Optimizing the unoptimizable

Optimizing and optimization have become common themes in both the media and technology sectors that focus on the consumer as the end target. There is a broad range of definitions for the term, which can vary from optimizing the efficiency of a process that targets the consumer, to optimizing the profitability of the relationship with your customers. For the work I’m involved with, we like our filters tight, so we tend to take the most Draconian interpretation of optimization; focus on optimizing the relationship with your customers so that every interaction results in a satisfying and profitable exchange for both sides. This means knowing exactly what your customers want, why, when, how best to reach them, and how to compel them to do business with you almost immediately.

Knowing what your customers truly want also implies knowing what they don’t want, so you don’t waste your time or theirs in unwanted solicitations. It also means understanding temporal drivers; some people like to shop all the time, others much less frequently, but everyone has an internal clock that that be set off by the right triggers, if you know which button to push and when.

So why is this level of targeting accuracy so hard for analytic vendors? The short version is insufficient data; as an example, demographic analysis (the most common type of consumer analytics) is driven by broad, arbitrary separation strokes such as age or zip code, that have nothing to do with an individuals interest or motivation in purchasing any product or service. Web analytic approaches such as clickstream analysis focus on a single dimension of consumer interaction, even thought the relationship between a supplier and their customers is always multi-dimensional. This isn’t just a matter of depth, it has to be depth and breadth, it has to be real-time, and it has to anticipate why?

Consumers leave a vast data wake as they move through a network or even a website, the problem is not lack of information, it’s lack of correlation. Knowing how to take the myriad of details available on consumers and combine them into something truly actionable has been the holy grail of consumer analytics, and one that is about to undergo a fundamental shift as we develop new analytic models that are designed to leverage the vast increase in available data while addressing core, fundamental motivations.