That’s a brain-powered one. What if you can have access to a lot of data? Past and present shopping behavior of all these people, not just by you but a lot of other places like Waze does when all the drivers use it, right? Would you trust your feeling and your thoughts or algorithms backed by the data? More and more people are doing it with data backed by the algorithm so therefore you cannot just avoid this topic because this is happening. And I’m not saying that this is all good for humankind by the way. I’m not saying that at all.

Right now I’m going to just hold my judgment about that because this may result in mass loss of jobs. But if you flip it around then if you’re a small retailer who’s not Amazon, so they have no chance to lay you off because you’re not part of Amazon’s system or even if something is replaced by the machines, maybe it’s an opportunity for you. If you’re small or medium size, and you’re thinking that you never had access to such technology before. And you thought that you should hire a lot of people to do this. Now you tell me I don’t need people because if there’s an algorithm backed by that data, then I could do this? Then it’s an opportunity if you think differently.

Damian: There’s a lot of retailing business models that are popping up recently that are very conducive to this whole idea of almost suggesting something for people. For example, I think of Stitch Fix or I think what Amazon Wardrobe may be trying to do where it has like a similar flavor to this whole Waze thing. That if it’s consistently suggested things that weren’t what you wanted, you’d pretty much tune that out. But if you got it right and because you had some sort of real-time data coming in, whether it was crowdsourcing or looking at transaction behavior or things it sold effectively, you’d be able to serve the right thing to people. And over time you’d just kind of defer to this decision maker.

Stephen: That’s right. If you build that trust if you’re right most of the time.

Damian: And you know in some ways I feel like the cost of being wrong is probably, you know, almost more of a thing to avoid than being right. Like the saying, you know, it takes like a lifetime to build a reputation. It takes a moment to ruin it. We talk about batch and blast (email campaigns) a lot, and ways to get it right.

Stephen: People don’t think about probability. It’s like this. Even the clock stopped completely is totally correct twice a day. So you’re not just measuring accuracy, how right are you, because the clock is right twice a day. It’s not how right we get. How wrong are you? I say this in the analytics business all the time. Sometimes you don’t know 100 percent. But the cost of being wrong should be lower. Now, why is batching bad? The idea is that is one way to get everybody, and out of everybody, somebody is going to buy. But what’s the cost of it? The cost of it is a ton of people I’m training to ignore my messages because I’m sending emails with the same message every day. And everybody knows I’m sending the same messages and irrelevance shows them that, “Hey, this is not relevant to me. Why am I getting this? I buy a lot of other things from you. Why are you showing me all this women’s wear?” So we’ve got to really think about the cost of being wrong and to improve the probability of being right. You’ve got to rely on data and algorithms. We cannot avoid those things anymore.

Damian: Yeah I completely agree.

Stephen: So what we want to talk about is that then. Okay, so fine, Amazon is doing this, Google’s doing it, all the big boys are doing it and finding things that those guys don’t even call big data, they don’t even use those words. How do retailers catch up with that, aside from how you can catch up with those guys? I see a lot of articles where you should just buy into this, but with AI there are some prerequisites that you have to get ready for. So if you think about the process here, and this is why I broke down the whole situation, you collect the data mine through crowdsourcing and have a preexisting match. Whatever it is, you have to have a kick-ass algorithm so you are right most of the time. If the algorithm isn’t wrong by the way then I may have to go to Queens by way of Long Island.

Damian: This is where I’m going to interrupt you for a second because avoiding being wrong and focusing on being right are actually a little bit different.

Stephen: Of course.

Damian: I’ll give you one example of a company who’s a master at this: Google.

Stephen: Yes.

Damian: Google is very, very focused on making sure that wrong things do not show up for the search intentions, more so than they are about serving the best particular thing. Right? So that is actually kind of like almost a flip in thought. Like, for example, if you turn to the trusting one that we can all understand, right? Always showing up to where you need to be is par for the course. When you don’t show up that’s when the problem occurs. Right? Because now you’re like the guy who doesn’t show up. You blew it.  

Stephen: I am saying that for most retailers the bar is pretty, pretty low. Let’s not try to be Google overnight.

Damian: I mean, give yourself a week….

Stephen: Yeah the reason why I’m saying this is that most retailers just batch and blast. They don’t even target the price. Just different products. And by the way, for the batch and blast people, fine, maybe they see that holding out somebody is a lost opportunity, but they are not thinking about the risk of turning people away. Right? If you’re sending emails to everybody all the time, fine, great, do it. But are you at least pampering your recipients in a different way depending on who they are?  Are you sharing different offers? Because we do this all the time at BuyerGenomics: If somebody is new make them feel excited. If they’re the most valuable buyer then pamper them and don’t give them discounts easily. They love you already but if they’re far gone they’re fading away then you do something else. One little thing changes a lot of things.

Now let’s extend it further. We can do things like different product suggestions or use different channels. So, therefore, the bar is kind of low because you’re coming from a world of batch-and-blast and just sending everything and let them think what they like. Instead, I’m going to do some suggestions in my emails and then see if I’m right and increase the probability of conversion, if not guaranteeing the conversion. And Google, by the way, guarantees that my search engine is always right.

Damian: Also I think we have to institute almost like an anti-conversion metric. So let’s put it this way: I love analogies to kind of make things more clear. Let’s say you had a medication that you were testing on a sample and you only literally measured like the way that we measure batch-and-blast campaigns, then the analog is how many people got better? But you only measure how many people got better, well, then sending a whole list is going to give you good numbers. If you don’t look at the context of how people got better along with how many people got worse, you can put yourself in dangerous situations and erode trust So I mean it’s such an important concept and it’s a metric that we have to kind of build into it, like what’s a conversion rate? I’m going to give it a name: the anti-conversion rate.

Stephen: You’re saying that everybody should raise their bar. In fact one of my mentors that’s the Wonderman recently passed away rest his soul, but he said this long time ago he’s the father of direct marketing for heaven’s sake. And what he said long time ago that yeah we developed a lot of techniques so that we can realize out of a mailing of like 2 percent conversion rate which is very high in our online world, If you do the conversion rate in the right way which is based on how many millions you blasted I see numbers like 23 percent. Okay. Do you know what that means if you achieve 2 percent conversion rate? You are 98 percent wrong. And we rejoice because you recover the cost of mailing? You know what? The bar should go up, especially in the world where you do batch blasting and your conversion rate is hovering at 1.3 percent. And by the way, let’s not do that based on some clickthrough rate. I’m saying if you look at how many emails you’ve blasted you’re training people to ignore you.

Damian: Right. I think that 98 percent, a lot of that is just ignored or thrown away and ignoring a mailing is different, but in the email situation without a good anti-conversion metric it doesn’t capture all of what you could know. You know I’m kind of done reading this guy’s e-mail and by the way, it’s an effort, isn’t it?

Stephen: For a person to go in that direction, then what you’re doing is you’re really really wrong. So what I wanted to leave behind is just the thought that you should really think about this and raise the bar. You don’t have to be Amazon. I have to think differently. And to do so let’s not forget that it starts with the data and algorithm. When the data and algorithm come together it has some exclusive value. As we saw in Google, as we saw in other ways for example. Now when you do that then the trust goes up because you’re mostly right. And that is what we want to do. You want to increase the probability of conversion if not guaranteeing.

Damian: Yeah. And I think in closing, what more virtuous way to use data than to build trust?

Stephen: That’s a really good way to think about it. Whether you do it for marketing or supply chain and analytics or marketing optimization work. In fact, I recommend a book called The Numerati. It was written a long time ago, but it’s a very interesting way of looking at the data itself. They use examples from real life and don’t use any statistical terms, so please don’t be scared. And they have chapter names such as shoppers, lovers, terrorists, workers, and such things. There are all those behaviors that we leave behind and these leave some kind of a data trail. And if you analyze them right you could predict who’s going to fall in love with you. How do you think all those dating apps work by the way? It’s data and knowing how to read it. Why do you trust that dating app on the Internet? Same reason why you trust all the data.

Damian: All right. Well, thanks again. Till next time.

Stephen: Thank you.

Damian: If you enjoyed today’s episode, we ask you please leave a rating and write a review. Or better yet, share it with another marketer. Be sure to subscribe to the podcast for new episodes. Also, check out the show description for complete show notes and links to all resources covered in today’s episode. If you’d like to speak to someone about any topics covered in today’s episode
please visit BuyerGenomics.com and start a chat with the BG team today.

By |2019-11-14T19:21:56+00:00March 21st, 2019|Inevitable Success Podcast|0 Comments

Creating Customer Journeys: The Guide to Marketing Nirvana [Step 3]

Segmentation Strategies

In Part 2 of our Marketing Nirvana series, you learned how to identify who your customers are (sometimes referred to as a buyer’s persona), where they come from, how they purchase your products, and their preferences.

We also broke down the Buyer Lifecycle (BLC) and how it relates to the natural progression of a customer/retailer relationship.

Now you have the ability to apply all of that information towards building intelligent, targeted, personalized, cost-effective marketing plans and strategies.

Keep in mind, a chief goal of curating Customer Journeys and Customer Relationship Management is to maximize the lifetime value of customers by curating an experience that turns customers into advocates.

In order to do that, you must design distinct segmentation, modeling, and messaging strategies based on customer wants, needs, and value.

This is where you take the segments you developed back in Step 2 and put them into action.

Value-based Segmentation

Another component of segmentation that we have not yet discussed is value-based segmentation. This practice helps you to identify and target a certain group of customers who spend more money, more often than the rest – hence rendering them more valuable.

Equipped with comprehensive customer transaction histories, a PMA can calculate each of their respective potential value – offering perceptive calculations about the future marketing investment that is appropriate for each customer.

