Rationalizing the Sometimes Irrational Customer

 
 
By Guest Author  |  Posted 2014-06-02
 
 
 
analyzing customer behavior

By John Lucker

Traditional business intelligence systems typically use management reporting and statistical methods to interpret customer behavior with one underlying assumption: that the customer is rational. It’s no wonder so many of these systems fail to live up to expectations of the businesses that have invested in them.

The fact is that customers are human, and humans are often not rational. Therefore, customers, by definition, are not always rational. These deviations from what is expected of customer behavior often confound companies and their leaders.

Being human, consumers can make emotional buying decisions. While many economic and business axioms are predicated on behaviors using the law of large numbers, on an individual basis, consumers can often move in contrarian directions to what’s expected.

Thus, if companies subscribe to traditional economic expectations, they risk not understanding what truly motivates their customers on an individual basis. This lack of understanding leads to asynchronous connections between companies and their customers, which, in turn, can translate into lost, missed or declining revenue.

Analytics can help businesses better understand, and even predict, seemingly irrational customer behavior. By interpreting signals (which are sometimes faint) and clues to customer behavior, analytics offers businesses a way to make sense of and capitalize on­­ idiosyncratic customer preferences.

Customer choices and actions can be statistically scrutinized to determine how and why behaviors may “make sense” in accordance with our logical assumptions—or perhaps violate much of what was previously thought to be true.  Despite the views of many classical economists, consumers do not necessarily optimize utility functions based on some fixed set of preferences embedded in their psyches.  

Factors such as life experiences, framing of situations, habits, self-control or lack thereof, social impacts, and decision fatigue are examples of important components for determining what makes people behave as they do.  In this manner, complex individual and market data can serve as the customer’s “window to the soul,” but it may not always perfectly predict outcomes.  The probability, circumstance and impact of imperfect predictions should be factored into all business solutions and actions.

The Roots of the Irrational Customer

Classic economic theory tells us that consumer behavior is born of certain traits: unbounded rationalism, willpower or restraint, even selfishness. Historically, these traits have been identified as the reasons humans make certain choices under specific circumstances. In business, these cognitive perceptions have long been relied on to inform business strategies, guide product development and give marketing executives their directives.

The problem is that consumers don’t always follow these rules of thumb or conform to our experience and wisdom. Marketers often find that traditional evaluative methods of supply/demand models, consumer surveys or observational focus groups can go only so far to help understand individual behaviors.

When faced with value choices, some consumers make decisions that appear counterintuitive. Perhaps humans are fickle, acting more often on whim than fact. But there are forces today that are changing human behavior faster and more radically than ever before.

For example, our insatiable appetite for connectedness is spawning vast online social networks where experiences and desires shape actions within large communities of “friends”—all with stated “likes” or “thumbs up or down” points of view. Thanks to mobile technology, instantaneous communication propels many toward decisions that are tethered to evolving beliefs, emotional actions and input from others with whom consumers are in touch with in real time, as they travel with their mobile devices through everyday life.

Confusing the issue is that most consumers think they are rational. While they may research a purchase in detail before making it, in reality, a tangled web of correlations–along with a network of influencers, followers and transient leaders—often sways their behavior without their being aware of it.

With the pervasiveness of product review information, a seemingly rational purchase choice can be driven by others’ nonfactual, subjective or irrational influence. Too often, misinformation can be viewed as accurate information.

Fortunately, as consumers travel through this interconnected network, they leave behind quantities of digital exhaust in the form of data. Within this data are clues to what customer behavior might be—with and without the externalities previously discussed—and how buying patterns are formed, and what might happen next. Because we are not always rational, superior predictions can result from granular observations of how individuals, and those like them, actually vote with their wallet and their feet.

Reading the Signals

Regardless of what they are purchasing, consumers buy certain products regularly–some planned, and some without conscious forethought. Throughout the consumer journey, customers emit “signals,” offering companies a wealth of traditional data that can be combined with newer big data sources to more accurately inform business and product strategy.

Analytics helps businesses peer into customer behavior and learn to interpret signals. By interpreting data to better understand customer perception and opinion, businesses can overcome their own cognitive and organizational decision bias and influence their customers’ buying behaviors toward what will be, rather than what product managers think should be.

Within the constraints of privacy requirements and end user license agreements, behavioral and transactional data can be fed back into the value chain. This can result in better offers, more targeted products and, ultimately, more willing buyers.

Taking it one step further, using predictive analytics allows businesses to get a clearer picture of what a given customer segment or an individual is likely to do next—embracing the irrationalities and reinforcing the rationalities. Predictive insights surface through an ever-evolving understanding of what triggers create specific outcomes. For example, knowing that certain customers are likely to contribute to charitable causes under certain circumstances may trigger product management leaders to include a social cause with its next offer to that segment.

Achieving Better Outcomes

To capitalize on today’s “irrational” customer attitudes, an organization should move from an assumption-based business culture to a strategy-driven, fact-based culture informed by analytics. Analytics allows businesses to view customer expectations and demands through the lens of behavioral science by reading signals and predicting actions.

Successful organizations can take several key steps to evolve their cultures from traditional to fact-based. These include:

·  creating a road map that outlines objectives for enhanced customer management;

·  applying end-to-end analytical process and technical solutions that bring the road map to life;

·  embedding broad thinking that incorporates new and more efficient ways to conduct business using analytics;

·  integrating technology components such as scoring engines, signal processors, enterprise rules engines and learning engines that make analytic outputs available to critical business processes in real or near-real time;

·  incorporating organizational change-management practices to guide the restructuring, education and training that can make analytics stick long-term; and

·  monitoring performance using a fact-based business intelligence platform that enables companies to tune business and technical processes to improve road map components.

By embedding analytics and moving toward a fact-based culture, businesses can gain a more accurate view of what makes customers tick and ultimately achieve better business outcomes. As we’ve established, customers don't act rationally all the time— or even most of the time. So, although the factors that motivate behavior don’t always make sense, businesses can use customer data to rationalize the irrational customer.

John Lucker (@johnlucker) is a principal and the global advanced analytics and modeling market offering leader at Deloitte Consulting LLP. He is also a U.S. leader in Deloitte Touche Tohmatsu Limited’s Deloitte Analytics Institute.

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