Panel data describes the quarter. It does not decide the next one.
Category data, promotion history and sell-out, combined with intelligence from over 230,000 external sources. Enabling you and your team to win every sales channel.
FMCG
Your own evidence for every retail conversation
FMCG teams negotiate listings, promotions and shelf space with retail partners who usually bring a lot of data to the table. Panel data, sell-out reports and category reviews are there, but they are often spread across systems and describe the past period rather than the next one. ZopaAI combines your sell-in, available sell-out and promotion history with external data on stores, catchment areas, purchasing power, competitor assortments and pricing, and scores every chain, store and SKU on its potential. This makes it easier to see which store, which product and which promotion deserve the next euro. Your key account and field teams go into their conversations with a view of their own.
ZopaAI stands for reliable, scientifically verified AI for use in companies, in order to avoid the problem of AI hallucinations.
Newspaper, Frankfurt
Eight building blocks, usable on their own, together a steering system
All modules are usable on their own or interconnected in a steering portfolio.
Pick your role, the most commonly used modules are highlighted.
Contact and visit steering
Contact planned by short-term and long-term potentials, rather than historical data. ZopaAI enables you to focus on the future-strong customers, from retail stores to restaurants.
- Contact frequency per account instead of rigid A/B/C classes
- Steering the needed contact measure
- Impact measurement: sales change after contact, per territory
“Region south needs attention, due to rising competitive pressure by CompetitorX, emerging revenue decline of -12% and listing depth development of -3%.”
Illustrative wording. Figures and names are not from a real client project.
Competitive analysis
Competitor movements and analysis on a granular basis, customer per customer. ZopaAI lets you see which customers pose a threat at which products, customers or regions.
- Quantified competitive pressure per account
- Forecasting the regions most under pressure
- Assortment and price comparison per retailer and category
”Category 07, drugstore channel: your share fell 1.8 pp over nine weeks, 1.1 pp of it to private label. Leaflet and online pricing data place two competitors 40 cent below your price point since week 31.”
Illustrative wording. Figures and names are not from a real client project.
New customer discovery
Retail head offices are finite. The store and wholesale long tail is not. ZopaAI identifies unlisted stores, regional chains or cash-and-carry partners, matches them against the structural profile of your strongest accounts, and estimates expected volume before the first contact.
- Unlisted stores and regional chains, matched on category fit
- Lookalike search built on your best-performing accounts
- Expected volume per prospect, before first contact
”Region West: 214 outlets carry no listing of your core range. 38 sit in catchments matching your strongest 50 accounts on purchasing power and store format; expected volume 1.4 m € p.a.”
Illustrative wording. Figures and names are not from a real client project.
Prioritisation
ZopaAI orders accounts, categories and SKUs by expected margin or volume contribution for the coming cycle, and shows explicitly where the focus should be.
- One ranked list per team, region and cycle
- Ranking by expected sales volume or margin contribution, not revenue history
- Plan-versus-model deviations shown, not smoothed over
”Q4, field force South: of 120 planned accounts, 31 carry 68 % of the expected margin effect. Regional category growth data move 22 further accounts below the cut-off.”
Illustrative wording. Figures and names are not from a real client project.
Account scoring
Every retail head office, store, drugstore and wholesale partner carries a score, that is explainable up to the lowest level. Guaranteeing insightful scoring, whilst allowing you to understand the very fundamentals of this score.
- Potential, margin quality, order rhythm, proximity and risk per account
- Every score decomposes into the variables that produced it
- Continuous update instead of an annual segmentation
”Account 0902, buying group: potential 62, margin quality 41, churn risk high. Order frequency fell 18 % over two quarters while catchment data show the category growing 3 %.”
Illustrative wording. Figures and names are not from a real client project.
Next-best-action recommendations
Not a dashboard to interpret, but one concrete next step per account. ZopaAI names the action, the expected effect and the comparison cases it is derived from.
- Recommended action per account, with its reason
- Prioritised by expected effect, not by ease of execution
- Withheld where the evidence does not carry the needed granularity
”Store 2210: propose secondary placement for SKU 4120 ahead of week 40, where leaflet data place the category's promotion peak. Outlets comparable by footfall and catchment gained 1.6× uplift with the same mechanic; margin effect 11 k € per quarter.”
Illustrative wording. Figures and names are not from a real client project.
Cross-sell & upsell
ZopaAI provides you with detailed cross-sell & upsell potentials, showing the sum that is left out and the potential of pushing this action.
- Range gaps per account, ranked by expected margin
- Substitution risk flagged before the second SKU is pushed
- Pack and size upgrades identified per channel
”Drugstore channel: 74 accounts stock your core range but not the premium line — all in catchments where purchasing-power data show the premium segment growing. Comparable accounts add 8.2 % category margin with no measurable cannibalisation.”
Illustrative wording. Figures and names are not from a real client project.
Region overview & territory planning
Territories cut by potential, not by a static logic, that misses out on important factors. ZopaAI puts potentials and risks on one map, allowing for an active planning, instead of historic practices.
- Potential and risks per territory, on one map
- Always know where your attention is needed
- Clear next best actions per region
”Territory South carries 2.1× the potential of Territory East at identical headcount, measured on store density and catchment demographics. Re-cutting three postcode areas balances workload to within 6 %.”
Illustrative wording. Figures and names are not from a real client project.
What changed at an international drinks manufacturer in 3 months.
Scope of the case study
- Client
- Drinks manufacturer, multiple categories, EU-based.
- Territory
- Germany, 6 retail chains, broad listings.
- Implemented
- Competitor analysis, Account prioritisation, field force steering, recommended actions & more.
- Period
- 3 months (ongoing project).
Full case study as a PDF
Two pages on initial situation, data foundation, methodology, results. Get access to the link.
The form could not be loaded, possibly because of a browser extension. Email us at info@zopaai.com and we will send you the case study.
Presented anonymised. Figures from the client's own project controlling.
Our field sales team finally has future-ready intelligence on its customers. We were able to successfully rework our high priority accounts and gain market share, with ZopaAI.
Reference from an ongoing project in FMCG
Questions we hear first
Short answers before your first call.
No. Panel data improves the picture where you already have it, but it is not a precondition. The external layer is built from our 230,000+ databases. If you do license panel data, we work inside the terms of your contract.
Choose your industry and time.We will match you with an expert.
Not another sales pitch, but an honest discussion focusing on your needs. 30 minutes, no sensitive data, no risk, no cost.
- Industry lead, not a generalist
- Valuable discussion, honest assessment.
- Focused on discovering potentials.