> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wayak.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Store benchmarking

> Compare locations against each other on sales, conversion, and inventory turns with scheduled reports

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.

***

## What you need

<CardGroup cols={2}>
  <Card title="Data sources" icon="database">
    * **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
  </Card>

  <Card title="Knowledge spaces" icon="book-open">
    * **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
  </Card>
</CardGroup>

**Semantic layer:** Define these in your ontology before setting up the agent.

| Component | Name                   | Definition                                                                                        |
| --------- | ---------------------- | ------------------------------------------------------------------------------------------------- |
| Object    | `Store`                | Maps to the store master table. Represents a physical retail location with its profile attributes |
| Metric    | `RevenuePerSquareFoot` | Total revenue divided by selling square footage over the period                                   |
| Metric    | `InventoryTurns`       | Cost of goods sold divided by average inventory value, annualized                                 |
| Metric    | `ConversionRate`       | Number of transactions divided by foot traffic count, expressed as a percentage                   |
| Dimension | `StoreRegion`          | Geographic categorization: Northeast, Southeast, Midwest, West, International                     |
| Dimension | `StoreFormat`          | Format categorization: flagship, standard, outlet, pop-up                                         |

<Tip>
  See [building a semantic layer](/quickstart/build-semantic-layer) for a step-by-step guide.
</Tip>

***

## Agent setup

<Steps>
  <Step title="Create the agent">
    Go to **Agent Space** → **New agent**.

    | Field    | Value                                                                                     |
    | -------- | ----------------------------------------------------------------------------------------- |
    | **Name** | Store Performance Analyst                                                                 |
    | **Role** | Multi-location retail operations analyst                                                  |
    | **Goal** | Identify performance gaps between stores and surface actionable improvement opportunities |
  </Step>

  <Step title="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.
  </Step>

  <Step title="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
  </Step>

  <Step title="Add skills">
    <AccordionGroup>
      <Accordion title="Benchmark stores by KPI">
        **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).
      </Accordion>

      <Accordion title="Deep-dive store analysis">
        **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.
      </Accordion>
    </AccordionGroup>
  </Step>
</Steps>

***

## Automation

### Playbook: Monthly store benchmarking report

<Steps>
  <Step title="Set the trigger">
    Schedule: **First Monday of each month at 8:00 AM**.
  </Step>

  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="Test and activate">
    Click **Run now** to test with live data, then toggle to **Active**.
  </Step>
</Steps>

***

## What's next

<CardGroup cols={2}>
  <Card title="Restock recommendations" icon="arrow-right" href="/use-cases/retail/restock-recommendations">
    Once you identify which stores need better inventory allocation, automate the restock process.
  </Card>

  <Card title="All retail use cases" icon="list" href="/use-cases/retail">
    See the full list.
  </Card>
</CardGroup>