Case study · Retail analytics

Turning 500 stores, 3 products, and 8 weeks of data into one clear picture.

A CPG brand selling across thousands of Walmart locations had no reliable way to see how their products were actually performing at the store level. We built them a live intelligence dashboard that changed how they make decisions.

Client
CPG brand, anonymous
Retailer
Walmart in-store
Service
Dashboard & analytics
Scale
514 store locations
The problem

Three blind spots holding the business back.

The brand had sales data — it just couldn't use it. Weekly reporting was a manual exercise that consumed hours, store-level performance was invisible, and underperforming locations went undetected until it was too late to act.

01

No store-level visibility

Aggregate numbers looked fine on paper. But individual store performance — which locations were struggling, which were thriving — was completely opaque.

02

Reporting took hours

Every week, someone on the team manually pulled, cleaned, and assembled sales data into a spreadsheet. Hours of work for a report that was already out of date the moment it was finished.

03

Underperformers went undetected

Without a systematic way to flag problem locations, poor-performing stores stayed poor. There was no early warning — just a quarterly surprise when the numbers finally rolled up.

The solution

A live intelligence layer built around their data.

We designed and built a custom sales intelligence dashboard pulling directly from their Walmart POS data. Not a generic BI tool configured to fit — a purpose-built system designed around exactly how this brand's team thinks and works.

Store-level performance tracking

Every location ranked, scored, and surfaced — with drill-down into individual store trends over time.

Live data, always current

No more manual pulls. The dashboard updates automatically — the team opens it and the numbers are already there.

Underperformer flagging

Stores falling below velocity thresholds are automatically surfaced — no hunting, no surprises.

KPI command center

Total units, revenue velocity, week-over-week trends, and top performers — all above the fold, at a glance.

Filterable store explorer

Search, sort, and filter across all locations by state, region, or performance tier in seconds.

Velocity trend analysis

Week-by-week sales velocity charted by product and location — so the team can spot momentum shifts early.

The question was never whether the data existed. It was whether anyone could see it clearly enough to act on it.

The outcome

Decisions in minutes, not days.

The dashboard didn't just save time — it changed how the team operates. Store-level intelligence that used to take a dedicated effort to assemble is now available to anyone on the team, instantly.

Weekly reporting eliminated. What previously took hours of manual work now happens automatically — the team opens the dashboard and the data is already there.
Underperforming stores now visible within days, not quarters. The flagging system surfaces struggling locations early — giving the team time to respond before a problem becomes a loss.
Store-level conversations with Walmart buyers, backed by data. The team can now walk into buyer meetings with specific, accurate store-level performance data — a meaningful shift in credibility.
A single source of truth across 514 locations. Everyone on the team — sales, ops, leadership — works from the same live data. No more competing spreadsheets.
Velocity trends identified before they show up in quarterly reports. The week-over-week charting gives the brand an early signal on what's gaining or losing momentum — weeks ahead of traditional reporting cycles.
Live demo

See the dashboard in action.

The following is the actual dashboard built for this engagement, with anonymized data. Interact with it directly — filter stores, explore the charts, drill into performance tiers.

lucernainsights.com · retail analytics demo
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Selling in retail? Let's talk.

If you're a brand selling in Walmart, Target, or any major retailer and you're still piecing together store performance from spreadsheets — we've solved this problem before and we can solve it for you.

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