Playbook
Plinc’s Machine Learning Playbook for CRM
A practical playbook for CRM leaders who want to understand how predictive models work, what determines whether they deliver commercial impact, and how they connect to the wider AI strategy conversation.
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What’s in the playbook?
Machine learning has been part of CRM for years in the form of recommendation engines, propensity scores, value models. These tools shape who gets which message, which offer, and when. In many businesses, they influence revenue and promotional efficiency every single day.
The question most teams are now asking is not whether to use them. It is how deeply they are embedded, how rigorously they are measured, and how they connect to the broader AI conversation happening at board level.
This playbook covers three models that appear most consistently across CRM programmes: Category Affinity, Product Recommendation and Future Value.
Read it to find out:
- What makes deployments genuinely effective rather than merely functional
- The four factors that separate effective ML implementations from average ones, and why three of them are data questions not modelling questions
- How predictive models connect to the wider AI strategy conversation
Highlights
A quick peek at a few of the findings inside.
- The differentiator is the data, not the algorithm. The real gap between good and genuinely effective ML deployments is almost always the quality, connectivity and structure of the underlying data. Identity resolution, data breadth, update speed and where intelligence lives in the architecture. These four factors consistently determine commercial impact.
- Predictive models are already a practical AI anchor. CRM teams that have been running propensity scores, recommendation engines and value models can demonstrate that they have been applying machine learning techniques in commercially accountable ways for years. That is a credible position from which to shape the wider AI conversation.
- 44% increase in second purchase within 30 days. At one retailer, deploying Future Value modelling early in the customer lifecycle and aligning onboarding journeys to the behaviours associated with higher projected value produced a 44% increase in second purchase within 30 days for targeted segments.
See it in action
Learn how Plinc helps some of the UK’s top brands connect, analyse and activate their customer data to drive business decisions and create exceptional customer experiences.
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