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Implementing Marketing Automation: The Way Retailers Win

The Guide to Marketing Nirvana [Step 5]

The world of marketing and advertising is changing at breakneck speed.

Programmatic advertising buys see changes in the cost of impressions from minute-to-minute. The cost of search terms rise and fall based on competitive bidding. Batch-and-blast emails are flooding inboxes.

In addition, competitive marketing dialogues with customers evolve dynamically as digital activities change the odds of customers purchasing from retailers.

So, how can a small marketing department keep pace with these factors and other dynamics that are changing the marketplace?

Predictive Marketing Automation

Today’s marketers find themselves in their own competitive cyberwar with their competitors and simply need to automate as much of their activities as possible.

A key tool, then, is a Predictive Marketing Automation Platform (PMA).  

A PMA accomplishes three things:

  • It utilizes machine intelligence to effectively predict who is going to buy what and when.
  • It detects when an individual has gone “in-market” and has a higher likelihood to spend inside a calculated purchase window.
  • Once it notices the change in that consumer’s buying potential, it takes action on its own by contacting them with the right offer, at the right time.

These machine intelligence capabilities grow smarter over time as we learn how each customer responds or does not respond to various marketing actions. In order to win with customers and reach Marketing Nirvana, a PMA is an essential capability.

Recap/What’s Next

So, let’s begin with a quick recap of the steps to Marketing Nirvana that we have discussed so far in our series of blog posts:

  • Enter customer data into a PMA — begin to keep comprehensive promotion and buyer history data over time.
  • Understand who the customer is.
  • Create Customer journeys based on value and customer purchases.
  • Design email/digital campaigns to maximize sales; create a plan to test and learn over time.

Our next step in this sequence is:

  • Leverage the customer’s relationship autonomously based on buying, promotion and engagement history. Enhance the customer’s journey by understanding where they are in their purchasing lifecycle and tailoring planned touches accordingly.  

All customer relationships have a beginning and an end. Moreover, those relationships vary from person to person and segment to segment. Each customer has his/her own marketing genetic code℠.  

While we discussed the Buyer Lifecycle in Step 2, its importance makes it worthwhile to revisit here in a little more detail.

Understanding the Buyer Lifecycle

The term Buyer Lifecycle (BLC) refers to the natural evolution of a customer/retailer relationship. It depicts your customer’s relationship and brand engagement from the moment that they become a prospect, an  in-market buyer, and finally inactive.

Many customers apply recency, frequency and monetary value algorithms as a surrogate for BLC. Unfortunately, those algorithms are typically manually intensive and error prone.

Every customer has their own unique lifecycle throughout their relationship with your company. A paramount goal of your brand should be discover customer behaviors while also influencing and creating new ones.

With PMAs, these actions are swiftly automated via predictive analytics and machine intelligence. They analyze the discrete events and metrics that either drive or diminish both revenue opportunities and customer value.

The notion that any customer file or database is mostly static is a misconception. In reality, your customers’ BLC is a dynamic, ongoing process that changes every single day.

A PMA utilizes email behaviors, past purchases, web behaviors and external data sources to understand how engaged certain customers and prospects are at any given time.

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.

This allows you to see exactly where your current/potential customers are by autonomously designating them into one of six distinct stages.

The Buyer Lifecycle

Example of a Buyer Lifecycle Analytics (BuyerGenomics).

PMAs freely shift each customer among the six different stages according to a range of established variables and key signals.

These signals can include answers to the following questions:

  • How often have they visited your website? How long since the last time?
  • What did they browse? How close did they come to making a purchase?
  • What are their respective buying patterns? How frequently/infrequently have they bought?
  • Are they opening/clicking your messages? Or ignoring them?
  • Have they made a trip to one of your stores?

BLC In Action

Due to advances in cloud computing, it is finally feasible for all marketers to maximize BLC visibility so that swift action can be taken whenever crucial shifts between stages occur.

For instance, a customer who recently shifted into the “Fading” stage requires a form of marketing intervention. This can come in the form of special discounts, privileges, gifts, or other offers that may reactivate the customer.

Meanwhile, for customers who have been labeled “Inactive” for an extended period of time, it may not make sense to waste anymore of your marketing resources on them.

On the other end, once a “Prospect” shifts to “Active,” you have your best shot at convincing them that your product is the one they want to buy not just once, but repeatedly and consistently.  

Send a personalized message, and strike while the iron is hot.

Each sector of the BLC involves a different approach and strategy. Therefore, a proper grasp of its intricacies helps to understand each customer better and consistently target them with relevant information.

AutoPilot

Obviously, keeping track of customer attributes and segments is a complex task. A PMA’s AutoPilot feature helps to simplify that process and make it actionable.

AutoPilot automatically allocates messages to customers based on their journey and BLC stage. Specifically, AutoPilot identifies those customers who need to be treated differently in your marketing campaign and then automatically triggers changes to outgoing customer communications.

These changes to planned customer journeys can range from personalized offers to attention-grabbing subject lines – all customized based on the probability of success.

To activate these automated treatments, simply click on a PMA’s enable menu to automatically initiate segmentation.

Wrap-up: Your Buyer’s Lifecycle and Automation

Buyers are the lifeblood of your business, and as is often the case, the Pareto Principle rules: 80% of profit normally comes from just 20% of your customers.

Earlier in Part 3 , we cited another compelling marketing statistic: increasing customer retention by just 5% increases profitability by 25 to 95%.

The marketing imperative is clear: Having an automated strategy to tailor messages to customers based on their buyer lifecycle and relationship with your company is the best formula to maximize customer retention and sales over time.

In our next section, we will discuss how PMA’s can help you acquire more customers through targeted acquisition strategies.

In addition, we’ll explain how competitive marketing dialogues with customers evolve dynamically as digital activities continue to alter the odds of customer purchase rates from retailers.

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 |2019-05-07T14:50:47+00:00May 7th, 2019|Blog|0 Comments

5 Risks of Selling on Amazon [And 1 Key Advantage]

The Amazon Effect

These days, there is no denying the power of Amazon. Given their enormous influence in the world of E-commerce (and beyond), they offer the opportunity to dramatically increase both your profits and customer base.

With 100 million dedicated Prime members worldwide and many studies confirming that over half of all product searches begin on Amazon.com, it makes sense why so many small and medium-sized businesses (SMBs) have utilized Amazon to expand their customer base.

In fact, there are many prominent brands who have directly attributed their success to Amazon. In 2017, more than 20,000 SMBs exceeded $1 million in sales through Amazon.