For instance, a casino may offer a free night’s stay (with meals included) to a rated player who has a high potential value. Meanwhile, a guest without a known gaming history might receive a lesser incentive to stay.

In this case, the return on investment (ROI) becomes the guiding metric. However, this is just one example of value segmentation. Retailers, airlines, and restaurants also target marketing offers based on customer value.

It is of great importance while developing customer journeys to create a dialogue that is both relevant to each individual customer and has a high return on promotional investment (ROPI).

This is a paramount priority when it comes to new customers who are interacting with your brand for the first time.

New Customers – One-time Buyers [High and Low Value]

To reach Marketing Nirvana, your aim should not be to just get any customer – but the right type of new customer.

For many retailers, a whopping 75 percent of buyers do not come back after their first purchase. These are called one-time buyers.

One-time buyers offer no recurring revenue and are much less profitable than customers who purchase frequently. As such, you should  provide new customers with a special dialogue designed to cultivate a new relationship and encourage repeat purchases.

It is also critical to factor in the difference between high and low value new customers. Unnecessary focus on the latter is not a recipe for sustained success – especially since it costs so much to attract new buyers in the first place.

Reaffirmation

Encountering a new customer is similar to going on a first date – where you have the opportunity to demonstrate both your brand’s value and the value of your product portfolio.

Most new buyers require some form of reaffirmation. To do that, answer these questions for them:

  • What makes your product(s) so special?
  • Why should they only shop with you as opposed to your competitors?
  • What type of personalized customer experience can you offer?

This opening dialogue – in which you educate customers about who you really are – is extremely important. You have to go in with the assumption that any new customer can ultimately become a Most Valuable Buyer (more on that to come).

In this instance, put your best foot forward by guiding the conversation towards the areas of interest where he/she is most likely to be receptive. This can be achieved by shifting the conversation towards certain products based upon past purchases.

Future Lifetime Value

With a PMA, you can identify behavior patterns or signals that indicate the relative likelihood of someone becoming a future Most Valuable Buyer (MVB). These are called predictors of Future Lifetime Value.

Examples include the frequency of purchase, amount they spend, level of engagement, segments/cohorts they belong to, and their respective lifestages.

As you begin to formulate this targeted, personalized relationship with your customers, they become more likely to be receptive to your messages, offers, and promotions.

Cross-sell and Upsell Journeys

Another way to generate more high value customers is through the implementation of cross-sell and upsell journeys.

Cross and upsell campaigns work best under a certain set of conditions:

  • When coordinated across channels,
  • When they  include dynamic product recommendations,
  • When they are delivered with relative timeliness compared to a customer’s latest purchase.  
  • When the offering is highly relevant to the customer

PMAs allow us to analyze sequences of product purchases and target the likely subsequent purchases of customers.

For example, if you sell golf equipment, and a customer just bought a set of golf clubs, then there is a good chance that they will buy a pair of golf shoes, balls, and gloves in the near future.

From there, see what else he/she has bought recently, and offer relevant product suggestions that they are most likely to buy. This is the practice of cross-selling – or tying similar product suggestions together in order to maximize profitability.

Other cases might call for upselling. Upselling is a method used to motivate customers to purchase a similar high-end product over the one in question.

For instance if a customer is only purchasing low-end golf balls or tees, then you should seek to up-sell – or drive them towards more high-end, expensive versions of the same items.

Another example would be if you are a sneaker retailer, and a customer is only buying low-end, discount sneakers. If you know he/she is classified as a bargain seeker, you could offer a 10 percent discount on a considerably more expensive pair.

Best Customers – Most Valuable Buyers (MVBs)

The fact is, not all customers are created equally – nor should they be treated as such.

  Cara Sarah
  High-Value Buyer One-Time Buyer
Potential Value High Low
Segment/Cluster Up & Coming Busy & Overworked
Location City Suburb
Income $100,000 $60,000
Channel Online & Offline Online Only
Last Purchase Date Less Than 6 Months Ago More Than 12 Months Ago
Life-To-Date Transactions 4 1
Product Categories Purchased 2 1

MVBs are the loyal, repeat consumers who are willing to spend more money, more often than the rest of your base.

According to the Pareto Principle (also known as the 80/20 rule), for most companies, roughly 80 percent of total profit comes from just 20 percent of the customers.

For retailers, the pareto principle holds true.

This is a major statistic that many marketers are not aware of. Therefore, companies should invest more into keeping their best customers and finding more like them.

Once you have identified a customer as an MVB, your objective is to keep them there. There are a number of tried-and-true ways to make this happen.

Surprise and Delight

The tactic of “Surprise and Delight” is a great way to reaffirm connections and reinforce your customers’ attachment to your brand through certain gestures – namely, by making them feel special.

One way this is done is by employing the software’s ESP (Email Service Provider) functions to send a special offer, privilege, gift, or discount for your product or service at a carefully coordinated point in time.

This is a simple practice that can be tailored for different purposes for all types of customers.

In the case of an MVB, you can send them a special gift or offer thanking them for their loyalty. This serves to validate their current spending habits, and perhaps encourages them to engage with your brand even further.

Churn [Customer Attrition]

As stated earlier, one of the ultimate methods of reaching Marketing Nirvana is to create advocates and reduce churn.

Churn is a metric that that measures the percentage of customers who terminate their relationship within a particular time period.

Most companies have their own respective definitions of what a reporting time period is (whether it’s a quarter or a year) before a customer is considered inactive. The most common is over a one-year period.

If you’re looking at the overall relationship and retention rate of customers, you should be keeping a close eye on churn rates over time – because if it’s decreasing, you’re doing something right. However, if churn rate is increasing, something needs to change.

Going back to the Buyer Lifecycle (BLC), It is important to look at the percentage of customers that are fading and those that have become active over time. This helps you know if you are serving our customers well, or if you are perhaps beginning to lose them and have to change your approach.

For fading/inactive customers, a certain kind of special offer, message, or promotion can be sent in order to lure them back towards spending with your brand again.

Lapsed (Fading) Customers

Market research studies indicate that acquiring a new customer is anywhere from five to 25 times more expensive than retaining an existing customer. Moreover, it is at least 5 times easier to keep a current customer than it is to acquire a new one.

This means that the act of “rescuing” fading customers through marketing intervention is extremely critical – particularly because increasing customer retention rates by just 5 percent increases profits from 25 to 95 percent.

The truth is, most customers have a start and end date to their relationship with any brand. As a marketer, seek to identify if a customer is starting to lapse by checking if they are no longer purchasing at the rate they did before.

If that is the case, it becomes your objective to get them back on track.

Two Types of Faders

There are really two different types of faders – profitable and unprofitable – which means that the amount of potential profits you can regain by “rescuing” or “reactivating” each customer is based on the segment they’re coming from.

Certainly, you should not waste your precious marketing resources attempting to retain fading/inactive customers that have not demonstrated any positive engagement or indicators of long term value in the past.

This goes back to the importance of identifying high and low-value customers based on their expected future value.

Meanwhile, if a profitable customer (high-value, MVB) begins to fade – or even worse – suddenly becomes inactive, the corrective action should be taken right away.

Again, this can only be accomplished if you have a tool that tracks shifts like these along the BLC.

A PMA can autonomously identify these movements and strategically reach out to faders/at risks before they become inactives, or implement a call to action for inactives to return.

The notion that we are driving towards here is that you can put stop gaps in place that help identify the early warning signs associated with churn – and take corrective action.

This is a remarkably beneficial byproduct of loading your data into this platform that closely monitors the buyer lifecycle.

Conclusion

This third section helps you understand how to best utilize targeted, omnichannel analysis/communication to identify which customers are the most profitable – or who you should invest the bulk of your time/money/resources catering to.

Once again, not all customers are – and should be – treated equally.

Everyone is on a different customer journey and trajectory. Their customer personas, BLC stages, and respective financial values to your brand are all different.

If you are a smart marketer, you can gain valuable insights from the types of purchases that were made, the amount spent, when they occurred, and the marketing actions that were successful in driving sales. From there, leverage that information to lead each customer along a journey that reaches their maximum potential value.

This makes a PMA platform is particularly valuable because it allows you to keep track of tenure, LTV, and your customers’ potential market basket. In turn, you can customize messages over time to keep your valuable customers engaged (and spending) while monitoring that “evil” factor called churn that drains profitability.

Such an approach leads to the notion of true, automated 1:1 personalization.

By utilizing a capability known as dynamic variable content, a marketer may personalize each communication with well-proven, relevant product offerings. This practice offers the ability to autonomously communicate with all of your customers in a familiar, intimate, personalized way similar to that of a small neighborhood shop owner.

This is truly the culmination of a longstanding marketing vision (stretching back multiple decades) that we are now finally reaching through this cutting-edge software platform.

Looking ahead to Step 4, entitled: Launch Customer Journey-based Email Campaigns, you will learn how to further apply all of these insights and strategies towards designing targeted, intelligent, successful email campaigns.

About the Author:

Gary Beck
Gary BeckChief Strategy Officer
Gary’s background includes over 30 years of analytics & database innovation for several leading Fortune 500 companies and Madison Avenue advertising agencies. Gary has been a frequent lecturer and author on the topics of database marketing and applied statistics. His articles have been published in DM News, Direct Marketing and the Journal of Direct Marketing. He recently was President of the Direct Marketing Idea Exchange and served on their Board. Gary received his M.S. in Industrial Administration from Carnegie Mellon University.

Any further questions or insight? Email Gary at gbeck@buyergenomics.com.

