FuseIntelligent Data Models
Intelligent Data Models

Your customer base, segmented and set in motion

One RFM-BT model reads every customer's recency, frequency, value and behaviour, then routes each into the journey that fits. This is that model, made tangible.

Meet the customers →Try the model
The Intelligence Loop

Not a funnel. A loop that keeps learning.

Score, segment, act, measure, re-score. The loop runs continuously, so a customer is never stuck in the wrong journey for long.

THEIntelligenceLoopevery loop gets smarter1UnderstandRFM2PredictChurn · pLTV3ActivateRFM → Braze4Measure5Learn6Decide
  1. 1
    Understand the customer, RFM lives here
    Databricks
  2. 2
    Predict what matters next, Churn · pLTV
    Databricks
  3. 3
    Activate the next best action, RFM segments drive the send
    Braze
  4. 4
    Measure what happened
    Braze
  5. 5
    Learn & optimise
    Databricks
  6. 6
    Power smarter decisioning
    Databricks × Braze

Databricks is the intelligence engine, Braze the activation layer. Each revolution makes the next interaction smarter. RFM spans Understand to Activate: it gives every customer a tier and a segment, then pushes them straight into Braze to pick the message, feeding the Predict models, Churn and pLTV, along the way.

The Model Catalogue

One model is the start. The power compounds.

RFM is the deterministic foundation. Every other model in the catalogue layers on top, and each one adds a dimension. They're all standalone, and you turn each on only when it earns its place. Some are quick wins you can run today. Others get better as purchase and spend history build. What you can switch on comes down to the data you have.

Deterministic

RFM

Recency, frequency and monetary segmentation. It tells you who your customers are and what to do next, and it's explainable enough to use in Braze from day one.

Needs: Purchases
Adds: The explainable foundation the other models build on
When: The base model. Start here.
Deterministic

Engagement Score

Scores digital engagement on its own, separate from spend: logins, sessions, saved items, app opens.

Needs: App / web session + interaction events
Adds: The engagement axis RFM leaves out, for activation and experience-risk signals
When: As soon as session events flow
Braze AI

Intelligent Selection

Braze automatically shifts campaign traffic to the best-performing variant, so you optimise without manual A/B reviews.

Needs: Campaign + conversion events
Adds: More from every targeted campaign, automatically
When: Available now. A quick win.
Braze AI

Braze Recommendations

Suggests the most relevant product, complementary range or offer based on how someone engages. Personalised next-best-content.

Needs: Purchase history + product catalogue
Adds: Personalises what each segment is offered
When: Once purchase history builds
Braze AI

Send-Time Optimisation

Works out the best moment to reach each customer on each channel, then lets Braze deliver when they're most likely to engage.

Needs: Message + engagement history
Adds: The 'when' for every campaign, with no manual scheduling
When: Available in Braze as history builds
ML

Churn Propensity

Predicts who's likely to lapse and pushes a risk score into Braze, so you can step in before they churn.

Needs: Behavioural / purchase / session events
Adds: A forward risk signal that recency only hints at
When: Starts on early data, sharpens over time
ML

Predicted LTV (pLTV)

Estimates what a customer is likely to be worth over the next 12 months, so you know who to invest in.

Needs: Purchase + spend history
Adds: Turns a value tier into a forward-looking investment dial
When: Plan it alongside RFM
ML

Next-Best-Action

A decisioning layer that picks the best action, offer and channel for each customer in the moment. The orchestration brain over the other models.

Needs: The feeder models + a decisioning framework
Adds: Turns many signals into one coordinated decision per customer
When: The apex, once the feeders exist
ML

Channel Optimisation

Predicts the best channel for each customer, whether that's email, push, SMS or in-app, instead of blasting every channel.

Needs: Cross-channel engagement events
Adds: The 'which channel' for every message
When: As cross-channel history builds
Analytical

Incrementality / Uplift

Measures the true causal lift of a campaign with holdouts, and works out which customers are genuinely persuadable.

Needs: Holdout groups + conversion events
Adds: Proof of incremental value, not just correlation, plus smarter targeting
When: Layer it onto any always-on programme
Analytical

Contact-Frequency Optimisation

Finds the right contact cadence for each customer: enough to keep them engaged, not so much they fatigue or opt out.

Needs: Send + engagement + opt-out events
Adds: The 'how often', governing frequency caps by value
When: Once send and engagement history exists
Analytical

Closed-Loop Intelligence

Connects activity, behaviour and performance into one learning cycle, so you can see which channels, timings and frequencies actually change behaviour.

Needs: Currents + campaign performance
Adds: The Learn and Optimise step that makes every model smarter
When: Compounds over time

On their own, each model is useful. Together they compound: RFM tells you who and what, Recommendations pick the offer, and Send-Time, Channel and Frequency decide how it's delivered. Churn and pLTV add risk and worth, Next-Best-Action makes the call, and Incrementality and Closed-Loop prove what actually worked.

01

The RFM+BT Model

The five dimensions, the two outputs, and the matrix every customer lands on.

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02

Customers

Example customers. See each one's signature and walk their journey, screen by screen.

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03

Try It

Move the controls and watch the model classify a customer live.

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04

Next Steps

From this demo to a live, measured programme, with true attribution and a closed loop.

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