Tesco
loyalty gamification through AI-computed personalized spending goals
Tesco targeted up to 10 million Clubcard members with spending challenges personalized by Eagle Eye's AI engine, distributing more than half a billion points across its 2024 campaigns, on a program through which 82% of UK sales flow.
Key points
- AI-personalized spending challenges in the Clubcard app.
- Eagle Eye decision engine on purchase history, backed by dunnhumby.
- Up to 10 million members targeted, over half a billion points in 2024.
- Evidence B, confirmed status, four six-week campaigns in 2024.
Objective
Bring Clubcard members back more often and grow their spend over a six-week period by offering them numeric challenges sized to their usual basket rather than uniform promotions.
The deployment
Clubcard Challenges are personalized spending challenges offered in the Tesco app. Each member receives goals (for example spending a certain amount in a category) and earns Clubcard points if they hit them over six weeks, up to 50 pounds in points. The assignment of challenges is computed by Eagle Eye's AI engine, which according to the vendor takes more than 190 decisions per member to set target products, spending thresholds, and reward levels from purchase history. Tesco ran four six-week campaigns in 2024, widening the included population each wave, up to 10 million customers for the last. The program draws on the Clubcard's data, through which 82% of Tesco's UK sales flow.
Results Proof B
A quantified case study from the Eagle Eye platform (reach, player conversion, points distributed) quoting CEO Ken Murphy, corroborated by eMarketer analysis on the weight of the Clubcard (82% of UK sales). A vendor source with an interest, but consistent with the analyst press.
How it works
Documented architectureThe stack in detail
- plateforme Eagle Eye Loyalty platform whose AI engine calibrates the challenges: more than 190 decisions per member (target products, spending threshold, reward) from purchase history.
- integrateur dunnhumby Tesco's data subsidiary, behind the Clubcard, which handles the customer modeling upstream of the engine.
- infra Application mobile Tesco / programme Clubcard Distribution channel for the challenges and source of the data: 82% of Tesco's UK sales flow through the Clubcard.
How it runs, concretely
For ops teams-
1Population selection Loyalty marketing
The loyalty team sets the wave's scope and the number of customers included, widened campaign after campaign.
-
2Challenge calibration AI
The Eagle Eye engine computes for each member the target products, spending threshold, and reward from their history.
-
3Delivery in the app Application
The challenges appear in the Tesco app with progress and the Clubcard point reward.
-
4Progress tracking AI
Each purchase updates the member's progress toward their goal over the six weeks.
-
5Measurement and iteration Data and marketing team
Participation, completion, and incremental spend are measured, then the next wave widens the target.
Each member's Clubcard purchase history. Without an identifier linking receipts to a person, the engine cannot calibrate a reachable spending threshold, and the challenge becomes either trivial or out of reach.
How your customers perceive this type of use
Sourced studiesLe paradoxe est documente des deux cotes : 71% des consommateurs attendent des interactions personnalisees et 76% sont frustres quand elles manquent (McKinsey, 2021), mais 75% declarent ne pas acheter aupres d'organisations auxquelles ils ne confient pas leurs donnees (Cisco, 2024). La « creepy line » est localisee : messages recus quelques secondes apres une recherche et suivi de localisation sont les pratiques qui mettent le plus mal a l'aise (Periscope by McKinsey, 2019).
Acceptance conditions
- La confiance dans le traitement des donnees precede l'achat : 75% ne achetent pas sans elle (Cisco 2024)
- Un cadre legal protecteur rassure : 59% des consommateurs disent que des lois fortes sur la vie privee les rendent plus a l'aise pour partager des informations dans des applications IA (Cisco 2024)
- La personnalisation elle-meme est attendue quand elle est consentie : environ la moitie des consommateurs (US 55%, UK 52%) disent s'inscrire souvent ou parfois a des services personnalises (Periscope by McKinsey 2019)
Red lines
- Le message declenche quelques secondes apres une recherche ou un achat : deuxieme ou troisieme cause de malaise selon les pays (Periscope by McKinsey 2019)
- Le suivi de localisation percu comme de la surveillance : 40% de malaise en Allemagne et au Royaume-Uni (Periscope by McKinsey 2019)
- Le mesusage des donnees personnelles par l'IA, devenu la premiere inquietude des consommateurs, a 53% et en hausse (Qualtrics 2025)
Sources: McKinsey & Company 2021 · Periscope by McKinsey 2019 · Cisco 2024 · Qualtrics 2025
How to replicate
Inference, not sourcedData prerequisites
- first-party transactional history per member
- a loyalty program that identifies each customer
- the ability to tie each receipt to the right profile
Org prerequisites
- an active loyalty program with a large base
- a CRM team running the campaigns
- an incremental measurement loop
Possible stack
- a loyalty platform with an offer engine (Eagle Eye, Talon.One, or equivalent)
- a CDP or customer warehouse
- a mobile app
The plan, step by step
- Step 1Define the challenge mechanic (duration, reward, eligible categories) and the scope of the pilot population.Deliverable: Challenge spec and individual calibration rules.
- Step 2Connect the offer engine to members' purchase history to compute reachable per-person spending thresholds.Deliverable: Thresholds and rewards computed for the test population.
- Step 3Run a first six-week campaign on a smaller population, with a control group and progress tracking in the app.Deliverable: Live campaign, progress visible to the member.
- Step 4Read participation, completion, and incremental spend against the control.Deliverable: Quantified review of the pilot wave.
- Step 5Widen the population wave after wave, adjusting the calibration on the learnings.Deliverable: Scaling plan and schedule for the following campaigns.
First step: Define a numeric challenge mechanic and connect the offer engine to members' purchase history to calibrate individual thresholds.
The same pattern, proven in other industries
Meta
AI feed and video recommendation (large-scale models / LLMs)
Netflix
content recommendation engine
Nike
personalized exclusive offers and access based on member engagement and history
Sources
- S1 Tesco Clubcard Challenges - Eagle Eye case study Interested party archive pending
- S2 How Tesco built a retail platform powered by loyalty data and AI Secondary archive pending
An error, newer info, a source?
This page lives on its accuracy. If a figure has moved, if the deployment has changed, or if you have a higher-quality source, tell us. Every sourced correction is verified before publication.