By |2019-11-14T20:53:38+00:00March 13th, 2019|Blog|0 Comments

Find More Good Customers (While Avoiding the Bad)

Who Are Good Customers vs Who Are Bad Customers

Your host Damian Bergamaschi & Stephen, discuss what makes a customer either good or bad. As the episode begins we learn that the topic question is a bit of a loaded question. As marketers, we have to dig in and define what a good customer is for your business. Once you figure that out on the whiteboard you have to then prove it with your data. By the end of the episode both them come to a realization about marketing and its ability to set the expectations that customers expect. This is an important lesson that could help you to avoid the bad customers that detract from your business’s success. 

Below is a lightly edited transcript of Episode 35 of the Inevitable Success Podcast.

Transcript:

Damian: Today we’re back and we’re going to talk a little bit about different ways to think about what makes a good customer and a bad customer. And I know we kind of independently in the office asked a lot of different people, “Hey what would you consider a good customer and a bad customer.” And we all had slightly different answers to it, which was interesting, and even more interesting, I don’t think any of them were flat out wrong. And maybe if we kind of incorporated all of them together we will have a really interesting comprehensive view of what a good a bad customer is. So with that, kick it off, Stephen what would you say?

Stephen: Yeah well, some people say that “I am a bad customer.” Now that’s interesting. There are no bad dogs right we have bad owners. Same thing here. There are no bad customers. So why are they bad? Are they bad because they don’t spend money with you?

Damian: In that analogy, I’m guessing that the bad owner is that like a good or bad marketer right.

Stephen: Pretty much a bad business.

Damian: Gotcha, I don’t want to say that.

Stephen: There’s the good, the bad, and the ugly but I want to qualify this question with one more word to it which is, bad for whatWe want to find out why because we want to sell more things to people right?

In fact, I joked around with my kids, when I’m dead, put “This man sold a lot of things that people don’t need” on top of my grave. Well that’s what we do. Actually, we help merchants to sell things that people don’t need. Well, that’s what we hope happens.

Now is that bad? Well, it gets the economy going, which is a good thing. At the same time if they don’t come back you know of course it’s bad. Good means many things. Again let’s talk about for what later on but let’s put all the qualifiers out in the market and then we decide what primer to use depending on what we want. So let’s start from the top.

Damian: I know some of that is in jest but I’m going to challenge part of that because I think it’s interesting.

Stephen: Absolutely. The first qualifier is the easiest one: People who spend a lot of money. But let’s think about a second qualifier. What if you spent a lot of money only once? The third qualifier. But he didn’t come back recently. All this busy buying behavior that was all three years ago. Is he still good? And then there’s a fourth qualifier which is, yeah but he came a lot three years ago even now he keeps coming back but he only comes back when there’s a big sale. He’s a bargain seeker. Is that good? Well, let’s think about it because you still make money with him. Is it better than not coming at all?

And is he loyal? Let’s talk about that too. What does that mean? That means when he wants to buy certain things, let’s say I’m selling, home audio equipment or, whatever it is. I mean you don’t call it home audio anymore. But let’s say that you deal with an audiophile, does he always come back to you when he has the need in that category? How loyal is he? Well, one more thing, and this is what you and I talked about earlier, will he or would he recommend this store to his friends?

Damian: Right.

Stephen: So we just talked to about six or seven different measurements of goodness. But depending on what you’re trying to do I think the way you look at these things change a bit. You cannot just go after the big spenders. If you’re in acquisition mode. First of all, you have to show up, as in your marketing has to be in front of the person. Will he respond to our offer? That’s another question. That’s even before they are good, bad, or ugly.

Damian: Yeah, I mean if I had to be general about the types of answers that we kind of came across. One was around, you know, the profitability of a customer and that totally makes sense. Obviously, we want profitable customers, more profitable equals better in most cases, right? Profitability is certainly one way to look at it.

The other way that I kind of put forward was, does that customer amplify your business? Or worse than that, detract? I mean I think in that case the dichotomy between a good and a bad customer is even more powerful. Because somebody that has like low profit versus high profit maybe gets lost. But at scale, if you have people who are amplifying your business where people who are literally like net detractors of your business, that’s a really big difference between those two. And it brought the idea that there certainly are things that if you look snapshot in time, and you took let’s say an NPS score of this person is a net promoter of the business and you could talk more about what an NPS is for people who don’t know. But, if at that point in time this person was a net promoter, what does their data story at that moment in time look like? And I bet you could profile people who fit that you know that high NPS just by looking at the data and then possibly either benchmark it or influenced that more.

Stephen: And that is not just by keeping his transaction history because let’s say you are selling, I mean we’re all wearing headsets now, so let’s just talk about that. Tell your boss you sold some amazing headsets. You just did. Yes and lucky so much so that you know I will actually take time, 10 minutes, and write a review and give them a five-star review because I love it so much. Now at that point, you are a one-time buyer, you just dropped about $300 for a headset but you took the time to write a review, which is favorable. Do you really care what the guy did or bought so far? What are you going to say “Oh its a one-time purchase”  so you discount his value? No sir not even in a different dimension at this point, conversely, let me ask you this part, which is a bad review. Have you ever written a bad review for some merchants or service?

Damian: Yes, I have. 

Stephen: Now why is that? Why would you bother to write a bad review? Think about it.

Damian: There’s probably different reasons and different situations but in one case I feel like it’s almost like my duty, you know.

Stephen: Right! So does that make you a bad customer or are you being a good citizen? There’s a dichotomy.

Damian: Yeah, I agree.  I like to say I’m a good citizen.  Said every bad reviewer ever.

Stephen: But let’s not be altruistic for a second. Let’s just think about it as a marketer, why people do that, its because they think there was a mismatch in what the person expected.

Damian: This is such a good segue because I want to go back and challenge something you said earlier. You had made, in jest, you know that you could sell a lot of things that people necessarily didn’t need or want. 

Stephen: We can all make a good argument we don’t know why we want the stuff we don’t need.

Damian: Right, but you know it kind of makes me think that a good business is really just a value exchange where the customer gets what they perceive as a higher value than the dollars in their pocket. And really when the customer says that’s good that’s when the expectations were met or exceeded. And when it’s not it’s when there was a miss. So you know ideally, all my best customers are the ones that I’ve met or exceeded the expectations because they’re going to be net amplifiers to my business. And its kind of almost when I thought about it that way it puts the onus back on, I think the business.

Stephen: That’s what I’m trying to say.

Damian: To be good owners.

Stephen: That’s right! What did I say? There’s no bad dog.

Damian: Right. And in some ways, like you know I think we talk a lot about like what tactic could you do to improve your marketing. And I actually think one of the best tactics you could do is to increase your value proposition. Because increasing your value proposition is what by definition it just makes you more compelling. Exactly. If you’re more compelling or at least you exceed expectations are often going to get amplifiers to your business, you should have higher profits. And guess what? You’re bringing value to people. So…

Stephen: And it’s so much easier to market. Yeah. You know the expression you have the product sales itself. Don’t we wish that we all deal with that kind of product? And by the way the other day I recommended this company that we help our digital marketing and I recommended that print to my kids love that they bought it. You know I feel really good about it. I felt like, oh wow. You know what? We’re helping people to sell things that people actually like. Although this is not the cheapest thing in the market and the category. And but they’re good.

Damian: It’s a good value.

Stephen: Exactly! Value is not about oh, this is the cheapest or most expensive, right? The other day and we kind of joked about it, we had a really expensive running around with that. Yeah, but you know we know where the money went. We’re not complete. But how bad is it? Oh my God! This is so bad that I felt bad for the chicken. They died for it.

Damian: You’ve taken that to a new level. Yeah.

Stephen: They did die for it.

Damian: But I agree with you.

Stephen: The point is we always think about it as bad customer, good customer. It’s not a one-dimensional thing.

Damian: How do you find, at scale, customers that really value your product? And how can you increase the value of your product so that you can basically reach more customers?

Stephen: We talked about jokingly the dimensions of defining the customer. We talked about dollars, frequency, recency, the loyalty, the bargain-seeking behavior, and also will he ever recommend your product to others from a scale of 1 to 10. 10 being yeah, I’ll recommend this hotel every single time. That’ll be 10, right?

Now that we know the parameters. and I sound like a broken record. You have to have some measurements in your database to create such a match so that when you need to find those people for different purposes. Ok, am I looking for high-value customers? No, I’m mixing things like I want a high-value customer who also come often and his last transaction they should be less than 12 months ago and his lowest and highest amount of, I imagine should be this. And now I’m saying this in terms of database parameters.

But imagine a situation where you only imagine what a good customer definition is. You can do that all day long. Huge difference, can you actually do it? You have the data sets that are ready for it. Now that is something that we always talk about. You know, all this customer 360 and all of that. What good is it you cannot answer this question? These are very basic questions, by the way. But he just stored the data on a transactional level and now you have to add up numbers by customers who owe you. And also, yeah, I know the lifetime total but do you know what he did the last 12 months, 24 months, 36 months and so forth? When was the last time he bought this item or that item? These things should be like (Snap) that.

You just want it, you get. That’s number one. Number two, let’s not be one dimensional. Now they have all this data arsenal. Let me give you an example of something that everybody experiences, right? Let’s talk about airlines, for example, we all have complaints about it. You know I always have complaints about their customers too. A lot of them are bargain seekers. They’re not loyal. They are on the plane because you just happen to offer the cheapest price to go to Chicago at that day at that time. That’s it. It’s not that loyal. How do you imagine you get your customers? It’s not that easy is it? OK. So people who pay the full price for that ticket. Is that good? What if you only did that once? People who do that often. Right? People have less time between booking from actual flight time. That means that this guy could be a really heavy duty business traveler and I want to him, right?

