I often get asked by founders and marketing leads: “Should we keep asking NPS, or focus on behavioural data — and can we combine them?” The short answer from my work with SMEs is: you need both. NPS gives you a direct line to customer sentiment; behavioural signals tell you what customers are actually doing. When you align them, you get a far better early-warning system for churn risk than either source alone.
Below I share practical, hands-on ways I use NPS together with behavioural signals to predict churn. These are methods I've applied with retail, hospitality and e‑commerce clients — simple enough for small teams and designed to deliver measurable outcomes quickly.
Why combine NPS and behavioural signals?
NPS is a powerful indicator of loyalty intent: promoters are more likely to repurchase and recommend, detractors can spread negative word-of-mouth. But NPS is noisy, infrequent and sometimes disconnected from recent customer actions. Behavioural signals — purchase frequency, visit cadence, product usage, basket size, email engagement — are objective and continuous. When a customer’s sentiment (NPS) and actions (behaviour) diverge, you get valuable early-warning signals. For example:
Combining both lets you prioritise interventions and personalise outreach so you spend retention budget where it actually prevents losses.
Core framework: Layer sentiment on top of behaviour
I use a simple three-layer framework that’s easy to implement in any analytics stack (Google Analytics + CRM, Segment + GA/BI tool, or even a spreadsheet for early-stage businesses):
What behavioural signals matter most
Not all metrics are equally predictive. Focus on signals with high signal-to-noise ratio. These are the ones I prioritise:
These map well to business outcomes and are generally easy to extract from order systems, CRMs or analytics tools.
Simple rule-based churn score to get started
Before you build ML models, create a rules-based score you can explain to stakeholders and act on immediately. Here’s a template I give to clients. You can implement it in a spreadsheet or your CRM.
| Signal | Trigger | Points |
|---|---|---|
| Recent NPS | Detractor (0–6) | +30 |
| Recent NPS | Passive (7–8) | +10 |
| Recent NPS | Promoter (9–10) | -10 |
| Recency | No purchase in 60+ days (for monthly buyers) | +25 |
| Frequency | Purchase frequency down >50% vs prior period | +20 |
| Engagement | No email opens in 30 days | +15 |
| Friction | Return or refund in last 30 days | +20 |
Score thresholds are business-specific, but a typical approach:
How to label and validate your predictions
Validation is essential. You should track whether customers flagged as high risk actually churn within a defined horizon (30/60/90 days depending on purchase cadence). I recommend:
When you have sufficient volume (thousands of customers), move to a simple logistic regression or tree model using NPS bucket and the behavioural features above. This typically improves discrimination and gives you probability scores you can prioritise by expected revenue at risk.
Practical activation ideas tied to risk bands
Not all interventions cost the same or have the same ROI. Match the action to the risk band and expected customer value.
Example: with a boutique skincare brand I worked with, passives with a 25% drop in purchase frequency received a tailored 20% off personalised bundle. That single action lifted 35% of the cohort back to previous frequency within 60 days, and almost all had high NPS later.
Small-team implementation checklist
If you’d like, I can share a downloadable CSV template for the scoring table above or a simple SQL snippet to join NPS responses to order histories — things I often give to clients to speed up implementation.
