Investments in Data Analytics play a major role in any Credit Union. The question is why to invest in data analytics, what future it has and how the members see future in a credit union. John Janclaes talked about the journey through his credit union when the cost of technology went down, and the members asked CUs to be more relevant and friendly with less friction in their interactions with the CU. Starting with Omnichannel or an Integrated Business Model there are four core areas to focus on- Learning about partners, Joining partners, Deepening relationships and Maintaining the relationship with the CU. There are financial chores to keep the relationship alive and as a Strategy person, these are Acquisition, Retention and Deepening Relationships. There should be clear articulation as to what to do with data analytics.
The first key area of importance is Personalization. It involves knowing the member and enabling their financial life, valuing the members and most importantly moving away from customization that is towards personalization. Secondly, take risk smartly.
Then Gap Analysis helps to know how far and how fast a CU could go over time to enable organization. Also, Role Charters relate to why, what you’re going to do when you’re going to deliver it, what are the key partnerships, what is the autonomy you have and talking that with peers across the organization. It facilitates the meeting in the middle of what is the vertical responsibility versus the horizontal responsibility.
Then comes rethinking the branch which is geo-mapping of where the branch is needed to be and relocating it using data analytics. Forward thinking and risk management is another learning aspect. It is crucial to have a Builder’s Mentality when thinking about the data sets being used and capabilities and making better decisions. Be a visionary and do not overestimate near future. When people, process and technology come together to make something happen the critical path is people. People need to understand how to use data to make decisions, drive business, and improve member experience and to take more risks. It is about progress and not perfection.
Shazia Manus started with data warehousing moving on to dynamic reporting which is efficiently presenting comprehensive data and then visual analytics in segmentation and interchange analytics. If the attention is on business problems that are worth solving for then it will lead to efficiency and drive to return on investment revenue. The framework to be followed contains what is data rich (problems for which data is easily readable and mining can be done in 9 to 12 months) and data analytics use cases which are multi-dimensional, that is, can be used internally (for projects) and externally (for clients). Member Attrition Model is more functional rather than card attrition model. The next thing is the use of Speech Analytics for Fraud Detection especially useful in account-takeover fraud.
Here again, the first area of importance is building logical rules that relate to what actions trigger what results to see the outcome. And the second area is client satisfaction and engagement. In the big data world not ‘why’ but ‘what’ is most important. In the attrition model, it was observed that loan or mortgage maturity date is the number one identifier as to why people attrite along with inactive cardholders. Three criteria decide winners and losers- Skill, Mindset and Data and data is the main differentiator.
Lastly, Greg Mitchell emphasized on Investment in Digital to know the members, anticipate their needs and deliver delightful experiences to maintain relevance. A member wants a credit union that is well managed, whose capital is preserved and has no reputation problems. Use data to do risk analytics to be effective as an organization. Artificial Intelligence and Machine Learning are mandatory exercises for credit unions, and in the end, the aim is to deliver solutions.
It is significant to be one step ahead of the members intellectually to understand technology. Financial metrics and dashboards allow BI teams to solve business problems without too many instructions. The shift from Human Analytics to Machine Analytics relates to progression; what are the problems being solved and the kind of tools being used. Data blending is upcoming for credit unions. Certain pointers to measure the success of investment are ROI or Return framework with revenues and marginal revenues, Right Time-Time Offer-Through the Right Channel, Line Item and its Impact and Member Experience.