Established model
RFM analysis
Direct marketing practice, formalised in the 1990s · 1990s
Sort customers by how recently they bought, how often, and for how much — three fields that predict a great deal.
Its place in the frameworkOmni Core›Personalization
What it does
It long predates machine learning and remains an unreasonably strong baseline. Recency does most of the work: how long ago someone last bought predicts whether they will buy again better than almost anything else on file, and all three fields are already in every order table.
- Reach for it when
- As the first personalisation anyone builds, and as the baseline any more sophisticated model must beat.
- Where it stops
- It describes what someone did, not what they want. It cannot help with a new customer, and it will keep recommending more of what they already bought.
Long-standing direct marketing practice; formalised in Jan Roelf Bult & Tom Wansbeek, “Optimal Selection for Direct Mail”, Marketing Science, 1995.
Why it sits at Personalization
Changing what a customer sees based on what is known about them — deliberately, within limits, and only where it demonstrably helps them rather than only the organisation.
A model is only useful when you reach for it at the right moment. This one answers a question that arises here — so it is filed here, and nowhere else. These are the working areas it serves:
- What is personalisedRecommendation-system practice, and the distinction between personalisation that reduces effort and personalisation that increases exposure.
- The signalsWork on inference from behavioural data, and the error rates of attribute inference that customers experience as being misunderstood.
- The limitsPrivacy-calculus research on the trade customers make between disclosure and benefit, and where it breaks down.
- Whether it worksControlled testing applied to personalisation, and the frequency with which untested personalisation shows no effect.
What it touches elsewhere
Nothing in a business is decided on its own. A conclusion reached with this model at Personalization lands in these other cores, whether or not anyone follows it there.
- Data CorePersonalisation runs on collected data, and the purpose it was collected for constrains this use.
- Brand CorePersonalisation that misjudges someone damages perception more than a generic experience would.
- Market CoreSegments are defined there; personalisation is what is done differently for each.
- Business CoreEvery personalised variant is something to build, maintain and get wrong.
Filed at the same place
These answer questions that arise at Personalization too. Where they disagree with this one, the disagreement is the useful part.
- Collaborative filteringRecommend to someone what people who behaved like them went on to choose, without needing to know anything about the item.
Elsewhere in Omni Core
- The service blueprint
- Moments of truth
- The customer satisfaction index
- Net Promoter Score
- The Customer Effort Score
- SERVQUAL and the gaps model
- Service recovery
- Nielsen’s usability heuristics
- The double diamond
- The Fogg Behaviour Model
- Behavioural and attitudinal loyalty
- The service–profit chain
- Showrooming and webrooming
These are other people’s models, named here so you can go to the source and use them properly. The Omnigoal is not affiliated with their authors and is not endorsed by them; nothing of theirs is reproduced here — no canvas, no diagram, no wording. Each is described in our own words, with the originator credited, because the framework is a place to put thinking, not a replacement for the people who did it. Model names and trademarks belong to their respective owners and are used here only to refer to the work itself.
All 125 models