Then there’s a good old point system. Every airline company has a mileage system so you could easily use the point system. But what if it’s a point of however and that like redeeming a lot of business? Not redeeming any of it is bad too because that means they’re not engaging with you at all. But there are people who are point seekers. OK so let’s talk about that and there are other people who buy extra services, for cash. Upgrades, you know, extra food or wine or whatever that they sell. Are they good? The question that I have all the time is you know that there’s no one answer.

The first quest, let’s not be one dimensional. The second quest, if you want to have say a full price ticket item, is your database ready to answer the question? Because I know for a fact that even an airline, by the way, they have like more two pages full of ticket codes. If you include all the ticket codes that they use with their affiliate program like Expedia, then it’s even more. So the market asks a very simple question, I want to know who paid full price for their flights. And I want to know how many times that person did out of how many flights per year. If you don’t know how to decipher these things you will take forever to do. So, going back to the original question is your data ready for these things?

And the third of course, is that changing the mindset of the users of this data? That said you know what, now that you have all this data, please think about the goals. In other words, OK is that a good customer? For what? For the longevity of the relationship?  For the value itself? Or the short term value of the long term value? Or will he do this next year type of thing? Or will he recommend this airline to others? For what is also another very important qualifier to that question.

Damian: Yup I mean even in the airline industry like there are certain airlines that they’re going to be of value to different people for different reasons. I’ll pick two what I would think of is extremes. One would be they get picked on a lot is Spirit Airlines. Just extremely you know, it’s basically it’s all about price and then let’s say like a higher end airline for this more luxury I should say like Emirates or something like that. Sure. Right?

Stephen: Emirates is not that expensive by the way.

Damian: Yeah, I know. But they’re going to have vastly different you know value propositions. Very different customers. They’re going to meet different expectations. The customers themselves are going to have different expectations. And I think getting that right match of the right customers when the expectation of the customer meets the expectation that the brand is setting. Then that’s ok. That’s a good customer.

Stephen: But you know what the bad customer is? I’m going to be selfish and say this. Sure. People who want a Mercedes level car with a budget for the Hyundai Excel. Now that’s a bad customer. You don’t even know what you want. I agree. You know that disparity between what you expect and what you get, well that gets frustrating. You cannot get a Tesla with like $10000. You just can’t.

Damian: Like the expectations minus reality. It’s a happiness philosophy.

Stephen: I don’t want to be too about it but I think the one thing that…

Damian: I agree.  You can’t please everybody.

Stephen: We can’t. In a capitalistic society, we are all entitled to something. The sense of entitlement cannot be too bloated either.

Damian: You know what I actually think this is one of the spots where… We covered a lot of stuff here. This is one of the spots where I think marketing in this truest sense can be very powerful because you know communicating something the right way through messaging, creative, even frequency and timing of when you do this can be very powerful and in some cases be almost polarizing to part the sea so you get the good customer and avoid the bad customer.

Stephen: Ideally, you have a really interesting point that I never thought about before, the function of marketing. It level-sets before somebody walks into the store, that yeah this is what you’re getting. If you like it, come. I’m going to make it sound like it’s the most important thing that you ever get. Of course in the market that’s what we’re going to do. But also you kind of mind these things like a guarantee that nobody will promise too much. It kind of levels sets what the value is. This is why you go all the way to Brooklyn to buy a TV set. Why? Because you are a bargain seeker and this guy is sitting in Brooklyn. Problem is that this is the cheapest TV price in New York. Well then, they’ll say it’s not. There’s no conflict. Yes. There’s no good or bad there.  But when that disparity happens, everybody is unhappy.

I mean you’re going to get bad reviews.

Damian: Like every relationship ever, most of it is communication. Right.

Stephen: So I mean I didn’t know you meant that. I mean let’s take it on and talk.

Damian: Well like I said we hit a lot. I think next time. Always fun. Makes me think every time we speak and you know I’m going to be more cognizant about setting expectations, communicating the value, and marketing as well making sure the data sets are ready so that I can act on things when I see it. That was a key message.

Stephen: But also, we wanted to provoke some thoughts today.

Damian: You provoke thoughts?

Stephen: Well here’s the thing if you want to learn something please read our blog. Yes. You want to just chat and think about things in a very different way. Maybe this is the right form. I agree.

Damian: Alright. Well, thanks again. Until next time. Thank you.

By |2019-11-14T20:59:44+00:00February 22nd, 2019|Inevitable Success Podcast|0 Comments

Transforming Your Data Into a Marketing Plan

The Guide to Marketing Nirvana [Step 2]

At this stage along the path to Marketing Nirvana, all of your POS and transactional data have been consolidated and filtered through PMA software.

This means that all of your retail information has been stitched together across all available channels – generating the template for the omnichannel, 360-degree customer view that the mega-retailers use today.

By using this comprehensive view of the customer, marketers can build their marketing plans with greater insights to address media spend, price promotions, special campaigns, and specific strategies for segments of their customer and prospect universes.

Insights include answers to the following questions:

  • Who are my customers?
  • Where can I find them?
  • What message do I deliver?
  • Which channel(s) should I use?

Who Are Your Customers?

The first plan of action is to enhance your customer transaction information with information that can help you build a marketing plan. List brokers, data brokers, and major compilers each offer hundreds of different customer attributes (or variables) for marketers to choose from.

Three critical types of actionable intelligence are available (from different sources, offering differing degrees of customer coverage):

  • Geographic/Demographic – income, age, region, dwelling location/type.
  • Psychographic – lifestyles, interests, hobbies.
  • Behavioral – actual buying behavior/purchasing tendencies.

These different types of data offer marketers the essential raw materials to build a rich understanding of who their customers are and how best to communicate with them. There are thousands of permutations that can potentially be of value.

As you would expect, all of these variables have a cost associated with them. The data is not free.

Therefore, understanding how to use all of this information both wisely and cost-effectively can quickly become overwhelming – especially with such a vast range of personalized variables for sale throughout the marketplace.

But if you’re following these steps, there is no need to worry. PMAs takes care of the hard work for you by finding segments of customers that behave similarly and profiling them for you.

In other words, PMAs automatically find groups (or clusters) of customers that help divide your total market into segments that behave in similar ways with respect to your product offering.

Market Segmentation

A logical question at this point is, how do PMAs help you arrive at the best segmentation solution for your total market? A common technique deployed is a statistical method called cluster analysis.

Cluster analysis creates groups of customers based on maximizing homogeneity within groups and heterogeneity between groups of customers.

Each of these groups (or segments) can provide extremely practical information about your customers’ lifestyles, lifestages, incomes, interests, and – perhaps most importantly – the likelihood that they will purchase your products in the future.

There are many marketers who are not familiar with 3rd party data solutions. Even those who have some rough familiarity have likely used them less than they would have liked because of their high historical costs. However, in today’s world of “big data,” those costs have gone down significantly.

With all of this precious information out there, retailers need to seriously consider how to take advantage it.

Theoretically, you could peruse through all of the data available on the planet and create a custom cluster analysis that points to which variables most closely match your ideal customer base.

However, while some very large companies pursue this path, such a method is time consuming, expensive, and (worst of all) potentially wasteful if you wind up making poor decisions along the way.

Through its efficient utilization of third party data resources, a PMA makes your data smarter via perceptive insights and practical profiles through the distillation of demographic and psychographic database attributes.

By already pre-clustering all of the consumers in the US specifically for you and your business, you have a running start on better understanding your customer universe without the high costs of distilling a custom solution.

Instead, you can spring into action right away. And, you can always add new data to your database later if it makes sense to do so.

Lifestage Migration

Powerful marketing opportunities arise when a member of your target audience transfers (or migrates) from one lifestage to another.

Your odds of customer acquisition dramatically increase when equipped with a repository (PMA) that can keep track of these shifts, respond to triggers, and autonomously deliver timely, personalized offers.

An example of lifestage migration is when an individual or couple moves from a rental apartment to a single-family home for the first time.

According to a NAHB study, buyers of new homes outspend non-movers by 2.6 times on such items as appliances, furniture, and remodeling.

Wouldn’t it be great to be able to send a timely, personalized discount offer for new furniture, kitchen appliances, roofing, landscaping products, etc. to someone you know for a fact is a new/prospective homeowner?

Well, it turns out that you can.

That ability alone gives you a significant competitive edge over the rest of the competition.

Actionable Segmentation and 1-to-1 Marketing

As with any marketing segmentation solution, the goal is to create a set of actionable segments that are both well understood by your organization and stable over time.  

These segments typically consist of a set of available buyer information (including external data and product information) that allows you to better engage customers, automate the right messages to send, further understand the buyer lifecycle, and create new customer journeys.

For example, there can be multiple categories of clearly distinct populations that shop at a particular retailer.

If you want to get the most out of each group, you must market to each separately with deliberately distinct, exclusive messages.

When initiating any form of customer communication, your message should be as relevant as possible. This ensures firm engagement and an increased likelihood to respond.

In the retail example, a younger demographic might be much more receptive to sales on the latest fashions, while an older demographic might be more inclined to lean toward more traditional, conservative brand offerings. In turn, target your messaging to those who are most likely to be receptive.

Thorough cluster and lifestage development allows marketers to identify the natural pockets of opportunity for each customer without overwhelming them with unnecessary noise.

Blindly blasting universal messages to all sectors of your base will skyrocket the amount of irrelevant messages delivered. At best, those messages will be ignored. At worst, it could turn customers off from your brand altogether.

Execution Considerations

While developing multiple segments for a retailer, the approach should always be as pragmatic as possible.

Start by asking yourself a few questions:

  • How many segments can your organization support?
  • How many segments will allow you to maximize marketing effectiveness?

The answers can get tricky, because when a retailer deals with multiple segments, they should work to tailor their approach differently for each one. While there isn’t one set of answers that works for everyone, the considerations are the same for all companies.