However, while this certainly sounds appealing, there are a variety of factors to consider before diving into a relationship with such a giant company that has gutted many well-known brick and mortar stores – rendering them casualties of the Amazon Effect.

While the rewards can be very high, they also come with a range of risks. While Amazon can help you sell much more than you could elsewhere, you have to do it their way.

Although Amazon has created an abundance of options and a high level of convenience for consumers, its rising dominion actually poses a number of obstacles and threats for sellers.

Let’s break them down one by one before offering a way to turn the tables and stack the deck in your favor if you do choose (or have already chosen) to sell with Amazon.

1. Amazon is a Buyer’s Market [Thinly Veiled as a Seller’s Market]

Although Amazon presents itself as having a plethora of potential customers that can seemingly generate a level of demand high enough to outstrip the supply any single retailer could possibly provide – the reality is not that simple.

A condition of the seller relationship with Amazon is affording them considerable power over your pricing, inventory, and brand identity.

Equipped with this power, Amazon systematically pits its sellers against each other. This practice is based upon a science that methodically measures and commoditizes every brand and product with mathematical certainty.

In doing so, Amazon dedicates itself to maintaining a marketplace with the largest selection at the lowest prices. Therefore, even if a customer purchased your product, he/she will be introduced to thousands of other sellers that compete with you both on pricing and product.

Amazon HQ2

Actually, Amazon operated in a very similar manner during the well-publicised quest for its second headquarters. In a sense, they treated potential host cities in the same way that they treat their own sellers by luring them into a bidding war.

After the announcement, cities all across the United States rolled out their respective red carpets – promising substantial tax breaks and subsidies despite any potential drawbacks associated with doing so.

New York City in particular planned to offer Amazon tax breaks of at least $1.525 billion and cash grants of $325 million, along with other incentives to have one of its HQ2 sites in Long Island City. New York wound up winning their bid, only to have it retracted by Amazon back in February in the wake of opposition from members of the New York State Senate.

After the bid was cancelled, New York Governor Andrew Cuomo basically begged Amazon to reconsider by reportedly calling Jeff Bezos personally. In addition, an open letter was placed in the New York Times pleading Bezos to change his mind, stating that Cuomo “would take personal responsibility for the project’s state approval.”

The message Amazon sent was clear: “We make the rules, and we will only work with you if it is in our best interest.”

Credit: Statista

2. Amazon Requires Additional Costs

Amazon has created a medium through which an incredible amount of commerce flows. As a result, they are in a huge position of power, and can make even more money by charging tolls for access along the way.

In what has been described as a “pay to play” model, Amazon charges multiple fees for third-party sellers – either on a per-item basis or as a cut of sales. The latter of which can be anywhere from 15-20% of the sale price in addition to listing fees. There are also extra fees for special promotions like Prime Day and Lightning Deals.

Meanwhile, if you decide to register with Fulfillment by Amazon (FBA) – in which Amazon warehouses and fulfills your products – you will usually pay both a listing fee and a fulfillment fee.

Selling through Amazon also often requires paying for both search rank and advertising – since sellers need to develop some sort of presence on the site to gain eyeballs.

Selling ad space on its site has become extremely lucrative for Amazon. In 2018 alone, they reported $10.1 billion for their “Other” category, which they say mainly consists of sales from ad services along with sales tied to their other “service offerings.” In fact, Amazon does not even release its distinct earnings from advertising alone.

Moreover, fighting for ad space can be extremely competitive, and there is no guarantee that consumers will see your ad in lieu of other higher performing products.

As margins get squeezed, only a small group are going to rise to the surface and make it to the top. Usually, only a few select sellers get all of the eyeballs, while everyone else scrambles for whatever they can get.

3. Amazon Owns Your Customers

When you first register to sell with Amazon, you have the option to choose the aforementioned Fulfillment By Amazon (FBA) or Fulfillment by Merchant (FBM). With the latter, you choose to ship your products to each customer through your own logistics and operational processes. Therefore, you have access to the postal addresses of your buyers (more on that later).

Either way, you are never, under any circumstances, allowed to attempt to lead them to your own site.

The reason for this is simple. They are Amazon’s customers – not yours.

Amazon makes this fact very clear in a section of their Business Solutions Agreement:

“Any attempt to circumvent the established Amazon sales process or to divert Amazon users to another website or sales process is prohibited. Specifically, any advertisements, marketing messages (special offers) or “calls to action” that lead, prompt, or encourage Amazon users to leave the Amazon website are prohibited. This might include the use of emails, hyperlinks, URLs, or web addresses within any seller-generated confirmation email messages or any product/listing description fields.”

If you break this rule, you run the risk of being banned from Amazon permanently. At the end of the day, Amazon wants to ensure that they remain loyal to themnot you.

4. You Can be Suspended or Banned at Any Time

If you do not meet Amazon’s customer service expectations or receive too many negative reviews, you can get suspended – or even possibly banned – without a moment’s notice as these processes become increasingly automated.

Furthermore, sellers who supply inventory to Amazon wholesale can abruptly discover that their product listings have been yanked simply because Amazon’s algorithm (which strongly dictates the relationship between almost all of their sellers) decided that they Can’t Realize a Profit (also known as CRAPping out).

5. Amazon Owns an Expanding Portfolio of Their own Competitive Brands

The truth is, Amazon can be a platform provider, partner, and a powerful competitor all at once.

After perfecting the model for selling other people’s goods, Amazon has doubled down by swaying consumers to purchase their own products.

By 2022, Amazon’s private label sales alone are projected to reach $25 billion. Currently, Amazons owns and operates 139 private label brands and 473 exclusive brands – selling a wide range of items across many categories, including clothing/shoes, electronics, food, furniture, healthcare and beauty, household goods, industrial, and pet/animal products.

While some of Amazon’s private label brands sport its name – like Amazon Essentials or AmazonBasics, there are many more “phantom” brands that don’t. As a result, there are numerous people buying goods directly from Amazon without even knowing it.

Make no mistake. If your brand hasn’t already been challenged by an Amazon brand, the odds are that it will be.

Let’s take a look at another excerpt from the Amazon Services Business Solutions Agreement:

“You grant us a royalty-free, non-exclusive, worldwide, perpetual, irrevocable right and license to use, reproduce, perform, display, distribute, adapt, modify, re-format, create derivative works of, and otherwise commercially or non-commercially exploit in any manner, any and all of Your Materials, and to sublicense the foregoing rights to our Affiliates and operators of Amazon Associated Properties.”

If a certain product or category is performing particularly well, Amazon can just start selling similar ones under a new competitive (yet inconspicuous) brand. And once they see which products are succeeding, they ramp up their production and increase their presence on the site via advertising.

