Why are multichannel consumers are more loyal? In this series, I’ll examine that question.
Four years ago, I started seeking scientific evidence to answer this question, even when it wasn’t a given that multichannel customers actually are more loyal. Since then, multichannel consumers have been empirically shown to be more loyal and profitable. But I haven’t come across any literature that really explains why from a scientific point of view. I’m basing my theories here on common understandings of neuroscience, the study of fear in animals, and how the brain makes decisions.
The theories I’m trying to prove scientifically are:
- Multichannel consumers are more loyal than single-channel consumers.
- Multichannel companies can win over single-channel companies if they focus on creating a good customer experience across all their channels. In this case “good” doesn’t necessarily mean “best,” a very important point.
- A unified brand across channels is imperative to nurturing a multichannel consumer.
Understanding the Science
Let’s start with the science behind my theories. To explain how decisions are made in the brain, we’ll use a fairly common test case involving a rat and two different boxes: a light box and a dark box. Assumptions to keep in mind:
- These theories are based on precognitive decision making, not post-cognitive (e.g., Maslow’s Hierarchy).
- Rats make precognitive decisions the same way we do.
- Our brains make decisions in a very complex, highly mathematical way. Each decision is broken up into several little decisions. This process is recursive, because the big decision is broken up into smaller ones and gets solves from the bottom up.
Rats are instinctually averse to light. When given the option of going into a dark or a light box, the rat will crawl into the dark box without thinking about it. Is this a decision? It is, but it’s one the rat makes without thinking about it.
Here’s how the first step of the decision is made:
Question 1: Do I go into the light box or the dark box?
Answer 1: The answer is solved by answering two other questions:
Question 1a: Is light good?
Question 1b: Is dark good?
Solving the problem recursively, step one looks like this:
Question 1a: Is light good?
Answer 1a: Yes: 40 percent; no: 60 percent
Question 1b: Is dark good?
Answer 1b: Yes: 90 percent; no: 10 percent
Looking at the answers with the highest percentage, “go into the dark box” wins because the rat will pick the “yes” answer if there is one. In this case the results for “Is dark good?” came back “yes” at 90 percent, while the results for “Is light good?” came back “no” at 60 percent.
Note that the questions’ final results (90 percent and 60 percent) aren’t directly comparable, as they describe their universes (dark or light). The percentages don’t directly compare dark to light. The rat’s natural instinct is to choose the most advantageous answer, in this case “yes,” the dark box is a better environment. Because “Is dark good?” resulted in a “yes” and “Is light good?” resulted in a “no,” this is a fairly easy example. It gets more complicated when each subquestion results in the same answer: when both “Is light good?” and “Is dark good?” equate to “yes.” In that case, the brain moves to step two, comparing the actual evidence that generated the percentages.
Let’s say you run two trials on four products, pitting medicine A against medicine B, and medicine C against medicine D. You discover that 60 percent of patients liked medicine A over medicine B, and 60 percent of patients liked medicine C over medicine D. The clear winners are A and C. But, which one would you choose, medicine A or medicine C?
Both are preferred by 60 percent of patients, so there isn’t clear winner as with the light/dark example. The brain moves on to step two, looking at the evidence that produced the results. If the A/B trial had twice as many people in it as the C/D trial, the brain would choose medicine A. While the percentages are the same, there’s more evidence for medicine A being good.
If there’s a clear winner in a trial — one result is “good,” the other is “bad” — we’ll choose the “good” result. That’s why the rat chooses the dark. It was the “good” answer; the “yes” answer had the highest percentage of all the answers to both questions. In the case of the four medicines, however, two results come back as equally “good.” The brain then goes to step two: consider the evidence. In this step, it determines how much evidence there is and weights the goods results accordingly. More evidence for one choice weights that choice better.
How This Relates to Multichannel Marketing
Now that we understand the basics about how decisions are made in the precognitive brain, we’re ready to prove the above theories. The next column will replace the above questions with, Should I shop at a Company A or Company B?
We’ll see how the fact that one of these companies is multichannel greatly affects the amount of evidence in determining which company is better, even when the channels Company A and Company B have in common are liked equally.
Questions, thoughts, comments? Let me know.
Until next time…
The use of psychology in marketing and sales is not new, but it may be more useful than ever in an attention economy where time is precious and focus is rare. How can you tap into a demanding consumer to check whether there is an actual interest in your product?
According to a survey conducted as part of OnBrand Magazine's State of Branding Report 2017, marketers are well aware of the new technologies that are expected to be important to their brands in coming years, but the majority aren't rushing to invest in them before they're fully-baked.
Two weeks ago, Foursquare announced what could be the most important component of its data business: the Pilgrim SDK. So what does it do, and what does it mean for location-based marketing?
Combining clickstream data with machine-learning technology, behavioral analytics helps enterprises create a tailored online experience for each visitor to their web or mobile sites.