Let’s say you sell food products. One segment of your base is vegetarian, one is gluten free, the second is both, and the third is neither. Each group should receive very different sets of promotions and recommendations.

This costs time, money, and resources to develop.

When you begin to grow and expand the number of segments you are marketing to, you also increase the amount of work necessary to cater to each one. This can get overwhelming fast, particularly for smaller retailers.

This leads to a key challenge – figuring how many segments are necessary to maximize effectiveness, while weighing that against the internal cost of maintaining the amount of segments and messages.

A PMA helps solve that quandary by providing swift personalization for each identifiable segment.

Regarding email messages, PMAs can deploy personalized, one-to-one blocks of relevant communication autonomously. This allows smaller retailers to be more productive in their marketing efforts across multiple segments with less overhead and greater traction.

Again, the heavy lifting is already done for you.

Where Do Your Customers Come From? How Do They Purchase Your Products?

Many small and medium sized businesses lack the capacity to track the range of factors leading up to the first point of contact with each customer.

This is a shame, because knowing where and how a customer relationship started may actually be more important than knowing the point of purchase itself.

With a PMA, you can capture valuable customer acquisition information, connect all of the dots together across channels, and build a strong data foundation going forward.

Acquisition Channel Analysis

The acquisition source – or the method/channel through which a customer relationship was initially formed – is highly predictive of how customer behavior will evolve down the road.

Say a buyer first came into contact with your company through your website. Perhaps they arrived at your site in the first place by clicking on a social media ad. From there, he/she subscribed to your email list and purchased a product on their iPhone with a discount code.

Meanwhile, another buyer actually walked into one of your retail stores and used a physical coupon from a mail-order catalog.

Wouldn’t you develop your discourse with the two differently? Certainly.  

If you have a group of customers who are online-only shoppers, stick to the digital realm. If others are frequent in-store shoppers, focus on that realm, but still always leave the option to purchase online as well.

By understanding which communication channels are most important to each customer in your base, you can develop the right respective combination of media to reach them through.

This practice helps to determine where to invest your time and money, reduce wasted efforts, maintain relevance, and optimize Customer Acquisition Cost (CAC).

From there, you can lead them further along  the right path on the customer journey by establishing the best messages to entice subsequent purchases.

This type of predictive element is extremely valuable. After all, the ability to accurately forecast what buyers are going to do next is something that all marketers strive for.

What Products Do They Purchase?

Obviously, you can glean a great deal of customer knowledge by tracking what types and categories of products they buy. From there, the objective is to maximize their lifetime value through new purchases and services down the road.

Market Basket Analysis

Market Basket Analysis is a form of statistical analysis used to determine which products customers normally purchase together.

For example, someone who purchases a surfboard is likely to buy another related item/accessory in conjunction – such as a wetsuit.

By understanding how products are purchased together, marketers can guide the dialog with customers in a way that is both helpful to the consumer and profitable to the company.

Another example could be after someone who purchases a particular brand of electric guitar, carefully tailored suggestions would arise suggesting typical pairings with certain amplifiers or effects pedals.

Meanwhile, a poor example of this method would be if a customer who just bought a lawnmower received subsequent suggestions about other lawnmowers instead of other supplementary products like hedge trimmers or leaf blowers.

Mistakes in the form of duplicate or irrelevant pairing suggestions dramatically decreases your chance of adding items into your customer’s cart, and can even damage your reputation in the buyer’s eyes.

Top retailers who successfully implement this practice consistently – such as Amazon – are able to maximize both quantity and price of each purchase through this practice.

This is because they had the resources to develop high-end software capabilities necessary to do so. Without a similar toolset (like those found in a PMA), how could others expect to do the same?

Cross Category/Single Category

How broad or narrow are your buyers’ interests?

If you sell winter sporting gear, an example of a single category buyer would be an individual who only purchases skiing equipment.

Meanwhile, a cross-category buyer would be someone who buys both skiing and snowboard equipment.

For instance, if you know a buyer is only interested in skiing, you would know to not waste your time suggesting anything beyond that category – even if it is technically something similar like snowboarding.

Accurately differentiating between various cross and single-category buyers serves to further personalize the offers you send to them. Is someone a hiker, fisher, and a camper? Or just one of those?

This is just another example of a simple, yet powerful concept and practice that are often overlooked by retailers.

What Are Their Needs [And How Can We Meet Them]?

Ultimately, marketing is about people, not products. Therefore, it is extremely important for retailers to understand the customers’ requirements for their goods and services.

How do customers want to be viewed and treated by the brands they purchase from? And, how does your product meet their needs better than the alternatives in the marketplace?

Simply speaking the customer’s language and catering to their needs accordingly holds a great amount of weight.

For instance, there are certain people who need to feel like their products are trending. Those who want the latest and greatest often desire feelings of prestige and recognition from both their sellers and their peers.

Some also wish to be viewed as insiders with access to exclusive offers, products, and treatment that others do not receive.

Meanwhile, there are others who always want to feel like they are getting the best deal possible. Focus on providing them with information on your latest sales and discounts.

With the proper tools necessary to conduct proactive data and survey analyses, you can easily understand the needs profile of each particular customer segment and design your mode of communication with each cohort differently.

Understanding the Buyer Lifecycle

The term Buyer Lifecycle (BLC) refers to the natural progression of a customer/retailer relationship. It depicts your customer’s relationship and brand engagement from the moment that they become a prospect all the way up until their final purchase.

All customers fall into one of the following stages:

1. Prospects Not yet customers.
2. Actives Individuals currently engaged and/or spending with you.
3. In Market Buyer currently shopping for your products and are prepared/likely to buy again
4. Faders Subjects no longer purchasing at the rate their customer profile suggests they can.
5. At Risk Buyers most likely to stop spending with your brand and fall into attrition.
6. Inactives Customers who have ceased purchasing your products.

Each sector of the BLC involves a different approach and strategy, and a proper grasp of its intricacies help to better understand each customer and consistently target them with relevant information. We’ll talk in-depth about the Buyer Lifecycle and suggested marketing actions in Step 5.

Conclusion

It is always the marketer’s goal to understand the triggers that are likely to create sales in order to build successful campaigns.

The best way to do this is to know who your customers are, where they come from, what their interests/inclinations are, which channels they frequent, the types of products they buy, and how they want to be treated.

A PMA platform stacks the deck in your favor by giving you a cost-effective repository of marketing information that supports smart, sophisticated segmentation right from the moment your rudimentary transactional/POS data is uploaded (see Step 1).

Therefore, not only can you possess an advanced, expansive database that captures and categorizes comprehensive customer information in real-time, you also have access to vast amounts of previously inaccessible discrete demographic, psychographic, and behavioral information.

All of these factors combine to formulate the core components of each individual customer’s genetic code – or Customer Genome℠.

Now that you’ve put in the effort to know who your customers are, you have the ability to apply that information towards building intelligent, targeted, personalized, cost-effective marketing plans and strategies.

This leads us to the third phase of our series – learning how to dig deeper and narrow the scope even further to Create Customer Journeys and Segmentation Strategies.

About the Author:

Gary Beck
Gary BeckChief Strategy Officer
Gary’s background includes over 30 years of analytics & database innovation for several leading Fortune 500 companies and Madison Avenue advertising agencies. Gary has been a frequent lecturer and author on the topics of database marketing and applied statistics. His articles have been published in DM News, Direct Marketing and the Journal of Direct Marketing. He recently was President of the Direct Marketing Idea Exchange and served on their Board. Gary received his M.S. in Industrial Administration from Carnegie Mellon University.

Any further questions or insight? Email Gary at gbeck@buyergenomics.com.

By |2019-11-14T21:03:55+00:00February 19th, 2019|Blog|0 Comments

How Do You Choose What to Automate?

A Marketer’s Dilemma: Choosing What to Automate

This is our last episode of 2018, and we’re thinking ahead to next year. One of the big questions is what are we going to automate in 2019? This episode discusses the importance of planning and setting specific goals when it comes to automation. We’ll talk about both the benefits and the limitations of automation, and how you can combine automation with a human function to get good results.

Below is a lightly edited transcript of Episode 34 of the Inevitable Success Podcast.

Transcript:

Damian: We’re thinking a lot about what we’re going to be doing in 2019, and one of the things that came up is what are we going to automate in 2019? Stephen, you had a very interesting response to that, which was…?

Stephen: Do you know what you’re automating? You automate things that you know how to do already. Automation is not about creating something out of nothing. That means you need to have a goal first. We talked about that when we talked about the benefits of modeling in general. Modeling doesn’t give you any answers—machine learning, or AI, or human-made models—it doesn’t matter what it is. You have to know what you want out of it, and deciding that is a uniquely human function. Nobody is going to do it for you.

Damian: Yea and one of the other things that I thought was interesting is if you try to automate things you really don’t know the answer to, who knows what the expected outcome is? What could you expect as the outcome? It would probably be bad.

Stephen: That’s right. The whole of automation is really two-fold. One is to cut time so that we do things faster. Two is to do it with fewer people. Not good news for a lot of the workforce out there, and there’s a reason why a lot of publications talk about how many jobs will be gone if AI takes over. Some of them are total science fiction, but some of them are not unfounded. A lot of people will lose jobs. Let’s face it, though—we automate things to cut time and to cut human resources. That’s it.

Damian: You know what’s interesting—to kind of flip that on its head a little bit—there are so many businesses out there that have one or two-person marketing teams, and their revenue doesn’t justify having more than that, regardless of whether they are amazing or not. I actually think that if a person really studied and became a student of how to be a good automator, that’s a massive opportunity, because then they could say, “I’m the guy (or the girl) who can do the job of ten people by myself, because I have this kind of technology.”

