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Multi-location retailers struggle to understand why some stores outperform others. The data exists — POS, traffic counters, labor scheduling, inventory — but it lives in different systems and nobody has time to compile a cross-location comparison every week. Performance gaps go unnoticed until they show up in quarterly results. Wayak connects your multi-location POS and inventory data, defines consistent benchmarking metrics in the semantic layer, and delivers scheduled reports that rank every store on the KPIs that matter. Your operations team gets a clear picture of who is leading, who is trailing, and where to focus improvement efforts.

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What you need

Data sources

  • POS database — Transaction data with store ID, revenue, transaction count, units sold, and date
  • Inventory management system — Stock levels, receipts, and inventory turns by store
  • Labor scheduling system — Scheduled hours, actual hours, and labor cost by store and date

Knowledge spaces

  • Store profiles — Store size (square footage), format (flagship, standard, outlet), region, and opening date
  • Operational standards — Target KPIs by store format, acceptable variance ranges, and improvement playbooks
Semantic layer: Define these in your ontology before setting up the agent.
ComponentNameDefinition
ObjectStoreMaps to the store master table. Represents a physical retail location with its profile attributes
MetricRevenuePerSquareFootTotal revenue divided by selling square footage over the period
MetricInventoryTurnsCost of goods sold divided by average inventory value, annualized
MetricConversionRateNumber of transactions divided by foot traffic count, expressed as a percentage
DimensionStoreRegionGeographic categorization: Northeast, Southeast, Midwest, West, International
DimensionStoreFormatFormat categorization: flagship, standard, outlet, pop-up
See building a semantic layer for a step-by-step guide.

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Agent setup

1

Create the agent

Go to Agent Space → New agent.
FieldValue
NameStore Performance Analyst
RoleMulti-location retail operations analyst
GoalIdentify performance gaps between stores and surface actionable improvement opportunities
2

Set the description

You analyze and compare store performance across locations. Always normalize comparisons by store format and size — do not compare a flagship to an outlet without adjusting. Present rankings with context: show the metric, the store’s value, the peer average, and the variance. Use a direct, data-driven tone. When identifying underperformers, suggest specific operational levers (staffing, inventory allocation, layout changes) based on which metrics are lagging.
3

Scope data access

Grant access to:
  • POS database (transactions by store)
  • Inventory management system (stock levels, turns by store)
  • Labor scheduling system (hours, costs by store)
  • Store profiles knowledge space
  • Operational standards knowledge space
  • Store object, RevenuePerSquareFoot, InventoryTurns, ConversionRate metrics
4

Add skills

Trigger: User asks for a store comparison or weekly operations review
  1. Pull revenue, transaction count, inventory turns, and labor hours for all stores over the requested period.
  2. Calculate key metrics: revenue per square foot, conversion rate, inventory turns, and revenue per labor hour.
  3. Group stores by format (flagship, standard, outlet) to ensure fair comparisons.
  4. Rank stores within each format group by each metric.
  5. Identify the top 3 and bottom 3 stores in each format group with their variance from the peer average.
  6. For bottom performers, cross-reference which metrics are lagging to identify the likely root cause (traffic, conversion, basket size, or operational efficiency).
Trigger: User asks about a specific store’s performance
  1. Pull all available metrics for the requested store over the past 12 weeks.
  2. Calculate week-over-week trends for revenue, conversion, inventory turns, and labor productivity.
  3. Compare each metric against the store’s format peer group average.
  4. Identify the single biggest performance gap relative to peers.
  5. Search operational standards for recommended improvement actions tied to that gap.
  6. Present a trend chart and a prioritized list of actions with estimated impact.

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Automation

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Playbook: Monthly store benchmarking report

1

Set the trigger

Schedule: First Monday of each month at 8:00 AM.
2

Build the workflow

  1. Query the POS, inventory, and labor databases for the previous calendar month across all stores.
  2. Query store profiles for format, region, and square footage data.
  3. Loop through each store:
    • Calculate revenue per square foot, inventory turns, conversion rate, and revenue per labor hour.
    • Compare against the format peer group average and the same month last year.
  4. Condition: If any store falls more than 15% below its peer group average on two or more metrics, flag it as “needs attention.”
  5. Aggregate into a formatted report with store rankings by format, trend comparisons, and a flagged-stores section.
3

Configure delivery

Send an email to the VP of Retail Operations and regional managers with the subject line: “Monthly store benchmarking — [Month Year]”. Include a dashboard-style summary and a detailed appendix with per-store metric tables.
4

Test and activate

Click Run now to test with live data, then toggle to Active.

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What’s next

Restock recommendations

Once you identify which stores need better inventory allocation, automate the restock process.

All retail use cases

See the full list.