SAS and Python Predictions enable the creation of ING's Proactive Targeting Framework
Which customers will respond best to a given product offering? It remains a central question in any business. The Customer Intelligence Department at ING Belgium relies on response modeling software to provide the answer. In 2006, ING and Python Predictions developed a new tool – the Proactive Targeting Framework. Based on SAS software, it predicts customer behavior in a fast, effective, flexible, and user-friendly manner.
Intelligent actions with customer intelligence
In recent years, the Customer Intelligence (CI) Department of the company has used internal customer data to guide its marketing decisions. "Historically, we were considered as an operational partner, delivering target groups at the request of other departments. Today, we have truly become a proactive player in the development of efficient and actionable marketing campaigns," states Dr. Martine George, head of the CI Department at ING Bank Belgium.
Rapidly changing environment calls for new modeling
"A first step was the construction of a data matrix containing more than 300 distinct pieces of information that are available for every client on a monthly basis," explains Pieter Dyserinck, Senior Customer Intelligence Analyst at ING Belgium. "While these indicators had proven their value in previous targeting projects, we were looking for an expert partner to help us mold this mass of information into a more powerful, flexible, and interpretable solution.”
Efficiently predicting customer behavior
In a close collaboration between the internal and external experts, the newly created models were carefully benchmarked with former modeling practices at ING. This not only demonstrated their superior technical performance, the process also resulted in a fruitful knowledge transfer for all parties involved. "We rigorously compared different algorithms using the flexible programming power of SAS software. Together with the internal experts, we carefully retained interpretable models, containing no more than 15 variables, which provided lean but powerful predictive performance," recalls Buckinx. "Additionally, we have implemented the selected final models into the SAS® Enterprise Miner environment, which offers the business users a powerful, flexible, and easy-to-use interface." Furthermore, the framework features a built-in quality check - a tool that automatically indicates when and where future adjustments are required.
When easy expansion is key
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(left to right) Martine George (ING), Pieter Dyserinck (ING), Geert Verstraeten (Python), Wouter Buckinx (Python)
Customer response modeling
SAS® Customer Intelligence, SAS® Enterprise Miner
1. More effective targeting of marketing campaigns.
“Our new Proactive Targeting Framework will greatly improve the efficiency and success rate of our Customer Intelligence Department.”
Dr. Martine George
Head of Customer Intelligence, ING