Walk into most brand review meetings and the story still starts with syndicated reports and last quarter’s shipments. Meanwhile, your shoppers are asking AI assistants for “low-sugar energy drinks that actually taste good” or “grab-and-go breakfast that will not spill in the car.”
In an AI driven discovery world, that gap is expensive.
Retailers and distributors sit on a constant stream of proof about what shoppers actually want. Point of sale data, search queries, click paths, basket composition, even the questions shoppers ask retail chatbots and AI assistants. The manufacturers who treat that data as a product feedback loop, not just a marketing dashboard, will decide the next generation of winning SKUs.
This article outlines how to pull retailer and distributor data into your product strategy, which signals matter most, and how to put a simple, repeatable loop in place with your partners.
The New Discovery Loop Between Shelf, Screen, and AI
Product discovery is no longer limited to shelf facings and search bars.
According to Salesforce’s Connected Shoppers Report, 39% of consumers, and more than half of Gen Z, already use AI for product discovery. Another study found 58% of consumers now use AI tools for discovery, up from 25% two years earlier. Capgemini reports that 71% of consumers want generative AI built into their shopping experiences.
At the same time, site search remains one of the strongest intent signals in digital commerce. Constructor calls site search a “strong intent signal that demands a sophisticated response” and links better search to higher conversion and fewer exits. Voyado notes that search analytics reveal hidden trends and unmet demand that standard reports miss.
For manufacturers, the implication is clear.
Retailer POS tells you what sold, where, and when.
Search, on-site behavior, and AI queries tell you what people tried to find, what they couldn’t find, and how they describe their needs in plain language.
That is product strategy gold. If you can see it.
The Data You Can Already Access From Retailers and Distributors
Most manufacturers already receive some flavor of retailer and distributor reporting. The issue is that it is often treated as a sales tool, not as a product lab. Here is how to reframe the major data sources.
1. POS and Inventory Data
Retail POS data gives manufacturers real time visibility into product performance, sales trends, inventory levels, and buying patterns at the item and store level.
For product teams, key questions include:
- Which pack sizes, flavors, or formats over-index in specific regions or channels
- Where the trial is strong but the repeat is weak
- Which price tiers or promotions drive sustainable lifts, not just one-time pantry loading
- Which SKUs drag down the set, tying up space without adding variety that shoppers value
Crisp and other data providers point out that POS is especially helpful for surfacing product opportunities by showing performance at category and competitor level, not just for individual SKUs.
2. Site Search and on Site Behavior
Modern onsite search is no longer “just a box.” Retail specialists describe it as a multi purpose product finder that, done well, reduces friction and steers shoppers to the right items.
Search analytics you should request from retail partners include:
- Top queries that lead to your products
- Top queries that should lead to your category but end in “no results”
- Queries that regularly lead to shoppers refining or abandoning their search
- Filters and attributes shoppers click most often when browsing your aisle
- Cross click patterns. for example, shoppers who view your 12 pack often click into a single serve competitor
MarketingCharts highlights that site search is treated as a strong intent indicator by leading commerce players.
When you treat those terms as unfiltered shopper language, they become a steady stream of product and packaging clues.
3. AI Discovery and Conversational Signals
Retailers are rolling out branded AI assistants, and consumers increasingly bring their own. Reports from Salesforce, Capgemini, and others show that shoppers now use AI to compare products, check dietary fit, and ask context rich questions about use occasions.
Instead of asking “protein bar,” they ask:
- “What is a high protein, low sugar bar I can keep in my car that will not melt”
- “Snack idea for my kid who has a nut allergy and hates chewy textures”
- “Energy drink that will not make me jittery, less than 10 grams sugar”
Those questions rarely appear in standard POS reports. Yet they are direct statements of need that can shape new products, new claims, and new bundles.
Ask retail partners and distributors who operate AI discovery tools or chatbots for anonymized, aggregated views into:
- Frequent category questions and complaint themes
- Repeated attribute requests that current sets cannot fully satisfy
- Confusion signals, such as shoppers asking if a product is gluten free when your packaging does not make that clear
Turning Scattered Signals into Concrete Product Moves
Once you have access to POS, search, behavioral, and AI discovery data, the next step is to structure it around decisions your executive team already makes.
1. Guide New Product Development with Real Demand
Instead of starting briefs with “we need a new flavor,” start with:
- “We see growing search volume for ‘caffeine free energy’ across three national retailers”
- “AI questions about ‘snack for long drives’ spike on weekends in the South and Midwest”
- “POS shows strong trial on our smaller packs in urban C stores but weak repeat on the largest pack in suburban grocery”
From there, your R&D and consumer insight teams can:
- Build concept boards anchored in actual search phrasing, not just survey language
- Run quick qualitative with shoppers who asked those questions, where data rules allow
- Design test SKUs that explicitly answer the discovered use cases
C store data specialists have shown that brands that continuously use store-level findings to refine their product mix are faster at spotting new behaviors and unmet needs.