Stephen: But let’s say that you’re a marketer, and you have a lot of jobs or things to do on your list. You have to break it down from the point of view of not just the things you don’t want to do, but also what is the most time consuming and what is repetitive. You automate those things first.

Damian: That’s a great thing. I think that’s something you should write down. Stop and think about what you did in 2018 (or any year), and think of the things you did over and over again. Those are repetitive tasks, so can you be thinking, write down some rules, find some sort of technology, to outsource those tasks to a machine.

Stephen: Now, if you decide that a task is right for automation, then what’s next?

Damian: As I was saying that, I actually think that one of the next questions to ask would be what are all the things I was supposed to do, but didn’t do? Because I probably won’t do them again next year.

Stephen: Well, if this is about writing a new procedure for something, then a machine is not going to do that for you; you have to do that yourself. Now, let’s say that you isolated a task or a bunch of tasks that you want to automate. Now you have to think like a machine, even if you’re not a coder. So, imagine you have thousands of offer codes, and it’s in really bad shape. You want to automate it, because you don’t want to go line by line and clean it up yourself. Well, there has to be some logical way to express that command, otherwise, no one can program it.

Even if you use the pattern recognition module of machine learning, you still have to teach the machine what it’s supposed to clean up. You have to do it in a way that converts your thoughts into logical steps. I heard about a bunch of enthusiastic young mothers who now not only teach their kids foreign languages and math skills early on, but some of them also wanted to teach them how to code. It’s a very noble idea. Do you know how they teach code to four or five-year-old= kids?

Damian: I’m very interested in this for two reasons, one because my first-born could be born at any minute—we’re shooting for the next couple hours, but it’s probably not going to happen—but the other reason, and I’ve said this a bunch of times, is that I think it’s an amazing thing to learn as a kid. So how do they teach it?

Stephen: They take a task of say creating ramen noodles. You want a machine to open up a packet of instant ramen noodles, put it in boiling water, and start cooking at the right temperature. You know how to do it, because you’re a human being. You know when it’s done. But let’s say that machine has no idea what cooked ramen looks like. Write an instruction from step one all the way to however many steps it takes. That is the first lesson in coding. What would be the first task, do you think, if you were making a packet of ramen noodles? Let’s say that you have pots and pans and a packet of ramen. What would be the first step?

Damian: So, I have all the ingredients? Oh man, this is a pop quiz.

Stephen: Yea, you just have to do it. There’s no right or wrong answer. By the way, a machine should be able to do it.

Damian: All right, I think it’d be something like, “Reach out with your dominant hand.”

Stephen: Well, a machine doesn’t have a hand!

Damian: Well, there you go.

Stephen: Let’s just say such a machine exists. I would say that you have to talk about the measurement of water. How much water do you need? Let’s say 450 mL. Then when does it boil? Up to what point? You have to teach the machine when the boiling point is, so that means you need the module that measures temperature, or some observation like there are a bunch of bubbles coming off the water. Now it’s boiling. What do you put in? Open the packet. How do I do that? Cut it from the top, from the bottom, sideways, or look for some indication of a cutting line?

Damian: Wow, we somehow turned this into a cooking show, I don’t know how we did it.

Stephen: Yea, because everybody can relate to this. The point is, let’s say that your task is to clean dirty data—I was just looking at some data this morning, and do you know how many variations of Facebook I saw there? Facebook is a good source, right? So, can you imagine all the variations I saw? You could have Facebook with a lowercase, it could be www.facebook.com, it could be m.facebook.com, or it could simply be FB, etc. The point is, now you’re not doing it, the machine is. Now you have to think like we did with the packet of ramen. Where do you start? Is that FB a combination, or do I give an example of what FB could be, or let the machine just do some deep learning? If it’s learning, you have to tell them FB is right and FBC is wrong, because the machine is not going to know.

Damian: That rule could be used in a lot of places, because most often, when I’ve encountered the need to write something like that, it’s been for reporting. I actually think that’s a great thing to automate if you can, because typically that’s something you know, and it’s repetitive, and sometimes it’s something you should be doing but aren’t doing, and sometimes it’s something you really shouldn’t be spending so much time doing, but you are.

Stephen: That’s right. You also have to think like a machine does. What if none of the rules that you give to a machine capture every error that there is, and yet you even have to think about the fact that if you do all these things in a very exhaustive way, and you still have some things to clean up, call operations, or whatever. But if you just don’t say it, the machine is not going to do it for you. You have to say it explicitly.

Damian: Yea, I definitely think there’s a place for troubleshooting and Q&A (quality assurance) on anything you go to automate. It’s actually a good point. When you’ve automated something that is not a last step, you need to look at that.

Stephen: That’s absolutely right. There’s a meme floating around—let me paraphrase it, because I don’t remember exactly. Do you know the difference between machine learning and deep learning and AI? Very simply, AI will correct their own errors, whereas machine learning still needs human beings to correct them. It’s a very simplified version of the explanation, but we use these words interchangeably anyway, and probably they’re not wrong. For a layman, who cares, as long as they’re not the one doing it.

But the point is, you are the one with the goal for what the machine is supposed to do. Is it about finding some patterns that are useful for a future sale? Is it about building an actual model to predict who is going to be the most valuable customer? Or is it about sorting things so you don’t have to sort them to find the ten most valuable leads? What is the goal? It’s not about math or whatever.

You have to have the goal, that’s number one. And number two is to determine if this repetitive and automatable? Let’s say that finding the best lead is the goal. Let’s say the machine produced a lot of high scores. From the machine’s point of view, with the data it has, that’s all it could do. Now you have to break the tie—how do you do that? In that case, there is human intervention. A person ultimately makes the decision based on the results the machine produces.

So that is the point: you don’t have to be the coder, but you still have to think in terms of a coder and think about what the machine needs to break things down. Think logically in terms of how you are going to instruct the machine, and do you have all the ingredients to do full automation. That would be my advice to marketers who want to go to the next level with AI to make their businesses faster and better. When it comes to what the machine is actually supposed to do, you have to think about it for awhile.

Damian: I agree. In closing, I would strongly encourage, if not challenge, the listener to write down two or three things that you plan to intelligently automate in the coming year. Then think about what you are going to do with all that time you just opened up, because that is where the ROI is.

Stephen: Right, well, there’s no shortage of work I hope.

Damian: Well, if you just saw automate what you did last year, and then don’t do anything with that freed up time, you’re going to get a flat ROI. You’ll have more time, maybe your golf game will get a little bit better.

Stephen: Hopefully you’ll think a little more about marketing, but you know, you’re right. There’s a joke among coders that the laziest coders write the best macros for automated modules because they don’t want to do it again. So, some laziness is a good motivation for automation, yes, but with that extra time, hopefully, you come up with a wonderful idea of how to sell better in terms of new ideas and new products, not just repeating things that you’re doing all the time.

Damian: Right, well, on that, Happy New Year!

Stephen: Happy New Year! This is the year of the pig, I believe. It sounds prosperous, so happy and prosperous new year to everybody.

Damian: If you enjoyed today’s episode, we ask you to please leave a rating and write a review. Or, better yet, share with another marketer. Be sure to subscribe to the podcast for new episodes. Also, check out the show description for complete show notes and links to all resources covered in today’s episode. If you’d like to speak to someone about any topics covered in today’s episode, please visit buyergenomics.com and start a chat with the team today.

By |2019-11-14T21:08:02+00:00February 12th, 2019|Inevitable Success Podcast|0 Comments

How to Build A Customer Database

The Guide to Marketing Nirvana [Step 1]

In this age of big data, data shortage is not a problem. In actuality, the opposite is the case. Retailers are frequently drowning in their own data, triggering an overwhelming sense of anxiety and disquietude.

Not only that, these companies typically do not have access to the resources than can transform this mess of data into something accessible, actionable, and profitable.

This first installment in our 7 Steps to Marketing Nirvana Series will outline how to eliminate data overload and transform your marketing team into data overlords.

Revitalize Your Customer Portfolio

The general reason most companies’ databases are inadequate, incongruous, and insufficient is that their Point of Sale (POS) systems were developed to solely ingest transactional data, which consists of five key categories:

  • Who bought? (Sometimes)
  • What product?
  • For how much?
  • When?
  • Through which channel?

While such information can be useful on its own, this is merely scratching the surface of what is truly possible. The fact is, these rudimentary systems have not been designed with the long-term relationship of a customer in mind.

For instance, POS systems do not track the trails leading from one purchase to another, and also do not attempt to understand the stimuli that incentivize each subsequent purchase. In addition, they are not directly actionable – rendering your data management abilities a far cry from their full potential.

Ingest/Filter Your Customer Transaction Information

Marketing software solutions have evolved dramatically over the past few years – offering small and medium sized businesses capabilities they have never had before. Predictive Marketing Automation (PMA) Software is one such class of software.

In contrast to POS solutions, this type of solution maintains customer databases, predicts the relative value of customers and automates marketing communications.

In turn, the first priority is to get your POS data organized, cleaned, and filtered for the sake of maximizing data hygiene. This is done by uploading all of the records into a PMA’s standard data model.

Think of it as cleaning a messy garage, fine-tuning a car in a body shop, filtering dirty water, assembling puzzle pieces together, or weeding a garden.  

Keep in mind, one of the main goals of marketing is to know as much about your customer as possible.

Following ingestion through a standard data model, all of your retail information becomes stitched together across all available channels. This generates the omnichannel, 360-degree customer view that the mega-retailers strive for.

Without such an expansive customer viewpoint, it is impossible to accurately calculate anyone’s customer journey or respective Lifetime Value (LTV).

By simply uploading your customer transactional data into a PMA, you are already well on your way towards developing the comprehensive customer portraits, strategies, and services possessed by the Amazons of the world.

