Alright, I've been looking at Read AI for the sales team. The promise is solid: turn meetings into actionable data. But if you're a sales manager trying to use this for rep reviews, the out-of-the-box "Team" dashboard is noise. You need segmentation, and you need it clean.
The core problem is that Read AI's default view aggregates everything. You can't just filter by participant and call it a day. The real need is to isolate metrics *per rep* across *their specific deal calls* and compare that to their 1:1s with you. Here's how I'd structure it.
First, you **must** tag your meetings with a consistent naming convention in your calendar. This is your source of truth for segmentation.
```
// Example calendar event titles
[Deal: Acme Corp] Q3 Review - Alice
[1:1] Weekly - Alice & Manager
[Internal] Team Sync - Product
```
Then, in Read AI, you use the filtering on the "Meetings" page. But that's manual. For a manager review, you need a dashboard. Since Read AI doesn't offer custom dashboards per role yet, you have two practical paths:
* **Export & Pivot:** Weekly, export the meeting data (CSV). Use a spreadsheet to pivot by rep (derived from the meeting title tag) and meeting type. Key metrics to pull: `talk_time_ratio`, `action_items_generated`, `sentiment_trend` (for deal calls). Compare the rep's deal-call metrics to their 1:1 metrics.
* **Leverage the "Highlights":** Use the "Highlights" feature, but create a tag for each rep. Filter the highlights feed by that tag before the review. This gives you talking points beyond raw numbers.
The metrics that actually matter for a rep review:
* **Talk/Listen Ratio on Deal Calls:** Are they dominating or actually listening to client needs?
* **Action Item Density:** How many concrete next steps come from their client calls versus internal meetings?
* **Sentiment Trend Across a Deal Series:** Are conversations with a specific client improving or deteriorating?
Avoid alert fatigue by not tracking everything. Focus on 2-3 metrics per rep, per quarter. If you try to monitor every stat Read AI provides, you'll drown in data and miss the signal.
--monitor
alert only when it matters
Hey OP, I'm a RevOps lead at a mid-market SaaS company running a team of 12 AEs. I handle our GTM analytics, including sales performance, and have been using Read AI in production for about five months, integrated with Salesforce and Google Calendar.
Here's how I'd break down using Read AI for rep-level segmentation, since that's exactly what I needed.
* **Segmentation Effort:** It's almost entirely manual post-processing. The core data (speaking time, questions asked, sentiment) is solid, but linking it to a specific rep and meeting type requires the CSV export. I spend 30-45 minutes weekly in Google Sheets using `FILTER` and `QUERY` functions to pivot the data by rep.
* **Cost and Scale:** At our tier, it runs about $18/user/month billed annually. The hidden cost is my time for segmentation. It works for my 12 reps, but I wouldn't want to scale this manual process past 20 without a dedicated analyst.
* **Where It Clearly Wins:** The accuracy of transcription and the "questions asked" metric are gold for coaching. Isolating a rep's talk vs. listen ratio on a specific deal call provides a concrete coaching point you can't get from CRM data.
* **Honest Limitation:** The dashboard is a black box. You can't build a custom view that automatically filters to "[Deal: *]" meetings for "Alice." You're stuck with the "Team" overview or manual filtering on the Meetings page. There's no API (yet) to pipe these rep-specific metrics into a BI tool like Tableau.
My pick is to use Read AI for the data collection, but you'll need to own the dashboard creation externally. I use the CSV export into a templated Sheets report that automatically segments by rep name and meeting type. This works for our weekly manager syncs. If you need this to be real-time or fully automated without manual exports, Read AI isn't there. Tell us your team size and whether you have an analyst who can own that weekly export-and-pivot process.
Your point about the calendar naming convention being the source of truth is critical, and something a lot of teams miss early on. It's the only reliable way to auto-segment if you're doing exports.
The manual export and pivot method you outlined is exactly what we landed on too. One caveat I'd add is that you need to lock down that naming convention as a team policy. If even one rep uses "[Deal Acme]" instead of "[Deal: Acme Corp]", your filters break and your weekly data prep becomes a cleanup job. It's worth a monthly audit of the calendar event titles to keep the data clean for segmentation.
mod hat on