Marketing to the 'Persuadables': Data Science, Behavioral Science, and Hypothesis (data) Driven Marketing.

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? What if we could collect specific data points that could tell us if a consumer is a 'persuadable' or not? Over the past few years, marketing and research have been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What Is Behavioral Science?

Behavioral science isn’t a new industry, but within the past few years is something of an emerging topic in marketing and research. At its core, behavioral science and the research that results from it, seek to understand the many aspects related to someone’s habits or decision-making. Most importantly, as we noted, it helps to understand why people make certain decisions.

If you think of that in the context of our marketing and product strategies, it’s clear why behavioral science plays a role in market research. There are a variety of methods that can get close to truly understanding consumer behavior, but many of them can fail to capture empirical evidence — sensory information captured through observations and documentation of behaviors through experimentation.

As a result, the importance and rise of behavioral science in marketing and research is no small subject. Just in the past year, there have already been numerous events discussing behavioral science specific to gathering and analyzing data to understand why consumers make decisions — but marketers and researchers, by and large, are still figuring out how to leverage it.

Behavioral scientists, in my opinion, should play a key role in marketing strategy as companies embrace, more and more, the hypothesis -> test -> persevere/pivot framework. By asking the 'right' behavioral questions, educated data points will be collected to determine if XY or Z populations are persuadable or not.

A strategy is only as good as page 2. Instead, the clever organization should create a hypothesis-driven environment so that actions are taken quickly once those hypotheses are proven or unproven (failed to prove).

 Leveraging Behavioral Data

Big data can be used as a possible solution for at least two reasons. First, it gives us access to more data than ever before, including data based on actual behavior from purchasing, web analytics, subscriptions, and more. As a result, big data can reduce the struggles we sometimes have with differences between stated versus observed behavior.

Second, there are big data sources that allow us to understand the motivations of consumers by examining the big 5 personality traits for millions and millions of people. By understanding different personalities, we can begin to realize if being “extroverted” or “conscientious” drive consumers’ purchasing. Some suggest that behavioral science and the resulting data on motivations behind decision making will be the new normal for market research. We agree that understanding what people don’t tell us in surveys is as important as what they do. Together, these two types of data give us a more well-rounded picture of consumer behavior, and with the right methodology, you can gain this knowledge quickly.

In a specific use case, a brand was looking to understand its target audience for new product innovation. They had a hypothesis’ about what this audience would look like, and likely could have gained that knowledge through standard quantitative research. However, by incorporating an approach that combines survey data and big data, they were able to understand who their audience is, but also what would motivate them to purchase this particular new product. The moral of the story? Consumers are more than just the people that buy your product.

Predictive Analytics

Marketers and businesses are always looking to better understand their customers and potential customers, seeking to know what influences the purchase decision, and what actions, triggers or encouragements will accelerate sales. Organizations started relying upon data science to gain valuable information including insights into customer behavior from the growing volumes and types of data – sometimes called “Big Data,” which, the critics call an informal way of referring to lots of data. In the past few years, organizations have shown renewed interest in the area of data science known as predictive analytics.

Traditional analytics is generally descriptive, providing statistics and reports on historical events or actions that have already occurred. Predictive analytics, on the other hand, is an area of data science where models are created using data, statistical algorithms and machine-learning techniques to analyze current and historical facts in order to foretell about future events – the likelihood of a probable outcome in the future.

Predictive analytics focuses on actions on an individual level – per person, per campaign, per store and the like. Massive amounts of data are available that encompass behaviors, characteristics, and outcomes for all kinds of individuals and activities, representing a vast array of experiences. Predictive analytic models use machine learning to analyze the volume and details of all this experience represented in the data in order to discern the predictable rules, patterns, propensities, and behaviors, subsequently used to predict the likelihood of certain behaviors, actions or performance. These analytics are also deployed to recommend specific actions to influence the outcome.

As these statistical models are tested using more data representing more historical experience, it leads to more precise predictions. While this area of data science is empirical in nature, it is more precise than guessing and therefore is of significant value.

Common Applications

A 2014 TDWI report found that the top reasons organizations are using predictive analytics are the following:

Identify trends.

Understand customers.

Improve business performance.

Drive strategic decision-making.

Predict behavior.

According to a report by Forrester Research, the most common applications for predictive analytics are cross-selling, upselling, determining customer profitability, promoting customer loyalty and credit scoring.

An enterprise might use predictive analytics to predict purchasing behavior so that it can better target its marketing efforts; predict probability of closure to prioritize leads; predict order cancellations to implement ways to improve customer retention and loyalty; or predict a customer’s product choices based on views or past purchases to make product recommendations personalized to that customer.

