KPN transforms its approach to direct marketing

Gaining new insight with a propensity modelling solution built on IBM® SPSS software

Published on 15 Dec 2010

We decided to test our findings by monitoring actual customer purchases over a six-month period, and we found that there was a very good correlation with what the IBM SPSS Statistics model had predicted... Customer response to our direct marketing and telemarketing campaigns increased by 400 to 1,000 percent when we used the propensity models.

Michiel van Straten, Senior Data Analyst at KPN


Computer Services, Telecommunications

Deployment country


KPN is one of Europe’s leading telecommunications and ICT services companies. In its home market in the Netherlands, KPN offers fixed-line and mobile telephony, internet and television services to consumers, as well as end-to-end telecommunications and ICT services. The company also operates several highly successful mobile brands in Germany and Belgium, and its subsidiary Getronics provides ICT services to companies within the Benelux region and across the globe. In total, KPN has more than 40 million customers and 33,000 employees, and reported revenues of €13.5 billion in 2009.

Business need
KPN has a portfolio of more than 100 products that it sells to over a million business customers. The company’s business marketing intelligence team needed to find out how best to allocate marketing budget in order to maximise cross- and up-selling opportunities – a task that required complex analysis of huge volumes of data.

KPN used IBM SPSS predictive analytics to create propensity models for more than 30 key products. These models are used to mine customer data to discover which products are most likely to appeal to which customers. The models were then united into a single overall model, enabling accurate segmentation and predictive analysis that supports a more intelligent allocation of marketing budgets.

Identifies the customers who are most likely to buy each product, helping to target direct marketing campaigns and increase customer response by 400 to 1,000 percent. Provides startling insights through predictive analysis: for example, that making one additional marketing interaction every six months could increase KPN’s revenue from certain groups of customers by 50 to 70 percent. Creates compelling graphical presentations that make it easier for non-specialists to understand complex analyses and make good business decisions.


IBM products and services that were used in this case study.

IBM SPSS Statistics Standard

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