In addition, you don’t just compete with Amazon’s private label brands – you compete with them for share of wallet. Their goal is clear – to obtain as much disposable income from both their customers and sellers as possible.

While Amazon states that third-party brands still make up the bulk of its sales, how much longer could that be the case given their increasingly aggressive private label brand expansions that pull sales away from their sellers? 

Turn the tables – Independently Build your Brand and Customer Base

Although you cannot email Amazon’s customers, there is no place in its long list of clauses and stipulations that restricts you from utilizing direct mail.

Instead of FBA, if you choose Fulfillment by Merchant (FBM), you will have access to your customer’s postal address – which is important to have in this case.

While direct mail is a more expensive alternative to email, calculated investments can ultimately pay greater dividends. If you are mailing someone who already bought your product through Amazon and offer incentives to visit your own website, you can divert eyes towards your own site and personal offerings.

Once that happens, you can now initiate a direct, personalized selling relationship with the customers – something that you could never have done otherwise.

Therefore, you can turn the tables by using Amazon to generate a large volume of buyers, and then convert a steady stream of those customers into your own personal customer base.

This strategy offers a tremendous opportunity to methodically grow revenue, profits, and loyalty without Amazon pulling the strings and taking all of the credit.

Conclusion

The point here is not that Amazon is a “bad” or “evil” company. In fact, what it has managed to accomplish since its inception in 1994 as a small online bookseller is nothing short of astounding.

However, while Amazon may not necessarily be “out to get you,” they are dead set on capturing all of the profits in all of the categories that have them for themselves. You are merely content – or a commodity. Therefore, Amazon ultimately stands to gain more from your relationship than you do.

So before you jump into a selling relationship with Amazon, carefully consider what the conditions (and potential ramifications) are. Know that you and your brand will not only obtain a lower profit margin from each sale, you will also have a giant wall separating you from your actual customers.

If you do elect to sell on Amazon (or already have), consider the direct selling method to take back some control of your customer base. When dealing with such a giant company that carries such a strict set of rules and regulations, take advantage of any outlet you can to bolster your own personal brand awareness and develop personalized customer relationships for yourself.

When you own the customer, you own the future of your business and your brand. Amazon knew this decades ago, and look where they are now.

By |2019-05-09T17:30:01+00:00April 29th, 2019|Blog|0 Comments

Launching Email Campaigns in the Omni Channel Age [Marketing Nirvana Step 4]

PMAs and Email Campaigns

We’ve already talked about customer segments, personas, behavior, and value. Now, we’ll focus on using this knowledge to design comprehensive, intelligent, and target customer journey-based email campaigns. This is an essential step towards maximizing the potential of every one of your customers.

For many companies, email is a primary channel through which they communicate with their customer base and obtain intelligence about their customer universe. In this article, we’ll take a closer look at the targeting capabilities of email when it is launched from a PMA – either alone or as part of a larger digital campaign.

PMAs – Changing the Way Organizations Think

As we’ve stated before, marketing is only effective when you send the right message, to the right person, at the right time. Sending too many (or too few) messages, irrelevant content, or poorly timed emails can cost you customers, sales, and possibly even your IP (Internet Protocol) reputation. This basic marketing axiom applies to all channels – digital or traditional.

PMA’s are unique platforms that capture promotion history, along with behavioral and purchase data. These platforms offer marketers an extremely powerful email marketing capability in their digital toolbox.

One of a PMA’s greatest benefits is the ability to supply the tools, framework, and intelligence necessary to be a continuously learning organization that supports a culture of testing and learning over time.

This includes tracking different segments of customers as they are exposed to various social media treatments, their e-commerce shopping behaviors, and communications through email.

A Continuously Learning Organization

A continuously learning organization strives to get smarter about all aspects of their marketing mix. These marketing agencies attempt to understand the sensitivities around positioning, media, and pricing through a rigorous ongoing program of marketing research and testing.  

Learning Cycles

The digital realm has essentially created a new culture in the marketing field – particularly regarding the rate at which learning cycles can be created.

In the old days of catalog marketing, firms would spend weeks deciding which content to put into a catalog. From there, it would have to be sent to a printer, bound, and mailed out. Then, more time would be consumed waiting for orders to actually come in.

As a result, there was typically a 3 month learning cycle associated with catalog and direct mail. Therefore, developing, implementing, and testing various strategies was a much slower, drawn out process.

Email – Speeding up the Cycle

Fast forward to the present. With email, an entire learning cycle can take place within 5 days or less.

Here are the three key steps necessary to execute a modern email campaign:

  • Draft email (1 day or less)
  • Configure email/images/graphics for sending (1-2 days)
  • Analyze results (2 days)

With a PMA, you have the ability to react quickly and evolve your strategies by sending out emails quickly with different subject lines and product offers. Within roughly 24 to 48 hours, you will be able to accurately gauge whether or not each particular design/message/cadence is going to be successful.

Over time, a PMA’s machine learning capabilities pull out patterns of repeated customer behaviors that can help highlight what products to cross-sell, upsell, or feature next.

From there, you can determine the best way to deliver personalized offers to specific segments of customers. Your conversion rates can certainly increase if you know who you’re sending to, what their profile is, and how they’ve interacted with you before.

Campaign Considerations

Typically, at the beginning of a customer’s journey with your brand, you have minimal practical information about them.

However, as time goes on, the scope of information grows. If you make it a priority to continuously test and learn more about your buyers and their respective purchase cycles, you will have many more actionable insights to incorporate into subsequent campaigns.

The list below elaborates upon essential components to consider throughout this process.

Holidays/Seasonality

When mapping out a customer’s journey, one typically looks at it across a marketing calendar. Marketers are always looking for legitimate reasons to communicate with customers, and two perfect examples are holiday and seasonal sales.

For instance, the automotive industry takes advantage of most holidays (i.e. President’s Day, Memorial Day, Labor Day) as catalysts to drive consumers into their showrooms.  Can you remember a major holiday that didn’t have “sales?” Probably not!

Seasonality is another important factor when planning your customer communication methods. Depending upon what product(s) you sell, people typically buy at different times of the year.

For example, a golf retailer surely sells more clubs and gear in the spring and summer months than fall and winter (at least in regions that truly experience all four seasons). Additionally, clothing companies certainly sell more t-shirts, shorts, and bathing suits when the weather is warmer.

Media Plan Synergies

It is undeniable that multiple impressions across channels are more effective than single channel campaigns.

Therefore, always coordinate your omnichannel campaigns to complement each other. For example, combining email and direct mail offer a very effective one-two punch!

