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Customer Modeling

Marketers who use customer data often talk about "customer modeling" instead of customer profiling. Modeling is kind of like profiling, but it is action oriented. Models are not about a static state, like "Customer is 50 years old". Models are about action over time, like "If this customer does not make a purchase in the next 30 days, they are unlikely to come back and make any further purchases with our company".

This kind of model sounds so mystical, and it is. To see a mathematical model predict customer behavior is astonishing, to say the least. The model says, "Do this to these people and they will likely do this". The marketer goes out and does what the model says, and a good bunch of the customers do exactly what the model said they would.

What is a model? Simply, it looks at customers who are engaging in a certain behavior and tries to find a commonality in them. The marketer might say to the modeler, "Here’s a list of our very best customers, and here’s a list of our former best customers. Is there any behavioral signal a best customer gives before they stop shopping? What does the data say?"

Here are some examples of customer modeling in action to give you a better idea of what is possible:

Example: I can rank the customers of your clients by likelihood to respond to an e-mail campaign. Instead of sending them all one e-mail, if you send two e-mails to the top 50% most likely to respond, you spend the same money on e-mails, but drive higher sales. This increases yield, or ROI, and makes paying for more e-mail campaigns attractive to clients.

Example: Many retailers over-discount. They always wonder how many of the people who used the discount would have bought anyway without it. I can tell you who these customers are, and what's more, show you how to set up a discount customization strategy that only delivers enough discount to get each customer to buy, and no more. Customers who need no discount get none, and still buy. This increases response rate while lowering discount costs, a double-barreled benefit.

Example: Looking at response rate and sales versus costs of advertising is the first step to optimizing campaigns. Buy what if you could predict the future sales of customers by keyword by search engine? Some campaigns looking like losers actually turn into very big winners when looked at this way.

Example: Of course, it's not enough to get customers to buy the first time. Your clients need customers who will buy again and again. My models will tell you where to find these customers, and how to create more of them out of the customers you have. And in the area of customer retention, you can use these models to predict which customers are most likely to stop buying, a critical element of best customer management or CRM.

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Customer Modeling