2. Adjust Packaging, Claims, and Architecture
Many questions that flow through retailer sites and AI tools are not about entirely new items. They are about confusion or friction with what you already sell.
Look for patterns like:
- Repeated search for “resealable” or “single serve” in your category
- AI questions asking “how long does this stay fresh after opening”
- Frequent filter use around pack count, size, or serving style
- Cart level data that shows shoppers pairing your product with an unanticipated occasion
Those signals can support decisions such as:
- Shifting a core SKU into resealable packaging
- Adding clear freshness, storage, or usage guidance to the back panel
- Introducing a trial size specifically for a new occasion that shows up in AI queries
- Rationalizing pack architectures by channel. for example, ultra value packs in club, smaller mixed packs in convenience
POS and field data connections already help brands spot which store conditions drive growth. Layering in search and AI questions keeps that same thinking pointed at packaging and claims.
3. Sharpen Channel and Account Strategy
Distributor and retailer data can also help you decide where and how to scale new products.
Combine:
- POS velocities by channel and region
- Site search demand by geography
- AI question themes by retailer or platform
- Location data, such as POI or trade area characteristics, where availabledataplor
Then ask:
- In which banners do we see strong “pull” signals before distribution is fully built out
- Where are shoppers clearly asking for a type of product that no current item in the set satisfies
- Which distributors have the right footprint to test a new pack or formula that responds to those signals
That structure turns retailer and distributor partners into co-authors of your channel plan, not just recipients of sell sheets.
A Practical Operating Model to Close the Loop
You do not need a massive reorg to start treating retailer and distributor data as a product feedback system. You do need clear ownership and simple routines.
Here is a workable model for most mid-sized and large manufacturers.
Step 1. Define a shared question set
Before you ask partners for more data, agree internally on the questions you want to answer, such as:
- Which unmet needs in our category appear most often in search and AI questions
- Where do we have a strong trial but weak repeat pattern by channel or pack
- Which attributes are shoppers looking for that are absent or hard to read on our current packaging
- Where are retailers losing shoppers in their digital path when they try to find our products
This keeps the effort grounded in product and commercial decisions, not in data for its own sake.
Step 2. Standardize partner feeds
Work with retail and distributor teams to receive:
- POS and inventory feeds at the SKU by store level, on a predictable cadence
- Search and browse analytics in a standard format, at least for your category
- Aggregated AI discovery themes, scrubbed of any personal identifiers
- Clear documentation of definitions, time windows, and data quality caveats
You do not need perfection on day one. Start with a few lead partners and grow from there.
Step 3. Build simple, shared views
Give cross functional teams a common view of:
- “What shoppers are asking for”. top search and AI themes by category
- “What they actually buy”. POS and basket views
- “Where they struggle”. zero result queries, high exit search journeys, repeat complaints
Marketing, sales, product, and insights leaders should all see the same dashboards or summaries.
Step 4. Tie data reviews to stage gates
For each major product decision, bake these checks into your process:
- Stage 0 or 1, use retailer search and AI questions to define problem statements
- Pre launch, use POS and test store data to size the opportunity and set realistic velocity targets
- Post launch, track whether you are reducing “friction” signals in search and AI questions over time
If your brand wants AI assistants to recommend your products, they should see consistent, factual product data and evidence that shoppers are satisfied. That requirement lines up neatly with the same data your retailers need to maintain category health.
Step 5. Share findings back with partners
Closing the loop also means giving something back. Bring retailers and distributors joint readouts that show:
- How you used their data to refine a pack, formula, or launch plan
- Early results in terms of reduced returns, better repeat, or more relevant search behavior
- Ideas for joint tests that respond to new discovery patterns
This deepens your relationships and makes it easier to secure richer data access over time.
From Marketing Metrics to Product Decisions
In an AI-driven discovery environment, shopper questions, clicks, and purchases are not just signals for ad teams. They provide live feedback on whether your products, packs, and channels match how people actually live.
Manufacturers that treat retailer and distributor data as a continuous feedback system will:
- Reduce failed launches by grounding briefs in real demand
- Adjust pack sizes and claims in ways that shoppers can actually feel
- Give retail and foodservice partners confident reasons to support new items
- Train AI assistants to talk about their assortment in clear, accurate, up to date terms
That is the real promise of closing the loop.
Not more dashboards. Better decisions about what you put on the shelf, how you describe it, and where you choose to compete.
