Bankers call for advanced analytics

Technology officials in banking industry are deeply interested in the future of business intelligence, specifically predictive analytics processes that can analyze customer behavior. A recent Computing report found that financial officials are able to draw deeper analysis than retailers. Both bankers and store owners are interested in creating conditions that could leave customers feeling free to spend, with banks eager to drive customer dollars to their own line of payment cards.


Targeted offers

As Computing pointed out, banks have access to an important and unique data source for analytics - transaction data from customers' credit cards. Each use of a credit card contains a wealth of information - where it was used, what type of merchant made the sale. Companies can combine these data points to create a picture of customer interests and allow them to create an environment the encourages further spending and incentives that cardholders will want.
"The data is broader than a retailer would get, so it can go very deep and build meaningful profiles of customers. They can then ask, 'Six months ago, this individual was shopping at John Lewis and now they're shopping in Primark. What does that tell me?'" analytics officer Andrew Jennings told the source. "Banks are not very good at this, but the competitive environment is driving them towards [being good at it]. That's what we're seeing today."
According to Computing, Jennings also stated that while banks have depth of data that cannot be matched by individual merchants, the stores are more experienced actually creating analytics models. He mentioned that there is room for alliances between stores and card providers. Banks can agree to give retailers payments for each transaction placed on that institution's payment cards. Financial institutions can also create programs that give rewards directly to customers if they spend at certain allied merchants.



Unique skillsets

TechTarget recently examined efforts by companies to take predictive information from their data. The source consulted with strategic analytics expert Jennifer Golec, who described the ideal analyst's role as threefold - programmer, data scientist and storyteller. They must have programming know-how to deal with the complex and large data sets needed to make a predictive model. The data science will come in handy when developing processes that employ multiple variables. The storytelling flair will help analytics teams explain their findings in clear, business-focused terms to the rest of the company.

P.S.

Soon is comming post about a Business Intelligence solution focusing bank customers and their behaviour. I applied at the DnBNOR innovation price, but was ignored in 2009, maybe because BI analytics was not that actual then. STAY TUNED!