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Customer purchase likelihood demo

MAUK demo build · retail conversion scoring

A retail scoring interface that shows its reasoning: five sub-scores, one verdict, and the customer signals behind each.

Move eight sliders describing a shopper and watch a purchase-likelihood verdict update, with the five sub-scores that produced it and the signals that pushed it up or down. The five scorers are hand-written rules that imitate how trained models behave: this build demonstrates the interface and its delivery, not a model trained on data.

Demo build. A working demo MAUK built to show a prospective client, or a market, what we could deliver.

What it is
Web tool
Status
Live
Year
2026
Visit the live websiteDiscuss a similar project

A demonstration of the interface. The scorers are hand-written rules, not models trained on customer data.

Purchase likelihood
Scoring workspace with a loyal high-income shopper selected: a Yes verdict at 96.5% confidence beside the five sub-score probabilities
A high-intent shopper profile, scored Yes with each sub-score beside it.

The problem

What needed solving

Scoring products usually hand back a single number, which nobody trusts and nobody can act on. The goal was an interface that makes the reasoning visible: what each part of the scoring thought, and which customer signals moved the result.

The solution

What we built

A scoring workspace that shows the whole chain, from customer signals to sub-scores to the final verdict.

Core functionality

  • Eight live inputs: age, gender, income, past purchases, category, time on site, loyalty and discounts
  • Five named sub-scores, each with its own confidence bar
  • Each input marked as helping or hurting the result
  • Five ready-made shopper profiles
  • The raw result shown beside the verdict

Screens

More screenshots

The signal contributions and the raw result.
A casual browser, scored the other way.
The workspace as it opens, with the five test profiles across the top.
The eight inputs in full.
Phone layout.

Captured from the live site, a demo workspace, or the product running on sample data. No customer records, passwords or private admin data are shown.

Business impact

What it achieved

  • Every sub-score and signal shown for each prediction, not just the verdict

    Verified

  • Runs on demand, with nothing to install

    Verified

  • Scoring covered by its own automated tests

    Verified

What sets it apart

Why it works

  1. 01

    Reasoning on screen

    A retailer sees why a shopper scored the way they did, not just the number.

  2. 02

    Stated plainly

    The scorers are hand-written rules, not trained models, and the project says so everywhere.

  3. 03

    Nothing to maintain

    It runs on demand in the cloud, with no server to keep running.

Services

Where this work fits

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