Sales reps don't carry a thousand product memos, give them an AI assistant that does
In-the-moment product recommendations for B2B sales reps visiting retail points. Account for point profile, sales history, seasonality, and learned patterns across 25+ segment types. FMCG-vertical specific.
Discuss a pilot →Quick facts
- Business size
- FMCG distributors, B2B sales operations with more than 50 sales reps and more than 500 retail points
- Timeline
- 5-8 weeks test, 3-5 months pilot, 6-9 months production
- Budget range
- Pilot from €45k.
- Hardware
- Cloud and mobile (rep app).
- Data needed
- Sales history per retail point, product catalogue, point characteristics (size, type, location).
- Evolution
-
- Genesis
- Custom-built
- Product
- Commodity
No product gives you this. We assemble and train it around you.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
A sales rep at a major FMCG distributor visits 8-12 retail points per day. Each point has a different size, customer mix, sales history, and assortment habits. A 200-SKU product portfolio is too much for any rep to memorize at the point-specific level. The result: reps default to what they sold last time, what is easiest, or what the customer asks for. They miss real upsell, cross-sell, and new-SKU introduction opportunities.
Conventional CRM dashboards show data, but they do not make recommendations. Sales training covers the catalog, yet it cannot be situation-specific. Retail-point profiles sit in spreadsheets that reps do not open during visits.
AI recommendations change the cadence. The rep opens the mobile app at the point and sees a ranked list of products to suggest here, given this point’s profile and history, with reasoning. The rep keeps judgment, and the system absorbs the analytical work that is impossible at scale.
What the Solution Does
A mobile-first recommendation system for B2B sales reps.
- Point profile: each retail point has a structured profile (size, type, location, segment).
- Sales history: order history per point shows what has been bought, what is declining, and what has never been tried.
- Recommendation engine: for each point, top-N product suggestions with reasoning.
- Mobile delivery: recommendations on the rep’s phone, integrated with the visit workflow.
- Feedback loop: what the rep actually sold informs the next recommendation cycle.
Where It Fits
This makes sense if you…
- Operate FMCG or B2B distribution with more than 50 reps and more than 500 retail points
- Have order-history data per retail point (12+ months ideal)
- Have a mobile app or CRM the rep already uses (we integrate with it)
- See real cost from rep coverage gaps, missed upsell, or slow new-SKU introduction
This is probably not the right time if you…
- Sell direct to consumer (a different recommendation problem, see personalized-recommendations)
- Have a small portfolio a rep can memorize
- Have no historical sales data per point
- Lack a mobile app for reps (the delivery surface is the bottleneck)
Business Value
Order-size lift. Recommendation-following visits typically show 8-18% larger orders. The range varies sharply with baseline rep training and SKU portfolio width.
Long-tail SKU coverage. Reps default to the top-100 SKUs. The recommendations surface long-tail items where they fit the point. Coverage typically improves by around 40-80% versus an untrained baseline.
New-SKU introduction. When you launch a new product, recommendations route it to the right points faster than rep training does. Adoption rates improve by around 30-60% at recommended points, by our count.
Rep onboarding. New reps who have not memorized the portfolio perform closer to experienced reps with the system’s suggestions. Onboarding time drops.
How It Works
Here is how the system builds and serves a recommendation.
1. Point profile
Each retail point gets a structured profile from your CRM and external data: physical size (store square meters), customer demographic (residential or commercial), location characteristics, and historical sales pattern. We have identified 25+ segment types in FMCG-distribution contexts, and each segment has a typical SKU mix and seasonality.
2. Sales history embedding
Per-point sales history is converted into embeddings (compact numeric summaries the model can compare) that capture what has been bought, what is declining, and what is missing.
3. Recommendation scoring
Multiple signals combine: similarity to high-performing peer points, seasonal pattern, current promotion calendar, inventory, margin priorities, new-SKU push targets.
4. Mobile delivery
The rep opens the app at a point and sees ranked recommendations with:
- The top SKU to suggest.
- The reason (peer-point similarity, seasonal uplift, declining trend, or new-SKU push).
- Expected uplift if accepted.
- A one-click action: add to order, mark as discussed, or skip.
5. Feedback
What the rep actually sold, against what was recommended, feeds back. The system learns rep-specific patterns and point-specific responses.
6. Behavioral segmentation
Underneath sits a segmentation layer (related to behavioral-segmentation) that clusters points into 25+ types with characteristic SKU patterns. New points get classified into a segment quickly, and recommendations use segment-level patterns alongside point-specific signals.
Stack
BERT-based embedder (shared with product-categorization). A recommendation scoring engine. Datapipe for the data pipeline. A mobile API integrated with your existing rep app. Metabase or Power BI for analytics.
What You Need to Make This Work
Data. Order history per retail point (12+ months ideal). Point profile data. Product catalogue.
Integrations. Read access to the CRM or order system. Mobile app integration (we ship a widget, or integrate with your existing app).
Hardware. Cloud-based.
Team. A sales-ops lead. A mobile-dev contact (around 25-40 hours during the pilot). Sales reps for feedback during the pilot.
Implementation Roadmap
1. Test (5-8 weeks)
Pick one geography and one rep team. Build the data infrastructure. Train an initial recommendation model. Validate against historical orders. Output: a working recommendation engine with measured offline accuracy.
2. Pilot (3-5 months)
Mobile-app deployment for the pilot rep team. A/B test against control reps. Measure order-size and SKU-coverage lift. Iterate on the UX. Output: a working production deployment.
3. Production (6-9 months)
Network-wide rollout. Continuous improvement. Quarterly recommendation-strategy review with sales leadership.
Keep in Mind
- Mobile UX matters. Reps will not use an app that is slow or cluttered. We invest heavily in the mobile UX during the pilot.
- Adoption is rep-driven. Senior reps trust their instincts and may resist algorithmic suggestions. Plan for change management: top-rep validation, sales-leader endorsement, and real measurement.
- Bad recommendations destroy trust fast. A few visibly wrong recommendations and reps stop opening the app. We tune conservatively at first.
- Point profile quality is the ceiling. Without good point data, recommendations fall back to portfolio averages. Sometimes the highest-value finding is “fix the point master”.
- New points are cold-start. Without history, recommendations rely on segment patterns. Confidence is lower, and we surface that.
- Margin and volume trade off. Recommending high-margin SKUs over high-volume SKUs is a strategy choice. It is not a technical one. We co-design it.
FAQ
Can this work for non-FMCG B2B?
Yes. The architecture holds, only the segment patterns change. We have discussed pharma distribution, industrial supply, and food service distribution.
Does the rep have to use it for every visit?
No. It is a tool. Reps use it where it adds value and ignore it where they have stronger instincts. We measure adoption and uplift separately.
How does this differ from CRM recommendations?
CRM dashboards show data. AI recommendations show what to do next, with reasoning. Different surface, different value.
Can the system learn rep-specific patterns?
Yes. The system captures which reps sell which SKUs effectively. Recommendations can be adjusted per rep persona.
What about seasonal / promotional pushes?
Built into the recommendation engine. A “new promo this week” routes to relevant points, and seasonal patterns adjust scoring automatically.
Ready to Discuss?
If you operate B2B distribution at scale and rep coverage or new-SKU introduction is a real strategic problem, this is a worthwhile pilot. We will walk through your rep workflow and your data, and tell you what to expect.