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Smarter segmentation for banks: from data foundations to real-time action
Faraday’s Dave Small sits down with Matthew Cecil, Associate Partner at Synpulse, to unpack what client segmentation really looks like when you combine a bank’s first-party data with predictive datapoints.
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What happens when predictive segmentation meets banking reality? Find out 👇
In this conversation, our own Direction of Partnerships, David Small and Synpulse's Matthew Cecil cover the full journey: identity resolution across fragmented systems, building propensity-based segments that map to actual products, and activating those signals in call centers, direct mail, and digital channels with transparency baked in.
In this video, you’ll learn
- How to build segments that perform: move from static demographics to propensity-based predictive datapoints (e.g., likelihood to open an auto loan).
- Why explainability isn’t optional: model documentation, per-person reason codes, and bias mitigation controls for regulated use cases.
- Identity resolution during M&A: unify records and features to power product recommendations across newly merged portfolios.
- Real outcomes: one lender cut $600k in 6 months in direct-mail spend while maintaining conversion by suppressing low-propensity households; a call-center program saw +20% conversion by prioritizing high-likelihood leads during peak volume.
- A practical path to “segment of one”: start with data completion (emails, addresses, profile enrichment), layer predictive datapoints, then automate downstream workflows for timely offers.
Who should watch
Retail banks, credit unions, and agency partners looking to operationalize segmentation with compliant, explainable predictive models—and turn data groundwork into measurable ROI.
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