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Predicting member value and retention with data

With predictive models, we helped a credit association better understand who their high-value members were and which members were most likely to leave.

Research & Analytics
Data Operations
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A credit association wanted analytical models to better understand their members so that they could grow revenue. They wanted to know who to contact and when with their marketing efforts. But siloed and incomplete data was getting in the way.

They came to g2o for help getting their data into shape because of g2o's expertise with customer data analytics. They had some specific goals in mind.

First, they needed to convert low-value members into high-value members, and secondly prevent those high-value individuals from leaving. These are simple goals, but ones that require a deep understanding of the company’s members:

  • Who are my high-value members, and why?
  • What is my retention rate, and who is likely to leave us?

With these insights in place, it would be possible to make predictions around lifetime value and future revenues, but the credit association lacked a firm grasp on this information.

A handful of environmental factors further complicated this initiative. Another strategic partner set the course for these revenue goals but failed to execute on the strategy. As a result, g2o was under pressure to deliver and demonstrate our ability to partner with the client’s on-site team. This project would prove to be a test of flexibility, partnership, and performance.

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Our team worked with the credit association to build analytical models that predicted conversion factors of low-value members to high-value members, and the likelihood of high-value members to leave.

We accomplished this in two principal stages:

  • Using the right data
  • Applying the right science

While predictive models typically get the glory, much of the heavy lifting rested in engineering the appropriate data. The credit association maintained records in siloed product systems: checking accounts, life insurance, healthcare, etc., hampering their intelligence. For example, the checking team knew that Chris Miller was a member, and the HSA team knew that Chris and Claire Miller were both members, but no one knew that Chris had both HSA and checking accounts, or that he lived with Claire. To be successful, our client needed to market themselves effectively, but they didn’t have the visibility to their audience to do so.

g2o was able to collect these datasets, enrich them with missing information, and aggregate them into a quality dataset suitable for analytical models. Once we had a single view of the member, we aligned that data to the member's appropriate address, so that the client could market to a specific household efficiently.

This process takes a rich understanding of data governance and careful curation of third-party insights. To inform our predictive models, we added location intelligence, life stage, wealth, and numerous other demographics and attributes to our client’s existing data. That meant that members wouldn’t just become lost business anymore when they changed their name or address.

Once we armed the client with a clear view of their members and how to reach them, we uncovered who needed their attention. To achieve this, we developed predictive models to test relevant variables until we formed the most accurate algorithm.

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The project provided the client with an iron-clad understanding of their members, precisely who was in danger of leaving, and which customers were ripe for upselling, by name.

Using this predictive model, this financial institution was empowered to serve their members better, ultimately impacting their bottom line. The client is now armed with strategies for smarter marketing, more effective targeting, and more efficient use of resources.

Changing and inconsistent member information, poor data entry, and other data quality issues can lead to a lack of context and understanding. A single view of the member removes ambiguity and supports better decision making.

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