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HR & RecruitingCompetitor & Market Research#compensation#benchmarking#HR

Comp Benchmark vs Competitors · Candidate Decision Lens

Bundle 3-5 competitor comp/benefits/culture into a side-by-side; show why our offer wins.

Pain addressed · Pain #2: HR knows it needs to look at competitor comp/benefits, but most of the time it's 'from memory' — no structured framework, no apples-to-apples.

Comp Benchmark vs Competitors · Candidate Decision Lens

Prompt·Advanced
Also available in 中文 →
You're an HRBP skilled at comp benchmarking. Build a candidate-decision-POV comparison against 3-5 competitors.

# Target role
{my_position}

# Competitor companies
{competitor_companies}

# Available data
{available_data}

## Axis 1: Cash
| Company | Base range | Bonus | Total cash (target) |
- Cite source + date
- Mark uncertain ones explicitly

## Axis 2: Long-term incentive
- Options / RSU / stock
- Vesting (4-yr vs 3-yr)
- Stage + realization odds

## Axis 3: Benefits
- Time off / health / flex / perks

## Axis 4: Soft (culture + growth)
Based on Glassdoor, LinkedIn signals, tech blog activity:
- Culture tags
- Tech maturity
- Growth path

## Axis 5: Hidden cost
- Commute / hours / stability / overtime culture

# Strategy output

## 1. Our "win" dimensions (2)
## 2. Our "lose" dimensions (1-2)
## 3. Candidate decision matrix
| Factor | Weight % | Our rank |
Total = 100%. Estimated win probability %.

## 4. Recs for hiring team + execs
- Adjust base/RSU? How much?
- What to emphasize?
- Need Tech Lead / CEO close?

# Rules
- No fabricated data — cite source + date
- Push hard on hidden costs
- Don't inflate our wins to please me
Saves ~120 min per use

Open directly on your favourite AI

One click copies the prompt and opens the platform in a new tab — just paste (⌘/Ctrl + V).

Workflow · 5 steps

  1. 1

    Spend the first hour collecting hard data from 3-5 competitors via Levels.fyi, Glassdoor, LinkedIn, candidate-relayed offers. Tag every datum with source + date.

  2. 2

    Fill the three placeholders and run the prompt.

  3. 3

    Axis 5 (hidden cost) is the one the model under-mines. Push back: 'What about RTO mandates, sabbatical policies, last layoff round, weekly average hours?'

  4. 4

    Re-check the decision matrix yourself — models tend to be over-confident on 'our rank'. Demand evidence.

  5. 5

    Send the recommendations block (with specific dollar deltas) to the hiring manager + exec — it's the comp-ratio adjustment ask.

Verify before you ship

AI's "almost right" is the #1 pain. Tick these off so reviewers can't find the obvious holes. (Checkbox state stays local — we don't track it.)

Done 0 / 5 checks

Why this prompt works

This prompt isn't a generic “write me a Competitor & Market Research”. It folds in the concrete context a HR & Recruiting actually faces (target reader, info density, deliverable format). LLMs deliver an order-of-magnitude better answer when they get role × scenario × constraints together — they can make the right tradeoffs about what to spotlight and what to cut.

When to use

Best moment: you know roughly that you need to do a Competitor & Market Research, but you're stuck on “how do I even ask the AI?” Muscle memory has no template. Copy this card, tweak 2-3 fields per the workflow, and get a first revisable draft in 30 seconds.

Common pitfalls

  • Send without tweaking placeholders · the draft reads as default-GPT and your reviewer spots it in 3 seconds.
  • Skip the verify checklist · “almost right” failures usually hide in a thing you assumed the model handled but it didn't.
  • Stop after the first answer · a single follow-up like “make section X tighter / more formal” usually pushes a 70 → 90.

Variants & extensions

Want to use this card more aggressively? Three extensions:

  • Swap languages: both EN/ZH prompts ship in the card — use the EN version when shipping to a Western audience.
  • Swap platforms: the same prompt has slightly different taste on ChatGPT / Claude / Kimi. The recommended-platform pill marks the best fit.
  • Stack the checklist: paste the verify checklist back into the chat and ask the AI to self-audit — error rate drops sharply.
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