The amount of information available about consumer interests and online behavior is striking and is growing exponentially. With the proliferation of AI, machine learning and data science as a field, more and more information about consumers is being extracted and individualized consumer personas can be constructed.
Although the insights being extracted are many and each adds value a deeper understanding of consumer preferences is in order to maximize the insights obtained from consumer personas. For example, companies are able to extract the sentiment associated with an online review or a post interaction survey in real time and can identify a consumer complaint quickly. What companies are less certain about is how to address this concern so as to optimize the likelihood that the consumer will remain loyal. As just one example, should you offer the consumer an additional service for free (a value-add) or cut the cost of a service (a discount)? Knowing the answer to this one simple question provides a huge amount of information, and is at the heart of the study of “preference”. For example, some consumers prefer a value add (pay full price for a hotel room but get free breakfast) and other consumers prefer a discount (10% off the full price of a hotel room). From a purely economic standpoint these two offerings may be identical. For example, if the room is $200/night and the breakfast is $20 then the value add means a room and breakfast for $200 and the discount means room and breakfast for $200. Although they are identical from an economic standpoint, people have very different preferences. Some people will clearly prefer a value add and others will clearly prefer a discount. These preferences are measurable and add significant value to understanding a consumer’s persona. Understanding a consumer’s preference tells you how to proceed in the current situation, and also tells you about this individual’s preference regarding sales promotions, in general. This provides information should you encounter another individual with a similar consumer persona, and about future marketing to this person and consumers with similar personas.
By optimizing the consumer persona for one customer through an understanding of their preferences, you are one step closer to optimizing it for other consumers in the domains of customer service but also marketing. If this increases sales by even a few percentage points isn’t it worth it? This is just one of many examples of how preference can be used to optimize consumer personas.