Goal: Move beyond aggregate statistics. Group individual customers by actual behaviour — how recently they rented, how often, and how much they spent — to create segments that can be acted on by a sales or CRM team.
RFM (Recency, Frequency, Monetary) is one of the most battle-tested frameworks in customer analytics. It doesn't require demographic data, survey responses, or complex modelling assumptions. It works entirely from transactional history — which is exactly what machinery rental data provides.
Each customer receives three scores:
These scores are combined to assign each customer to a behavioural segment.
[INSERT CHART: RFM segmentation output — e.g. scatter plot or segment size bar chart]
| Segment | Description | CRM Priority |
|---|---|---|
| Champions | High R, F, M — recent, frequent, high-spend | Protect & upsell |
| Loyal Customers | High F & M, slightly lower R | Re-engage proactively |
| At-Risk | Previously high-value, now going quiet | Reactivation campaign |
| Potential Loyalists | Recent but low frequency | Nurture toward repeat |
| Lost / Hibernating | Low across all three dimensions | Low-cost win-back only |
💡 Business implication: The At-Risk segment is the highest-value intervention target. These customers have proven they spend — they just haven't returned recently. A targeted outreach campaign (account manager call, loyalty offer, contract renewal incentive) is far cheaper than acquiring a new customer of equivalent value.
RFM segments don't just inform CRM — they reshape fleet planning. Champions concentrated in specific product categories signal where supply reliability is most critical. A Champion customer who can't get the machine they need becomes an At-Risk customer within one rental cycle.