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

# Module 3 · Visualize & Go Deeper

> Charts, reports, and live dashboards as real products — publishing, filters, refresh, maintenance — get a full workshop: Dashboards & Reporting. (~30 min)

## 3.1 · Your first chart

```text Prompt theme={null}
Show me [metric] by month for the last 12 months as a chart.
```

<Check>
  **You'll see:** an interactive chart rendered in the thread — hover for values; it's saved with the conversation.
</Check>

<Frame caption="Charts render right in the conversation and stay saved with it.">
  <img src="https://textqllabs.github.io/workshops/self-service-analytics/assets/m3-first-chart.png" alt="A chart rendered in the thread" />
</Frame>

## 3.2 · Direct the visualization

```text Prompt theme={null}
Make that a stacked bar chart by [segment], add the trend as a line on top, and highlight the months where we declined.
```

<Check>
  **You'll see:** the chart rebuilt to spec. Other useful asks: "log scale", "sort descending", "show only top 8 and group the rest as Other", "add a target line at \[value]".
</Check>

## 3.3 · Segment — who's driving the number

```text Prompt theme={null}
Segment [metric] by [customer tier / region / plan / cohort]. Which segments are growing, which are shrinking, and which one explains most of the overall change?
```

<Check>
  **You'll see:** a segment-level view plus a narrative pointing at the segments that matter. This is the workhorse move of self-service analytics.
</Check>

## 3.4 · Trends and seasonality

```text Prompt theme={null}
Plot [metric] weekly for the past year. Is there a trend after accounting for seasonality? Anything unusual — spikes, drops, level shifts — I should know about?
```

<Check>
  **You'll see:** trend analysis with anomalies flagged and quantified, not just eyeballed.
</Check>

## 3.5 · Ask "why" — root-cause a change

```text Prompt theme={null}
[Metric] changed by [amount] in [period]. Decompose the change: how much came from each segment, and was it driven by volume, price/intensity, or mix? Rank the contributors.
```

<Check>
  **You'll see:** a contribution breakdown — the kind of analysis that used to take an analyst a spreadsheet afternoon — in one turn.
</Check>

## 3.6 · Compare and correlate

```text Prompt theme={null}
Is there a relationship between [metric A] and [metric B] across [customers / regions / weeks]? Show a scatter and tell me how strong the relationship is — and warn me about anything that would make it misleading.
```

<Check>
  **You'll see:** the relationship quantified, with honest caveats (correlation is not causation, outliers, confounders).
</Check>

## 3.7 · A quick forecast

```text Prompt theme={null}
Based on the trend, project [metric] for the next quarter. Show the range of uncertainty, and list the assumptions baked into the projection.
```

<Check>
  **You'll see:** a forward projection with confidence bands and explicit assumptions — clearly labeled as a projection.
</Check>

<Note>
  **Go deeper** —

  Charts, reports, and live dashboards as real products — publishing, filters, refresh, maintenance — get a full workshop: [**Dashboards & Reporting**](/workshops/dashboards-reporting/overview).
</Note>

## Troubleshooting

<table>
  <tr><th>Symptom</th><th>Fix</th></tr>
  <tr><td>Chart is too busy</td><td>"Show only the top 5, group the rest as Other"</td></tr>
  <tr><td>Wrong chart type for the story</td><td>Name the type: "make it a line chart" / "horizontal bars"</td></tr>
  <tr><td>Forecast seems overconfident</td><td>Ask: "what would have to be true for this to be wrong?"</td></tr>
</table>

### ✅ Checkpoint

* [ ] You produced and restyled a chart with plain-English direction
* [ ] You ran a segmentation and identified the driving segment
* [ ] You decomposed a metric change into contributors
* [ ] You got a forecast with uncertainty and assumptions stated