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

# Measure Retail Execution Impact

> Separate field activity from commercial results, build a fair comparison, and join FieldKo data to sales data to estimate return on investment (ROI).

This page is for sales leaders, finance partners, and managers who need to show what the field program achieves. The outcome is an impact estimate you can defend: it separates what the team did from what changed on the shelf and in sales, uses a fair comparison, and counts the full cost.

> [!NOTE]
>
> **What FieldKo does today:** FieldKo records what field teams did and saw, as Salesforce records. Sales, margin, inventory, and cost data come from other systems. Estimating commercial impact or return on investment (ROI) always needs that external data.

## What Good Looks Like

* Results are reported on a ladder, from effort to net contribution, and each step is labeled.
* Commercial claims use a comparison group, not only before and after.
* Costs include field time, travel, materials, management time, and rework.
* Operational gains, such as better availability, are reported as operational gains, not dressed up as revenue.
* Assumptions and limitations are written down next to the result.

## When to Use It and Common Failure Patterns

Use this page for a pilot review, a budget case, or a quarterly business review where someone asks what the field program is worth.

Common failure patterns:

* **More visits called ROI.** Activity goes up, and it's reported as value.
* **Before and after only.** A seasonal lift is credited to the field team.
* **Reverse cause.** A promotion that sells well also draws more visits, so visits look like they caused the sales.
* **Missing costs.** Only field wages are counted.
* **Changed definitions.** A survey or scoring change makes the "after" period look better.

## Operating Procedure

### Report on a Measurement Ladder

| Level | What It Shows | Example Measures |
| - | - | - |
| 1. Capacity used | Effort spent | Visits, field hours, travel, campaign spend |
| 2. Work performed | What the team did | Tasks completed, displays built, ranging checks |
| 3. Execution result | What changed in store | Observed on-shelf availability (OSA), campaign compliance, verified fixes |
| 4. Commercial effect | What changed in sales | Incremental units, margin, distribution, reduced waste |
| 5. Net contribution | What it was worth | Incremental gross profit plus avoided costs, minus program cost |

Each level is context for the next, but doesn't prove it.

### Choose a Fair Comparison

1. Pick a group of stores that gets the change and a similar group that doesn't, over the same period.
2. Match or adjust for past sales, store format, season, promotions, price, stock, and local demand.
3. If you can assign stores to the groups at random, do.
4. If you can't, write down the assumptions behind the comparison.

### Count the Costs

Include field labor and travel, materials, management time, services, and rework caused by the change. Keep fixed and variable costs separate, so you can see what scaling would cost.

### Calculate and Document

* Incremental units: the difference against the comparison group, not before versus after.
* Incremental contribution: incremental units × contribution margin per unit.
* Net contribution: incremental contribution − incremental cost.
* ROI: net contribution ÷ incremental cost, when the cost is positive.

Record who estimated each number, what costs you included, and what else could explain the difference.

## How to Implement It in FieldKo

| Ladder Level | FieldKo Data | Availability |
| - | - | - |
| Capacity used | Visits, **Actual Time Spent (Mins)**, and Attendance **Actual Time Worked** | Built-in (Attendance needs the FieldKo Native package) |
| Work performed | Task status, survey answers, field campaign **Total Visits** and **Percentage of Visits Completed** | Built-in |
| Execution result | Core ranging OSA, survey-based compliance | Built-in data; compliance metrics are configured with reports |
| Commercial effect | Sales, margin, inventory | External data |
| Net contribution | Costs and sales | External data |

To set up a comparison and join the data:

1. **Define the groups.** Save the test stores and the comparison stores as account sets, and run the change as a field campaign on the test set. See [Run Field Campaigns](https://support.fieldko.com/managers/field-campaigns.md).
2. **Report the FieldKo side.** Build reports for levels 1 to 3 by account, using [Retail Execution Metrics](https://support.fieldko.com/managers/platform/retail-metrics.md) and [Trend Reports Over Time](https://support.fieldko.com/managers/platform/trends.md).
3. **Bring in sales data.** Either load sales and margin data into your org as your own object linked to the account, or export FieldKo data to the tool that holds sales data. Join on a stable store key, such as your enterprise resource planning (ERP) account number. See [Export FieldKo Data and Photos](https://support.fieldko.com/admin/export-data.md) and [Integrate FieldKo with Other Systems](https://support.fieldko.com/platform/integrations/integrate.md).
4. **Freeze the definitions.** Don't change survey questions or scoring during the measurement period. See [Keep Questions Consistent](https://support.fieldko.com/platform/customize/design-surveys-for-reporting.md#keep-questions-consistent).

