Know who to call before you pick up the phone.
An admissions team with 4,000 applicants and a week to work them does not need another dashboard. It needs the 200 worth calling first. RFM, Likelihood and Next Best Product compute that on every profile.
0.0on G2The scores are computed for you, not modelled by you.
RFM sorts every customer into six named buckets from their own transaction history. Likelihood scores any outcome you define. Next Best Product recommends per person rather than surfacing a bestseller list. None of the three needs a data scientist to stand up.
- Six RFM buckets: Champions, At-Risk, Promising and more
- Likelihood scores for any outcome, updated from live behaviour
- Next Best Product per person, not a bestseller list
A ranked call list, not a dashboard to interpret.
The team asking who to call this week does not want a model. They want names in priority order. Scores land on the profile, segments resolve against them, and the list is the output.
- Scores sit on the profile, so any segment can filter on them
- Membership updates as behaviour changes, with no rebuild
- The same segment feeds the dialler, email and analytics
The five questions behind every call list
These are the questions a team actually asks before working a list. On Segment each one is a modelling job that lands on the data team, because scoring people was never what the product did.
| What lands on you | On Segment | On Intempt | Who does it |
|---|---|---|---|
| Who is most likely to enrol if we call them? | Model it in the warehouse, sync the score back | Likelihood attribute, already on the profile | Nobody - it runs |
| Rank this week's list by that score | A query, then a CSV | A segment filtered on the attribute | The team that makes the calls |
| Who has gone quiet since last term? | Recency logic written by hand | RFM moves them to At-Risk on its own | Nobody - it runs |
| What should we offer this person specifically? | A recommendation model to build and host | Next Best Product, per profile | Nobody - it runs |
| Push the list to the dialler and to email | Sync to each destination, reconcile the two | One segment, every channel reads it | The team that makes the calls |
Who does it is the column that matters. Every row reading “Nobody - it runs” is a model somebody would otherwise have to build, host and keep current.
Connect every trusted source.
Plug into the tools your team already runs on.
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Your customer data stays yours, and stays secure.
The teams that made the switch
We were losing visitors before they signed up. Intempt's personalized experiences changed that - we started meeting people where they were instead of guessing. Once they're in, Intempt's automated email takes over and keeps the relationship moving. Acquisition and retention finally feel like one connected motion instead of two separate problems.
Jim Stromberg, CEO
StockInvest
Case Study
StockInvest needed to turn anonymous traffic into registered users before any retention strategy could work. With Intempt's Experiences, they personalized the anonymous visitor flow, surfacing the right content and CTAs to boost signup conversion. Once users signed up, automated Journeys nurtured them through onboarding and deeper engagement, steadily increasing lifetime value.
Frequently asked questions
RFM, Likelihood and Next Best Product, computed on every profile.
No. RFM, Likelihood and Next Best Product are computed by the platform from the events and transactions you already send. You define the outcome you care about; you do not build, train or host the model.
The list, ranked, without the modelling project.
Connect your sources and the attributes start calculating. Free to 1M events a month.