Make no mistake – without this level of comprehensive knowledge about your customer base (along with the ability to manage that knowledge effectively) your chances of succeeding – let alone competing – on a major league level are incredibly slim.

Define and Create Reference Tables

Reference tables are constructed from the various types of transaction codes used to track retail or online sales in POS systems. These typically include customer data, transaction data, and product data.

For example, if a buyer enters a furniture store and purchases a couch on clearance (where the price has been dramatically reduced), that particular item would have a special transaction code indicating the markdown in price.

This simple set of information can be used to form a clearer profile of each buyer. For instance, if the person who bought the couch also purchased other items on sale (either that same day or over a longer period of time) he/she could be categorized as a discount buyer.

Every nuance of each purchase (full-price, discount, coupon, time/date) that is captured by POS systems can be ingested into a PMA and built to form reference tables. Or, in other words, PMA’s ingest transactions codes and provide the magic “decoder ring” to make sense of the data.

Compile and Access Promotion History

One of the most valuable elements of a PMA is the establishment of a promotion history facility. This systematically tracks and deduces how each customer responds to various forms of communication and/or solicitation (email, messaging, offers, etc.).

The digital world we inhabit is a closed system in which any form of online activity can be readily tracked and stored.

Therefore, if your company sends out a promotional email to its customer base, a PMA can tell you what particular strides each recipient made towards a purchase. For instance:

  • Who opened the message?
  • Did they click through to the site?
  • What items did they browse?
  • Overall, how engaged is the customer with your brand?

Any of these bits of information can offer powerful clues about exactly where each of your customers are along their respective journeys and how to best manage the dialogue (or curriculum of communications (COC)) with them.

For instance, if a customer left an item in their cart, send a simple reminder within the next few days. This little nudge can be the final trigger towards making the actual purchase.

Marketing is all about relevance. Your chances of securing new customers or inciting repeat purchases from your existing base directly correlates to your ability to send the right message, to the right person, at the right time, via the right channel.

Define Your Metrics

Another component of this process is to define the most important set of metrics (also known as Key Performance Indicators (KPI)) that are specific to your business. These are ways of measuring your customer management and overall company performance.

Different cohorts of customers should always be treated differently. Some metrics can be more valuable than others in different contexts – depending upon the terms of the behaviors you are attempting to trigger in your customer base. Therefore, the most pertinent metrics should be defined and consistently evaluated.

Customer Acquisition Cost (CAC)

A crucial metric to evaluate in this context is Customer Acquisition Cost (CAC). CAC is determined by dividing all of the costs spent on obtaining new customers by the actual amount acquired in a particular time frame.

Ultimately, the goal is to minimize your CAC as much as possible by carefully assessing your Return on Investment (ROI) for each customer won.

Lifetime Value (LTV)

CAC should always be measured in tandem with another important metric – Customer Lifetime Value (LTV). This determines how much a customer is worth to you over their lifetime.

Customer value metrics can also be assessed in relation to a number of different factors and time frames (i.e. 1-year, 2-year, historical, etc.)

Historical Value

A simple metric is to examine how much a how much money a customer has spent in the past over various time frames.

For instance, say that a frequent flier has flown 1 million miles with an airline over his lifetime, but now no longer flies as much as he used to. Meanwhile, another customer has flown less overall (say 500,000) but much more within the past year.

Despite the fact that the former has flown with the airline twice as much overall, he would likely not receive the same degree of perks as the latter. This is because the frequency of recent miles outweighs the overall number of miles over a longer period of time.

Projected Future Value

Projected future value (or potential value) is a key metric that is often overlooked. If you blindly assume that every customer has the same potential value, then you are likely missing out on a range of opportunities.

You should always aim to recognize customers for what they’ve done in the past while also incentivizing future behaviors leading to more subsequent purchases.

For instance, if you do not know what a target or cohort’s potential is, how would you be able to truly define your level of marketing success? Your company should have the ability to answer a few key questions:

  • Are we achieving the full potential of each type of customer that we have in our database?
  • How much should we invest in each customer in the future? Via which channel(s)?

The bottom line is that a PMA system can evaluate both historical and current customer behavior to calculate the respective value of any customer down the road. This is an easy, accessible way for marketers to justify marketing expenditures by using the software to show their finance departments exactly how investments in particular omnichannel campaigns are delivering the required ROI.

Category Value

Category value is a calculation of how much and how frequently a customer spends on your particular product or service in proportion to others..

While many marketers would love to have a clear, accurate view of category value, they often lack the resources and technology to do so.

Let’s go back to the airline example. Say a customer flies a total of 100,000 miles per year, but only travels 25,000 with your airline. A PMA can dive into survey data, cluster/cohort analysis, and gaps between activity and explain why that particular customer is spending their money elsewhere.

With this beneficial knowledge at your fingertips, you can tailor your interactions and offers to that customer in order to win back a greater percentage of their buying potential while gaining an edge on your competition.

Conclusion

By this point, your customer and transactional data have been uploaded into PMA software, where it has been systematically organized and filtered.

Not only do you now have a clean garage, you also have a well-oiled, fully fueled vehicle ready to hit the road on your path to Marketing Nirvana. But in order to get there, you need a roadmap identifying who your customer base is, and where to find them.

For a breakdown on how to design your custom customer roadmap, be sure to move ahead and dive into our second installment in this series – Transforming Your Data Into A Marketing Plan.

About the Author:

Gary Beck
Gary BeckChief Strategy Officer
Gary’s background includes over 30 years of analytics & database innovation for several leading Fortune 500 companies and Madison Avenue advertising agencies. Gary has been a frequent lecturer and author on the topics of database marketing and applied statistics. His articles have been published in DM News, Direct Marketing and the Journal of Direct Marketing. He recently was President of the Direct Marketing Idea Exchange and served on their Board. Gary received his M.S. in Industrial Administration from Carnegie Mellon University.

By |2019-11-14T21:12:19+00:00February 6th, 2019|Blog|0 Comments

Trusting Your Marketing Results: Causation & Correlation

Causation vs Correlation

Correlation can be an amazing tool to discover causation, but sometimes it’s just too expensive or not worthwhile to even go that far. If the correlation works and you test into it, that doesn’t mean you break out an extra million bucks. You test into it and if it holds up and it’s true over time then make money with it. Don’t worry about it. Go solve another problem.

Below is a lightly edited transcript of Episode 32 of the Inevitable Success Podcast.

Transcript:

Damian: Google is literally saving lives. Are they? Maybe, maybe not. So, in a recent study that we had found since 2006 to 2011 the murder rate in the United States has dropped every single year a near-perfect correlation with people shifting away from Internet Explorer and Edge to Google Chrome. So is Google actually improving the safety of the Americans? Or is this correlation versus causation?

Stephen: The short answer is we don’t know. Maybe, maybe not. And if you took any economics classes in college they say, “Yeah every time there’s a war, the U.S. economy grows.” So war is good for the U.S.? Well if you just look at it from an economic stance it’s not war. Is the war the cause of all this? Maybe, but we are not here to have a philosophical discussion about causality vs. correlation. We’re here to say that marketers, especially when you’re dealing with a lot of data, we see interesting correlations all time but do we jump to conclusions or do we take a step back and say, that sounds interesting but do we act on it? I guess the long and short of it is, no just act on it if the coalition is really, really strong and if it makes sense, not all the way digging back to causality.

Damian: If we kind of go back to the Google example, I think it’s cute and it’s funny. It’s most certainly not true that it’s causing it. It’s certainly true that it is correlated though, and I think in today’s world as everything that turns into data and there are more data sets that are easy to compare to each other, you’re going to find more and more correlations. So I think the point that you’re making is that sometimes these correlations can tell you stuff that is actionable and can make you money, and sometimes you can be wrong on the causation and it can still work and I think that’s what we’re talking about.

Stephen: That’s what I’m trying to say. And also in the predictive business, we talked about predictive analytics some time ago, let’s bring back what it does and doesn’t do. Well actually they do a lot of things, but there are easier things to predict and harder things to predict. For example, predicting who’s going to do something. The who part, yeah that’s really established. Do you want to sell something? Who’s going to buy what, we know how to do that pretty, pretty well. So who means – okay who is more likely to go on a luxury cruise? Okay. With all the demographic data in past behavior can predict that. If you flip that and say that this person is coming to the store all the time, what is he going to buy next? We can do that too. How do you think that all the collaborative filtering happens on Amazon – if you buy something – oh he must be interested in that too. Well, they’re predicting what you’re going to buy the next. The second hardest thing is when. Okay, fine you’ve predicted that somebody is into luxury goods. Will, she buy some really expensive Italian handbag this Christmas? Now that’s hard because now you have some other type of empirical data to know exactly when. This is why in the marketing world what we call hotline names is so important. Or anything, like for example, I just moved by the way, and I must have left a lot of trails. In fact, it was a little spooky because I said something about moving in Facebook and before you know, it all the Street Easy ads are starting out on my wall.

Damian: Right.

Stephen: So I said, “Well this is interesting, they must be listening to everything that I say now. It’s OK because I kind of bought into it and this is what I do for a living too, so I got to say you know it’s okay.” You know? But it’s still innocuous. The point is we know how to do these things, we know how to read, so even the when part is not impossible. Yeah, this guy’s giving user data. In fact, there’s no model, there’s no predictiveness, they just responded to what I said. Now, what is the hardest thing to predict? It’s why? Why do people do things? We don’t know that.

Damian: Well actually, I wanted to see – we were talking a little bit earlier and I know you have an example from your past client experience where the correlation was very profitable.

Stephen: Oh it happens too.

Damian: Yeah? And I have an example from my past experience where the correlation was very unprofitable. So let’s, I think we can go through both. Why don’t you jump off, I think it there was the septic tank example.

