Lately I’ve become fascinated with behavioral economics and related fields of study that examine and challenge our assumptions about how people make both simple and complex decisions. Of course, in interactive marketing we sometimes use tons of data as a proxy for true guidance and rely heavily on predictive models that primarily use past behavior and the behavior of those similar to you to present you with advertising vehicles, marketing offers, and messages. Is this the best we can do?
Relevance signals are clearly present in our behavior when we search using specific language cues or visit and return to specific pages with precise content. But to motivate or influence decisions we need to better understand the real influencing factors, which may not be as simple or linear as our current models imply.
Behavioral data models in use to serve relevant ads and purchase opportunities online can capture at least part of the decision process, but we might be able to craft a more ideal approach or decision environment on e-commerce sites and in shopping situations online. To do that, we must challenge the assumption that people make rational decisions based on the directly relevant factors weighed at the time of choice.
Consider the decision to buy a new electronic gadget. In your rational mind, you would search out the best deal based on its feature set, price, availability, etc., and weigh the options against your needs and budget. Of course you will take the best deal presented to you – won’t you?
Maybe, but maybe not. It depends on the mindset you’re in when presented with the options, earlier choices you may have made, the path you took to arrive at the option set, the complexity of the decision task, how the options are bundled, what else you are comparing it to, the size of the investment and the risk inherent in the decision, the mechanics of the choice, the way the information is presented, the timetable of the expected return, and a billion other factors.
Presumably, you would be moving toward the best deal for you, at that moment and space in time given a set of personal variables. Some of which have nothing whatsoever to do with the product, price, or offer and many of which you have no idea are influencing your decision.
Marketers of the new electronic gadget would be looking for patterns of behavior that suggest product interest to serve you ads and present purchase opportunities. They might pair that information with demographic data that implies that you can afford this gadget – maybe even the deluxe version.
They might segment the ads or opportunities based on their assessment of whether you’re doing serious research, just kicking the tires, or if you’re close to a purchase. They might use specific language, colors, or presentations based on regional preferences or other more granular distinctions, but still our data models describe what people should do given a neutral set of influences and a rational decision process.
More sophisticated e-commerce players adjust the research and shopping environments with dynamic content and personalization that help to move consumers through a conversion process to sale. The bundling and pricing options are carefully crafted to maximize profits and collaborative filtering (nearly a household word now) is used to upsell in the ubiquitous “if you like this product you may like that other thing” presentation.
Now commonplace features like product ratings and reviews are one clear approach to attempt to influence product and value perceptions. Subtleties found on sites like Amazon that emphasize when you could expect to receive the shipment of your gadget (making it almost yours already), or showing both positive and negative reviews to validate thinking are strong signals that these smart marketers understand and will continue to test other influences on our decision process.
Direct marketers, both traditional and online, have long used constant testing across multiple variables to help achieve optimal results. Are they the right variables?
We’ve made great strides in the sophisticated analysis of campaign insights and site-side results with ever increasing data sets, but if most of the underlying models are built on the assumptions of consumers making rational decisions, we could be repeating old style marketing – just doing it more efficiently and faster. Now we may have more insights and a new way of understanding, predicting, and maybe even influencing how consumer decisions are being made.
I don’t have any academic training, experience, or credibility in these fields of study, but indulge myself in reading the works of Gladwell, Ariely, Brafman, Taleb, and others whose theories and questions about how people make decisions should be central for marketers and make for fascinating explorations. I wonder how their insights can be applied to our field of online marketing.
Behavioral targeting and e-commerce leaders keep their algorithms to themselves. Perhaps some of the irrational effect is already captured in the sheer volume of data used to feed their models. Perhaps their secret sauce already includes a proxy for frame of mind and oddball, seemingly unexpected influences. I wouldn’t know how to capture that, which is why I defer to the experts in this area and urge them to broaden their thinking, recast their models with a set of new assumptions on the influences that matter, and the actual human decision calculus – which appears to be anything but rational.
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