You may have heard that customer intelligence is the key to optimizing marketing, products, and process in retail finance.
And it’s true: data, when synthesized as insights and intelligence, helps cut through the extraneous noise and “fluff” found in far too many processes and products that financial services companies offer.
Cutting this “fluff” leads to a win-win for customers and finance marketers: the customer gets what they want faster/better/smarter (win) and the marketer delivers content, services, products or support more efficiently (win).
So What the Fluff is Fluff?
Fluff is every extra step, redundant form field, un-informed touchpoint, stretched “hold” time, irrelevant messaging, or any other distraction from the customer’s perspective in accomplishing a task.
Every time your customer logs into your app from their mobile device and is required to enter a long, complex password instead of using biometric authentication or a retinal scan/facial recognition, they experience fluff.
Every time an already-authenticated user is required to enter their name and address into a field on your website, that’s fluff.
If your customer is applying for a credit card and is unsure of what happened to their application after submitting, you’re introducing fluff (also, distrust, but that’s for another day).
If that same customer has questions about what to expect in the application process and you aren’t offering myriad, easy ways for them to get support, you’re adding fluff.
And when that customer does connect with support, if the support rep (or chatbot) doesn’t recognize that the customer submitted an application and has to ask basic questions about the nature of the support inquiry, that’s fluff, baby.
Let’s dive deeper into a true-life example of fluff:
I recently connected a bank account to my investment platform. To verify the connection, the investment platform placed two micro deposits in my account; and I then had to confirm the amounts of those deposits in the investment platform. Until those amounts are confirmed, I cannot take any further action. This sounds trivial, except that the platform was indicating that the deposits were made, but my bank was not showing any new deposits. What happens now? I, the customer, have to make a phone call. I have to carve time out of my own day (during the investment company’s operating hours, which happen to be my working hours) to find the phone number, sit on hold, then share all of my information, explain the issue, find a resolution and hope that it works.
That was fluff.
It was only about 20 minutes of my day, but it reshaped my perception of the investment company’s platform (and the brand behind it). It came at a time when I was busy and those (stolen) 20 minutes required were almost the straw that broke the camel’s back.
This may sound harsh. But for your audiences, your digital experiences are your brand.
Fluff is extra. Fluff requires additional time and attention. Valuable attention from your customers in an economy where everyone is fighting for attention. And it can be any minor-to-major frustration in your customers' eyes.
How to Fight Fluff
When it comes to financial services, the customer runs the show. It’s their dollars that get pushed, pulled, and resized in an investment. Their dollars that get lost, won, or recouped. Their time and attention that it takes to perform a transaction. Make a withdrawal. Talk to a teller. Click a link. Call and scream.
But the opportunity lies in how you show the customer they are known. And when you truly know the customer, you can unlock intelligence about their perspective—letting that be the beacon for campaigns, product development, support, content strategy and the like—through better data and feedback loops.
If that investment platform I mentioned had recognized that I was experiencing issues with connecting my bank, they could have shortened, or potentially even eliminated the need for my call, thus reducing the fluff. On both our ends.
They didn’t have the sophisticated customer intelligence required to do so. Or the technology to make it possible.
Customer Intelligence vs. Data
Data used for customer intelligence can come from a lot of different places:
Web and application analytics
User research, customer surveys, focus groups, interviews, intercept tests
Customer information, transactions, product/purchase history, support inquiries
Social media listening
Internet of Things (IoT) streams
Software & system logs
Text messages (including emails and SMS)
A multitude of other marketing and business systems (most of which likely aren’t connected)
Financial Services companies possess extraordinary amounts of data about how customers behave. Those who use analytics creatively to unlock the information inside (and then combine that with other research) will be the pioneers in giving customers what they really want while saving money in their marketing bottom-lines.
Customer Intelligence: the Fluffbuster
The sheer volume and continual influx of data in finance can be, at times, unmanageable if not cared for and curated properly. Pulling customer intelligence from the data, however, is more possible with a commitment to process and people. (For now, I’m excluding platforms. You’ve likely got enough platforms and until they are maximized, adding one will only increase the fluff you’re serving up.)
Finance marketing executives should establish a team to surface and share customer intelligence with various groups across the company in an effort to optimize customer interactions.
This team should be comprised of influential members from a swath of departments and should include competencies in data, technology, and communications (eg, Data Engineer; Data Scientist; Marketing Technologist; etc.)
Cutting the fluff starts with asking questions to frame up a business problem. For example, “Our new portfolio service for high-net-worth investors isn’t getting a lot of traction. Why?”
The process begins with pulling data from all the relevant systems and normalizing it. In this case, that might be campaign data from promotions (emails, digital ads, social), call center data, CRM data and website analytics, as well as demographic data and market research.
That data is analyzed using advanced computational techniques, predictive modeling, and often artificial intelligence to identify trends, patterns, anomalies, and triggers.
Your data scientist might identify patterns, for example, showing that only 1% of customers who started a form actually completed and submitted it (Wouldn’t it be great if the issue was always this easy!). That’s a great opportunity to analyze, review, and optimize your form—it probably has too much fluff.
The process of turning data into intelligence is cyclical: a problem is identified, data is pulled, normalized and analyzed, testable insights are produced, the theories are executed on as tests and with those results, the initial problem is revisited.
You’ve heard it before; you hear it everywhere: your customers have high expectations of your institution. This means it's your job as a finance marketer and leader to cut the fluff and ensure you’re shaping a positive brand perception. Using data and analyses (along with a commitment to the right processes to identify opportunities for more intel) at all touchpoints is critical.
Now stop fluffing around and go make some money.