Hidden Costs of Customer Analytics: Data Collection and Implementing Results

We talk a lot about the nuts and bolts of predictive analytics on this blog - how we build customer analytics models, how we interpret them, and how we establish whether or not they’re working in production. However, one item we have yet to tackle - and a very important item at that - is how to integrate data mining and/or predictive models into your organization and your company’s routines. Indeed, the black box in the middle should be the necessary data integration (getting the data into the right format for the models) and running a bevy of models against the data to see which one projects as the most effective. But it’s the before and after this black box that really make or break any attempt to incorporate predictive models into a company’s processes: data collection and incorporating data mining results into your processes.

Do you need to collect more data than you already have?
The main problem that companies run into is the need to collect more data in order to build the necessary predictive models. For example, if a company wants a model that tells them which of their products a specific customer might buy next, does it already collect the data necessary to support a model that does that? Ultimately, the question is: Is it worth the time and cost needed to invest in collecting more data that may not yield any better results when supercrunched? New collection methods take months to set up and sour clients on predictive models before the fun has even begun.

We believe that it’s important to try to work first with the data that our clients have and add new sources of data over time. This is a handy tip for business users who really want data mining to work in their organization: start small by using the data you have and get more sophisticated as you go. I know it may be tempting to put in that clickstream collection database right now so that you can use online behavior to segment and market to your customers. But trust me, get the buy-in from your organization first by proving that predictive customer analytics work. Then ask for the stars.

Are you ready to completely change the way that you do direct marketing?
Some companies would have you subscribe to a whole new way of doing business in order to use their models. Customer-centricity is my favorite new business philosophy. Though I firmly agree with the need for firms to be customer-centric, is it realistic to expect companies that use offers, coupons, and holidays to draw new and existing customers to their websites - and have for years - to change their direct marketing approach to accommodate a new set of tools? Not really. Company culture and routines are slow-developing and even slower-changing.

So the question at the end is really: What are you going to do with all this newfound predictive power? How are you going to fit in customer-centric model results - We’re 99% sure that Brad Pitt will buy two Baby Bjorns with lumbar support - to your next email blast that has a summer theme and features hats and sunscreen? And what about the extra creative necessary to relay that customer-centric message? You’re going to have to make a new email blast featuring the Baby Bjorn with lumbar support, in addition to the summer-themed email.

Well, I’m pretty sure that the summer themed email blast was probably going to draw some business but I’m also positive that luring customers with what they want when they want it is a lucrative way to market. The best practice is to initially skim the cream off of the predictions, to take those that have the highest probabilities of succeeding, and run with them. Collect a group of customers that has the highest probability of buying the Baby Bjord - Mr. Pitt among them - and send an intermittent email to just that group. Now watch your open rates and clickthroughs soar.

Tags: Clickthrough, Coremetrics, customer analysis, Data Integration, Data Mining, email, Predictive Analytics

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This entry was posted on Monday, July 28th, 2008 at 8:15 pm and is filed under Clickthrough, Customer Analytics, Data Integration, Data Mining, Email Marketing. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.