Published on 15-Jun-2011
Validated on 03 Dec 2012
"This is a clear result. It shows what can be achieved through an intelligent data analysis and customer approach. We’ve used IBM SPSS Statistics and IBM SPSS Modeler to run other campaigns since this one and they have all achieved good results." - Dirk Meggert, Manager of CRM and Database Marketing, DekaBank
Business-to-Consumer, Business Intelligence
DekaBank is a central institution of the German Savings Banks Financial Group and operates in the wholesale banking and mutual fund segments. With a group balance sheet total of €115 billion ($144.2 billion) at the end of 2005, assets under management of more than €140 billion ($175.5 billion) and equity capital of €3.7 billion ($4.6 billion), it is one of Germany’s leading financial services providers.
DekaBank needed a data mining tool that could help savings banks within the German Sparkassen Group decide which customers would be most likely to purchase certain investment funds.
DekaBank selected IBM® SPSS® Statistics and IBM® SPSS® Modeler to differentiate between the range of services and products that can be offered to various banks’ target groups.
IBM SPSS predictive analytics solutions help maximize customer value and minimize risk by transforming data into applied customer insight. Analyze data from every channel – ATM transactions, web data, textual data such as notes from call centers and applications – in a closed loop that generates results to improve interactions, often in real time. Companies are then able to focus on the financial needs of each customer segment while simultaneously balancing opportunities against risks.
By using IBM SPSS Statistics Base and IBM SPSS Modeler, DekaBank has gained the ability to: • Better determine the characteristics of typical purchasers • Target the customers who are most interested in certain mutual funds • Optimize marketing campaigns
To read a German version of this case study, please click here.
In Germany, savings banks are everywhere and branches can often be found even in small communities. DekaBank is a central institution of the German Savings Banks Financial Group and operates in the wholesale banking and mutual fund segments. With a group balance sheet total of €115 billion ($144.2 billion) at the end of 2005, assets under management of more than €140 billion ($175.5 billion) and equity capital of €3.7 billion ($4.6 billion), it is one of Germany’s leading financial services providers.
Efficiently select the right offer for the right customer
Like many banks, DekaBank is committed to cultivating personal relationships with customers. The goal of campaign management work is to use knowledge about each customer to make the best offer for each individual, as well as overall sound marketing decisions.
Increasing numbers of savings banks from the German Sparkassen Group rely on DekaBank’s analysis and campaign services to market their mutual funds. DekaBank needed a data mining tool that could differentiate between the range of services and products that can be offered to various banks’ target groups.
The Customer Relationship Management (CRM) and Database Marketing Unit of DekaBank, the central fund service provider, uses IBM SPSS Statistics and IBM SPSS Modeler to help savings banks target the right customers more easily – and increase their success. “We want to make our campaign offers more attractive to our exclusive sales partners, the savings banks, and we know that there is an enormous potential in our collected data,” says Dirk Meggert, manager of CRM and Database Marketing.
“The IBM SPSS solutions were already well-known to our experts and we were offered a fair price for our needs. All of the important statistical functions and processes are available in IBM SPSS Statistics and the program can handle large amounts of data from millions of customers,” explained Meggert.
Easily obtain target groups and develop appropriate campaigns
In order to further simplify the campaign process, DekaBank chose our data mining solution, IBM SPSS Modeler, to write suitable program streams, develop campaign scores and control the use of these scores. Minimal training is required to use Modeler and the software makes it easy to modify the streams for current or new queries.
The issue of a new guarantee fund (a product similar to a certificate of deposit) was the system’s first practical test. The CRM staff at DekaBank began by analyzing the typical characteristics of previous purchasers of similar funds. The results were given to consultants in the local banks, who used them to target eligible customers. “We determined that the typical purchaser of this financial product, for example, would be an older and often long-term, particularly intensive fund user. We had to adapt our approach to these clients in all areas: from mailing texts and the layout of our flyers to the telephone guidelines,” said Meggert.
In order to select customers who would most likely be interested in guarantee funds, Meggert and his team relied on statistical procedures to develop a scoring model with ten appraisal classes. The model used current and past customer data, along with socio-demographic, product-related and geographic variables already stored in DekaBank’s system. All “candidates” were appraised on the basis of the model and ranked accordingly. The customers best suited for a targeted approach were in the first class, and those least suited landed in the tenth class.
DekaBank put together a service package containing sales and promotional materials, as well as a list of the customers most likely to be interested in the new guarantee funds. This enabled consultants at each savings bank to focus on approaching those customers. They were first contacted through mail, and then later approached directly by a service employee.
Optimize campaigns with statistical analysis and data mining
The campaign was a complete success. The number of transactions of the 160 participating savings banks rose on average by a factor of 3.3 – the highest one rose by a factor of 8.8 – compared with the savings banks that didn’t participate.
“This is a clear result,” explained Meggert. “It shows what can be achieved through an intelligent data analysis and customer approach. We’ve used IBM SPSS Statistics and IBM SPSS Modeler to run other campaigns since this one, and they have all achieved good results.”
So far DekaBank has achieved a six-fold return on investment by using Statistics and Modeler to target those customers best-suited for the guarantee fund campaigns. Active collaboration with the local savings banks regarding their initiatives has also been an essential factor in DekaBank’s success.
“IBM SPSS Statistics has convinced us. A targeted offer, such as for the guarantee funds, wouldn’t have been possible without this software. Selections are the key component DekaBank’s campaign offers for the savings banks,” said Meggert.
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