FinTech
AI & Data Strategy

Customer satisfaction up from 2.5 to 4.1.
ROI hit 1,200% in year one.

An AI insight platform reads footfall and behaviour in real time while GPT-4 handles routine customer service — together they lifted retail revenue by 36% and automated two-thirds of the service workload.

2.5→4.1 CSAT score
uplift
36% retail revenue
increase
65% inquiries
automated
1,200% ROI in
year one

Client

A UK FinTech consulting firm operating across MENA and European markets.

Goal

Drive AI and data-led transformation in the FinTech sector, fostering innovation and growth while holding to strict regulatory compliance across MENA and Europe.

Store layout, customer service and revenue growth were three separate problems.

Retail environments were too complex for generic AI. Store layouts and product placement needed optimising for sales, customer engagement needed lifting, and none of it counted unless it showed up as measurable revenue growth.

Those goals were being pursued separately, with no shared view of customer behaviour to tie decisions together — so improvements in one area rarely moved the numbers anywhere else.

Three constraints were non-negotiable:

  • Designing AI tailored to complex retail environments
  • Optimising store layouts and product placement to boost sales
  • Growing customer engagement into measurable revenue

One platform to watch the store, one AI to handle the chat.

An AI-powered customer insight platform went in first, using Edge technology for real-time footfall analysis, heatmaps and path tracking across stores.

Edge technology read the store in real time; GPT-4 read the customer — staff were left for the queries that actually needed a person.

GPT-4-driven automated customer service handled routine enquiries directly, cutting response times and errors, and freeing staff to focus on complex queries that actually needed a person.

Customer behaviour data was centralised across both systems, so store optimisation and revenue decisions were made from the same evidence rather than guesswork.

Two-thirds of the workload automated, revenue up more than a third.

2.5 → 4.1 CSAT rose as automated service resolved routine enquiries and freed staff for the complex ones.
36% Retail revenue increase, driven by data-led decisions on store layout and product placement.
65% Of all customer service inquiries automated, with service quality and satisfaction improving, not just speed.
1,200% ROI Delivered in the first year, across 16,200 customer service requests handled every month.

Centralising customer behaviour data turned two separate initiatives — store optimisation and automated service — into one feedback loop. CSAT climbed from 2.5 to 4.1, revenue grew 36%, and the service team absorbed a 16,200-request monthly volume at a 1,200% first-year ROI.

* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.

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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.