Stephen: Oh septic tank yes, this happened in real life. We were helping out a luxury furniture catalog and online store. And we were building models to find out, again let’s talk about who. Who is more likely to buy furniture through a catalog.

Damian: Right.

Stephen: This is not cheap furniture by the way.

Damian: Okay so premium catalog for furniture, okay.

Stephen: And then they’re building models with all kinds of data, all kinds of behavioral data, behavioral meaning that he something similar in other places that type of thing, and the demographic helpers, also income, what’s the gender, head of household, age, all that stuff. And then all of a sudden this census-level data popped up and it was a percentage of septic tanks in a neighborhood, popped up in a model as a very strong variable. And by the way even when something is really highly correlated, we don’t use just one variable, that’s not even a model, that’s more like your gut feeling. But we don’t do that. But that popped out and we all scratched our heads. What does this mean? So again, is this causality? If you have a septic tank you do this? And then we realized that no, it’s telling us something. We’ve got to trace back, trace back to see if it makes sense.

Damian: It certainly was correlated though.

Stephen: It was strongly correlated. So we said, okay so let’s just say that what people have a septic tank? Well their house would be a bit large right to have it, and the town should be pretty far away from the city center to have a septic tank, you don’t even have a sewer system connected to the house? It is telling us something and what we said was, yeah it is a weird variable. We would have never picked it without math on our own, there’s no way. But it’s telling us something and let’s use it. So we used it, and it worked, because it was telling us all those things that I said here: certain size of a house, certain type of a household, single family unit, pretty far away from city center, certain income level, were all correlated to this particular furniture catalog. So we said, I don’t know why – again I stop asking the why part, but let’s use it and it worked. So I’d like to hear your story about when it did not work.

Damian: Sure. So one of the things about that, that when we were talking about it, it’s okay, it gave you a hint to something that you could wrap your head around well why could it – you start using a computer in your head to figure out why, why that would occur.

Stephen: Sort of human function actually.

Damian: Yeah.

Stephen: By the way all the machine based models, they just do it. They don’t really reason as humans do. Funny thing about it is that when you have a lot of variables, the machine will find substitutes anywhere.

Damian: Yeah, but I mean I think there are situations where that correlation could break down into unprofitability. So for example, it’s very rare but maybe there’s a growing city that still doesn’t have their sewer system yet, and you live one block away from, you know, a place that you can walk in and buy furniture, that correlation will break down from profitability because the premise, the cause was that they still had a beautiful house but they were just too far away to get in the car and go drive.

Stephen: That’s why you should never use just one variable.

Damian: Right.

Stephen: This was one of like 10-12 variables in that model. So it’s never the one thing. So that’s another thing that I want to point out is that when we say build a model, by the way, even machinists when they build a model they never use one variable. In fact, we use about 10 variables, if the one variable is really, really obscenely too strong and it takes up like 80-90 percent of predictability power, we throw that out, because if that one variable doesn’t work then you’re really screwed later. So modelers, mathematicians, they’re all about hedging bets and what is a regression model? Regression is nothing but a curve that has the least amount of error rate on the average. The curve that is the least wrong. That’s the regression curve. So yeah we don’t want to hedge all our money in one variable, we don’t have it –

Damian: Correct.

Stephen: Yeah that’s a big caveat that I want people to remember.

Damian: So the story that I have was, I don’t know, maybe this could have been 5-7 years ago or whatever, but I remember I was looking at Google Analytics accounts for some e-commerce websites and I even remember like, especially earlier in my career you’d read articles that say you know, like it was hard to track things back then. So page views were like a really easy thing to track because everyone had access to it. And there was this like running theme in marketing forums and vice versa, all those places that if you could increase the number of page views in your sessions, then those were more engaged and they had higher conversion rates. And I remember digging deeper and deeper, deeper into it and I was kind of buying into it because I was looking at all these different accounts, and I saw that yeah that’s true. Like the pages that the sessions that have all these high engagements judged by that metric were extremely correlated to very, very high conversion rates. And then I looked just a little bit deeper and I realized that wow, all of these websites have multi-page checkout steps. So by definition, if you went to check out you increased your page views by 5.

Stephen: Oh right.

Damian: So if you, in hindsight like you couldn’t buy unless you had that many page views, therefore like was it really describing a good session that was engaged or were those the people that you know, you had to have that many pages to check out? And then it kind of started this whole other process where, is a landing page really great if you were, or a website really great if you have to go to so many many pages to check out? And then it was like well actually maybe the best sessions and this actually proved to be true, the best converting sessions were the ones where somebody landing on the landing page went straight to check out. There was no navigating or shopping, it was buying. And that actually is one where if you bought into, I should encourage people to keep having more page views, it was wrong. It actually hurt, it was the inverse. And I was just I guess that’s my story.

Stephen: That is a very good example. And this is why what you just did here is exactly why humans will have someplace even in the machine-driven world, is we reason. The second point is that the reason we have to dig deeper into not just pure data, but you have to even think about how the data is collected. And I have a similar example when I was at a data vendor really or a compiler, and we had no shortage of data, and we were building a model for a certain client and we found out that certain regions, by the way, when you’re in a compiler business you know that in certain states it’s hard to collect certain types of data. So when that data popped in –

Damian: What do you mean? Give me an example.

Stephen: In other words, when you compile the data, you don’t know everybody’s home value by the way. So a lot of things are outsourced and somebody actually sometimes stands in line in the local city government and finds out what all the house prices are. Well, they can troll the web, but the point is there are some variables that are collected that way. The point that I’m making is that certain variables if you know the history of it, you have to tell the difference between actual consumerist behavior or some loophole in the way we collect the data. So you’ve got to really think about not just what you see in front of you, oh yeah it looks like it’s highly correlated. And that’s what you just did, think why so many page views? Because the website is poorly designed. In my case it was more no, no, no in certain states it is hard to collect such and such data, and if that’s popping up so prominently.

So you know what, let’s look at this, compare this with a store footprint because you cannot argue that if you have a lot of store footprint you have more concentration of people in those states right? And it was almost an identical match, so that variable should be thrown out. This is why, again going back to the point number one, humans still have a place to reason and make sense of all this, but that does not mean that the analysts who do these things should have an endless pursuit of oh I want to know why. Because the why part, and this is why the why part is the last and hard to predict. Sometimes you just have to ask why. We talked about three types of data, about a few episodes ago. You have behavioral data, demographic data, and attitudinal data. Attitudinal data is scarce because you have to actually stop and ask questions in the form of primary research, or survey, or even social media listening. But it’s impossible to listen to everybody and it’s impossible to know everybody who answered it either. It is really hard to marry such data on a personal level with all the other behavioral and demographic data. In the pursuit of why that’s what you need to do. So, I’m not saying that asking why is not important, even when you see a variable you know, in a really well-built model you have to pursue to find out, okay what’s the background of all this data? Does it make any sense? Why are a septic tank and all those things showing up in my model? Yes, you have to think about it. That doesn’t mean that you have to stop and pursue the why so hard that you have to start primary research.

Damian: Right.

Stephen: Sometimes you just have to act on it.

Damian: I think the essence of what I take away from you’re saying is, one, correlation can be an amazing tool to discover causation, and two sometimes it’s just too expensive or not worthwhile to even go that far. If the correlation works and you test into it, that doesn’t mean you break out an extra million bucks. You test into it and if it holds up and it’s true over time then make money with it. Don’t worry about it. You know, go solve another problem.

Stephen: That’s right. And I’m trying to communicate the price of prediction. There are a lot of marketers ask that question first. Now the marketing is a part of the product planning stage and I have met such people in Korea actually, there is an amazing company that does all that social media listening, and they were helping companies like LG, Samsung, and all those companies and they actually figured it out by listening to the social media comments that some company made a very small washer and dryer set, thinking that yes single people might buy this thing. The assumption, great in all scientific research –

Damian: I know this story. You’ve told me this story, it’s a good one.

Stephen: Yeah. So and then they realized, wait for a second, we made this thing – they don’t buy them.

Damian: So single people didn’t buy the smaller washer and dryer.

Stephen: Because you know why? They’re too busy socializing, basically they follow tweets that they make you know? They want to have a big washer and just have one load once in a while. The lifestyle makes sense.

Damian: Basically they don’t want to do laundry all the time so they’re like I’m going to let this laundry pile up in a corner and then I’ll do it all at once.

Stephen: That’s exactly right.

Damian: And I don’t want to spend all day doing it. I want to do it one time.

Stephen: In fact, my wife who washes quite frequently, doesn’t even need, because she washes so frequently that she doesn’t even matter for her that much. The moral of the story is this, the company spent a lot of money doing this because they were actually planning a new product. You don’t want to build a wrong product to have to listen and ask, do the survey and do the panel research, you’ve got to do all these things right? But when you are in a one to one marketing mode, let’s not go crazy. Sometimes you find a good correlation, count your blessings and act on it, if it doesn’t work, go to Plan B.

Damian: I think that’s a great place to end. And you know in the meantime, if you’re going to use Internet Explorer versus something else, make sure that you do it in the winter because we also found that ice cream sales are extremely correlated with murder rates. So there are lower murder rates in the winter. So that should cancel out your risk of using Internet Explorer.

Stephen: Stay safe.

Damian: Stay safe people. Take care.

Damian: If you enjoy today’s episode we ask that you please leave a rating and write a review. Or better yet share it with another marketer. Be sure to subscribe to the podcast for new episodes. Also, check out the show description for complete show notes and links to all resources covered in today’s episode. If you’d like to speak to someone about any topics covered in today’s episode please visit BuyerGenomics.com and start a chat with the BG team today.

By |2019-11-14T21:22:19+00:00December 19th, 2018|Inevitable Success Podcast|0 Comments