A retail sales chart from Bloomberg shows that traditional retail department stores are in serious trouble. In-store retail sales have steadily declined from 9 % of total retail sales in the early nineties to just 2.67% today. By contrast, total sales from non-store retailers such as Amazon have increased from under 4% to 10.44% during that same period. In total, non-store retailers have seen sales increase for 23 straight months, far and away the longest active streak among sub-sectors.
One obvious reason for this shift is convenience. Consumers simply like being able to tap their phone or open a web page and get a product delivered to their doorstep.
But another reason for the shift is that non-store retailers are dominating the data game. For instance, Amazon doesn’t just track what their users purchase; they also track everything their users look at as they shop. Amazon then serves personalized ads and messages to these users for the specific products they just browsed and displays these ads across the internet — even on sites that don’t belong to Amazon. This marketing initiative keeps these products top of mind, making users more likely to make a purchase.
This example illustrates what analysts call the data flywheel effect. The more users browse Amazon, the more user data Amazon obtains — and the more data Amazon obtains, the better Amazon is at personalizing the customer experience, thereby making more sales and improving the overall experience. It’s a rapid win-win cycle. The flywheel spins faster and faster as data and engagement feed off each other – all while Amazon’s lead against the competition gets wider and wider.
The Data Flywheel Effect and Banking
Retail banks and credit unions have a tremendous opportunity to take advantage of the data flywheel effect in a similar way. After all, financial institutions collectively capture trillions of data points (from transactions to social media interactions), creating a goldmine of useful insights.
Among many things, banks and credit unions can learn how and when money leaves their institution to a competitor’s institution via a myriad of transaction types (including credit card and loan payments). Through social media tracking, life stage events can be identified long before competitors can take note. Knowing this information, financial institutions can then serve up hyper-personalized communication to these exact users — just like Amazon.
There’s no question that this move to advanced analytics is a difficult hurdle to overcome. Many banks haven’t traditionally needed to hire data specialists, nor have they had to partner with financial technology companies to amplify their data efforts. This transition can be messy, as banks wade into uncharted territory.
However, banks that start turning the flywheel now will win the gains that come with momentum. The more they make use of the available data, the better they will be able to personalize their services to their audience. In the process, they’ll win more users and get more data, which in turn will improve the user experience.
Investing in Advanced Analytics
Because of this advantage, it’s little surprise that more and more firms are investing in data solutions. A survey cited in the Harvard Business Review found that 63% of firms plan to invest more than $10 million in 2017 (compared to 24% in 2012). Similarly, 70% now say that big data is “of critical importance” (compared to 21% in 2012). It’s clear that companies are starting to see just how much the data flywheel matters.
Let’s look at a few financial institutions that are leading the way on this front.
Wells Fargo’s “Activist Analysts” — Wells Fargo hired Charles Thomas, EVP and Chief Data Officer, who trains employees to be what he terms “activist analysts” — meaning that they improve the process from gathering data to gaining insights to putting the insights into action and finally to measuring the results.
Specifically, he’s interested in sharing data across operation lines so the bank can offer up an ad for a mortgage to a customer who has just searched for a mortgage, or so they can avoid making blunders like pitching a sale right after a customer has filed an online complaint. This process ultimately leads to higher customer satisfaction and, in turn, better word of mouth.
TD Bank’s Customer Mapping — Like Wells Fargo, TD Bank also works to unify data from disparate departments. As recorded in a MoneySummit interview, TD finds ways to “blend the analytical and emotional aspects of a customer’s thought process into a comprehensive strategic roadmap.”
Specifically, they first “observe the customer in as natural and unguided setting as possible,” then they “blend this anthropological research data with analytics from past behavior tracking.” With that raw data at hand, they trace the path the customer took from interest to purchase and create “a linear process map.” As they refine the map bit by bit, they get closer and closer to providing an experience that matches exactly what their customers are looking for.
USAA’s Personalized Advice — USAA has found that in addition to using complex algorithms, they can gather user data in simpler, more obvious ways. For instance, they’ve built a simple online experience that asks users a series of five questions about their finances and then offers different advice depending on how the user answers. In addition, USAA has built personalization into its mobile app so that the experience differs based on the products that the user has with their institution.
The Data Flywheel Effect Widens the Gap
Leaders in the collection and use of data are paving the way for years to come. As the data flywheel spins faster, they’ll see the gap between them and their closest competitors get wider and wider.