# Inside a Scoop Investigation

Follow a headline result through the evidence to a useful management response

A Scoop investigation connects a measured change to a useful management question. It examines the components of the result, tests relevant explanations, and identifies what the manager can act on or needs to check.

This walkthrough uses the [fictional eight-store retail example](https://docs.scoopanalytics.com/docs/sample-performance-briefing). All figures and recommendations are illustrative. It shows one investigative path; each location's evidence and operating playbook determine the questions that matter.

## 1. Look beneath the district result

District sales grew **4.4%**. A1 sales fell **9.3%** and A2 sales fell **9.4%**. Growth elsewhere offsets both declines, so the district total is insufficient for planning the next visits.

The question becomes: **what accounts for each store's change?**

![District scatter marking similar sales declines and different transaction changes at A1 and A2](https://docs.scoopanalytics.com/assets/scoop-example-v1/A02-scatter.png)

*The scatter shows why the two stores deserve different follow-up questions. The current and comparator periods both cover August 1–15.*

## 2. Separate volume from average sale

Sales combine transaction count and average sale. At A1, transactions fell **10.0%**, while average sale grew **0.8%**. The transaction loss accounts for the sales decline. A broad claim that shoppers are spending less per transaction would not fit this result.

![A1 transaction and average-sale trends diverging over recent months](https://docs.scoopanalytics.com/assets/scoop-example-v1/A05-trend.png)

*The chart puts the current comparison in a longer history. Its title states the finding; the marked span points to recent evidence.*

The question becomes: **where did the store lose transactions?**

## 3. Find the concentration

The category breakdown identifies Home. Its transactions fell from **493 to 355**, and sales fell from **$22,185 to $15,975**. Average sale within Home remained **$45**. The other categories grew about 2%.

![Home marked as the declining category while other category sales grow](https://docs.scoopanalytics.com/assets/scoop-example-v1/A06-category.png)

*Home's 28.0% decline is materially different from the rest of the store.*

| A1 evidence | What it establishes | What it leaves open |
|---|---|---|
| Store transactions −10.0%; average sale +0.8% | Fewer transactions explain the store sales decline | Why transactions were lost |
| Home transactions −28.0%; average sale unchanged | The loss is concentrated in Home volume | Availability, traffic, conversion, or another local condition |
| Other categories grew about 2% | The weakness is not uniform across categories | Whether the affected category faced different conditions |

## 4. Test the explanation before choosing the response

The data weakens the explanation that Home sales fell because each Home transaction became smaller: average sale stayed at $45. It supports a volume finding.

It does not separate fewer visits from lower conversion or unavailable products. The briefing therefore directs a concrete check: inspect Home availability and presentation, then review recent customer requests with the team. That operating evidence determines the appropriate response under company policy.

```mermaid
flowchart TD
    accTitle: A1 investigation from result to visit question
    accDescr: A1 sales declined as transactions fell while average sale held up. The loss was concentrated in Home, where average sale was unchanged. The supported finding is Home volume loss. The visit checks availability and customer requests before selecting an operating response.
    result["A1 sales declined"] --> components["Transactions fell; average sale held up"]
    components --> category["Loss concentrated in Home"]
    category --> check["Home average sale unchanged"]
    check --> finding["Supported finding: Home volume loss"]
    finding --> visit["Check availability and customer requests"]
    visit --> decision["Agree the operating response"]
```

Your [operating playbook](https://docs.scoopanalytics.com/docs/your-operating-playbook) shapes these questions. It establishes what the measures mean, which comparisons are useful, which exceptions matter, and which checks and actions fit the manager's responsibility.

## 5. Connect the finding to an action

The recommendation is to find what is limiting Home transactions. It carries the rationale, concrete steps, responsible role, and review horizon. The goal in this example is to agree a response and a measure to review in the next comparable period.

![Action card for investigating Home volume, including manager response controls](https://docs.scoopanalytics.com/assets/scoop-example-v1/A08-action.png)

*An action can resolve an open question before committing to a corrective measure. Its goal is a proposal, not an achieved result.*

The manager may know something the data does not contain. Recording that context when accepting with changes, deferring, rejecting, or giving feedback helps the Scoop team review the recommendation. [Working with Action Plans](https://docs.scoopanalytics.com/docs/working-with-action-plans) explains the controls.

## 6. Follow the different path at A2

A2's sales declined **9.4%**, close to A1's result. But transactions fell only **2.1%**, while average sale fell **7.5%**. The next question concerns the value and mix of those transactions.

Outdoor's share of A2 sales fell from **39.3% to 30.3%**, while Everyday gained share. Category prices and margins are unchanged in the illustrative data. A larger share of lower-value, lower-margin Everyday activity reduces both average sale and overall gross margin.

The store's gross margin moved from **44.0% to 42.1%**, a decline of about **1.9 percentage points**. That is not a 1.9% relative decline, and the aggregate change is not evidence that category pricing deteriorated.

The question for A2 is why Outdoor contributed less and the mix shifted. Its manager should review recent demand and availability in those categories. The same headline sales decline has led to a different investigation and a different visit.

## What carries into the next cycle

Supported findings, manager responses, and remaining questions provide context for the next review. Where prior items and verified progress data are available, later briefings can report progress. This example contains one reporting cycle, so it makes no claim about later action completion or recovery.

A useful investigation leaves the manager with a clear account of what changed, the evidence for it, and the next decision. See the [complete briefing](https://docs.scoopanalytics.com/docs/sample-performance-briefing) or compare that recurring work with [BI and AI analytics tools](https://docs.scoopanalytics.com/docs/scoop-and-business-intelligence).