CU Employee Community Chair

Role of Marketing Analytics In Credit Unions

Marketing Analytics plays a vital role in any credit union. This includes making analytics more accessible to the marketing intelligence team, knowing the most influential members and using data to drive marketing priorities and content.

The first panelist, Brian Knollenberg, talked about Machine Learning Trial at BECU, which is the largest community charter Credit Union in the United States standing at number 86 in the United States out of top 100 Financial Institutions with 18 billion dollars in assets and over 1.1 million members. He talked about Digital Marketing as a drive for marketing and sophistication of marketing programs and its evolution and roadmap as at BECU at three levels.

  1. Back in 2016 contained marketing capability to be analytics capability, limited segmentation, scattered testing, static content, limited cross-channel integration, and one-off reporting.
  2. In 2017 established analytics marketing by segment and further included a suite of behavioral triggered programs, dynamic automated dashboards, Data Management Program powered paid media, fully planned and scheduled roadmap.
  3. Contains long-term goals which are Channel Orchestration, Machine Learning, Real-time segmentation, full attribution, personalization regardless of channel, analytics differentiation, DMP driven acquisition and CRM integration.

Machine Learning has grown exponentially and is more about what is happening and has been foretold for years. An example of machine learning in action is the automated response in Gmail. They have been using ‘Amparo’ which is a local startup for next-generation marketing test. It is self-optimizing personalization and test platform for marketing powered by machine learning algorithms that can uncover pattern and identify lift which humans cannot possibly do. It helped them in building ‘Time Series’ which they were not able to do before. By building a time series profile for each member and storing it, it allowed identifying patterns that humans couldn’t possibly find.

Machine Learning, therefore, has three perspectives-

  1. Think hard about what you want to accomplish (KPIs, channel, automation and creative are all considerations as well as data)
  2. Be prepared for a long-setup.
  3. Consolidate data early because what matters is right data and right amounts.

Mike Terzian, second on the panel, talked about making analytics more accessible to the non-BI team, he focused on integrated marketing which is being driven digitally. Being a part of Disney, he discussed two key ingredients- Disney Intellectual Property and Cast serving Cast comprising social media findings, survey feedback, and social currency. Social currency through members being more about Integrated Marketing. The core of their success was their Disney Connection and Engagement Training. Through this connection, they could use any exclusive Disney IP accepted as a key competitive advantage while targeting was the other piece through the partnership with marketing and BI. A variety of methods and tools were used to collect and aggregate dataset via the BI team. Analysis at department level using basic applications demonstrated predictive variables with a high number of relationships across multiple variables and was indicative of member’s interaction with partners.

The three methods adopted by them were as follows

  1. Engagement Score is the sum of values of each variable for each member. This number is relative with only a lower limit absolute of 0 and an infinite upwards scale. The higher the engagement score, the more interaction the member has with the credit union.
  2. Engagement is a weighted measure based on the predictive variables and their relative weighting in the original correlation models. This score shows a tighter range but better reflects the value of each variable.
  3. Engagement Decile is the stacked rank of the member based on the engagement, a .9 would place the member in the top 10% of engaged members while a .1 would mean they are in the bottom 10% of engaged members.

The next discussion on ‘Influence- What would you do if you know who are your most influential members’ by Naveen Jain posed a business challenge to drive the right kind of new membership with deeper engagement and reduced attrition.  Recommendations from friends are the most credible form of advertising. A study indicated that they reach less than 3% members on social media and there are two options left them, either to spend marketing dollars to reach out to the target audience or to find the most influential members as brand advocates helping take the messages to prospects.

This implies to turn top prominent leaders into brand advocates. You can invite them to member advisory boards or focus groups, host at community events and feature them on your campaigns to establish a brand personality. The social profile provides social connections such as followers on Twitter, LinkedIn, and Google, LinkedIn information such as employer, title, location and education background, university/college, etc. This information can be used to drive products and services within new member segments and regions, drive adoption of new products or services and new online/mobile banking apps and to reduce attrition of most influential members. Getting started with Influence Amplifier is simple, to begin with, provide encrypted members email address then find the most influential members and then engage these members to drive your message.

In the end, Rob Silverii spoke about ‘Using Data to Drive Marketing Priorities and Content’. The importance of using data is converting it from signals to data, then information followed by knowledge and finally into insights. Data is used to drive to the top of funnel content. The question is how to get an engaged audience through the conversion funnel. One way to do this is to align on one key metric to measure success. He then discussed Net Annualized Recurring Revenue or NARR.

The crux of his discussion was to use data to tell your story with the help of marketing team and data team working closely, aligning on a single metric, moving quickly and iterating, avoiding zombie dashboard and not to reinvent the wheel and to use and evolve existing tools at your disposal.


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