How banks can beat new finance boys with data
The rise of Apple/Google smartphone payments and new fintech start ups present challenges to traditional banks. Banks can fight back, but they need to understand how to better use their data to understand its customers.
By Adrian Kingwell, Founder and MD of Mezzo Labs.
Marketers in financial services are facing a massive challenge: Apple and Google are turning smart phones into payment devices; peer-to-peer lenders offer lower-APR loans and higher-rate saving schemes; and each new fin-tech startup greedily eyes up its slice of the pie.
Hamstrung by legacy systems and a risk-averse culture, the humble high-street bank needs to fight back. And fight back fast.
But how will they deal with the disrupters? How will they win back the hearts and minds of fickle internet-savvy customers?
The answer is the same for every other retailer: Know Your Customer.
Banks have, in theory, data on how every customer bought every product they ever sold. Stacks of data. If data is the new oil, banks are sitting on reserves the size of Texas. If only they knew how to refine it into petrol and pump it into the car…
A stitch in time
There has been a lot of noise recently about leveraging 3rd party data, mainly because of the rise of DMPs and programmatic marketing solutions that target waste in paid media. I wouldn’t go so far as idio’s Andrew Davies and say 3rdparty is a Folly, but its rewards might have been over-promised.
Sure, connect your advertising to Experian, stop paying for clicks with a bad credit rating. Add an onsite tag or two, tweak bids to pull the hottest prospects back. With a bit more work, you can target wealthier individuals and increase your average order value.
But let’s be clear: leveraging 3rd party data gives you no competitive advantage. Why? Because 3rd party data is available to your competitors too.
Also, bear in mind that in something like credit scoring data, there are a stack of customers that were unfairly given a bad score. They are shut out of EVERY marketer’s hit list, because every system references the same three credit scorers – and they cross-reference each other.
So 3rd party data is not great, but it has a useful bi-product: a unique identifier that can help stitch together customers across different devices, and can help link data from 3rd party sources (ad-serving and offsite databases) with 1st party sources (web analytics, email, and with a little work, CRM).
Banks have been trawling through customer data – data collected AFTER the sale – for years, but what is different now is the ability to add data from BEFORE the sale into the mix. At last pre- and post-sales siloes can be joined together with this identifier (or string of identifiers) and we get something highly useful: a Single Customer View of every visitor’s interaction and demographic. Producing unique competitive insights. Insights no other competitor would have, as they don’t have your 1st party data.
A bank’s competitive advantage is not just its 1st party data – it is how effectively it stitches its data together.
The birth of the Data Architect
So with a Single Customer View, a half decent analyst could find segments that are most likely to buy and so most worth targeting, right?
Without a common understanding of data between systems – a dictionary of definitions – the results could be meaningless. Someone needs to define what each data type means and ensure that all systems use that common language. There has been much talk in the web analytics community about data layers – JSON tables built into each web page that allow tags from different platforms to use the same set of data. This helps all web data speak the same language but it needs to reach into back office systems too.
Ideally data would speak the same language before it was stitched together. In reality a data blending platform is needed to produce analyst-ready numbers.
If this sounds complex, you’re right. It can be. Lots of different systems, lots of different data types, all needing to be defined and blended and stored before being queried. As a result, we are seeing a new superhero: the data architect. The data architect designs how things will flow around the data ecosystem.
Show me the money
So the most progressive banks are working hard to deliver this architecture, this Single Customer View, this Single Point Of Truth. And what are they doing with it?
Firstly, finding segments of customers worth targeting.
Secondly, targeting them, with emails, offsite ads and onsite promotions.
Thirdly, finding out if the targeting worked.
You might say that’s nothing terribly new. But what is interesting about banking is the high volume of touch points a current account holder has with you, and the historical length – and breadth – of that relationship. You want to target rich colonels who previously bought your home insurance? Your 1st party data could go back decades. Factor in the cash value of the relationship and the huge amounts of marketing budget that gets spent (and often, wasted) finding these people, and you have a very compelling reason to dig for oil.
Has anyone talked to Compliance?
There are some big challenges in connecting legacy systems together – particularly in the secure areas of the website – but the biggest challenges lie in banking processes. While digital marketers are full steam ahead, it may be some time before the conscience of banking – Legal, Risk and Compliance – get their heads around it.
The smarter the targeting, the greater the chance of a promotion being shown to the wrong audience. This is a level of complexity that Compliance just hasn’t seen before. And level of reputational risk that is completely unknown.
But the potential is huge. Joining up online and offline is especially exciting. Banks spend huge amounts getting the right DM to your door mat at the right time. Don’t get me wrong, I’m not averse to Direct Mail, but the conversion rates are eye-wateringly low. However, add online data and it’s a very different game. A customer gets an online car insurance quote, gets a targeted email in their inbox straight away, and a targeted offer through the post the next day. Holistically, one channel is supporting the other to win the same customer.
And there’s opportunity in branches too… If we can stitch the online customer ID with the mobile app ID, or even a bank card, a customer walking near a branch could get targeted via their phone, and in the branch via the ATM or interactive billboards. It’s a Minority Report view of the future, but in theory we could do it now.
So if you want to beat the new boys, look at your customers, look at the data you have about them, and work it smarter. Over the past ten years the banking mantra was “Know Your Customer”. In the next ten, it’ll be “Know Your Customer Better”.
- Private: How Applications of Big Data Drive Industries
- Big Data Assessment – Key Business Drivers, Expected Benefits and Common Challenges
- US Open Data Action Plan and Datasets