> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getthread.com/llms.txt
> Use this file to discover all available pages before exploring further.
# ROI Value Realization Dashboard
> Prove Thread's ROI with dollar savings, hours saved, FTE equivalent, ROI multiple, AI coverage, and triage deflection — year to date and month over month, by feature and board.
The ROI Value Realization dashboard is built for QBRs, renewal conversations, and any moment you need to show the return your customers are getting from Thread's AI. It combines the money-and-time side of ROI with the service-quality side, so you can show that faster and cheaper also meant better.
Open it from **Thread admin → General → Analytics**.
## What this dashboard answers
* How much **money and time** has AI saved us so far this year, and what is that worth in headcount?
* What's our **ROI multiple** — how many dollars saved per dollar spent on Thread?
* Is performance **improving month over month**, or flattening?
* Which **AI features** are driving the savings, and which moved backwards last month?
* Where do the numbers sit at the **board and feature** level when someone asks us to prove one?
## Filters and time windows
This dashboard is organized around fixed windows rather than a filter bar:
| Window | Where it's used |
| ----------------------- | ------------------------------------------------------ |
| Year to date | The top-level scorecard |
| Month over month | The trend and feature comparison sections |
| Month × board × feature | The Insights table, for drilling into a specific claim |
## What's on the dashboard
### Year-to-date summary
A single scorecard covering the year so far: total estimated dollar savings, hours saved, FTE equivalent capacity freed, monthly recurring revenue, ticket volume, AI coverage, triage deflection rate, ROI multiple, voice call volume, and Messenger deployments.
This is the section to screenshot for a QBR opening slide.
### Month-over-month comparison
The same headline measures trended across each month of the year — savings, hours saved, ticket volume, AI coverage, deflection rate, response times, sentiment, CSAT, and after-hours coverage — so you can see the direction of travel rather than a single snapshot.
### Month-over-month feature comparison
Breaks savings, session volume, and FTE-equivalent time saved down by individual AI feature — categorization, triage, prioritization, and so on — comparing the current month against the prior month and flagging whether each feature moved up or down.
Use this when overall savings look flat: it's usually one feature slipping while others hold.
### Insights
A detailed table that puts the ROI figures (dollar savings, hours saved) next to the service-quality figures (AI coverage, triage deflection, response times, sentiment, ticket volume) at the **month × board × feature** grain. This is where you go to answer "which board is that number coming from?"
## Metric definitions
| Metric | Definition |
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| Estimated dollar savings | Time saved by AI events, valued using a standard fully-loaded technician cost benchmark. Based on AI activity, not on logged time entries. |
| Hours saved | The time saved by AI events, before it's converted to a dollar figure. |
| FTE equivalent | Hours saved expressed as a number of full-time staff freed up. |
| ROI multiple | Dollars saved divided by monthly subscription cost — return per dollar spent on Thread. |
| Triage deflection rate | The share of support requests resolved without a technician touching them. |
| AI coverage | The share of tickets touched by any AI feature. |
| After-hours coverage | Service activity handled outside business hours. |
## Things to watch
**Dollar savings are an estimate, not an invoice.** They're derived from AI activity valued at a standard fully-loaded technician cost benchmark — not from your billing, and not from logged time entries. Present them as modeled savings.
* **FTE equivalent is capacity, not headcount removed.** It answers "how much technician time did we get back", which most MSPs redeploy rather than cut.
* **A flat savings line rarely means AI stopped working.** Check the feature comparison — savings usually shift between features before they fall overall.
* **Response-time and sentiment figures here are service-quality context**, deliberately sitting next to the savings so a QBR doesn't tell a cost story without a quality story.
* **Data refreshes daily**, so the current month is always partial. Compare complete months when you're trending.
* **Numbers should track your PSA** but can differ by roughly a day. See [*Things to know & gotchas*](/analytics/things-to-know-and-gotchas).
## Next steps
Turn these figures into a written ROI narrative.
The leadership view of what to measure and when.
## Related topics
- [Magic Analytics Topics You Can Explore](/analytics/topics-you-can-explore.md)
- [MSP Owner / Leadership](/start-here/roles/msp-owner-leadership.md)
- [Getting Started with Magic Analytics](/analytics/getting-started-with-magic-analytics.md)