Know Your Customer… Before They Buy

How bank and credit union marketers can stitch data streams together to yield a rich, powerful view of consumers.

Financial marketers are facing a massive challenge. Apple and Google are turning smart phones into payment devices. Peer-to-peer lenders offer loans with lower APRs and higher-rates of return. And each new fintech startup licks its lips while eyeing up its slice of the pie.

Hamstrung by legacy systems and a risk-averse culture, the humble main street bank needs to find a way to fight back. And fight back fast. But how will they deal with these disruptors? How will they win back the hearts and minds of fickle internet-savvy consumers?

The answer is the same as it is for every other retailer: Know Your Customer.

Retail financial institutions have, in theory, data on every customer and every product they ever bought. 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 fuel and pump it into their marketing…

Stitching Data Streams Together

There has been a lot of noise recently about leveraging third-party data, mainly because of the rise of data-management platforms and programmatic marketing solutions that target waste in paid media. While idio’s Andrew Davies gone too far characterizing third-party data as “folly,” its rewards might have been overstated.

Sure, you can — and should — connect your advertising to Experian, so you stop paying for clicks from consumers with a bad credit rating. Then add an onsite tag or two, and tweak bids to pull the hottest prospects back. With a bit more tinkering, you can target wealthier individuals and increase the value of your average conversion.

But let’s be clear: leveraging third party data gives you no competitive advantage. Why? Because third-party data is available to your competitors too.

Also, bear in mind that credit scoring data, there is a significant slice of consumers that were unfairly given a bad score. They are shut out of every marketer’s hit list, because the system is built around the same three credit scorers (and they cross-reference each other).

So third-party data isn’t the penultimate answer, but when it’s applied in the right way, it can arm you with unique identifiers for each consumer that can help you stitch together profiles across different devices. You can glean a wealth of insight by linking data from third-party sources (e.g., ad servers) with your proprietary first-party information (e.g., web analytics, email, and even CRM with a little work).

Banks and credit unions have been trawling through customer data for years, but the data collected has always been after an account was opened. What is different now is the ability to add data before someone becomes a customer — during the marketing phase of the relationship, prior to their decision about a specific provider. At last, pre- and post-marketing siloes can be fused together using these unique IDs to tag consumers. You can build a single view of every visitor’s interaction and demographic, yielding unique competitive insights — insights no competitor would have, as they lack your first-party data. In short, your competitive advantage doesn’t hinge on what you do with your own first-party data, the real marketing mojo depends on how effectively you can stitch it together with other third-party data streams.

The Birth of the Data Architect

So armed with a single view of the customer, a half decent analyst should be able to identify segments that are most likely to buy, and therefore most worth targeting, right?

Wrong.

Without a common understanding of data between systems — a “dictionary” of definitions — the results could be entirely meaningless. Someone needs to determine what each data type means, and ensure that all systems use that same definition. What value or significance does this data point have? And how does is correlate to other indicators? There has been much talk in the web analytics community about “data layers” (e.g., 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. But in reality, a data blending platform is needed to produce numbers ready for analysts.

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, a new superhero is beginning to emerge among the ranks of financial marketers: the “data architect.” The data architect designs how things will flow around the data ecosystem.

Show Me the Money

So the most progressive retail financial institutions are working hard to deliver this architecture, this single view of the customer. And what are they doing with it?

1. Finding segments of customers worth targeting.

2. Targeting those segments — with emails, off-site ads, and onsite promotions.

3. Figuring out how well their targeting worked.

You might say that’s nothing terribly new. But what is interesting about banking is the high volume of touchpoints a checking accountholder has with you — the historical length and breadth of that relationship. You want to target rich retirees who previously bought your home insurance? Your first-party data could go back decades. Factor in the huge amounts of marketing budget that gets spent (and often, wasted) finding these people, and you have a very compelling reason to drill down for this kind of oil.

What’s Next?

There are some big challenges in connecting legacy systems together, particularly with respect to secure areas of the website. But the biggest challenges actually lurk in banking processes. While digital marketers may be ready to plow full steam ahead, it could be some time before the rest of your leadership team — legal, risk and compliance — can get their heads around it. With smart targeting, there is a level of complexity that these folks just haven’t seen before.

But the potential is huge. Uniting online and offline is particularly exciting. Banks spend huge amounts getting the offline direct mail pieces delivered to consumers’ doorsteps, but the conversion rates are eye-wateringly low. However, once you start adding online data, it becomes a very different game. Prospective customers can get a targeted email in their inbox straight away, followed by a targeted offer through the regular mail the next day. Holistically, one channel can be used to complement the other.

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 in a branch could get targeted messages via their phone. Or someone walking near an ATM might trigger an interactive billboard. It might sound like something from the movie Minority Report, but this isn’t some exotic view of the future. It’s possible to do this stuff now.

So if you want to beat the new kids trying to disrupt the banking industry, you’ll have to think big. Look at your customers, look at the data you have about them, and work it… smarter. In the last decade, banking mantra’s might have been “Know Your Customer.” But in the next ten, it’ll be “Know Your Customer… Better.”

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