Email/Direct Mail Combinations

Both email and direct mail can be designed in a way that they complement each other.

The following statements are based upon the old adage of 1+1=3:

  • If you send an email, you’ll get one sale.
  • If you send a catalog, you’ll get one sale.
  • If you send both simultaneously, you’ll get three sales.

Send an email to let the customer know that a catalog or mail piece is coming. Then, once the direct mail piece hits the mailbox or the doorstep, send another follow-up email to reinforce the previous one.

Multiple impressions across channels strengthens your brand awareness and increases your likelihood of conversion.

Testing

Always strive to close the loop on your marketing campaigns by testing different strategies and learning over time.

Use these insightful metrics to review the success of each campaign:

  • Gauge efficiency by measuring revenue per thousand emails, in addition to opens, clicks, and conversions.
  • Analyze which items your customers purchased or browsed on-site after clicking through your email (considering they did in the first place).
  • Compare your results against your key driving KPIs.

Combined, these metrics divulge how well your campaign truly performed, and help you gauge both customer behavior and intent. You can then apply these learnings to your future campaigns to both meet your current goals and set your sights higher for new ones.

A/B Testing

You may not initially know the best way to interact with different parts of your customer base. You can obtain this knowledge through both a PMA’s Test and Roll and Split Test features.

Your subject line is the first thing your customers see in their inbox, and a carefully crafted one can substantially increase open rates.

Therefore, send two different subject lines to a subset of your customer base. Your PMA can be set up to test two or more subject lines in parallel, providing statistically significant results.

Once statistical significance is achieved for open and conversion rates, the PMA will automatically send emails with the winning subject line to the remaining members of your base. This process is referred to as Test and Roll.

The same type of test can be implemented for creatives/content as well.

Meanwhile, a traditional split test is similar to a test and roll, except that you implement the test for your entire customer base. One half gets one subject line/content, while the other gets a different treatment.

Timing and Sequencing (Cadence)

A carefully crafted message accomplishes little if your target never sees it, and the top of the inbox is a competitive piece of real estate. If you miss the mark and send your email just a few hours away from a customer’s ideal timeframe, you could easily wind up 50 emails down the list – out of sight and out of mind.

Key factors to consider regarding cadence include:

  • Time of day
  • Day of week
  • Number of emails per week
  • Date of last open
      1. Long gaps since the last open can indicate customers fading or inactive in your Buyer Lifecycle. If this gap lengthens, consider dropping from them from your list.
      2. This also helps maintain your IP reputation by avoiding getting categorized as spam.

It is remarkable how different the results can be (in terms of open rates, click throughs, conversions, and sales) by simply adjusting your timing and sequencing. Different people, companies, and products each have a varying level of receptivity simply based on the time of day.

Discerning the best messages for different groups can increase percentage points (like open rates, click throughs, conversions, and sales) that quickly add up to your bottom line.

In turn, a PMA can help you test out different strategies in order to optimize your campaigns. Meanwhile, its automation features free up some of your precious time so that you can focus your efforts elsewhere.

Conclusion

Ultimately, your emails serve as the face and voice of your brand. Proper usage of this powerful channel drives revenue and distinguishes your brand from the rest.

Not only does email allow you to send out a variety of messages to different customer segments, it also allows you to gather useful intelligence both quickly and inexpensively.

PMAs help organizations continuously test and learn while handling the basics, blocking, and tackling of campaigns.

Consistent, accurate, informed testing can greatly impact your conversions and bottom line. By engineering controlled tests and gathering verifiable data, you can determine precisely which marketing strategies work best for both your brand and your product(s).

Keep in mind that this is a process, and that multiple types and forms of testing can (and should) be executed over time to gather more actionable intelligence. Remember, if your organization is not continuously learning, then you are likely not growing at the rate your competitors are.


Today’s competitive marketplace requires continuous learning about customers and the best ways to communicate with them. As a result, marketing tools are rapidly evolving. Artificial Intelligence (AI), machine learning, and expert marketing systems are being built into PMAs – providing marketers more sophisticated approaches to gain marketing efficiencies.

Up next in Step 5, we’ll examine two new leading edge capabilities, during which you will learn how to use a PMA’s AutoPilot software in tandem with the Buyer Lifecycle.

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 |2019-04-10T20:19:09+00:00April 10th, 2019|Blog|0 Comments

Understanding Causation vs. Correlation in Marketing

How to Infer Causation 

A key component of marketing success is the ability to determine the relationship between causation and correlation. Namely, the difference between the two.

First, let’s define the two terms:

Correlation is a relationship between two or more variables or attributes. From a statistics perspective, correlation (commonly measured as the correlation coefficient,  a number between -1 and 1) describes both the magnitude and direction of a relationship between two or more variables.

Causation indicates that one event is actually the direct result of the other(s). It is the basic notion of “cause and effect” – in which one event is identified as a consequence of the other. Essentially, causation is the “why” for any given outcome from a marketing action.

The ability to properly gauge causation supplies you with the fact base that’s necessary to make informed, sound marketing decisions. Therefore, causation is all about finding the exact “marketing genetic code”  or data elements of specific customers that are predictive of future behaviors.

Meanwhile, understanding buyer behaviors and attributes help to predict their future relationship with your company and how to best manage that relationship.

Causation can be proved through rigorous experiments and testing. By doing so, you can firmly deduce that there are underlying reasons behind the connection between variables.

If these indicate positive behaviors, they should be further explored and taken advantage of.

Correlation Does Not Always Indicate Causation

While the definitions themselves are relatively straightforward, improper use of exploratory data analysis techniques can lead to a wide range of inaccurate conclusions. This occurs during instances where events are correlated, but the correlation is not due to a causal relationship.

In marketing, simply assuming that correlation implies causation without rigorous testing and experimentation can prove to be problematic, and ultimately lead to costly mistakes.

Below is a famous example in which there is a correlation between two factors, ice cream consumption and educational performance scores, but not causation:

While a simple glance at the correlation coefficient of these various countries indicates a high correlation between ice cream consumption and educational performance scores, simple logic indicates that the two things have absolutely nothing to do with each other.

Nonetheless, if you were to hypothetically test these two variables against each other, how would you do so? The best way to prove (or disprove) causation is by setting up a scientific experiment.

  • Tell half of the subjects in each country to eat ice cream everyday for the duration of the experiment.
  • Tell the other half of the subjects that they cannot eat any ice cream at all.
  • Run this experiment for a calculated period of time. At the end, have all of the subjects take the same exam.
  • Examine the results of those students who consumed ice cream, versus those who did not.

