Imagine the situation: You are sitting on a gold vein, but lack the right tools for digging and washing. This is probably how those responsible for product management, marketing or sales at cooperative banks and savings banks feel from time to time. With a history that spans many centuries, cooperative and savings banks are amongst the oldest in the European banking world.
There are more than 900 cooperative banks with over 18 million members in Germany alone. The Savings Bank Finance Group has 385 financial institutions and, with 50 million customers, is the industry leader among banks in Germany. New data records are created continuously in day-to-day business – for each customer and for each individual transaction. This is where Big Data really becomes ‘big’. But how can this enormous wealth of data be used to increase revenue?
Smart Data Analytics Enables Personalised Cross-Selling
Like almost all financial institutions, savings banks and cooperative banks also face the challenge of exploiting new business potential more efficiently through cross-selling and upselling. For years, banks have been relying on a bancassurance concept, in which partners from the cooperative or savings bank association provide products and solutions that could be of interest to customers and are intended to increase the revenue per customer.
However, customers in the digital financial world actually prefer to obtain information independently. The well-known ‘Sparkassen’ flyer with information on the building loan contract or fund savings plan is now just gathering dust because no one wants to read it. Instead, custom-made offers tailored to individual customers are in demand. The key to successful cross-selling lies in personalisation - and the key to personalisation lies once again in the intelligent analysis and categorisation of data. In short, the answer is Smart Data Analytics.
But what does an efficient Smart Data Analytics solution look like for savings banks and cooperative banks?
Let’s consider the good news first: In comparison to newcomers in the financial world, savings banks and cooperative banks do not suffer from a lack of access to customer and account data. Whilst FinTech start-ups enter the market with new services and first have to make existing customer and account data usable via banking APIs due to the lack of customer history, established financial institutions find themselves right at the source of that data.
Analytics Platforms Transform Big Data into Smart Data
Rather, the challenge facing savings banks and cooperative banks is to use modern categorisation and analysis to prepare this enormous wealth of data for cross-selling and upselling. In the conventional marketing and sales approach adopted by many savings banks and cooperative banks, products and services from the bancassurance network have thus far been offered according to the watering can principle. As a result, cross-selling campaigns were less customer-centric because the prerequisites for personalisation and customisation were absent.
There is now a solution developed for an innovative marketing and sales approach which cooperative banks and savings banks can use to make their wealth of data usable: Analytics platforms that specialise in the evaluation and categorisation of raw data in the banking world. (Disclaimer: We at FinTecSystems also recently began to provide this service to financial institutions).
By evaluating raw data via an analytics platform, savings banks and cooperative banks gain deep insights into customer behaviour that they can use for cross-selling.
In order to better understand the behaviour patterns of their customers, cooperative banks and savings banks should exploit the full potential of specialised analytics platforms:
Data transfer: A financial institution can use an interface call to transfer the customer and account data to be analysed to the analytics platform in industry-standard formats, such as CAMT format (XML). The provider of the analytics platform reads the data in its platform via an adapter and analyses the customer and account data.
Data categorisation: Once the account data has been transformed, it is categorised by the analytics provider and, for example, prepared in the form of an expenditure account with key figures for the savings bank or cooperative bank.
Data evaluation: After categorisation, the institution can better evaluate its customers’ behaviour patterns and derive cross-selling potential for each individual client.
Data usage: If a customer buys baby products for the first time, it is likely that they have just started a family. At the same time, if low rent indicates that the customer lives in a small apartment, the savings bank or cooperative bank could offer the customer a financing offer to help them purchase a larger apartment.
These Smart Data Analytics approaches provide savings banks and cooperatives that are on the lookout for marketing and sales innovations with a concrete solution that enables them to get more out of their data and to work in a more customer-centric way. This not only opens up new revenue and earnings opportunities through cross-selling, it also improves the customer experience and encourages long-term customer loyalty.
For those that want to read more: Everyone is talking about Big Data. But what role does this phenomenon play in the banking world at large, which has been working with data-driven business processes for a number of decades? Our blog post will answer this question and more. I want to read this!