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

# Pipeline analytics

> Conversion rates, time in each stage, deal velocity — see how your pipeline is actually performing.

The **Pipeline → Analytics** view answers the questions every operator wants:

* How many leads turn into bookings?
* Where in the pipeline do deals get stuck?
* How long does an average deal take, end to end?
* What's my forecasted revenue?

## What you'll find on the page

### Funnel chart

A visual showing how many deals are in each stage right now, plus how many moved through each in a chosen time period. Steep drop-offs between two stages tell you where your conversion bottleneck is.

### Conversion rates

For each stage transition (e.g., Lead → Quoted, Quoted → Booked), the percentage of deals that made it through in the chosen window. If only 30% of your quotes turn into bookings, that's a clear lever to pull (better quote follow-up, faster response time, sharper pricing).

### Time in each stage

Average dwell time per stage. Long average times signal slow points (e.g., quotes sitting too long → leads going cold).

### Stale deals

Deals that haven't moved in longer than your configured stale threshold. Click through to deal with them.

### Forecasted revenue

Based on:

* Active deals × their values × stage-specific conversion rates
* Expected close dates

A rolling 30/60/90-day projection. Use it as a directional read, not a guarantee — it depends entirely on your value estimates being honest.

### Source attribution

For deals closed in the period, where did the lead come from?

* **Widget** — chat conversations
* **Voice** — phone calls handled by the AI Receptionist
* **Form** — website forms
* **SMS** — texts to your business number
* **Manual** — added by hand
* **Reactivation** — re-engaged via a campaign

This lets you see which channels are actually paying off and where to invest.

## Time period selector

Top-right of the analytics page lets you scope the data to:

* Today
* This week (rolling 7d)
* This month (rolling 30d)
* This quarter (rolling 90d)
* All time

The metrics recalculate instantly.

## Filters

You can filter by:

* **Stage** — restrict to one column of the kanban
* **Tag** — only deals on contacts with a specific tag
* **Assignee** — only deals owned by a specific teammate
* **Source** — only deals that came from a specific channel

Useful for "how's my commercial pipeline doing?" or "what's Alex's close rate?"

## Limitations

* We don't (yet) track win/loss reasons in detail. Marking a deal **Lost** is just the binary signal.
* Forecasted revenue uses simple averages, not predictive models — fine for owner-operators, less precise at scale.
* Multi-deal contacts are counted per-deal (not per-contact) in the funnel, which is usually right but can mislead if you have one customer with many small deals vs. customers with single large deals.

## Reading the data

A few patterns to watch for:

* **Lead → Quoted \< 50%**: you're losing leads before quote stage. Probably means the AI Receptionist isn't qualifying well or you're slow to respond. Check **Knowledge Base** + auto-text settings.
* **Quoted → Booked \< 30%**: your quotes aren't converting. Could be pricing, follow-up cadence, or the kind of leads you're getting (low-intent leads from broad ads).
* **Long time in Booked**: your booked appointments aren't getting started. Maybe scheduling further out than ideal.
* **High Lost ratio in late stages**: you're spending time on deals that don't close. Better qualifying earlier would save the effort.

## Next

You're done with Pipeline. Next:

<Card title="Inbox" icon="inbox" href="/inbox/overview">
  Your unified communications view — everything's there.
</Card>