A leader in predictive analytics

Technology update
The company’s predictive analytics technology connects data to effective strategic action by drawing reliable conclusions about current conditions and critical future events. More than 250,000 commercial, academic and public sector customers rely on SPSS technology to help increase revenue, reduce costs, improve important processes, and detect and prevent fraud. More than 95 percent of the Fortune 1000 companies are SPSS customers. Founded in 1968, SPSS is headquartered in Chicago, Illinois.

SPSS’ UK customers include major corporations and government departments such as Barclays Bank, British Airways, BT, Credit Suisse, Customs & Excise, Egg, HSBC, DEFRA (Department for Environment, Food and Rural Affairs), Electrolux, FT.com, Gala, HBOS, Inland Revenue, Unilever and Waitrose.

Spaarbeleg transforms service calls into €30 million in revenue

The growth strategy of Spaarbeleg, one of the larger financial services institutions in the Netherlands, is based on expanding sales to its existing customer base. Aware of the dangers associated with over-saturating its customers with unsolicited messages, Spaarbeleg takes an innovative approach by converting inbound calls into its service call centre into new sales opportunities. Spaarbeleg achieves this using SPSS predictive analytics software, which has been integrated with an existing homegrown call centre environment.

The SPSS solution provides Spaarbeleg’s call centre agents with highly accurate, personalised product offering recommendations during service calls. Using business logic, the application generates real-time predictions regarding each individual customer’s needs, recommending the product most likely to be of interest. Additionally, Spaarbeleg uses the SPSS solution to increase the response rates of its conventional direct marketing campaigns.

During the first year of implementation, SPSS predictive analytics software detected a potential cross-selling opportunity for 18 percent of Spaarbeleg’s one million inbound calls. Offer recommendations were communicated through a pop-up window on the agent’s desktop. In those cases in which an offer was made, 50 percent of the customers responded positively and were sent additional information. Of this group of customers, 75 percent converted, resulting in the sale of 22,000 products. The bottom-line impact for Spaarbeleg is that €30 million in additional sales was generated in one year.

Credit Suisse’s marketing campaigns increase profitability

Competition in the financial services industry is intense, and obtaining new customers is an expensive proposition. Credit Suisse, one of the world’s leading financial institutions, implemented SPSS predictive analytics software to maximise profitability, generate targeted customer leads and tailor marketing programs to appropriate customer segments.

The organisation uses SPSS’ data mining solution to analyse a data warehouse storing information on its 2.5 million customers. The analysis is used to identify potential leads among Credit Suisse’s customers so that it can intelligently market to them based on their individual preferences and histories. In addition, detailed segmentation of its vast customer base allows Credit Suisse to target its clients with customised solutions.

Credit Suisse understands that it is not enough to know whether customers are interested in a product. It needs to know if they will actually follow through and make a purchase. SPSS software allows Credit Suisse to analyse situations in which customer interest in a service did not correlate with a purchase. Many times, customers did not have good enough credit and were subsequently refused service. Refinement of the models factored in credit-worthiness. As a result, the percentage of customers interested in purchasing a service but who were refused due to bad credit was reduced by almost half in subsequent campaigns. This represented substantial cost savings and enabled Credit Suisse to recoup the total cost of the project in just two years.

“With the help of SPSS predictive analytics software, Credit Suisse’s data mining activities – analysis and modelling – have been fully integrated into our business process and have proven their value in many different applications,” said Dr Alex Nippe, Head of Data Analysis/Data Mining at Credit Suisse. “The demand for data mining within the bank is rising all the time, and the strategic component is becoming increasingly important.”

For additional information, please visit: www.spss.com.