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Financial Services organisations are increasingly turning to and leveraging big data.

There are a number of reasons why they do this. Regulatory compliance, fraud detection and prevention are examples, but it is also used in more of a retail format to map their customers buy behaviour, and gain a greater visibility into a customer’s buying journey.

This can have a positive impact on improving customer conversion rates, avoiding costly churn and lowering customer acquisition costs. Plus it allows them to develop new data-driven personalised marketing campaigns and products to increase sales.

Big Data Today

In today’s fast-paced instrumented and integrated technological landscape, customers have an increasing amount of interaction points. The problem for organisations is that this increases dramatically the complexity and variety of data types they will need to blend with more traditional CRM and mainframe data to analyse.

The Value Of Big Data

The real value of Big Data comes when you are able to analyse all of these data types together – The insights and value you can yield will have an incredibly positive impact on your business. Based on the blended data and technology like Streams you can start to spot patterns realise who your high-value customers are  and what motivates them to buy more.

You can get a sense of their behaviour patterns, and how and when it is best to try and reach them to improve customer sales and loyalty. Instead of leaving customer buying journey’s to chance you can now design specific customer-centric  campaigns to improve your product conversion rates. This approach gives the customer: what they want, when they want it, how they want it, at a price they will pay.

The other benefit of this is to analyse why sales do not convert? You can identify points of failure in the acquisition path and act upon it to make your sales more successful. The big draw of big data for financial services is the ability to gather all of the data, blend it together, and analyse it all at once regardless of its source, type, size, or format.

 

What’s The Catch?

Traditional enterprise data warehouses are critical for enabling business operational reporting and analysis. As you start to bring in external data sources the types, size, and complexity of the data increases massively.

Eventually, you may hit the limit of your traditional data warehouse. An example of this might be that processing time increases substantially and starts to fail to meet the business needs. Your costs could spiral, and you could struggle to analyse all of the new data types – this is a serious problem.

Decision makers today can’t afford to have delays in the insights, it becomes an integral part of the company’s success strategy. No longer is it a “nice to have” – it becomes a “must have now”.

Technologies do exist that ease this problem and can make it more manageable. Hadoop, DashDb, and Spark allow you to cost effectively scale to any volume of data and store and analyse any and all data types together – both structured and unstructured.

For more cost effective storage you can extract structured data from you DWH and store it in DashDB, Sparc or Hadoop, and then request it back to the DWH as and when it is needed for analysis.

 

In Summary

There are many advantages and benefits to both financial organisations and customers alike by using Big Data technologies.

You can test “what if” scenarios, and understand your customer’s interactions better, and develop and identify the most appealing campaigns for them. You can understand your customers likes and dislikes to reduce and avoid churn rates. Being able to understand what’s happening across your customers buying journey, and knowing which campaigns will accelerate revenues and increase customer experience and loyalty are extremely valuable insights to have.

If you recognise any of the above, or you would like to discover more about Big Data, my advice would be to start to develop an experienced partner ecosystem that can advise and guide you on your journey.

Posted in Big Data

July 13, 2016