Gaining a deeper insight into your customers

The holy grail of consumer marketing has always been the ability to reach an individual consumer with an offer that resonates so solidly they are compelled to immediately do business with you. Everyone has a point of resonance, sometimes it’s near the surface, sometimes it’s buried so deeply it never surfaces, but most of the time the resonance point is a relative event that can be triggered by the right message at the right time, delivered in the right media. What are the core components needed to achieve resonance that is driven by a holistic view of the customer?

Who are they? This is demographic information, which is normally gathered at the point of website registration or product/service purchase. This is the best (and possibly only) opportunity to gather as much personal information as possible, and if the request is phrased properly, most people will not have a problem disclosing details such as contact preferences, etc.

What have they done? This includes both absolute and relative information. Absolute information is gained by transactional analysis of the customer’s buying behavior; the further back the transactional analysis can go, the better position the vendor will be in to derive long term changes in customer behavior and anticipate future changes. This also includes clickstream analysis of the customers website visits (browse vs. search, etc.) The relative information is derived from collaborative filtering (people like you also bought things like this).

When did they do it? This includes a temporal overlay tied to transactional analysis (including clickstream behavior). Temporal patterns can often be anticipated, since most people are creatures of routine and habit. Integrating temporal analysis to purchase behavior can indicate the best time to send a message to a customer, based on their past behavior.

How did they do it? There are two aspects to this; where did they come from, and how did they get to you? Search engines have long been the gatekeeper that brings consumers to e-commerce and entertainment websites, however social networks are quickly becoming a driving force, and the rise of micro-blogging applications like Twitter offer a potential gold mine of time-sensitive opportunities, once people figure out how to work the system. The how did they get to you part has extensive implications (which will be addressed in detail in a separate white paper), but the short version is that there are two primary means for consumers to come in contact with your business; either on-line through a fixed access point, or on-line through a dynamic (mobile) access point. The mobile access point also has implications for website design; looking at a website through a 21 inch monitor is very different from looking at it through a 2 inch smart phone screen, and it’s safe to assume that over the long run, the default access device is likely to be portable. If your website is not optimized for mobile access, this is something that needs to be addressed sooner rather than later.

Where were they when they did it? This aspect correlates closely with mobile access information, and is the province of geo-spatial analytics. What you’re looking for are behavioral patterns that are tied to specific locations; again, most people tend to do the same thing at the same place every day. Knowing where they are when they access your site can provide a treasure trove of actionable information.

Why did the do what they just did? Of all the consumer modeling technologies, this is probably the most controversial, and by far the most effective. The why of consumer behavior is derived from psychometric profiling; people are motivated by different things at different stages of their lives (or even by time of day, time of year, etc.). Psychometric profiling can be used to address specific behavioral attributes (is your customer impulsive, an early adopter, price sensitive, technophobic, etc.). All of these behaviors can be captured and quantified through profiling questionnaires; the questions need to be applied to a statistically significant sample of your customers (2%-3%), and can then be correlated and extrapolated to the remainder of your customers. These types of extrapolations tend to run at approximately 85% + accuracy during the initial iteration, then subsequently improve as the system gathers more learned behavior.

The who, what, when, where, how, and why dimensions of customers can be combined using advanced multi-variate analysis techniques to create an N-dimensional model of the consumer (referred to as a hypercube), that provides a a unique profile of what your customer is truly like, and therefore can be used to define the optimal marketing approach. While this may sound like an elegant solution, it is an elegant solution for a single customer, which makes it interesting, but ultimately useless. In order to make this a truly useful marketing tool, this profiling methodology needs to be tied to an intuitive visualization tool that allows manipulation of data at an aggregated level, but provides capabilities to execute in a highly segmented manner.

An example of this would be a marketing specialist who wants to know which customers are most likely to be interested in a new product, and what is the most effective way to reach them. Assuming that behavioral data on customers (who, what, when, etc.) can be integrated with existing product schemas tied to profitability models, it becomes relatively straightforward to determine (in rank order), which customers are most likely to be interested in product X vs. product Y. If contact preferences have been captured as part of their demographic profile, then you also have the optimal approach vector (given to you from the source of truth, your customer). Combining product profitability attributes with channel preferences delivered in the context of an N-dimensional hypercube model allows the marketer to finely slice and dice their customer base, while still dealing with a critical mass that is large enough to justify the associated marketing expense. This not only provides an integrated, holistic view of the customer, it provides linkage between back-end operational systems and outbound marketing systems that can be used across divisional lines, allowing companies to fully leverage their systems to deliver a quality experience to their customers.