In recent months we’ve been seeing a rise in the amount of interest in predictive analytics from our clients, so we’ve decided to run a series of blog postings to explain what predictive analytics is, how it’s used and so on. This posting is the second in the series.
Manypeople first hear about predictive analytics in the financial services industry, where it’s used in applications like credit scoring. Information such as someone’s repayment history is used to predict whether they will keep up future payments on a loan, and predictive analytics is also used to help identify financial cybercrime and fraudulent insurance claims.
In fact predictive analytics is used for many purposes. A major application of predictive analytics in many industries is in the areas of direct marketing and customer relationship management. For example, marketers use predictive analytics to predict who is likely to respond to particular offers. The prediction is often more accurate with existing customers because there is more historical data to predict from, and this is one of the reasons why loyalty cards have increased so much in popularity with businesses in recent years. The personalised sales data collected via a loyalty card scheme allows a business to spend their money only on sending you offers that the predictive model says you are likely to say yes to, so they reduce the money they waste on sending you offers that aren’t of interest to you. The extra data enables them to profile you and allocate you to one of their market segments.
To make this real, here’s an example from a company we’ve worked with in the telecoms and media sector. They worked hard to build up what many companies call a “single view of customer”; in their case that means the ability to understand someone as a member of a household. One of the things they wanted to achieve was to decrease the churn of profitable customers.
When they mined their data, one of the patterns they discovered was that households containing a father and at least one child, who had a sports TV package, had a much lower churn rate than similar households without sports TV. Thanks to that insight they began a successful campaign offering heavily discounted sports packages to households who fitted that profile, because the lower revenue achieved from the discounted TV package was more than offset by the increase in revenue and profit from retaining such customers for longer.
The next posting in the series will give further illustrations of how predictive analyitcs is used by businesses in practice, before we move on to compare it with other future-oriented techniques such as strategic planning and forecasting.
