Models

Reusable data models, made tangible

Each model is built once and re-skins to any business. Pick a vertical and watch the whole catalogue adapt: the language, the KPIs, the personas. Then open any model to see what it does, what it needs, and try it live with sample data.

The catalogue

One model is the start. The power compounds.

RFM is the deterministic foundation. Every other model layers on and adds a dimension. They are 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.

DeterministicLive demo

RFM

Recency, frequency and monetary segmentation, plus behavioural trends. It tells you who your customers are and what to do next, and it is 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.

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DeterministicLive demo

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

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Braze AILive demo

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.

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Braze AILive demo

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

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DeterministicLive demo

Reachability & Consent

Shows the addressable base per channel and the value you can actually touch, and what growing consent is worth. A perfect score is worthless if you cannot reach the person.

Needs: Consent flags + contact tokens per channel

Adds: The addressability lens every other model depends on

When: Day one. The consent data already exists.

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DeterministicLive demo

Purchase Propensity

Everyone has a personal purchase rhythm. This model reads it and times the nudge to the moment they are due, not the moment the campaign calendar says so.

Needs: Timestamped purchases per customer

Adds: The daily 'who is ready right now' trigger, the mirror of churn

When: Useful from week one, sharpens with history

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Braze AILive demo

Send-Time Optimisation

Works out the best moment to reach each customer on each channel, then lets Braze deliver when they are 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

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Bespoke Churn

Predicts who is 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

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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

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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

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Channel Optimisation

Predicts the best channel for each customer, whether that is 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

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AnalyticalLive demo

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

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AnalyticalLive demo

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

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Offer Sensitivity

Who buys anyway, who is genuinely persuadable, and who reacts badly to contact. Stops the discount budget going to people who were buying at full price.

Needs: Offer + redemption history, holdout results

Adds: The targeting half of incrementality: incentives only where they change behaviour

When: Trained by the experiments you run in Prove It

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AnalyticalLive demo

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

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Braze AISpec only

Braze Predictive Churn

Braze's built-in predictive suite scores who is likely to lapse, so you can test churn-triggered journeys without building a model first.

Needs: Message + engagement history in Braze

Adds: A churn signal you can switch on now to test the play

When: Available in Braze as history builds

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Bespoke Recommendations

A recommender built on your full first-party data, tuned to your catalogue and margins, going beyond what the out-of-the-box engine can see.

Needs: Full purchase history + product catalogue + margins

Adds: Recommendations you control, tuned past the Braze defaults

When: The upgrade once Braze Recs proves the play

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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 Contact-Frequency Optimisation decide how it is delivered. Bespoke Churn and Predicted LTV add risk and worth, Next-Best-Action makes the call, and Incrementality and Closed-Loop Intelligence prove what actually worked.

A model on its own is a score in a warehouse. Getting it into Braze, measuring what it changed and keeping it honest is a separate body of work.

The services See how it fits together →

Marketers first

We come at CRM from the campaign in, not the warehouse out. The output is a decision a planner can act on.

Every model shows its working, so your team can read the logic and stand behind it in a meeting.

Built for Braze

A CX platform specialism, not one integration among many. Models are designed backwards from the campaign.

Never from scratch

A proven, best-practice codebase per platform. Your build starts from something already running in production.