Retail & Consumer
AI that moves the numbers retailers live by
Pricing, markdown, demand, range and personalisation, rebuilt on your own data. Working software in weeks, tied to profit and margin rather than a dashboard for its own sake.
Free diagnostic, three minutes. No follow-up unless you ask.
What AI for retail means
AI for retail means using machine learning and generative AI to make the decisions a retailer makes every week, on price, markdown, promotions, range, space, stock and marketing, on evidence rather than intuition. For a grocer or supermarket that usually means clearance, space and range decided store by store. For a brand it means demand forecasts that hold through a supply shock, and marketing spend that follows the customers who respond.
Our retail work is measured in profit and margin. Clearance automation identified an £18m revenue uplift for a leading UK supermarket. A custom clearance tool unlocked a £3.5m annual profit uplift for another UK retailer. Conflict detection found more than 120,000 pricing errors and £25m of revenue at risk in a travel retailer's cruise division. A postcode-level model of vegan demand informed a UK grocer's store-specific ranging across more than 750 stores.
We are an AI consultancy of around 70 strategists, data scientists and engineers, building AI and analytics systems since 2016. We are not a software vendor, so the recommendation is not tied to a licence. When the answer is a product, it may be RetailCube, our own macrospace tool; when it is a model on your own data, we build it and you own it.
Built around how retailers actually make money
The four places retail and consumer businesses ask us to start, each drawn from work we have already delivered.
Pricing, markdown and promotions
Clearance, markdown and promotional pricing rebuilt on evidence rather than intuition. Our clearance and promotional tools have surfaced multi-million-pound profit and margin opportunities, and detected more than 120,000 pricing conflicts a manual process had missed.
Demand forecasting and range
Machine-learning demand forecasting and range optimisation that hold revenue while cutting complexity. One range review cut core SKUs by 46 per cent while retaining 95 per cent of revenue; another localised a vegan range across more than 750 stores.
Personalisation and marketing ROI
Recommendation, timing and lead-scoring models that lift conversion and revenue per send. Personalised emails have delivered 25 per cent higher conversion and up to five times the revenue per send of standard campaigns.
Supply chain and data foundations
A single optimisation view across demand and supply, and the data strategy underneath it. We recovered revenue through a Covid supply shock and mapped a £100m revenue opportunity for a major franchisor by fixing its data foundations.
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RetailCube
Macrospace optimisation: put shelf space where it sells, store by store.
What we have built for retail and consumer brands
Specific engagements, specific results. Anonymised by agreement where the client asked for it.
Turning a Covid supply shock into $3.8m of recovered revenue
A premium global footwear and lifestyle brand
A premium global footwear and lifestyle brand was left simultaneously over- and under-stocked when the pandemic disrupted its supply chain. QuantSpark built a single optimisation model, powered by machine learning demand forecasting, that recovered $3.8m in net revenue over six months.
Boosting Retail ROI: Data-Driven Promotional Pricing Optimisation
PE-backed European Retailer
QuantSpark helped a PE-backed European retailer transform their promotional strategy, moving from intuition-based decisions to a data-driven approach.
Personalising email timing to lift engagement and revenue
A UK high-street retailer
QuantSpark built a behavioural trigger model that personalises the timing of marketing emails, delivering five times the revenue per send and two and a half times the engagement of standard campaigns.
Product recommendation engine lifts email conversion by 25%
UK menswear retailer
A menswear retailer's marketing emails were sending repeat customers on long searches for products they had bought before. QuantSpark's recommendation engine personalised those emails and lifted conversion by 25%.
Marketing budget allocation tool cut cost per acquisition by 10%
A high-street womenswear retailer
A high-street womenswear retailer wanted to allocate digital marketing spend across channels and geographies to maximise return. A bespoke decision-support tool cut cost per acquisition by 10 per cent while the business kept growing.
Unlocking £3.5M Profit Uplift with Retail Clearance Optimisation
Leading UK Retailer
QuantSpark developed a custom clearance tool for a retail client, optimising markdown strategies and workflow to unlock a £3.5m annual profit uplift opportunity and dramatically reduce labour.
Unlocking £100M Revenue Opportunity for a Major Franchisor with Data Strategy
A major multi-site franchise network
QuantSpark partnered with a major franchisor to identify and resolve data pain points, uncovering a £100M revenue opportunity by addressing 10% annual sales leakage through a new data strategy.
Quantifying an £18m Revenue Uplift in Supermarket Clearance Automation
Big-4 UK supermarket
QuantSpark identified an £18m revenue uplift opportunity for a leading UK supermarket by designing a roadmap to automate their complex stock clearance and discounting processes.
