Marketers love to talk about segmentation, big data and predictive modeling. But then what do they do? They put up a billboard and run some radio ads. Ugh! Here are six tips to building a data-driven digital marketing strategy for acquiring new checking account relationships.
Have you ever noticed that most savvy brands on earth take a much different approach to marketing than financial institutions do? For instance, you don’t see any Apple billboards, do you? At least not since the iPod rolled out over 15 years ago. And while you may see the occasional Starbucks ad on TV, you can be sure that these limited seasonal media buys represent the tiniest sliver of their overall marketing budget.
It does not matter if your marketing channels are traditional, digital or both, don’t forget: you must always be targeting the right audience. With your acquisition strategy, targeting is key, and it starts by first figuring out who your best prospects are.
The good news is that financial institutions have a wide array of rich data streams that can be tapped when trying to determine who is a potential customer and who isn’t. Then the trick is translating those insights into a targeted acquisition marketing campaign.
1. Profiling Current Customers
Customer specific data should be part of any predictive model. Obviously this consumer segment clearly exhibited the one common behavioral patter: they took action and chose you. What can they tell you? It’s your job to peel back the layers of the data onion to find other similarities in preferences, concerns, needs and demographics.
If the goal of your outreach is not acquisition but rather deepening existing relationships, your own data will likely be the dominant component in any look-alike model you build. You don’t just know what products they have, you know where they spend their money!
2. Big Data
Today we have an almost unlimited amount of information available. But it’s worthless without the tools, skills and processes necessary to make sense of it all. The key to big data is to determine what data points are most relevant — those that have a strong, predictive correlation (either positive or negative).
For instance, you can leverage mobile device data to analyze people’s habits, behaviors, tendencies and geographic patterns. Some of this data you have through users of your mobile banking app and mobile visitors to your website, and some of this data can be obtained through third-party providers.
In the Old Days, such kind of analysis was difficult, if not impossible, and not just because we didn’t have mobile devices; we lacked the computing power to crunch the numbers. Even if we had been able to run deep layers of statistical analysis on vast sets of complex data, what would we have been able to do with it? It’s not like the media options that were available back then lent themselves to any kind of targeting. You could be sitting on the best customer intel and richest insights known in the history of marketing, but you still would have been left with crude options to get your message out there — e.g., TV, print, radio and billboards.
But in the Digital Age, targeting is absolutely critical — both in terms of defining your audience, and how you ultimately reach them. Why? Because few consumers looking to switch primary banking providers are going online and just searching around aimlessly. Think about it… it’s not very likely that very many people (anyone?) sits around Googling “checking account” or “local bank”. When someone is ready to move on, they usually already know which institution they want to move to, or at least have it whittled down to a very short list of options.
With your digital marketing, you have to be able to get in the head of your next potential customer BEFORE they know that they are looking for a new banking provider. THAT is what we are talking about when use the term “predictive analytics.”
3. Geographic Proximity
Most community-based institutions generate 95% (or more) of their new relationships in the branch. While consumers may handle the vast majority of their transactions online, they still start that relationship, at least the core relationship, at the branch. As the branch is still important, the geographic reach from that branch should be determined. But this should not be the sole factor you use to figure out whom you should market to. It’s just one data point that you can use in your analysis when building a targeting model.
4. Integrated, Targeted Omni-Channel Marketing
In the past, hanging a banner in front of your branch and laying some printed materials around inside might have been enough to get the job done. These days, old school tactics like branch signage and direct mail are still effective, it’s just that you can’t operate your digital and traditional channels in silos. You need an integrated approach.
Once you have built a predictive model that helps you identify your most-likely prospects, you need to think through your media channels in the context of today’s multi-channel customer journey, then get in front of your high-value targets in a one-to-one context whenever possible.
Tactics like IP targeting or targeting people through their social media profiles fit the mold perfectly. Contextual targeting based on life changing events provides another valid option.
Bottom line? Today, your approach must be highly targeted.
5. Frequency, Frequency, Frequency
Digital channels allow marketers to reach consumers at a much higher frequency. They also allow the flexibility of reaching your target segments with a wider range of targeted messages that are better customized to each user’s specific circumstances and/or preferences. Just remember, it will likely take several touches before a prospect responds to your digital marketing, so frequency is critical.
6. WIIFM? Don’t Forget to Align The Offer With Your Target Segment
Your offer will always be one of the most important factors determining whether or not your acquisition and targeting efforts will succeed… or not. Your offer must be customer centric — i.e., focused on what benefits them, not what features you provide and like to push. Having a solid product underpin your offer is essential. And, of course, offering incentives can also be very effective.
But all these things — the product, its benefits and your incentive — must correlate back to the insights you learned when creating the models that yielded the targets you’ve identified. Otherwise, all this talk about data analytics and predictive look-alike marketing is pointless.
After all, if you’re just going to run one generic offer for everyone, you may as well go back to running billboards.
Achim Griesel is the President of Haberfeld Holdings, a data-driven consulting firm specializing in core relationships, customer and profitability growth for community-based financial institutions. You can send Mr. Griesel an email or call him at 402-323-3793.