With customers this phenomenon is even more dramatic. Unlike the people in your life, your customer is at arm’s length and as a result lacks a personal connection. Also, people have an innate resistance to being ‘sold to’. Each time you ‘talk’ to your customer that’s exactly what you’re doing — and they know it. It’s not unlike that feeling you get when a friend, renowned for borrowing money, says to you, ‘I need to ask you a favor’.

So we’re faced with two primary obstacles to establishing communication with our customers. First, we need to break down that initial resistance and second we need to ‘talk’ to our customer in a manner appropriate to that customer — a communication that resonates. Before we can do that, however, we absolutely, positively must understand (in empirical terms) who our customer is. This is that ‘back-of-the-paint-can’ part of the show.

To be effective, every MarCom plan must know:

• What the customer universe ‘looks’ like (number of clusters, sizes, etc.)
• What message works best for each cluster (through experimentation)
• Baselines for each cluster (response rates, purchases, etc)
• Predictive response scores for each cluster (created through modeling)

Initially, we derive this information from historical data and build upon it as each subsequent program’s results become available. Using this information, it is not only possible to develop resonant messaging but to predict cluster-by-cluster response (and revenue) as well. Since clusters/segments differ not only demographically/psychographically but also perform at different levels, it is important to estimate the profitability of each cluster for a given MarCom effort before launch. Only then can targeting decisions be made intelligently.

 

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