Updated: Jan 11
How to think of the economics organization in terms of the flywheel effect. Go beyond "The Matrix" data view of your customers.
The following is part of our article series: Companies Need A Nudge: Create a nudge unit flywheel to drive happy customers and business success
My experience includes behavioral economics and operating leadership in large banking and consumer products organizations. I have led behavioral economics integrated, nudge unit teams. I also led operating divisions that integrated nudge unit-like teams and cultures. Naturally, I was VERY fortunate to be surrounded by many talented, dedicated people! We used a wide range of analytical techniques (some AI-related), data sources, Randomized Control Trial (RCT) techniques, and other behavioral techniques to manage bank credit loss exposure and optimize lending program performance. We used the same techniques to optimize the customer experience and deliver long-term growth. We were large enough that we had our own testing operation, which meant we had dedicated customer agents trained for behavioral testing. We also had systems designed to quickly integrate results with the test design parameters. In my career, I have managed or overseen thousands of RCTs. These were some of the coolest jobs I ever had!
Top 10 list for Nudge Unit success
Listen to your customers! Go beyond ”The Matrix" data view of your customers. Have customer round tables and focus groups. Great testing ideas come from listening to your customers.
Data scientists should build "real" customer context. Personally, I think all data scientists and related should have some kind of regular customer interaction. This helps make the messy world of emotions and behavior real for the data scientist.
Seek unique data about your customers. This could be an insight from existing data or it could be new and unique data.
There is a balance between the data scientist and data collection. In general, you want to keep your data scientists focused on building customer insight via the data. The data scientist should not spend too much time collecting and preparing the data.
Some "data digging" is ok. Data Scientists often do not like data digging. Data digging is code for the messy ETL-related data processes needed for less structured data sets. It can be grinding work. I call this the "meta metadata." That is the story behind the data dictionary. It can be time-consuming and take away from primary data analysis. While I hope Data Scientists spend the majority of their time analyzing, some data digging can be both instructive and can lead to a "digging for gold" outcome by finding unique competitive insights.
Test new Artificial Intelligence techniques. My observation is, usually, new analytical techniques are not always better than "tried and true" techniques like Regression and Decision Trees. However, we always learn something new and useful in the process, beyond the fact that new AI techniques were not always effective. It is worth the exploration, just not for the reasons you may expect.
Test execution is critical. Commit the resources for proper test execution. Testing systems may include:
Testing program guides,
Coding to differentiate test and control groups,
Collecting performance results,
Scripting for agents or customers,
Availability of characteristic data and related testing information.
Analytical resources to analyze and provide post-test results and recommendations.
Test with a successful scaling outcome in mind. Meaning, assuming success, how will this test be rolled out and scaled in our base business? Unfortunately, I know successful tests that failed to impact business results. This occurred because of a failure to scale.
Causality is key! RCT is necessary to drive confidence in the causal nature of your results. It will also help business leaders understand the value of risk testing. Often, a small (but statistically significant) percentage risk test gain will lead to a significant bottom-line improvement. By the way, not every test is suitable for RCT. If you test without a control group, be very explicit about what you hope to learn and potential learning limitations.
Useful for many organizations. With today's information technology, Nudge units can be useful for many products or services companies.