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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

Achieving Holistic 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 |2019-03-11T16:37:33+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