Maximizing the value of in-house data

Many companies, not just within the financial sector, have been looking recently to sell off anonymised data about its customers. Putting aside consumer concerns over privacy, are those organizations missing the real value of that information to their own business?

Much of the data financial organizations hold today that is the most valuable in terms of building customer relationships, maintaining loyalty and increasing repeat business is held within emails, chat logs, call transcripts, social media etc. The little snippets of information held in this unstructured, user generated content would be like gold dust, if only it was available at the right time. However, typically by the time it’s been collated, sorted and analyzed months have passed and whilst it’s useful for longer term trends, it doesn’t meet the needs of a sector that thrives on agility.

In addition, the data needs to be sliced and diced in different ways to suit different parts of the organization. Account managers might want to know which pension-age customers have been looking for child-focused products as part of a “grandchildren” campaign, whilst marketing might want to know the best time to target students. When it’s difficult to predict what information might be needed from one week to the next, rigid analysis results only restrict maximizing the value of the data even further.

For example, statistics might quickly show that a higher than average number of people are closing particular account, but it can be several weeks before analysis shows the reason why.  Perhaps it is something as simple as a perceived disadvantage to the account that could have been easily rectified or maybe consumer opinion has changed about an issue and it requires a strategic change in tactics – fast.

But the problem often with all this information is not just the volume or that it’s unstructured, but that it’s free format text. If the analysis is to be useful then accurate interpretation is essential. ‘My bank is bad’, could mean it’s awesome or it has some serious problems. Automated intelligent understanding of the context of the entire text, not just a couple of words, is essential if it is to make a speedy impact on the business.

The likes of Siri, and Google Now haven’t just highlighted the advancements in speech recognition, but the interpretation and the intent of the user too. By applying this technology to the analytics of business communication, organizations can not only have a greater, more accurate understanding of their customer base, but access to up-to-date information too.

When financial institutions have so much actionable information about exactly what customers are looking for at that moment in time, why sell it!

 

Originally published on Finextra

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