Opportunity with Data does not knock until you have all kinds of it.

It’s 2019 and we’re sitting on massive chunks of data. Everything we do, we speak and interact with is considered as data. A bank can churn so much data about their customers that it can almost seem that they can predict the next move of a customer. If data is not your priority then prepare to be outdated very soon. Right after a digital strategy, every bank today needs a data strategy. You’ve got many customers leaving their digital footprints with you. But how serious are banks today looking at data and trying to formulate success and improve ROI with it?

The answer may vary for those who see the real gold in data. If so much can depend on the data that is being produced, your bank’s data needs to be constantly worked with – a robust, well-oiled data strategy needs to be put in place!

Where does the data come from?

There can be various sources for obtaining data like for example from banking applications, social media, banking channels. Identifying these outlets has got to be your very first priority.

Here are the three most important types of data you’ll need to consolidate to understand your customers the 360 degree way.

  1. Reference Information

This is the single type of information that is required and maintained by banks today and only because customers themselves give this data. This kind of data includes personal information, location and preferences. From a banking perspective, you must make sure the data that is entered is error-less. This is a monotonous and tedious task for which you might want to introduce a bot here to take care of these activities smoothly, on time and most importantly without any errors. Making sure the data is correct – needs to be stressed here as this builds the foundation for your AI roadmap. Make sure you use what RPA has to offer to save time.

  1. Banking transactions

Study how your customers interact with you with the footprints they leave with you. This section of data typically includes transaction history, service history, complaints and feedbacks. Most commonly banks leave this piece of data type out of the equation. For example, Customers get annoyed very quickly when there is a service representative calling them for a credit card purchase when they already have the same. This usually happens when there is no common customer database across multiple products/services. Having a single view of a customer is priority to cross sell and up sell. Banks can leverage a lot of insights about their customers’ buying patterns and financial needs when this data is clubbed with a customer’s reference information.

  1. Social Interactions

While you figure out a way to provide great experiences to your customers, they probably are speaking about your services to their friends, colleagues and their entire social network. The social interaction data that you should be looking out for are engagement history, google search and social media activities. By bringing this together banks can have a 360 degree understanding of who their customers really are, what they want and also predict customer churn. The very digital nature of things functioning today pushes us to evaluate on going social interactions. A negative feedback on twitter can cost you an unforeseeable fortune and this is why analyzing social information is crucial.

Final Thoughts 

We’ve just finished classifying the three most important types of data that you will need to build a data strategy around. There’s always going to be a need to fetch this data for you to understand your customers better. There is so much noise around Artificial intelligence and machine learning these days. You will need to understand where you are in terms of data strategy in order to incorporate AI in your roadmap. AI can reap a lot of benefits for your bank only if the data you’ve collected is correct, consistent and characterized and this is where ETL Processing via Master Data Management can help.


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