How current are the scores?

They recalculate in real time as new events arrive, so a person who goes quiet moves buckets without anyone rerunning anything.

Can our own team pull the list, or does it come back to us?

They can pull it. The scores sit on the profile as attributes, so a segment filtering on them is built the same way as any other segment - by describing it, not by writing a query.

Audience AI

Score every profile, no model to build.

RFM, purchase likelihood, and next best product from first-party events. No hosting, no data science hire.

Start for free

Purchase likelihood, RFM and next best product scores per user

RFM · Likelihood · NBP

Three score types built in

Real-time

Updates on every event

On profile

Segments read directly

No code

Nothing to build or host

0.0on G2

1B+events

7+years profitable

50+companies

What sits on the profile already

Three parts of the scoring pipeline already handled. Nothing lands on your team to build.

Three models, no data team

RFM buckets, Likelihood, and Next Best Product are computed by the platform. No warehouse round-trip, no scoring model to build or host.

Live-updated on the profile

Every event moves the person to the right bucket. A profile that goes quiet moves to At-Risk without anyone rerunning anything.

One segment, every channel

The ranked list feeds dialler, email and analytics from the same source. Build the segment once, use it everywhere.

Scores where segments already read

RFM, Likelihood and Next Best Product, on every profile.

The 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

Explore more ways Intempt puts scores to work.

Connected outcomes across the platform.

In the words of50+ live tenants.

01 / 03

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

How Audience AI actually behaves on your data, and who touches what.

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 start.