Skip to content

Performance

GTM performance reviews, grounded in HubSpot

Anyone can pull quota attainment. Claude pulls the deals behind it: cycle length, expansion versus new customers, and forecast changes. The review is about how the number happened, not just that it did.

Start free trial

14-day free trial · No card required

Taito.ai assembles Hannah Reid's Q2 performance review, grounded in 7 closed deals and 42 activities and pulling source signal from Taito.ai and HubSpot, then drafting the reasoning: Hannah reached 118% of quota in Q2, sourced $1.4M in new pipeline, and led the Northwind expansion while winning three new customers. Forecast accuracy has held within 5% since April, with growth opportunity in shortening mid-market discovery… It follows up with a suggested coaching conversation agenda: Start with the Northwind expansion and what made it succeed. Move to the two mid-market deals that were delayed by a quarter and where discovery took longer, then agree on a Q3 target for cycle length.

How it works

What explains the number

Set the cycle up once. Claude reads the deals behind the attainment number: cycle length, expansion, and forecast changes. It drafts the review in Taito.ai performance reviews.

The number, and how it happened

Cycle length, expansion mix, and forecast changes are shown beside attainment, not hidden behind it.

Territory separated from performance

Same window, same metrics, every salesperson. Each one is benchmarked against the team median.

Coaching, not counting results

You spend the review time on the two deals that went wrong, not on assembling a spreadsheet.

Prompt

In Taito.ai, set up the Q2 GTM performance review cycle.

Participants:
  – Everyone in the GTM job families: AE, SDR/BDR, CSM, Sales Engineering
  – Include GTM leadership (managers reviewed by their leaders)

Questions each manager answers per direct report:
  1. Number delivery — quota attainment, pipeline sourced, deals closed
  2. Deal quality — expansion vs. new customers, retention, forecast accuracy
  3. Activity and craft — outbound volume, discovery quality, demo skill
  4. Collaboration — how they supported their sales team, marketing, and customer success
  5. Growth — what to invest in next quarter

Schedule:
  – Opens July 1, closes July 15
  – Calibration meetings scheduled for July 17–19
  – Reminders at T-7, T-3, T-1 for anyone with open answers
  • Claude
  • Taito.ai MCP
  • HubSpot MCP

Frequently asked questions

Which HubSpot data does it read?
Deals, pipeline, activity, and forecast history for the review window — the same records the manager could open themselves, pulled with their own permissions.
Does this just rank people by quota?
Attainment is one input. The draft also covers deal quality, cycle length, expansion versus new customers, and forecast accuracy against the team median, so territory luck is easier to separate from performance.
Can GTM and engineering cycles run side by side?
Yes. Both are review cycles in Taito.ai and only the evidence source differs — HubSpot here, Linear for engineering reviews.

Built to work together

Keep exploring

More workflows on the Taito.ai MCP server, or browse all MCP use cases.

  • Engineering performance reviews, grounded in Linear
    Performance

    Engineering performance reviews, grounded in Linear

    Every claim in the draft links to a real issue. Delivered work, PR reviews, and cycle history are pulled live from Linear.

    • Claude
    • Linear
  • Ask HR anything, in Slack
    Employee experience

    Ask HR anything, in Slack

    Time-off balances, policy answers, headcount lookups — answered from live people data in a DM, scoped to the asker.

    • Slack
    • Claude
  • Team availability, posted to Slack every Monday
    Employee experience

    Team availability, posted to Slack every Monday

    Every Monday the Taito.ai app posts approved leave, work-from-home days, and public holidays to #general, with coverage gaps flagged.

    • Slack