Don’t Ignore Business Rules When Building Predictive Models

The development of predictive models does not occur in a vacuum. The model-building process requires input from several key stakeholders, many of whom may not directly use the models that result. In several cases, an often overlooked stakeholder is the organization’s compliance officer.

Yes, you read that correctly. Laws, regulations, and internal policies restrict the use and application of data in marketing promotions, planning, and other organizational decision-making. These policies, known as “business rules,” have different degrees and levels across organizations and industries, but their importance is the same: ignoring them when developing your model can get you in a lot of hot water, as one of my past clients found out.

A financial services firm had once retained me to develop a series of prospect propensity models. The client had several types of data available about prospects: demographic overlay data, census data, and summarized data on credit and affluence. The client had obtained all these databases from third-party vendors in order to understand the customers and prospects in the areas where it did business. The client also had hoped to use this data to make smart marketing promotions to non-customers.

After being sure we were in compliance with financial services regulations and internal policies, I went ahead and built the propensity models, a two-month process. The marketing campaign team couldn’t wait to start deploying them. The strategic planning group was eagerly awaiting them to get estimates on future business. We were all excited. UNTIL….

A few months after the modeling engagement ended, the financial services firm renewed its contract with the vendor of the summarized affluence data. The terms of that contract included something the client had overlooked at the start of the engagement: the data was to be used for customer profiling and development, not prospecting!!!

Had the client retained me for building “best next offer” models for its existing customers, there would have been no problem. However, the wealth data had been used to construct prospect propensity models, so the client could not use the models that were built, lest it invite a lawsuit by the vendor. As a result, the client had to re-retain me to rework each model where there was at least one variable from the wealth data – and it turns out all models contain variables from the wealth data. And since the omission was on the client’s part, it had to pay for the rework. And, as if to rub salt into the wound, the marketing campaign team couldn’t use the models until they were redone and thus missed great opportunities in the interim.

The moral of the story: you could save your company thousands of dollars – both in model building costs, time, and opportunity costs – if you heed the business rules that govern the use of your data. Before undertaking a modeling project, make sure you understand the legalities of how you will use the information available to you. Talk to your company’s domain experts about these rules and make sure those constraints are always top of mind when you build you models. Otherwise you can end up like my client, or worse, on the wrong side of a lawsuit.


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