01
The wrong SKUs get pushed
Inventory builds up against products the marketplace has already moved on from. High-demand attributes are missed because no one is watching them at category level.
Program 01 · Retail & eCommerce
Product & Market IntelligenceApparel · Beauty · Home & Lifestyle
Catch the pattern before your competitor does.
Benefit from a continuous read on the marketplace: where you sell, which SKUs are converging, which attributes are gaining, which price bands are moving, or where the regional demand is shifting. Your team makes the call before the evidence shows up in your own sell-through, not after.
Decision intelligence · live
“Which attributes are climbing in the women's outerwear category this cycle?”
Quilted · Wool-blend · Cropped88↑12
Faux-leather · Belted · Mid-length71↑09
Puffer · Synthetic · Oversized42↓11
Recommendation: Reallocate ~18% of the next buy cycle from oversized puffers toward quilted wool-blend cropped silhouettes in the $148–$184 band.
- Confidence: high
- Evidence: review velocity, rating-volume growth, price-band conversion deltas
illustrative · not a real customer
The cost of deciding without intelligence
Most retail brands don't lack data. They lack the connection between data and the next decision.
Product decisions still get made on intuition, lagging reports, and the same internal data the team has always had. Meanwhile, the truth about what is winning is sitting in plain sight, on the marketplaces where the brand sells.
02
Pricing is set in a vacuum
Price bands are picked from cost-plus logic or last year's playbook, not from a live read of which bands are converting on which platform, in which sub-category.
03
The same listing runs everywhere.
Brands push identical SKUs across every platform, even when each platform has its own demand shape. The cost shows up in slow movers, missed bands, and dilution.
The shift
Master the Science of Demand: Predicting What Sells and the Perfect Price Point.
The point is not more reporting. The point is to change the question the team is answering on Monday morning.
Before
“What can we sell?”
A view from inside the company. Anchored in past performance, current inventory, and what the team already knows. Predictable. Insulated from what's actually happening in the market right now.
After
“What should we sell, where, and at what price?”
A view from outside-in. Anchored in marketplace signal: what's trending, what's converting, which attributes are gaining velocity, where the price band is moving. Decision-grade. Updated weekly.
What makes this different
Four layers of intelligence. Not a dashboard pretending to be a strategy.
Most retail analytics tools stop at the first layer. Product & Market Intelligence runs through all four (descriptive, conversational, predictive, recommendation) because that is how decisions actually get made.
Layer 01 · Descriptive
What is happening on the marketplaces, right now.
A live, executive-grade view of trending SKUs, converting attributes, price-band performance, and regional demand variation. Built for a CMO's or merchandising head's weekly review. Every chart answers a question, not just displays data.
Example output
Quilted wool-blend outerwear in the $148–$184 band is gaining 14% review velocity month-over-month on marketplace A; flat on marketplace B.Layer 02 · Conversational
Ask the data anything. In plain English.
A natural-language interface that returns both a visualization and a written explanation, including which data was used, which filters applied, and what time window was analyzed. The system declines questions it cannot answer rather than guessing.
Example query
"Which design attributes are driving the top-reviewed women's outerwear listings on marketplace A under $200 this cycle?"Layer 03 · Predictive
Where the market is going next.
Explainable predictions on which attributes are about to gain share, which price bands are tightening, and which sub-categories are likely to over- or under-index in the coming cycle. Every prediction is traceable back to specific evidence.
Example output
Linen-blend casual dresses in the $58 to $72 band are projected to outpace the broader category by 14% over the next 60 days.Layer 04 · Recommendation
Specific actions, ranked by impact.
Recommendations a senior merchandiser would take seriously, ranked by expected impact and confidence, traceable to the underlying evidence. Accept, modify, or dismiss; the system captures the feedback. Generic suggestions don't make it past this layer.
Example recommendation
Reallocate ~18% of the next outerwear buy from oversized puffers to quilted wool-blend cropped SKUs in the $148–$184 band. Evidence: review velocity, rating-volume growth, price-band conversion deltas.How it works
A three-phase approach that creates value for your business at every stage.
The architecture is deliberately decoupled. Use your own marketplace APIs, proprietary enterprise data, or start with what we provide.
01Data Stage
Aggregates marketplace listings, customer reviews, attribute data, price history, and regional signal. Designed to plug into your own marketplace APIs or licensed enterprise data sources when the program scales.
02Intelligence Stage
Processes the data into demand patterns, attribute performance, price-band movement, and predictive signals. This is where the four intelligence layers live and is descriptive through recommendation.
