Many Fintech companies are offering analytics add-ons to the core processing packages their clients are using. Many offer products that provide a comprehensive analytics platform, but are they the right solution for credit unions adopting analytics to grow their member base?
As many senior marketing managers can attest, the analytics presentations from the larger Fintech companies are spectacular. These software solutions can do everything from assigning a member lifetime value, predictive scores on the next product purchase and provide an estimated share of wallet by household. At first glance, these software packages look like the answer to effectively growing your member base. But once you scratch through the thin veneer of these product presentations, the analytics solutions for a small credit union are not as simple as it would first appear.
The first consideration when looking at an analytics solution is that of scale. With a first year price tag ranging from $40,000 to $100,000+ and ongoing costs of $10,000/year to $60,000/year, how can a credit union with only 20,000 member accounts expect to achieve an acceptable ROI? These costs are just for the software solution; additional personnel, marketing expense to leverage this new information and the incremental organizational challenges are just some of the additional expenses to consider. To break it down to a member level, 20,000 accounts would most likely yield 8,000-11,000 member households, since a lot of customers have more than one account relationship with their CU.
Looking at the acquisition of new members the designated market areas of many small CUs are approximately 100,000 or less in total population. These are relatively small audiences from which to expect an acceptable ROI. There are also many factors that will impact the results of your new data driven marketing capabilities including; competition, your products, pricing, service delivery and the strength of your credit union’s brand in the community.
Also, it is important for your credit union’s President to go into an analytics presentation with a clear sense of the total picture, not just “the analytics” generated by the software solution. There are three basic questions a President should be able to answer before sitting through an hour long BA/BI sales presentation:
- Do I know where (and how) to get the data to drive this new software solution?
- Does my credit union have the expertise to generate valuable insights from our data analytics solution once the vendor is gone?
- Is my CU culture/organization equipped to take on an avalanche of new member and market insights?
At the expense of making a big, one-time commission, many Fintechs are ignoring the educational component of inserting these new software packages into credit unions and the elongated revenue stream it would produce. For example, if management doesn’t know the basics like the cross-sell ratio of your members, or what your “best” member household looks like; how do you expect your marketing and sales people to embrace analytics that assigns values to predetermined customer segments?
It just seems to make sense that small credit unions need to organically grow into their analytic solutions. When you were in college you didn’t start with advanced trigonometry, you started with the basics of math and each year you built on what you learned the year before. Why should developing an effective analytics strategy be any different?
This organic growth in implementing an analytics solution seems to go against the urgency of big data analytics created in the press, online and by many BA/BI vendors. Antidotal evidence indicates that only 20% of all credit union analytic solutions are still in place after 5 years. This presents a marketing opportunity for Fintechs to develop a longer-term product that encourages learning and that grows in complexity as their client’s analytic capabilities expand.
My guess is that the total revenue generated to a fintech in a “growth scenario” would be greater over the life of the client engagement than the “quick sale scenario” being practiced now. For example, looking at the website of a large Fintech company, the way they promote their analytics solution is very telling. Out of the five pages of charts and a listing of the functions that can be performed; there is one paragraph discussing implementation. And nothing is presented on the applications, personnel required and the organizational retooling that will be required to make this new solution a success.
There is, however, a process that can bring smaller credit unions into the world of data-driven decision making, in a more common sense, practical manner. True, it doesn’t provide an estimated share of wallet on day one, but it does provide a longer term focus on building an analytics function and some initial customer information to help educate management and to begin improving marketing tactics with real customer insights. This two part process is not a new idea but simply the bundling of existing capabilities and processes in a new way that better serves the needs of your credit union.
First let’s take a look at the longer-term part of this process. Enlisting all the credit union’s senior managers into this project is critical for success. However, as we all know, this group has too much work to do already. The solution focuses on an interactive online workbook that provides structure to the process and a framework for each manager to identify goals, metrics, success criteria and data that resides in their respective departments. This begins to lay the ground work for a requirements document that is essential for reviewing any analytics solution.
At the same time your senior managers are taking on the drudgery of completing their workbook, we insert some real customer data. This comes in the form of a fully functional database, with the client’s customer data and appended demographics. This helps senior managers get their hands dirty with real data and they begin to see what all excitement is about. For a leadership team that has never seen what their customers look like – this can be a real eye opener on many levels.
First, even the most basic of questions that have not been answered become part of your institution’s knowledge base including; overall cross-sell performance, the most profitable member profile, the most common gateway product and the geographic concentration of members. And best of all there will be some low hanging fruit that your marketing team can sink their teeth into…for example, high balance, single service, checking only members are ripe for cross-selling.
Unfortunately many data scientists will turn their noses up at this type of basic analytics. They’ll say there is so much more you can do with your customer data – and they are right. However, we will use these more sophisticated analytics when your credit union is in a position to maximize the value of these insights by integrating them into your core marketing/sales processes. Selling a sophisticated analytics package into a CU that is not ready for it frustrates everyone involved; unless the vendor is ready to take the long view on the client engagement and bring their client up along the learning curve.
How does a credit union get started? Are there common sense breakpoints where your institution can get started on its analytics journey?
Described in broad terms, analytics packages become more complex as the result of two factors; the frequency at which they update their data or MCIF and the amount of data collected and utilized. A simple solution updates at a frequency of every three months. At this update frequency the data and trending for customers is more strategic. It can, however, look at campaign results and provide broad brush adjustments to strategy. At this frequency CUs can begin to look at developing their own segmentation scheme or using a pre-build one. Identification of best members, best member wanna-be’s and the beginnings of a CRM strategy can be developed and implemented.
Variance reports at each update can begin to provide more insights into specific trends. After a period of time at this level your management team will be looking for more information and functionality. Now your CU is at a place where real change occurs.
At this point if your institution is ready to make the commitment you can take the next step in complexity and functionality – this could be accomplished with moving to a monthly update and adding some predictive analytics from your data vendor. Now you are trending more closely to tactics and testing on discrete campaign elements, you can support a sales pipeline and be able to more closely match member behaviors with their demographics.
The part that isn’t really articulated anywhere is the change that will be happening to your credit union’s organization and culture as you bring these new member insights to your management team. At many analytics conferences a common sales theme is that your new analytics software purchase will drive change to your organization’s culture – I don’t believe that is true – management changes the culture and then the purchase of an analytics solution supports and grows that new culture. Educating your senior managers on the advantages of data analytics and letting them drive the implementation of your new analytics capabilities is critical for success.
Finally an MCIF that updates daily or in real time provides strategic, tactical and sales support functionality. Linked to an automated marketing platform you can now begin to respond to inquiries in real time and pro-actively rifle shot promotions into individualized member behavior with matrix mail/email. Your line managers will use Key Performance Indicators (KPIs) that will offer insights into both successes and failures.
This final scenario is the ultimate goal for many credit unions; unfortunately many times the road to success is different and/or longer than we anticipated. It has been my experience that no two financial institutions go through this process at the same rate or in the same manner. The one thing most of these credit unions have in common is they have developed a cost effective, impactful analytics resource that is embraced by their internal stakeholders.