From there, you will have the opportunity to answer the question – did the consumption of ice cream make a difference for the children enrolled in the study during this particular time period?

Another example of correlation not being causation is the idea that smoking is correlated with alcoholism, but does not cause alcoholism.

The same approach applies to marketing examples. The following is a real-life instance in which we implemented testing to prove causation for one of our clients at BuyerGenomics.

A Marketing Example

A question that many specialty retailers ask is whether or not they should send catalogues to their customers, in addition to their other marketing efforts.

One of our clients, a direct to consumer retailer with mass distribution, had a recurring program of sending catalogs to their customers. While they knew that there was a correlation between their catalog program and sales, they wanted to see if there was actually causation between the two, and if the catalog was delivering the desired return on investment..

In order to do that, they selected a hold-out sample as their control cell, selected from the universe eligible to receive the catalog. For this group of customers, they chose to not mail them any catalogs at all. Meanwhile, the remainder of their customer base received catalogs just as they normally did.

Keep in mind, by administering this test, the company was willing to potentially sacrifice some short term sales for the sake of information that would prove useful in the long run.

In other words, the test required a small investment that allowed their marketing manager to determine the value of their marketing catalog. Through this, they were able to understand how much leverage they had for future catalog campaigns.

In turn, it was key for the sample group to be representative of their customer base as a whole. In addition, that control cell had to be as small as possible (to minimize lost sales) while still providing statistically significant results.

Therefore, a relatively small sample group was selected, while catalogs were mailed out to the remaining customers.

What was the result?

The customers receiving the catalog delivered sales of several multiples above those in the control cell – a validating experience for all involved!

By realizing the incredible effectiveness of their catalog campaign, they have catalog attribution evidence to support future campaign strategies.

Using a PMA to Infer Causation

Predictive Marketing Automation (PMA) platforms provide marketers with the tools necessary to perform similar tests and experiments as described above.

With a PMA, you can perform these five key steps to design well-conceived experiments that tease out causal variables:

  • Create test and control cells.
  • Target each cell differently with marketing communications.
  • Match the sales back to the test and control groups.
  • Compare the results.
  • Based upon the results, roll out the winning strategy to the entire population.

While this is a relatively straightforward process, following a consistent process to create and track your random samples is vital. Maintaining the integrity of your samples and marketing treatments will ensure that your experiments deliver the intended insights.

It is also important to have a stable database environment to work from and that your database administrator keeps you informed of changes to your systems environment. This is key, because if there are changes in the way that data is collected or a certain variable’s value has changed over the course of a test, the accuracy of the whole experiment can be thrown off.

Make no mistake – unanticipated new data or system aberrations can wreak havoc on the whole exercise.

Conclusion

When implementing marketing plans, you always want to have the best information possible.

Determining causation allows you to understand the levers at your disposal to impact customer behaviors. Equipped with this knowledge, you can better plan, develop, target, and implement your marketing budgets.

While correlation on its own offers clues, marketers should not be basing their plans on correlation alone. Instead, they should be based upon experiments designed to determine causation.

Ultimately, continuous marketing tests serve to evolve the knowledge of your organization. A major benefit of a PMA platform is that it provides the data ecosystem that allows you to perform and even automate these tests more rapidly and efficiently than ever before.

From there, you can learn and evolve more quickly – basing your marketing decisions on data-supported facts instead of mere guesses, hearsay, or gut feelings.

There has never been a time in recent history when this capability has been more pivotal. Our digital devices allow marketers to collect more data about consumers than ever before. Meanwhile, customer profiles are also richer than ever. Therefore, the companies who best understand these resources and how they relate to their customers will be the clear winners in the years to come.

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 |2019-03-26T16:57:49+00:00March 26th, 2019|Blog|0 Comments

How to Achieve Holistic Marketing Personalization

Personalization is about the Person

At its core, 1:1 marking is about people – not products or channels. In fact, not just “people,” but individuals.

But the truth is, most marketers treat consumers as one-dimensional commodities, or worse – extensions of their last product purchased. This leads directly to improper personalization with irrelevant messages, which both squanders potential leads and drives away existing customers by training them to ignore brand messages.

Therefore, it is critical to clarify exactly what constitutes properly executed, holistic personalization, and precisely how brands can provide the best customer experience through such efforts..

When done right, personalization becomes a core component of marketing success.  Simply put, relevant messages sell better.

Proper Personalization

These days, consumers have come to expect – and even demand – a wholly comprehensive, personalized experience. Econsultancy recently referenced a consumer survey that found 64 percent of respondents now expect an individualized experience, while 85 percent consider an individualized experience to be important.

With so many brands battling for their attention everyday across a wide array of channels, customers feel that they deserve to be viewed and addressed as individuals with a pulse – not just faceless components of larger masses.

Meanwhile, marketers are constantly sifting through vast data heaps in an attempt to get to know their customers better and generate truly personal relationships.

However, even with greater technology, mathematical abilities, and more available data than ever before, they are still making key mistakes along the way. Such mistakes wind up taking a heavy toll on Customer Acquisition Cost (CAC) and Return on Investment (ROI).

Prevent “Pesky” Personalization

Few daily life annoyances rank higher than receiving unwarranted or irrelevant messages or pitches from anyone – let alone marketers.

Nowadays, if you are going to ask a consumer to spend his/her valuable time and money purchasing your product, make sure you are speaking to the right person, at the right time, with the right pitch, and via the right channel.

If not, the best case scenario is that those messages are simply ignored. At worst, a slew of impertinent messages could turn customers off from your brand altogether.

Refine Your Personalization Engine

In order for personalization engines to run properly, all ingested data must undergo an extensive refinement process. This requires a degree of commitment and awareness that many companies lack.

Truthfully, there is a great deal about customers that marketers simply do not know. Even what we call Big Data consists of many holes and gaps, while most personalization engines are only built to act upon available data (or “known information”).

In turn, when marketers stick to targeting the customer only with data already available to them, they wind up blitzing some poor individuals with messages until they simply cannot take anymore. This is particularly unfortunate due to the drastic disparity between the amount of people with “known” behaviors and the rest who are unaccounted for.

In fact, the former generally make up less than 5 percent of the total accessible universe. Therefore, under such circumstances, 5 percent (known) are unnecessarily harassed, while the remaining 95 percent (unknown) are completely neglected.

Surely, we can do better than that.

Transform “Unknowns” to “Opportunities”

Enter segmentation and modeling techniques. Various forms of segmentation is a common technique deployed by many marketers to categorize and prioritize various cohorts and sub-groups of their respective customer base.

