How confident are you that your organization’s analytics initiative is truly delivering real value?
Did you know, more than half of all analytics projects fail because they simply did not deliver the features and benefits that were intended at the onset of the project?
Your analytics project should contribute significantly to loan, deposit, and customer/member growth while also improving risk, retention, and profit. Analytics and even advanced analytics (machine learning based models) are empowering larger financial institutions, while many community-based financial institutions struggle to enjoy significant bottom-line impact.
To accelerate your analytics journey, consider these four topics:
1. Accelerate to Avoid Incremental Learning
Every journey begins with the first step. However, after every long journey or project, there are always things we wished we’d done differently. Advanced analytics, like machine learning, is often hampered by not knowing what we don’t know. Establish the 4 Cs of the process first: Collect, Cleanse, Compute and Consume.
Disparate data sources limit a complete picture of the members’ relationship. Once Collected and Cleansed, low hanging fruit is revealed within these data sources that would empower immediate action for the lines-of-business. The Compute phase should result in meaningful metrics that provide leaders feedback and measure progress. The Consumption layer permits leaders to view trends and identify key segments for action.
Each of the 4Cs has multiple iterations. More granularity is needed for deeper insights. As an example, some FIs have all debit card transactions grouped together into a single transaction code versus having separate PIN vs. POS transaction codes to reveal important insights.
Analytics can be used in identifying several recurring debit card transactions such as gym memberships and utilities. Another example is the integration of internet banking and mobile logins: helpful data in the computation of engagement, retention, and segmentation of digital vs. traditional users. The compute phase is obviously limited when the “right” data elements are not present. Worse, experience shows that the most statistically-predictive components of an advanced analytics equation are synthetic-variables. That is, a calculated metric derived from other data elements. The process to identify these best-elements takes time. Avoiding incremental learning can accelerate your analytics journey 2-3 years!
2. Accelerate to Avoid Incremental Learning
For most community FIs, the minority of customers provide the vast majority of many things. Dozens of sorts reveal immediate opportunities, such as:
- Pinpoint the members that provide the majority of deposits and loans.
- Determine how many customers have a checking product and use their checking account in a way that demonstrates your institution is clearly their primary FI.
- Answer key questions like, of those members with a debit card, what percent generate the most transactions. In most cases, less than 20% generate 80% of all transactions.
- Calculate profit and/or contribution. Many FIs can calculate that the top 1% of consumers typically generate around 100% – 150% of profit. Percentiles 2-15, generate another 150%. Percentiles 16-89 contribute a negative contribution of 80% – 100%. The bottom 10% (familiar through bankruptcy notices and legal bills) contribute a negative contribution of 100%. The net result remaining is the net profit or contribution.
- Identify top performing consumers. If we open 100 new relationships today, how long does it take to identify those with the potential to be in the top 1% or top 15%? Can you measure on-boarding success? Can you measure customer experience revealed by an engagement metric?
What gets measured gets done!
3. The Role of Math and Machine Learning
So much talk of analytics and too little math is also a recurring obstacle to avoid. Often, math and machine learning are typically applied with little understanding of product and industry knowledge. Two entertaining illustrations:
- A client engaged a multi-month machine learning consultancy that informed the Credit Union that the primary share account was their #1 product; nearly everyone has one…obviously!
- A larger FI client, with Ph.D. mathematicians and statisticians on-staff challenged a complex model equation to minimize risk and loss. The equation did not include “customer account balance” as it had no predictive contribution. The bank staff disagreed as their findings showed that it was the #1 predictive component. The bank staff used the balance after the account was already negative. If the FI charges-off accounts on day 45 as a business rule, there’s a high probability that a negative account on day 44 will be charged-off tomorrow. However, that doesn’t enable a business leader to make an informed decision! Once they adjusted their challenge-equation to consider “customer account balance” 30 to 15 days prior to the account going negative, they agreed that balance was not helpful. This is another example of incremental learning.
The reality is, it takes industry and product knowledge coupled with statistical experience to reveal actionable insights. Dumping data into a neural network model or machine learning tools will have you chasing statistically significant, but likely-irrelevant, findings. Machine learning is best applied to improve or identify a specific segment on an existing equation or model. That is, Machine learning is an evolutionary, not revolutionary, tool.
4. Put Actionable Insights to Work: Measured Action
Being able to collect the right data is one thing, but making it extremely useful and action-oriented requires a different skill and mindset. Contributing an immediate ROI should be your top goal. Initial success should be easily achieved because many opportunities will be revealed through the right concentration and segmentation steps. You can measure success by comparing targeted action against historical baselines. Over time, actions will be targeted on the right individual, at the right time, via the right channel, using the right message. Expected probabilities will be assessed and tested against previous results.
Soon the FI will formulate best practices and metrics through the data and insights. Your segments will become more narrow and refined. The FI’s culture will evolve into a data-driven culture, welcoming team suggestions and recognizing the best performing region, branch and individuals. On-boarding and experience goals measured at two days, two weeks and two months will result in improved retention and member/ customer service. Top performers can be recognized and learning organization theory can lift the performance of others. Over time, the bank’s culture begins to shift, strategic initiatives are measured, resources are provided to team members in the form of information and action lists, and performance can be assessed.
Are you ready to accelerate your analytics journey?
Let Coastline Analytics accelerate your journey by two years with our unique approach to establishing your organization’s advanced analytics capabilities.