Library/Leadership, Coaching & People Management/Strategy, Planning & GTM Leadership/Benchmark Total Rewards for a Sales Role Family
TestedLeadership, Coaching & People ManagementQuick Win (under 10 min)
About this prompt
This prompt builds a total rewards benchmark for Sales Managers and Directors who need compensation data to retain and attract talent in specific role families and geographies. It covers salary bands, equity guidelines, variable pay structures, and benefits adjustments. Use it when planning comp reviews, responding to counteroffers, or designing a new sales compensation structure. Fill in the {{role_family}} and {{markets}} placeholders before running.Competitive compensation is a retention lever as much as a hiring tool, and this prompt gives Sales Directors and Managers the structured benchmark data to stay ahead of the market. It summarizes salary bands, equity guidelines, variable pay models, and benefits adjustments for a defined role family across specified geographies — useful for comp review cycles, counteroffers, or redesigning incentive structures. The two key inputs are {{role_family}} — such as 'enterprise AE' or 'SDR' — and {{markets}} — the geographies you're benchmarking against. This is a planning and analysis prompt for leaders, not a rep-facing tool; the output informs internal decisions about pay structure. Pair the results with your current team's comp data to identify gaps and prioritize where adjustments are most urgent.The prompt
As a Compensation Lead, benchmark total rewards for [ROLE FAMILY] across [MARKETS]. Summarize salary bands, equity guidelines, variable pay, and benefits adjustments.
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Variables to replace
ROLE FAMILY | MARKETS
How to run it
- Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot — it's tuned to run in any of them.
- Replace the variables — swap each bracketed token for your deal's specifics. The variables list above explains every field.
- Make it yours — run it, keep what's strong, and tighten your inputs on the next pass. The prompt improves with your context.
What good output looks like
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