One common statistical method of segmentation is cluster analysis, which creates distinct segments by mathematically optimizing similarities and differences among the target audiences. Typical results of cluster analysis are any number of distinctive groups that share common demographic and behavioral characteristics.

Once the grouping is complete, each of these segments can be further described with the members’ aggregated demographic profiles, life stages, interests, and – most importantly – their collective behavioral profile and outlook regarding specific products or services.

While segment-based profiles may not yield explicit descriptions of each individual, they provide important insights about the target base, and guide marketers for better personalization in the beginning stages of such endeavors.

Beyond Segmentation

Segmentation is a long-established, familiar practice that continues to play a vital role in marketing. After all, it certainly is much more effective than using raw, unorganized, and unfiltered data for customized messaging. Or even worse – doing nothing at all.

With segment descriptions at their disposal, copywriters can message as if they are speaking to the target personally. Undoubtedly, designing copy for a segment full of people described as “Comfortable Golden Years” or “Up and Coming Young Suburban Households” would be much easier than doing so for unknown, faceless targets.

However, this traditional process does have its shortcomings. For instance, many segments can be far too broad – lumping loads of people into cohorts who may share some demographic commonalities, but not necessarily all of the same behavioral characteristics.

These types of issues become much more visible when such a “descriptive” tool is employed for targeting purposes. An above-average index value of a particular characteristic within a segment does not rationalize treating thousands (or even millions) of people in a target group the same way.

For example, not everyone inside a “luxury” segment is necessarily driven towards luxury cars/vacations, or high-end fashion/jewelry. Even among luxury buyers, their affinity towards specific types of products would not be simply distinguished by basic segmentation.

After all, every individual has multiple predominant characteristics. We are not so simply homogeneous, which is why – if the goal is to create a truly holistic customer experience – it becomes necessary to dig into more detailed profiles on an individual level.

Producing Personas

To understand individual targets more comprehensively in situations like these, gaps in data must be filled through statistical modeling techniques.

Remember, even in the world of Big Data, it is still impossible to know everything about everyone – and many organizations lack the ability to both collect data properly and harness useful insights from collected data.

One of the most important roles of modeling in this data-rich environment is to summarize complex arrays of information into “answers to questions.”

Examples of such questions include:

  • Who is more likely to be a cutting-edge technology buyer?
  • Who is more likely to buy children’s products?
  • Who is more likely to be into fashion?
  • Who would be a full price purchaser?
  • Who would be a bargain seeker?
  • Who is more likely to be a repeat customer?
  • Who is more likely to respond to particular type of offers (e.g, free shipping vs. 15% off)?

Therefore, in order to obtain the most comprehensive, 360-degree customer view possible – even with incomplete sets of data – certain types of statistical modeling methods must be implemented. Personas – in comparison to simple segments – would allow marketers align better with their target customers.

Propensity Scores

A customer-centric data view is achieved when all transactional, demographic, geographic, and behavioral data is centered around “each” customer or prospect. From there, such “event”-level data would be transformed into “descriptors” of individuals, which would then lead to model scores that would provide answers to specific questions.

Comprehensive individual-level descriptors detail – beyond demographic profiles – personal spending patterns including categorical purchases, browsing history, amount/consistency of purchases, price levels across each channel/category, and sets of time series variables neatly aligned around each person.

Then, through modeling techniques, such information will be further converted and summarized into forms of “personas” – or propensity scores – commonly expressed (for simplicity) on a scale of 1 to 10.

For example, if the question is to see each individual’s propensity to be an “Early Adopter of Technology,” all users would have to know is that a score of 10 would indicates “highly likely to be an early adopter,” without having to go through a series of seemingly unrelated data points laboriously.

This is where your analytics engine kicks into a higher gear and works to fill in the holes of unknown territories in your customer base. In this way, “everyone” in the target universe is viewed in a much more detailed, nuanced manner.

For instance, while all people are inclined to be “discount-buyers,” but not necessarily to the same degree. Only a certain amount of people are willing to wait in line all night for a television sale on Black Friday. Clearly, those people would score highly in the “bargain seeking” category. In a case like this, if a decision maker has access to a persona called “Bargain Seekers,” such intelligence would be readily available to treat target customers differently and more effectively.

And there is plenty of room for other personas, as well. With multiple persona characteristics for each person, you are able to identify each of their predominant traits (or highest/lowest scores). In fact, depending upon the marketers’ needs, it is entirely possible to have any number of personas for an individual.

Naturally, some people may score highly in multiple categories simultaneously. With standardized score format (1-10, for example), marketers – or even machines – can easily rank  persona scores within one target, at the time of offer. If a target scores high in both Early Adopter and Frequent Traveler categories, which one should win out? This actually represents extra opportunities where the marketer would link up multiple sustainable offers for each target, and rotate them over time.   

Ultimately, marketers will be able to increase coverage of their personalization efforts by employing personas that are available for most targets – not just depending on “known” information with much scarcer coverage.

With such a sophisticated scoring system, machines can be trained to send the best messages to the right customers at the right time, granted that a content library is also aligned with such personas – as in what personas should see what creative version. Consider the possibilities if done for millions of people in real-time.

Segments vs. Personas

To answer a common question regarding the key differences between segments and personas, below is a chart contrasting clustering/segmentation methods against model-based personas.

Since they are designed for one particular behavior or proclivity at a time,  personas are quite versatile and flexible. On the other hand, segments are more rigid by design, as they are grouped into limited numbers (at times predesignated numbers) of segments – typically anywhere between 10-70. There, each individual is forced into one segment at a time.

These technical differences lead to huge differences in usage. For instance, targeting would be less precise with segments, since each segment might be too large or too small, mixed in with multiple conflicting traits among their members (e.g., not everyone in the “Luxury” segment will respond to a specific product offer the same way).  With personas – or with any model scores – users can finetune the size of the target using specific models depending on the purpose of marketing campaigns (e.g., target with “Luxury Vacationer” model, mixed with “Likely to Response to Last Minute Offer” and “Fall Cruise Season” models).

For messaging, the notion that each segment or persona should be linked to a particular version of creatives is the same. Since lack of creative version is often the biggest bottleneck in an organization, using segmentation may seem suitable in most cases.  

However, for more diverse dynamic contents (especially for personalization of inbound traffic) personas would be much more suitable due to their built-in flexibility. Equipped with an array of personas for “each” visitor, once the identity of the visitor is revealed, the content can change based on one or more dominant characteristic of the target. Even a machine can be tuned to identify such dominant traits of a person in real time, because personas by design can be compared side-by-side – for one target at a time.

Flexible design also makes any necessary updates or adjustments much more seamless.  For instance, if certain model scores appear to be drifting away from your original design, simply update the anomalous ones – not the entire stack of personas.

