Challenge for M&S
Discover the challenge and submit your proposal for the Open Innovation Program 2026 before September 6.
How might we use virtual personas and digital twins, built from Sparks, social media and location data of our customer base, so that we can create hyper-personalised product launches, manage inventory per store, and make every M&S feel like it is “My M&S”?
M&S wants to use customer digital twins and virtual personas to improve how products are launched, stores are ranged and customer communications are personalised.
The challenge aims to combine synthetic Sparks, social media and location data into a simulation layer that helps teams test demand, preferences and launch scenarios before committing stock or activating campaigns.
M&S
Marks & Spencer (M&S) is a leading British retailer offering high-quality, great-value food, fashion, homeware and beauty products. Serving millions of customers in the UK and internationally, the company combines a strong heritage with a continued focus on product innovation and modern retail experiences.
Current Situation
Customer insight, launch planning and stock allocation currently operate on separate foundations. Sparks, web, social and location signals are not combined into a single simulated view of the customer, while product launches are planned nationally with limited store-level tailoring.
Stock allocation relies mainly on historical sales and store grading. Current systems forecast from past behaviour rather than simulating future demand, and personalisation generally remains at segment level.
Current pain points include missed local demand differences, overstock and markdowns in some stores, availability gaps in others, underperforming launches and customer communications that feel generic rather than individually relevant.
Desired Situation
M&S wants trading, CRM and store teams to work from a living digital twin of the customer base, built from Sparks, social and location signals.
Teams should be able to simulate product launches before committing stock, predict demand at store level, tailor ranges to local customers and build persona-based audiences for personalised campaigns.
The ambition is to improve sell-through, availability and offer engagement while reducing markdowns and waste, making each store and customer interaction feel more like “My M&S”.
Stakeholders
Internal stakeholders
Trading and buying teams, Sparks and loyalty, personalisation and CRM, Data & Analytics, Digital & Technology, supply chain and allocation planners, retail operations, store managers, marketing, legal, privacy and information security.
External stakeholders
Customers, franchise and hospitality partners, current and prospective suppliers, and social media platforms acting as potential data sources.
Primary users
Trading and buying teams using simulations for launch and ranging decisions, personalisation/CRM teams building campaigns on personas, and store managers acting on store-level demand predictions.
Possible Solutions
- Customer digital twin and virtual persona platforms.
- Store-level demand simulation and forecasting solutions.
- Product-launch scenario modelling tools.
- Hyper-personalisation and audience-building platforms.
- Retail decision-support tools for trading and buying teams.
- Advanced analytics for ranging, allocation and replenishment.
- Customer data integration and identity-resolution platforms.
- Explainable AI and model-monitoring solutions.
- Dashboards for trading, CRM and store teams.