## Fictional Example

Fictional example: a made-up juice brand, Coral Bay Juices, adds a weekly display check in 30 test stores for 8 weeks. Thirty matched stores keep the normal call plan.

| | Test Stores | Comparison Stores |
| - | - | - |
| Units sold, previous 8 weeks | 10,000 | 10,000 |
| Units sold, test 8 weeks | 12,400 | 11,300 |
| Change | +24% | +13% |

Without the change, the test stores would be expected to grow like the comparison stores: 10,000 × 1.13 = 11,300 units. Incremental units are 12,400 − 11,300 = 1,100. At a contribution margin of \$2.50 per unit, incremental contribution is \$2,750. Incremental cost is 60 extra field hours at \$30 (\$1,800) plus \$400 of display materials, so \$2,200. Net contribution is \$2,750 − \$2,200 = \$550, and ROI is \$550 ÷ \$2,200 = 25%. This is a teaching example, not a benchmark.

## What to Measure

| Metric | Definition | Population | Denominator | Exclusions | Owner |
| - | - | - | - | - | - |
| Incremental units | Test-group units − expected units from the comparison group's change | Test stores over the test period | Not applicable | Stores that closed, changed format, or lost the listing | Commercial analyst |
| Execution result change | Change in observed OSA or compliance, test versus comparison | Test and comparison stores | Product checks or stores evaluated | Products not ranged at the store | Retail execution lead |
| Incremental cost | Field labor, travel, materials, management time, and rework caused by the change | The test program | Not applicable | Costs that would happen anyway | Finance partner |
| ROI | Net contribution ÷ incremental cost | The test program | Incremental cost | Report only when cost is positive | Finance partner |

## What to Do When It Goes Wrong

| Symptom | Likely Cause | What to Do |
| - | - | - |
| Both groups grew by the same amount | No measurable effect, or the change reached both groups | Report the execution result, and don't claim a commercial effect |
| The comparison group behaved oddly | A promotion, stock problem, or retailer event hit one group | Note it, and adjust or rerun the comparison |
| Execution improved but sales didn't | Sales lag, or other factors dominate | Report the operational gain on its own, and measure again later |
| Store data and sales data don't join | Different store identifiers | Agree one store key, and fix the mapping. See [Keep Data Clean](https://support.fieldko.com/platform/data/data-quality.md). |
| Results look better after a survey change | The definition changed, not the shelf | Restate both periods on the same definition |

## Checklist

* [ ] Write the business problem, the change, and the primary outcome.
* [ ] Choose test and comparison stores, and save them as account sets.
* [ ] List possible confounders: season, stock, price, promotions, retailer events.
* [ ] Freeze survey and metric definitions for the period.
* [ ] Agree where sales, margin, and cost data come from, and the store key to join on.
* [ ] Calculate each ladder level, and document the assumptions.

## Industry Best-Practice Context

FieldKo's guide [How to Measure Retail Execution Impact Without Fooling Yourself](https://fieldko.com/best-practices/retail-execution-roi/) explains the measurement ladder and fair comparisons. To plan your own evaluation, use the [Outcomes and ROI Worksheet](https://fieldko.com/best-practices/toolkits/retail-execution-roi-worksheet/).

## See Also

* [Measure What Matters](https://support.fieldko.com/retail-execution/measure.md)
* [Retail Execution Metrics](https://support.fieldko.com/managers/platform/retail-metrics.md)
* [Run a 90-Day Retail Execution Pilot](https://support.fieldko.com/retail-execution/90-day-pilot.md)
* [Trend Reports Over Time](https://support.fieldko.com/managers/platform/trends.md)


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