Frequently cited examples include the following:

Target uses a customer’s shopping patterns to predict the customer’s pregnancy and then direct product offers specifically useful or necessary to newborns, identifying 30% more prospects

Hewlett Packard uses analytics to predict about employees who were likely to leave their jobs so that managers could take actions to retain them or otherwise be prepared

Google uses predictive analytics to provide search users with high-quality pages in their search results

Predictive analytics is used by Amazon to offer personalized recommendations for other products, based on the visitor’s product views which comprise 35% of its sales

Uplift Modeling

Uplift Modeling (sometimes called Persuasion Modeling) is a specific predictive analytics technique that has gained recognition in recent years. The technique is used to find members of a target audience who are “persuadable.”

It gained publicity after the statistical modeling team for the Obama for America 2012 campaign used an uplift modeling program to precisely identify voters who were leaning Republican but were likely to be receptive to the Obama message. The models used demographic, geographic and political data to statistically identify the characteristics of persuadable voters in swing states; and used the models to determine which voters should be targeted with television ads, which with door to door solicitations, calls or mailings.

Uplift modeling has obvious applications in a marketing context. The idea is that an audience of potential customers includes the following:

Those who have already decided to purchase a specific product regardless of any contact

Those who will absolutely not purchase the product even if contacted

Those who would react negatively to being contacted

Those who can be convinced or persuaded to purchase with contact.

Clearly, it is more efficient for a marketer to target the group that can be persuaded. Marketing resources are wasted on those who have already decided one way or the other, and it is counterproductive to expand on those who would react negatively to contact.

Uplift modeling allows marketers direct resources more efficiently and potentially accelerate sales by targeting ads and other efforts at persuadable consumers.

For example, a company undertaking a direct mail campaign might use uplift modeling to predict who on the mailing list are unpersuadable, unlikely to respond to the company’s offer and remove them from that mailing. The company would save marketing dollars, have a better-targeted audience and increase the response or conversion rate.

Predictive analytics is an exciting area of data science with wide applications. For business, it has significant potential to more efficiently allocate resources, accelerate sales and increase revenues. It allows vast amounts of historical data to be analyzed which can be used to predict the likelihood of certain behaviors, and then recommend specific actions to influence the outcome in the desired direction.

Targeting Only the 'Persuadables'

It turns out that it’s not that interesting to know what customers will do. Knowing a customer will click, or doesn’t click, will move left or move right, isn’t useful data. The key is to know which of your customers you can persuade to behave the way you want them to.

By focusing on persuadables marketers can achieve better results. Next, to that, it could just help improve marketing’s reputation.

 Predictive analytics techniques like Uplift Modeling zone in on so-called persuadables. With their black-box models, data scientists can divide your customer base into people that are likely to react to a marketing message and people that are not. Targeting is more effective to persuadables.

The only problem is that these black-box models don’t tell you why persuadables are persuadable. Behavioral science is more suitable to identify reasons why someone can be persuaded. 

Combining behavioral science and data science Dr. Maurits Kaptein came up with persuasion profiling. His technique opens up new ways to identify persuadables and take advantage.

Using Persuasion Profiling to Identify Persuadables

Already in the 1950s people like Solomon Ash ran experiments trying to figure out how to persuade people. Robert Cialdini’s six keys to persuasion are well known in marketing. For example, we know that people tend to follow the herd, obey authority or are influenced by the perception of scarcity. The research by Kaptein has shown that people react consistently different from persuasive messages.  

With this scientific discovery, he also revisited many conclusions done years ago. In these experiments, results such as 30% more people reacting to a persuasive message than to a neutral message is a staggering yet common result.

But it leaves out the 70% of people that didn’t react. More importantly, it ignores the people that would have reacted adversely by a specific kind of persuasive message. These kind of insights are invaluable for marketers.

By building persuasion profiles you can identify persuadables, but you do that on a level of persuasive DNA. In the above example, Mike is most persuaded by a social proof message. And he seems to be even less persuaded by a scarcity message than a normal (neutral) message. 

Next, to identifying Mike as a persuadable, you also know how to communicate with him. Personalizing content to customers’ persuasive DNA increases satisfaction.

Even with persuasion profiling in place, you will have a group of people that are non-persuadable. On the one hand, it’s every marketer’s challenge to find out the exact persuasive DNA of each customer. Another way of looking at it is that these people really don’t want to get a marketing message. 

Improving marketing’s reputation

On a final note, something which I think is a nice side effect of thinking about your customers in this way.

Marketing has been a field of tiny successes. A lot of customers receive a lot of marketing messages without the message having the desired effect. Focusing on persuadables also means we don’t have to bother people that wouldn’t be persuaded in the first place.

Leaving unpersuadables alone will also help improve marketing’s bad reputation.