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When should you Update Predictive Models?

on December 17, 2012

Rethinking Business Intelligence: Information or Decisions

on January 21, 2013

Why you should update predictive models

After writing my previous post, “How Often Should You Update Predictive Models”, it was appropriate to followup with a post regarding the consequences of not updating predictive models.

Predictive models use the patterns in historical and transactional data to identify risks and opportunities. Since the conditions and the environment are constantly changing the accuracy of predictive models need to be monitored. Once a predictive model no longer reflects reality it needs to be updated. Most of the time this is because the assumptions behind the model need to be updated.

Take for example a community bank. Internally every new transaction, deposit, withdrawal, application, or transfer creates new data. For most individuals, these transactions are occur several time every day, and that means you’re compiling thousands of new data points. Over time the customers environment is changes, this  is reflected in each data point collected. Did they get a raise or a new job? Is there car breaking down? So although this community bank may have a relatively modest customer base, their customers are experiencing change all the time.

Also, their are external changes that impact a customer’s behavior. For example interest rates change, new competitors enter markets, competitors invest in marketing, consumer confidence changes, and competitors merge. It makes sense then that they would need to update their predictive models to keep up with all of these changes. When these changes start to represent structural changes a new model needs to be developed.

For a typical community bank, strategic sales, marketing, and planning decisions happen at least once a quarter. If a bank doesn’t update their predictive models in preparation for these events, they are at a high risk of using obsolete information when making decisions.

What are the consequences of using this obsolete information?

  • Your pricing models don’t reflect changes in the competitive environment.
  • You recommend outdated products.
  • Your marketing material isn’t targeted at the right groups. They might not exist any more.
  • Your business development team begins chasing the wrong types of leads. For example, it might not be a profit environment to pursue new home mortgages.

So if you’re planning on making an investment in predictive analytics, make sure you consider the implications of using your data as well as the consequences of using outdated information.


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