The single-customer view is the key building block in any analytical capability. The most basic start in data is to consolidate all your current structured customer data and create that single customer view. Nothing fancy, just a bit of master data management (matching) and data clean-up - boring but very necessary. We are all aware of the 'annoying' experience of the next great offer 'empowered by analytics' when we already have the product or it is not relevant to us.
Once you have this in place, you can enter the world of analytics, not the cheap 'McDonald's' versions (often just correlation engines) that almost any technology and data company can offer you but advanced modelling techniques such as Baysian networks.
An example of this is a recent success ANZ enjoyed as part of a university collaboration program we are running. We won the Best Industry Paper award at the Australasian Data Mining Conference 2014 for our project on The Market Segmentation Of EFTPOS Retailers, where we applied clustering techniques on finding groupings of retailers who use the Electronic Funds Transfer at Point Of Sale (EFTPOS) facilities. Other approaches had focused on clustering the consumer but our approach allows a fresh way to look at data to gain business benefits beyond traditional approaches.
The key here is to find the data scientists who can work with these models, a skill not easy to find nowadays.
Imagine you have found a great analytical outcome, the next step is to explain this in the context of your business. For instance, a strong possible correlation between the success of Justin Bieber and the decline of the US economy post GFC is interesting but useless if you can't explain it.
This highlights the importance of heuristics (in computer science and mathematics, a shortcut to problem solving when traditional approaches would be too slow, including approximate solutions when there are no exact ones) and common sense which compliments the analytical approach to data and the introduction of self-learning technologies found in cognitive machines such as Watson (IBM).
Complementary to this artificial intelligence, the human brain can process complex data from visual stimuli and identify patterns far quicker and more accurately than computers are currently able to do. This means data visualisation, when done well (e.g. multi-dimensional), adds significant value in the speed and accuracy of decision making and sometimes provides more insight than pure analytical data crunching.
Last but not least, we humans like to play. Gamification technology allows us to explore scenarios in a “fail safe and fail fast" environment. Gamification can help you to take all this insight created from previous capabilities and bundle them all together in a compelling, engaging and fun interaction. That's exactly what Big Data should be about.
CUSTOMER-CENTRED ANALYTICS – A NEW APPROACH
This brings me back to the real value of improvements in data and analytical capabilities.
We all know most banking products are commodities with very little room for differentiation. We can either be the trendy, social media obsessed, product bank that, after exhausting all (often defensive) marketing options, will probably end up differentiating on price. Or we can be the intelligent, customer-centric organisation that looks further than products and wants to play an important role in helping people and companies succeed.
When we complement our data sets with discovery and learning, we shift from products and services to value-adding advice, creating better outcomes for our clients and ultimately better results for our businesses - or in the words of Jack Byrnes (Robert De Niro) in Meet the Parents, we can be “in the circle of Trust".
Patrick Maes is Chief Technology Officer and General Manager GTSO at ANZ. You can find him on Twitter here (@DrsPatrickMaes) and on LinkedIn here.