Conversely, it is quite challenging to update segments using clustering techniques while maintaining similar segment characteristics that users are accustomed to. However, to be effective, models and segments must be updated with newly available information periodically.  

With the continuous evolution of AI and machine intelligence capabilities each year, the whole process could actually be automated – from updating models to sending omnichannel messages.

Conclusion

Ultimately, the end goal of personalization is to decisively gauge which types of messages and product offers best appeal to your multifaceted customer base.

Although marketers are generally more familiar and comfortable with it, the practice of segmentation is insufficient on its own. For one, segments are fundamentally designed to create broad, generalized message groups – not individual-level personalization scores with deeper sets of variables.

Meanwhile, model-based personas offer a nimbler and more adjustable approach that helps deepen customer relationships through relevant messages designed to fit the persona profile, leading to higher chances of conversion.

Practically, marketers must rethink the way that they assess customer data altogether. Doing so requires dedication and coordination as a company, with a strict adherence to the chief objective of fully actualized, individualized personalization.

While some parts of this endeavor may utilize machine intelligence, it should be guided by humans to ensure proper individual-level data management, target setting and analytical work for model development, and proper deployment of persona-based omni-channel messaging – leading to an optimal customer experience (actual processes to be illustrated further in future articles).

Essentially, a fully personalized, relevant customer experience facilitates trust, increases loyalty/retention, makes customers feel important, and further drives sales.  This is the most critical role of advanced analytics in a time of plentiful, but never complete data.

About the Author:

Stephen H. YuChief Product Officer
Stephen H. Yu is a world-class database marketer with a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from over 30 years of experience in best practices of database marketing. Previously he served as Head of Analytics & Insights for eClerx, and VP, Data Strategy & Analytics at Infogroup. Earlier, he was the founding CTO of I-Behavior Inc., which pioneered the use of SKU level behavioral data.
By |2019-05-07T15:35:24+00:00February 28th, 2019|Blog|0 Comments

Predictive Marketing Automation [vs. ESP, CRM, CDP]

What is Predictive Marketing Automation?

Predictive Marketing Automation (PMA) is state of the art machine intelligence applied to solving marketing problems. PMA begins with an intelligently designed database to identify key changes in the customer behavior and potential value of an individuals in real time or near time.

However, a PMA platform does more than just perform analytics pertaining to marketing opportunities. It actually bridges the gap between insight and action autonomously – utilizing its peerless level of intelligence to target and deliver only the most important messages/experiences to individuals showing the highest potential for spending (or risk of decaying value) at any given time.

The end goal – and ensuing result – is simply, the maximization of profit volume.

In fact, not only does Predictive Marketing Automation outperform other well-known, commonly used platforms, a modern PMA transcends their entire scope of capabilities combined.

As knowledge of this emerging versatile tool spreads, it will surpass – and eventually outlast – all that came before.

The Evolution of Marketing Platforms

With technology perpetually evolving, only the most adaptive, useful, and efficient software manage to achieve sustained success while maintaining relevance.

Ultimately, when a new platform emerges and proceeds to eclipse the competition, the rest are not simply rendered dated –they grow obsolete.

This is comparable to the natural world – in which a particular species becomes extinct after it is dramatically outcompeted by another. In the wild, the noncompetitive eventually become nonexistent. The same set of rules applies to technology.

PMA is the natural evolutionary progression of marketing platforms – going beyond consolidations, data summarization, insights, and machine intelligence to the ever more seamless execution of the most valuable touches at the ideal time.

A strong PMA platform also observes response and folds its learnings over time – improving its targeting and tailoring capabilities on its own.

Platforms of the Present

With a plethora of marketing platform names and labels like CRM, CDP, ESP, DMP, etc.,  identifying and understanding each of their respective capabilities can be – to say the least – a convoluted process.

At a certain point, viewing and comprehending a list of all these ‘acronyms’ can seem like staring into a bowl of alphabet soup – where each term begins to blend amongst the others like an indiscernible jumble.

This is especially the case with overlapping functions across separate platforms, as well as frequently exaggerated claims by developers.

Overall, this can create confusion and initiate a wide range of possible misunderstandings.

The following is a breakdown of the capacities of a few prominent digital marketing tools today – Email Service Provider (ESP), Customer Relationship Management (CRM),  and Customer Data Platform (CDP) – in relation to those of a PMA.

ESP (Email Service Provider)

An ESP is a platform whose basic functionalities include creating email templates while utilizing send engines to distribute emails to subscriber lists. Essentially, they are designed to deliver sets of messages to a large base of subscribers.

While ESPs perform their basic functions well, a major limitation is that their capabilities do not extend beyond sending out messages. Even those equipped to store data cannot do so as efficiently as a PMA.

Two key points:

  • An ESP’s core function is simply to send out bulk groups of emails (batch and blast) – nothing more.
  • The more mail sent out, the more expensive the software.

The latter does not constitute a recipe for success. Sending out more generalized messages to more people (instead of data-driven, personalized messages) does not result in more sales.

In fact – if not targeted and deployed properly – this practice of traditional “batch and blast” messaging will likely turn off a large portion of both your current and potential customer base.

Why? Because this is the age of one-to-one marketing. If consumers are getting too many impersonal messages too often, they will likely veer away from your product and turn towards another that knows when and how to capitalize on their (often fickle) attention spans.

CRM (Customer Relationship Management)

Like a PMA, a CRM utilizes data to form customer profiles that develop stronger connections with consumers. One of its greatest strengths is its ability to capture user input and compile unstructured data in order to drive customer retention and increase sales. They are able to capture user data, consolidate qualitative experiences, and organize them in a way that best describes the customer experience (CX).

For instance, a CRM can track and list a range of customer experiences/engagements – such as when they enter a store, access a website, or click an ad on social media.

However, CRMs are still deficient in a number of areas compared to a PMA.

These include:

  • Lack of a core database model utilized in predictive analytics.
  • Not designed to filter enormous quantities of data from so many sources.
  • Limit in the amount of detail of ingested data.
  • No advanced identity matching capabilities across channels.
  • Restricting outside access to internal databases.

Essentially, CRMs work well for targeted tasks within specific channels, but lack the depth and versatility to fully manage modern customer data like a PMA.  

CDP (Customer Data Platform)

A Customer Data Platform software is a web-based interface that consists of three core components – a database, the ability to connect to multiple channels, and a marketer-friendly interface – that help drive marketing and sales initiatives.

In the past, CDPs were traditionally custom databases for marketers – whereas nowadays they are more cloud-based.

