A CPG brand in the natural and specialty channel had data coming in from their KeHE and UNFI distributors, Whole Foods, Sprouts, and Walmart — each in its own spreadsheet, each reviewed too late to act. We unified all of it into one live portal. Not one API credential, IT ticket, or integration project. Just the exports they already download every week.
Most brands think a cross-retailer view is something they'll get "later" — once they can afford a platform that plugs into every retailer's API. This brand didn't wait. Each of these channels already sends them a report every week or month. We built our own methods to read those exact files, so all five now live in one portal, side by side — without a single live connection to maintain.
Store-level POS, lost-door detection, OOS vs. demand root cause across 500+ doors.
Category rank against competitors, velocity, and distribution in the natural channel.
Store-level sell-through across every door — the same rigor as Walmart, a different banner.
Distributor sell-in and movement with fill-rate — what shipped vs. what moved.
Sell-in vs. sell-through, month-matched — so shipments are never mistaken for demand.
This is the part that surprises people. The well-known cross-retailer platforms require live API access to every retailer and distributor — credentials your team often can't get, provisioned through an IT project, and priced from $20,000 a year up. Lucerna took a different path: we built our own parsing engine for the exact files these systems already generate. You download the report exactly like you do today and drop it in. We handle the rest.
The brand had Walmart data access and weekly POS data from a standalone portal. What they didn't have was any structure around it. The raw export — rows of store-week-SKU combinations with no visualization and no way to ask follow-up questions — wasn't an insight. Insights happened quarterly, in spreadsheets, after the window to act had already closed.
They were counting ghost doors in their ACV number — stores in the distribution estate with zero lifetime sales — without knowing they existed.
Lost doors were invisible until a quarterly broker review revealed them — often months after the store had gone dark and recovery was no longer possible.
Without inventory data layered against POS, they couldn't tell the difference — and the wrong response to each is completely different. Call the 3PL or call the field rep?
A SKU could go dark in a store for 10 weeks before anyone noticed. The store kept selling other items — so nothing looked wrong at the store level.
Field rep time was allocated based on gut feel and relationships, not data. There was no prioritized action list — just raw signals buried in the weekly export.
When new stores activated, there was no way to track opening velocity or flag early underperformers before they became chronic problems.
Every channel gets its own purpose-built treatment — here's what that looks like for Walmart, their deepest and most store-dense channel. We process Walmart POS and inventory snapshot data into a layered analytics environment, with every panel designed around the specific questions their team needed to answer week over week. The period selector — L4 / L12 / L24 / L52 / YTD / individual weeks — controls every panel simultaneously.
Walmart POS sales, week-over-week delta, and active selling store count — surfaced in the first 10 seconds on the page, with directional indicators so the team answers the most important question before anything else loads.
A dual-axis chart pairing POS revenue against units sold across every week in the period. Clicking any bar opens a drill-down showing top performers alongside every no-sale store, root-cause-tagged, with a consecutive-silence counter.
When inventory snapshot files are present, a stacked bar chart shows OOS, thin-stock, and healthy stores per week. A KPI strip surfaces OOS rate, pipeline coverage %, and — most urgently — OOS combos with no recovery pipeline in flight.
The entire store universe segmented into Tier A (top 20%), B (middle 60%), and C (bottom 20%) by average weekly revenue. An insight line quantifies the concentration ratio: how much more an A store contributes versus a C store, per door per week.
Each SKU tracked individually, normalized by its own distribution footprint — not blended into a total that masks per-SKU dynamics. Color-coded week-by-week UxSxW table with a period-average column.
Which stores drive 80% of each SKU's cumulative volume over the trailing four weeks. An "All-SKU" tab surfaces stores in the Pareto-80 tier for every SKU simultaneously — the brand's most critical doors for broker prioritization.
Beyond the main dashboard, twelve purpose-built reports give the team and their broker the tools to diagnose and act on specific signals — each week, not each quarter.
Aggregates signals from every other report into a single prioritized list — Urgent, High, and Medium — tagged by signal type and store. The hub the broker opens before every call.
Stores that were active sellers but have gone completely silent, ranked by weeks dark. Shows last sale date and peak weekly velocity before the store went cold.
Every store in the distribution estate with zero lifetime sales — doors counted in ACV that have never actually moved a unit.
Contiguous out-of-stock periods with no pipeline coverage in flight — tracked by streak length, resolved vs. unresolved, and average recovery time.
Store-weeks where velocity collapsed 40–70% versus the prior week, each root-caused: OOS-Driven, Demand-Driven, or Unknown. Filters by cause type and minimum baseline to suppress noise.
Stores that used to carry a specific SKU and have gone silent on that item — while potentially still selling others. The SKU-level version of Lost Doors.
Stores that recorded their first-ever sale within a configurable lookback window. Shows opening velocity and weeks since activation — flags slow starters early.
Every store ranked by longest consecutive no-sale streak across all history. Separates a one-time blip from a chronic pattern of going dark.
Stores that have fallen in the bottom velocity percentile for a configurable number of consecutive weeks — not just stores that had a bad stretch.
Full store-by-store distribution picture for any SKU: active carriers, stores that have never carried it, and lapsed stores. Geographic density map included.
Enter any store number and see its complete sales and inventory history — every week it has ever appeared in an upload, with no date filter. Deep-linked from every panel.
Identifies stores with missing weeks between first and last upload date — so analyses are never run on incomplete histories without knowing it.
Beyond individual panels, the platform introduced a standard set of metrics that the brand, their broker, and their leadership now share. When everyone uses the same definitions, the right conversations happen faster.
The question was never whether the data existed. It was whether anyone could see it clearly enough to act on it.
The portal didn't just save time — it changed how the team and their broker operate. Store-level intelligence that used to take hours to assemble is now available the moment anyone opens a browser.
Request a demo and we'll walk you through the platform using your own data — whatever retailers and distributors you sell through today. If you can export it, we can build it. No slides, no sandbox, no API project. Just your actual numbers in Lucerna.
Whatever you sell through — Walmart, Sprouts, Whole Foods, KeHE, UNFI — if you receive the export, you have everything you need to go live in Lucerna this week. No API, no IT project, no expensive live-connection tier. Request a demo and we'll show you your own data inside the platform.
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