Sourcing a senior data analyst to restart a retailer's analytics programme
A leading UK retailer
QuantSpark Talent sourced and onboarded a senior data analyst for a UK retailer whose analytics programme had stalled, restoring capability across pricing, marketing, personalisation and strategy.
Rebuilding macro space planning on a modern analytics platform
A leading UK supermarket group
QuantSpark replaced slow, hard-to-maintain Excel space-planning models with a bespoke Python analytics platform that is now the backbone of macro space decisions across the estate.
A minimum viable range: cutting SKUs without losing revenue
A private-equity-backed UK pet superstore chain
A private-equity-backed UK pet superstore chain carried a dog food range that had ballooned to around 1,930 SKUs, yet most of its sales came from a few hundred. QuantSpark’s fixed-fee, 10-week range review cut core SKUs by 46% while retaining 95% of revenue.
Mapping emerging demand to localise a retailer's vegan range
A leading UK grocery retailer
QuantSpark built a probabilistic demographic model and heatmap to estimate vegan population density at postcode level, informing store-specific ranging that was rolled out nationally across more than 750 stores.
Uncovering £25m of revenue at risk in cruise retail pricing
International travel retailer (cruise division)
An international travel retailer's cruise division was losing margin to thousands of undetected pricing errors. QuantSpark's analytics surfaced more than 120,000 pricing conflicts, equivalent to £25m of revenue at risk.
Lead scoring model lifts sales conversion by 20%
Private equity-backed price comparison and switching service
A price comparison and switching service wanted to point its finite outbound sales team at the highest-value leads. QuantSpark's propensity model delivered a 20% increase in conversion rate.
How we work with retailers and brands
Retail AI pays back fastest where decisions are frequent and the data already exists. We sequence the work so the first result funds the next.
Start where the margin is
Markdown, clearance and promotional pricing usually pay back first, because the decisions repeat daily and the result shows up in the next trading period. A data-driven promotional pricing platform identified €3.4m of potential margin for a PE-backed retailer.
Then range, space and stock
Range and space decisions carry more risk, so they run on models that explain the trade-off. A customer choice model let a PE-backed pet retailer cut its dog food range by 46 per cent while keeping 95 per cent of revenue, and a Python analytics platform replaced slow Excel space-planning models to become the backbone of macro space decisions across a UK supermarket's estate.
And the customer
Personalisation earns its place when it is measured per send. A behavioural trigger model delivered five times the revenue per send of standard campaigns for a UK retailer, a recommendation engine lifted a menswear retailer's email conversion by 25 per cent, and a budget allocation tool cut a womenswear retailer's cost per acquisition by 10 per cent while it kept growing.
Proof, not promises
Net revenue recovered over six months for a premium global footwear and lifestyle brand, through ML demand forecasting and supply-chain optimisation
Figures are drawn from completed QuantSpark engagements. Clients are anonymised by agreement; on a call we will walk you through how each number was measured and, where the client has agreed, put you in touch with a reference.
One ladder, each step earned
Every engagement starts small and earns the next step. Book a discovery call and we will tell you where to start, honestly.
Readiness Diagnostic
Free. Ten questions, a scored readiness band and an honest diagnosis. No follow-up unless you ask.
- Scored readiness band
- Plain-English diagnosis
- Recommended next step
AiRE Diagnostic
A short list of AI bets ranked by value, feasibility and risk, a recommended roadmap, and an honest read: roll out, build, buy, or wait.
- Ranked AI bets
- Value and feasibility scoring
- Recommended roadmap
- Honest build / buy / wait call
First Agent
A production-candidate agent, live on your own stack in five weeks, built with your team so you leave owning both the agent and the ability to build the next one.
- One core workflow mapped (week 1)
- One production-candidate agent on your stack
- Your people building alongside us
- Runbook, eval harness, code and IP, all yours
AiRE Rollout
Fractional AI implementation across your existing stack. From AI experiments to AI operations.
- Tool deployment on your stack
- Team enablement
- Governance and guardrails
- Monthly reviews and Slack support
Labs Discovery
A working prototype on your data, in your workflow. Not a slide deck. Validated value and a scale roadmap.
- Process and data mapping
- Working prototype
- Validated value case
- Scale roadmap
Labs Build
An enterprise-grade system embedded in your operating model. You own the code and the IP.
- Production build
- Integration and embedding
- Team handover
- 12 months support
Every project is scoped individually. Book a discovery call and we will provide a detailed proposal within 48 hours.
Retail AI questions, answered
How is AI used in retail?
Where does AI pay back first for a grocer or supermarket?
Can AI improve demand forecasting and range decisions?
Do we need a new data platform before we start?
What is RetailCube?
How do we start?
Talk to an AI consultancy for retail that has done this before.
Start with the honest read
Three minutes, ten questions, no follow-up unless you ask. Or talk to a human first.