The goal of a CDP is to organize and unify customer databases. What separates a CDP from a CRM is the fact it can identify any customer beyond your database – whether they are known or unknown.

In addition, a CDP extends beyond customer interactions and gathers more information about calls, emails, purchases, etc.

After that point, however, the work stops. This is because:

  • A CDP doesn’t actually deliver messages on its own.
  • A CDP’s main function is to ingest data. It is not directly actionable and requires requires “piggybacking” onto other platforms/software in order to do so.

PMA – The Platform of the Future

As evidenced, there are a number of explicit distinctions that set a PMA apart from the other platforms listed above.

While CRMs, ESPs, and CDPs were all designed with similar objectives in mind (and even perform some of a PMA’s functions), none employ a firmer, more comprehensive grasp of the Buyer Lifecycle (BLC) and a true 360-degree customer view.

With a PMA, every possible function is tied together in one singular package. There is no need to integrate, assimilate, or “piggyback” onto anything else. This simplifies the process while generating more palpable, powerful results.

In addition, other platforms lack the predictive intelligence that a PMA possesses and deploys. At best, they consist of old-fashioned triggers with simple, rules-based logic that cannot produce inherently actionable insights.

Meanwhile, a PMA covers the entire process all the way through to profit – delivering real, tangible revenues.

Predictive Pioneers

One of a PMA’s key functions, predictive analytics is gaining momentum as the size, scope, and variety of Big Data continue to increase.

By definition, Predictive Analytics is the utilization of data, statistical models and machine learning in order to gauge the probability of future results. Its insights are derived from historical, demographic, and other behavioral data (both online and offline).

Therefore, not only can a PMA consolidate, unify, and clarify unstructured omnichannel data, it renders it useful by building models that produce actionable answers to relevant questions.

A few examples of such questions include:

  • Who will be a valuable customer in the future?
  • “When will my customer make a another purchase?”
  • “What new kind of product or category could they branch out towards next?”

Obtaining the most accurate answers to questions like these is similar to  molding a set of keys. Each key can be used to unlock more doors into the minds of your buyers – revealing a clearer view of their respective tendencies and inclinations.

This practice creates a whole new landscape of possibilities previously unseen and/or unknown (by both your company and your competition).

Forecasting Buyer Behavior

At its core, marketing is all about getting to know people – specifically your buyers.

For instance, all retailers deal with loyal and valuable buyers as well as one-and-done bargain seekers. The latter are undesirables. Without a doubt, attracting the most loyal and valuable buyers (Most Valuable Buyers, or MVBs) who not only come back, but continue to buy again over a long period – is the key component of true retail success.  

With predictive analytics, a PMA helps to:

  1. Procure high-potential customers, or Most Valuable Buyers (MVBs), which generally drive 75-80% of a company’s profits – despite making up only roughly 15-20% of its total customer base.
  2. Use predictive modeling to retain those MVBs and procure more out there who are just like them.

This is accomplished by constructing models that calculate a scores to define highly refined targets before taking action. This is done perpetually in real time – every second of every day.

Full Stack Automation

Another key highlight of a PMA is automation. This allows marketers to synchronize, streamline, and execute omnichannel marketing campaigns from a single, centralized apparatus automatically through machine intelligence.

These days, the process of simply identifying and understanding the past, present, and future browsing/buying habits of your current and potential customer base is insufficient. In order to initiate a sale, that knowledge must transform into insights that are actionable.

A PMA’s automation capacities allow marketers to reach the right customer, at the right time, on the right channel, with the right message. With all of the bases covered, this constitutes full stack automation – where the system is consistently fed new predictive information that has both material and monetary value.

In fact, the system predicts and detects peak changes in either the behavior or lifetime potential value of a customer as they transpire in real time. This enhances customer engagement, maximizes overall Customer Lifetime Value (CLTV) and increases sales.

Windows of Opportunity

There are particular points along each customer’s respective buyer lifecycle where precious windows of opportunity open. This is where the likelihood of a sale significantly spikes, and marketers must strike while the iron is hot.

The best part of PMA automation is that most of its functions are preset into the software itself – negating the necessity for the seemingly insurmountable degree of manual functions it would take to track each customer’s position along the customer journey at any given time. This is achieved through cutting edge, data driven, machine intelligence.

In many cases, this type of advanced, personalized automation can make all the difference between winning or losing a customer.

This is extremely critical – because once a buyer loses interest and decides to move on, you’ll have to fight twice as hard to win them back.

The Germination of PMA

Without a doubt, PMA represents the next evolutionary phase in digital marketing. In fact, many large, established marketing companies recognize this, and are already merging together in order to acquire and incorporate PMA elements into their existing software.

However, this type of reverse integration process comes with three critical downsides:

  1. Less flexibility than an organically-developed PMA.
  2. Higher costs for setup and implementation.
  3. Substantially lower time-to-value ratio.

This leads to a key question for marketers: Would you rather conduct business with an older platform that has been patched up and added onto (like renovating an old building or repairing a used car) or a new, innovative one that stitched and molded together the needs of customers organically and independently?

Conclusion

The utility of today’s popular marketing platforms is steadily declining. They are becoming outmoded and outdated. Through the natural progression of technology, marketing platforms are becoming increasingly more agile and able to handle more complex tasks.

A PMA represents the true evolution of all preceding platforms. It is a superset of all of the technology and capabilities found in an ESP, CRM, and CDP. Frankly, it does not just match their functions, it supersedes them – maximizing the power of your data assets and putting your business in the best position to succeed.

Again, this is the age of one-to-one marketing, where reaching buyers on a personal level via the proper channel is more crucial to than ever.

Equipped with valuable information about each individual buyer’s interests, inclinations, and locations along the customer journey, PMAs are autonomous decision-making tools that develop and deliver carefully targeted, personalized conversations to anyone, across any channel, at anytime.

BuyerGenomics is a prime example of a premier PMA. As a marketing pioneer and thought leader, we ultimately leapfrogged the rest of the field.

This was achieved through a steadfast commitment to uncovering the source of insight it takes to design and run a truly modern, cutting edge platform.

It is precisely this kind of frictionless connectivity that weaves together the entire CX and maximizes both ROI and sales capacity in the simplest, yet most sophisticated way possible.

At BuyerGenomics, a well-educated client is our best customer, and we’ve poured our experience and resources into this piece to that end. If you picked up even one or two new insights, you’ve succeeded today.

Interested in learning more about BuyerGenomics Predictive Marketing Automation Software? Click here.

By |2019-02-28T23:08:18+00:00January 8th, 2019|Blog|0 Comments