Build a Custom Retail Analytics Dashboard Using AI in Minutes
Describe your Retail Analytics Dashboard in plain English. Emergent handles design, development, and deployment, giving you a live build in minutes. No code needed.
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Emergent Key Features for Building a Retail Analytics Dashboard
Footfall, conversion and availability measured together and normalised, so stores can be compared fairly.
Normalised Store Comparison
Sales are adjusted for floor area and trading hours, so a small format store is judged fairly against a flagship rather than simply looking worse.
Footfall to Conversion Linkage
Visitor counts are joined to transactions by hour, separating a traffic problem from a conversion problem, which need entirely different responses.
Category Mix Effects
Margin change is decomposed into mix and rate effects, showing whether profitability moved because of what sold or what it sold for.
Lost Sales Estimation
Unavailability is converted into estimated missed revenue, which turns an availability metric into a commercial priority.
Staffing against Footfall
Labour hours are compared with visitor patterns by hour, exposing the periods where the store is busiest and least covered.
Store Manager Views
Each manager sees their own store against the group from one build, so performance conversations start from a shared and fair comparison.
Retail Analytics Dashboard Use Cases You Can Build in Minutes
Compare Stores in a Way Managers Accept
Stores ranked on sales, sales per square metre, footfall, conversion, average basket and items per transaction, normalised for size and trading hours, each against the group median with movement since last period.
window.awbMockup = { storePerformanceComparison: "Build a retail analytics dashboard ranking stores on sales, sales per square metre, footfall, conversion rate, average basket and items per transaction, normalised for size and trading hours, showing each against the group median, movement in rank since last period, and top and bottom quartile grouping.", footfallAndConversion: "Build a retail footfall dashboard showing visitors by store, day and hour against transactions to give conversion rate, footfall trend against the prior year, conversion by day part, staffing levels against footfall by hour, and the hours where high footfall meets low conversion.", categoryPerformance: "Build a retail category dashboard showing sales, margin and units by category and subcategory across stores, category share of sales by store format and region, categories growing and declining fastest, margin mix effect on total profitability, and stores where a category underperforms its estate average.", lostSales: "Build a retail availability dashboard showing on shelf availability by store and category, lines out of stock with days unavailable, estimated lost sales from unavailability based on normal sell through, stores with the worst availability, and the relationship between availability and conversion rate."};
Separate a Traffic Problem from a Conversion One
Visitors by store, day and hour against transactions to give conversion rate, footfall against last year, conversion by day part, staffing against footfall by hour and the hours where high traffic meets low conversion.
window.awbMockup = { storePerformanceComparison: "Build a retail analytics dashboard ranking stores on sales, sales per square metre, footfall, conversion rate, average basket and items per transaction, normalised for size and trading hours, showing each against the group median, movement in rank since last period, and top and bottom quartile grouping.", footfallAndConversion: "Build a retail footfall dashboard showing visitors by store, day and hour against transactions to give conversion rate, footfall trend against the prior year, conversion by day part, staffing levels against footfall by hour, and the hours where high footfall meets low conversion.", categoryPerformance: "Build a retail category dashboard showing sales, margin and units by category and subcategory across stores, category share of sales by store format and region, categories growing and declining fastest, margin mix effect on total profitability, and stores where a category underperforms its estate average.", lostSales: "Build a retail availability dashboard showing on shelf availability by store and category, lines out of stock with days unavailable, estimated lost sales from unavailability based on normal sell through, stores with the worst availability, and the relationship between availability and conversion rate."};
See What Mix Is Doing to Your Margin
Sales, margin and units by category and subcategory across stores, category share by format and region, categories growing and declining fastest, mix effect on profitability and stores underperforming their estate average.
window.awbMockup = { storePerformanceComparison: "Build a retail analytics dashboard ranking stores on sales, sales per square metre, footfall, conversion rate, average basket and items per transaction, normalised for size and trading hours, showing each against the group median, movement in rank since last period, and top and bottom quartile grouping.", footfallAndConversion: "Build a retail footfall dashboard showing visitors by store, day and hour against transactions to give conversion rate, footfall trend against the prior year, conversion by day part, staffing levels against footfall by hour, and the hours where high footfall meets low conversion.", categoryPerformance: "Build a retail category dashboard showing sales, margin and units by category and subcategory across stores, category share of sales by store format and region, categories growing and declining fastest, margin mix effect on total profitability, and stores where a category underperforms its estate average.", lostSales: "Build a retail availability dashboard showing on shelf availability by store and category, lines out of stock with days unavailable, estimated lost sales from unavailability based on normal sell through, stores with the worst availability, and the relationship between availability and conversion rate."};
Put a Number on Empty Shelves
On shelf availability by store and category, lines out of stock with days unavailable, estimated lost sales from unavailability based on normal sell through and the relationship between availability and conversion.
Put a Number on Empty Shelves
On shelf availability by store and category, lines out of stock with days unavailable, estimated lost sales from unavailability based on normal sell through and the relationship between availability and conversion.
Build a custom retail analytics dashboard in 4 simple steps
Compare stores on footfall, conversion, basket and category performance, so you know which locations are trading well and which need attention.
Start with sales per square metre, footfall and conversion rate, average basket size and items per transaction, category mix and stock availability. Emergent builds around your store estate, so formats and regions are represented properly.
Link your point of sale, footfall counters, inventory and staffing data. Emergent normalises for store size and trading hours, so comparison between a flagship and a small format store is meaningful.
Ask to include a new store, regroup a category, change the normalisation basis or add a footfall source, and every comparison rebuilds. Estate changes keep the ranking fair.
Publish one link where each store manager sees their own performance against the group and head office sees the estate. Store conversations start from figures the manager already saw this morning.
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Why choose Emergent?
Most tools give you a demo you have to rebuild. Emergent gives you a product that is ready to run.
| Comparison | Other Tools |  |
|---|---|---|
| Built for | Demos and MVPs | Products you keep growing |
| What you get | Front end shell only | Full stack, wired end to end |
| Backend and database | Set it up yourself | Built and connected for you |
| Customization | Surface level styling | Deep workflow control |
| Integrations and APIs | Manual glue work | Connected from a prompt |
| Code ownership | Locked to the platform | Clean code you can export |
| Time to launch | Weeks of patching and setup | Live the same day |
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