Turning Big Data into Big Answers

Jasmine Butler Burnham

Marketing Manager

The Financial Services Forum

Financial services providers can now tap into the power of the data within the business. The growth of computational power makes it possible to process the large volumes of data required for affordable analytics. Customer insight and engagement is just one area where decisions can now be made in real time, writes DOUGY WATT.
Can you answer the question, what is actually new about big data? Could you confidently say there is a difference between the data we have now and what existed five to ten years ago?
Big data is defined by Wikipedia as ‘a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications’. This would suggest the main difference is just that there is more of it around thanks largely to the technological revolution and a wider range of digital media.
But despite this logical evolution ─ more connected devices means the availability of more information ─ big data is now a topic of conversation at organisations of all shapes and sizes. The main question people ask is, how should we be using it?
Connecting different data
The difference between the data we have now and what was available five years ago lies in the connections we can make with it. We now have the ability to combine data to answer specific questions. For me, ‘big answers’ are as important as the big data that provides them. We are now able to start with the outcome rather than the data, allowing for a more efficient and cost-effective process. In principle, I don’t believe there is any business question that can’t be answered with the application of the right data.
However, I do worry that big data will get a reputation; much like CRM did, where lots of things that aren’t really big data will get tarred with the same brush, causing confusion. Don’t get big data confused with just good, old fashioned analysis!
Interrogate existing data
In many cases, businesses still think first about the data they will need, rather than researching the outcome and seeing what data is already available. This causes extra cost and wastes time, both of which could easily be avoided. Most businesses will have a huge amount of data already available, whether it’s from travel, such as Oyster Cards, online activity or our purchasing habits. The solution more often than not lies in data available in a format outside the organisation’s existing sources.
Take aggregators for example, which are simultaneously viewed as both the scourge and the saviour of financial services in the twenty-first century. As a general rule, aggregators are quite poor in their use of data and don’t seem to mine the vast quantities very effectively.  In my experience, some are quite efficient at re-mailing policy holders as a renewal date approaches; others seem hot on cross-selling activity. But what they do doesn’t always feel very personal. I’m also not aware of any aggregators using data outside their organisation to generate income, e.g. selling to third parties, although I’ll stand corrected if I’m wrong. However, the point is that the amount of data available, especially within this industry, means the opportunities to connect with individuals in a more timely and relevant manner are endless and there to be taken advantage of.
If aggregators currently appear to be missing a trick, I am happier to report that there are some good examples of companies starting with the outcome first and working backwards. Sainsbury’s and Tesco have become adept at using Nectar and Clubcard loyalty data to cross-sell financial services products from car insurance to loans to their regular shoppers.
As an example, a seemingly obvious use of the data – but one which actually necessitates a combination of strategic thought and smart analysis – is the ability to offer pet insurance to supermarket customers who may not have considered the supermarket bank. This can be extended to include travel money and insurance with purchases of sunscreen; or for cross-selling loans and savings accounts when the contents of a shopper’s basket suggests the birth of a baby
Improve the customer experience
Another key question is how providers can use the data they have inside their organisation, for example contained on customers’ statements. A statement has far more data than is actually used; often as well as the name of the retailer it has details of the item, certainly the retail sector. This can be used to find out much more about the customer and their habits. I suppose you could call this a statement of the obvious! But it’s not evident in the big banks’ data and marketing operations. Too much time is spent building ‘perfect’ data solutions and not enough effort is put in at the front end, allowing the data to be used to create a more rounded, service-led experience for individual customers. After all, customer service is vital, especially when switching banks has been made simpler not only by aggregators but also by the recent government initiative to facilitate seven-day swapping of current account providers.
Making better sense
The revolutionary element of big data is not just the volume or velocity, which has been growing exponentially, through many devices and across many sectors, but the connections we can make. By combining data sets rather than creating and analysing one single set on its own, we can now answer specific questions. I have developed the following 4Cs model to explain this in practise:
Choose – start with an ending. Ask yourself questions like, ‘What am I trying to achieve?’ and drill into this in more detail. What is the intended outcome and what does it look like? So for example, if the problem is that you want to retain more customers you can ask yourself, ‘What does retention mean in this case?’; ‘Which customer do I want to retain and why’; and ‘What behaviours do I want to know more about to help me retain these customers?’. In addition to the problem, also choose the data to use: how can you get access to the data you need?
Cycle – whatever happened to test and learn? Test your theory, run it and refine it. Based on an agreed outcome, and measurement of success, form a campaign to test the hypothesis.
Check – once the test has run, and a control is obviously in place, measure the success of your test.  And be honest with yourself, did it really work?
Climb – after testing, drop the bad and refine the good. Continue until you are confident that the results are what you need.
Here’s an example of how the 4Cs method can be implemented in the financial services industry. Occam, part of St Ives Group, was asked by Royal Bank of Scotland several years ago to build a pilot set of 10 event/trigger campaigns. These were existing campaigns that were executed in real time when a particular event happened (for example when a customer moved house) rather than when the campaign was originally scheduled by the marketing team to go out as a blanket execution. As such, the main variable being tested was timeliness of the offer.
Of the 10 test triggers set up, eight were very successful and saw an uplift of between 25% and 50% compared to the scheduled campaigns. However, of the remaining two campaigns, one resulted in no discernible difference and the other was a disaster and produced, literally, no response. As a consequence, the stronger eight were implemented to become live campaigns; the other two were refined and tested again along with new triggers.
Big testing of big ideas
Test and learn is essential to the success of projects, and I am confused as to why this practice seems to have been in decline in recent years. Perhaps it’s been a budgetary issue when money has been tighter, but that appears counterproductive. I would encourage all those dealing with big data, to test each and every campaign or promotion before going live, and learn from those that aren’t successful – much like we did with the Occam work for Royal Bank of Scotland.
Big data is enabling the financial services industry to create a more rounded view of the customer.  In theory this should result in a better service, as well as allowing for more targeted campaigns. This could either be generated from the consolidation of data from internal systems; addition of data from external but ‘known’ sources such as social media; or from external but acquired third-party sources such as mobile phone network providers or supermarkets. The opportunities afforded by big data are endless; but organisations regular inability to look through the other end of the telescope means they are missing a trick.
By starting with the problems you would like to fix, it’s easier to identify your data needs and to quickly and efficiently decide whether new sources will be required in order to answer your big data questions. My guess is that in most situations the data will be readily available and can be used to address key questions of acquisition, retention and cross-selling. The big answers that the data can provide are the true evolutionary element of big data, and financial services companies must begin to take advantage.

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