03Action Stage
Translates the intelligence into specific decisions: which SKUs to list, which attributes to back, which price points to test, which regions to push. Delivered to the team in the format their workflow already runs in.
What the business actually sees
Decisions, not dashboards. Outcomes the merchandising and growth teams can act on.
The output is built for the people who run the brand, not for analysts who consume reports. Every view leads back to a specific decision someone is supposed to make this week.
SKU and assortment decisions
Which SKUs to emphasize on which marketplace, which to phase out, and which gaps in the assortment matter most.
Attribute prioritization
Which colors, patterns, materials, and design features to back in the next production or procurement cycle, based on what is actually converting.
Price-band strategy
Which price points are viable for which segments, by sub-category and platform, and where the brand is leaving margin on the table or pricing itself out.
Regional demand reads
Where regional demand variation justifies targeted inventory placement, regional marketing spend, or differentiated assortment.
Marketplace positioning
How the brand's offering compares against category trends on each platform, and where each platform is over- or under-indexing for the brand's strengths.
Trend signal, ahead of the curve
Which attributes are gaining velocity, which are losing, early enough to react with production, marketing, or inventory shifts rather than after the cycle has turned.
Who this program is built for
Growth focused retail and eCommerce brands. Apparel-first, with beauty and home extension-ready.
This is not for everyone. Below is an honest read on whether Product & Market Intelligence is the right program for your stage.
This is built for you if:
- You are a US-based apparel, beauty, or home & lifestyle brand selling across marketplaces and your own channel.
- Your team is making weekly or monthly decisions about assortment, price, and which products to push, and those decisions are still mostly intuition or last-cycle data.
- You suspect the marketplace knows things about your category that your internal reporting doesn't surface in time.
- You're tired of dashboards that describe the past instead of changing the next decision.
This is probably not for you if:
- You are an early-stage brand. The decision frequency and data complexity don't justify the program yet.
- You sell only on your own DTC channel and don't compete in marketplaces. The outside-in view assumes you're playing where the signal lives.
- You want a tool you can install in a week. This is a decision intelligence program, not a SaaS subscription.
- You're looking for a generic "AI for retail" capability. This program is specifically built around product, market, and price decisions.
How an engagement runs
From conversation to decisions in your team's hands. Four steps, no surprises.
STEP 01
Discovery call
A 30-minute conversation with a senior team member. We confirm category, scope, and whether this is the right program before anything else.
STEP 02
Diagnostic & scope
A focused diagnostic on your category, marketplaces, and decision cadence. We agree on the decisions the program is meant to improve.
STEP 03
Build & deploy
We stand up the data foundation, the four intelligence layers, and the action surfaces your team will actually use. Architected to scale into your stack.
STEP 04
Run & expand
The program runs in the rhythm of your business, weekly or monthly. We extend into beauty and home & lifestyle, or into adjacent retail programs, when it earns the right.
FAQ
Questions, answered.
What buyers ask before booking a call about Product & Market Intelligence. Don't see yours? Talk to a senior team member.
01What is product and market intelligence in retail?+
Product and market intelligence is an outside-in decision capability that reads what is actually moving on the marketplaces where a brand sells (which SKUs, which attributes, which price bands, which regions etc.) and turns it into specific decisions on what to list, push, price, and produce next. It differs from internal reporting because it answers what should sell, where, and at what price, instead of what already sold.
02How is this different from a marketplace dashboard or retail analytics tool?+
Most marketplace dashboards stop at descriptive reporting. They tell you what happened. Product and market intelligence runs through four layers: descriptive, conversational, predictive, and recommendation. The output is a specific, evidence-backed action a senior merchandiser would take seriously, not a chart that needs to be interpreted by an analyst.
03Which retail categories does this program work for?+
The program is apparel-first, with beauty and home & lifestyle as extension-ready categories. The underlying capability (reading marketplace signal at attribute, price-band, and regional level) applies whenever those decisions matter. Innovatics typically works with US-based brands in the US$50M to $120M revenue band selling across marketplaces and their own channel.
04What does an engagement actually look like, and how long does it take?+
An engagement runs in four steps: a 30-minute discovery call, a focused diagnostic on category and decision cadence, a build-and-deploy phase that stands up the data foundation and four intelligence layers, and an ongoing run phase that operates in the brand's weekly or monthly rhythm. The architecture is decoupled, so the program can scale into the brand's stack rather than replace it.
We don't start with a contract
Start with the marketplace read. Build the system from there.
A 30-minute working session with a senior team member. We listen to the decisions your team is trying to make better, then write the proposal around those decisions — not around a template.
No commitment30 minutesSenior team member, not a BDR