As the next generation begins making financial decisions, credit unions will be able to comfort them with data-driven product recommendations.
Recently, my wife and I were shopping for a mattress. We began the process by “trying out” mattresses by how they felt. My wife thought she preferred firm mattresses, while I thought I preferred soft ones. As we tried mattress after mattress, my wife would ask me, “what do you think about this one”, in which I would usually reply, “It feels pretty good to me”. We became frustrated by a complicated search for a large budget item until we found a mattress store that comforted us with data. The mattress store (Becker Furniture World) is locally owned with only 8 locations (does this sound familiar to your credit union?). They approached mattress shopping from a data-driven way. By using an analytic data model (developed by Sleep to Live Institute), they are using analytics to aid customers in their mattress investments through data sensors and user input. The data comforted us enough that we decided to purchase one of the mattresses it recommended.
Data Acquisition from Users
When we walked into the Becker Furniture World, it was different than all the other mattress stores. There was a futuristic-looking canopy near the front of the store. Curious to see what this machine was, we asked a store associate and were informed that it collected data from our bodies and sleeping patterns to recommend the best mattresses. Before entering the contraption, we entered in personal data about ourselves using ranges for age, weight and height, along with other qualitative data including where we currently have pain and our sleeping preferences. After entering in our personal data, we both laid down on the bed (hooked up to data sensors).
When we laid down on the data-collecting bed, sensors began collecting valuable data about our bodies and the pressure we were applying to the mattress. This data was then sent to the Sleep to Live’s data pool, and a report was printed for us. The report displayed statistics about us and recommended mattresses throughout the store (with the option to order a custom made mattress).
Analytic Data Model
Sleep to Live Institute used their analytic data model to give us a data-driven recommendation for mattresses that would give us the best sleep possible. Utilizing this data, we found the mattress that would meet both our needs. Feeling empowered by the analytics conducted by Sleep to Live, we finally made the decision that was stressing us out.
Utilizing the latest sleep science, Sleep to Live is giving people the power to make data-driven decisions about their precious sleep. The days of buying a mattress based on how it currently feels are coming to an end. A new era of data analytics is transforming sleep (as well as the credit union industry), and how we make decisions to improve our health. This is a critical decision for people and can make or break a relationship with their mattress provider. A positive experience can convert a customer for life and will establish a strong level of trust with the mattress brand.
Financial Health Problems
Just as my wife and I were initially confused in the investment decision of a new mattress, many credit union members are confused when it comes to making decisions to improve the financial health of their family. They have tried a lot of products by how they “feel” and what they have heard from friends and other untrustworthy sources. Unfortunately, like buying a mattress, the decisions made on financial products have consequences and members have to lie in the bed they made (pun intended). Credit unions should be collecting data from members to improve their financial health.
When members interact with their credit union, they should be presented with a similar program that Becker Furniture World presented to my wife and me. They should be able to enter their financial goals and pains for a personalized output. The good news is, credit unions have the “sensor data” of members’ financial lives, and it is located within the transactions that members have been conducting on a daily basis. By integrating transactional data and member-input data, with the guidance of a member-centric data model, product recommendations can be presented to members in a comforting experience.