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Every customer gets a different experience. Built for them.

Real-time personalisation across product recommendations, content and communications — driven by behaviour, context and intent, not by segment averages.

See it in action

The problem

Customer segments too broad to drive real personalisation

  • Segments that are too broad to be useful

    Grouping customers into 5 segments means 80% of them see experiences that aren't quite right. Relevance suffers. Conversion suffers.

  • Recommendations that miss the moment

    Static recommendation rules don't adapt to what a customer is doing right now. They show what's popular, not what's relevant.

  • Personalisation limited to email

    Most teams personalise email but leave the website, app and support experience generic. The gap between channels destroys consistency.

  • Experiences that feel inconsistent

    Personalisation in one channel contradicts another. Customers notice the mismatch and trust—and conversion—suffer.

How it works

Relevance without creepiness—using data you are allowed to use

Step 1

Unify consented profiles

Identity, preferences, and behavioural signals are merged under consent and retention rules.

Step 2

Score and rank

Offers, content, and journeys are selected with explainable features for marketing and privacy review.

Step 3

Test and learn

Experiments feed back into models and guardrails so winners scale safely across channels.

Feature stores and batch exports plug into your existing CDP or data warehouse.

Every customer gets a different experience. Built for them.

What's included

What you get when you run this with Thinkia

A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.

Real-time behavioural signals

Processes session behaviour, purchase history and contextual signals to personalise in the moment.

Product and content recommendations

Surfaces the right product, article or offer at the right point in the journey.

Cross-channel consistency

Personalisation layer applies across web, app, email and support interactions.

A/B and multivariate testing

Built-in experimentation framework to validate personalisation decisions with data.

Privacy-compliant architecture

Personalisation without relying on third-party cookies or non-consented data.

Segment-of-one targeting

Individual-level personalisation that goes beyond cohort and segment logic.

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Results

What changes when this runs in production

Results vary by traffic volume, catalogue size and data maturity.

+15–25%

Improvement with real-time personalised recommendations vs. generic

+12%

Uplift from contextually relevant cross-sell and upsell

+35%

With personalised content vs. segment-average messaging

How we work

From batch blasts to relevance your customers notice—and you can defend

Identity & consent

Week 1–2

Customer graph, consent flags, and suppression rules are the non-negotiable foundation.

Feature & policy

Week 3–5

Ranking logic, fairness checks, and brand guardrails are encoded before any live traffic.

Holdout test

Week 6–9

A/B and uplift measurement prove revenue and engagement without breaking trust.

Omnichannel scale

Week 10+

Web, app, email, and care channels consume the same decisioning service with shared governance.

Data richness and channel count affect build; we prioritise journeys with measurable lift.

Get started

Ready to scope this for your context?

We start with a focused session—no commitment—to map constraints and a sensible path.

See it in action Explore AI Solutions