Online, every click is measured. On the shop floor, visibility ends at the door. AI for retailers changes that: it measures the journey and guides customers in the aisle, without compromising trust.
of value generative AI could unlock in retail, worth 1.2 to 1.9 margin points
of gross sales lost to in-store inefficiencies, up from 4.5%
of store staff have watched a customer leave without help
Online retailers trace every step from click to purchase. Point-of-sale systems know what customers bought, and footfall counters know how many came in. But neither answers the questions that decide a sale:
AI-powered in-store analytics answers all four in two layers. Analytical AI works in the background, using computer vision to reveal movement, dwell and waiting times, while consent-based, anonymised conversation analysis shows what customers need and where service breaks down. Interactive AI works in the foreground, guiding customers to the right product or person.
The principle is simple: analyse patterns, not individuals. Focus on topics, advice quality and process times, not employee performance.
Customer experienceShorter waits, proactive engagement from staff and interactive AI, and clearer orientation
SalesCross-selling opportunities identified and themes surfaced, with routine questions handled by interactive AI
Staff upskillingAggregated conversation patterns as the basis for targeted coaching
OperationsWorkforce scheduling built on actual footfall insights from analytical AI, rather than intuition alone
AI-driven customer journey analytics is already making previously invisible in-store interactions measurable. Zühlke has deployed computer vision and consent-based conversation analysis across three stores for a leading Swiss telecoms retailer.
This whitepaper shows how to turn that visibility into value, meet regulatory requirements and customer expectations, and build the evidence to scale. You’ll learn:
Fill in the form to receive the whitepaper "AI in retail for industry leaders: From blind spot to learning store", including the edge-versus-cloud comparison, the EU AI Act classification table, and a phase-by-phase roadmap for your first store.
Discover how we’re helping retailers use data and AI to understand customers better, improve operations, and create more engaging retail experiences.
Together, we’ll identify your strongest levers. Where is your shop floor losing revenue today? Which use case can create measurable impact fastest? And what could a realistic architecture and pilot look like?
You'll speak to the authors of the whitepaper:
In-store analytics measures what happens on the shop floor: how customers move, how long they dwell and wait, and what they ask for. AI adds what point-of-sale data and footfall counters miss, so retailers can track the store KPIs that decide a sale.
