Do we need a data scientist to use this?

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, and that is the strategy layer; building, training and hosting the model is a skill the Data Analyst agent already has, with no handoff back to a data team and no analyst to staff for the refresh.

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 - a skill the Data Analyst agent already runs. No hosting, no data science hire.

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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

10B+events

500M+users

7+years profitable

50+enterprises

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, and no analyst to staff for the refresh.

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 off the same resolved profile. Build the segment once, use it everywhere, with no export step in between.

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 bucketsChampions, At-Risk, Promising and more.
Likelihood for any outcomeDefine the outcome and the score updates from live behaviour.
Next Best Product per personRanked for the individual rather than pulled off 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 live on the profileAny segment, journey or report can filter on them.
Membership follows behaviourSegments re-evaluate as scores shift. Nothing to rebuild.
One segment, three surfacesThe dialler, the email and the analytics read the same definition.

Explore more ways Intempt puts scores to work.

Connected outcomes across the platform.

In the words of50+ live tenants.

“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

“Intempt helped us turn real browsing and purchase signals into personalized experiences that drive repeat buying. We finally have one system that sees the whole customer journey.”

Eric Gardner

COO, FieldsUSA

“With Intempt, we built a signal-led pipeline driven by real behaviors. Follow-ups are triggered by intent signals instead of timelines, so we only focus on users who are truly engaging.”

Tadas Kertenis

Co-founder, Hoperfy

Frequently askedquestions.

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

01 / 04

We're stitching HubSpot free plus Mailchimp plus Calendly plus GA4 with duct tape. What are the scores computed on?

One profile per person, which is the part the duct tape never gave you. Four tools with three seams means no single view of ad click to signup to trial to paid, so a likelihood score built inside any one of them is scored on a fraction of the person. Point the sources at one taxonomy and RFM, Likelihood and Next Best Product compute on the whole history. The scores then sit on that profile, so the ranked list is filtered in place rather than exported to a second tool to be useful, and the journey recipes that act on it are already built.

One profile per person, which is the part the duct tape never gave you. Four tools with three seams means no single view of ad click to signup to trial to paid, so a likelihood score built inside any one of them is scored on a fraction of the person. Point the sources at one taxonomy and RFM, Likelihood and Next Best Product compute on the whole history. The scores then sit on that profile, so the ranked list is filtered in place rather than exported to a second tool to be useful, and the journey recipes that act on it are already built.

Intempt Data

The list, ranked, without the modelling project.

Connect your sources and the attributes start calculating. Free to start.