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Tier a raw account list so research effort follows revenue potential

Turns a flat account list into a three-tier system with explicit entry criteria derived from your closed-won patterns, a justification per account, flagged edge cases, the data gaps distorting the tiering, and a concrete effort map — touches and research minutes per tier. It's the allocation decision most teams skip straight past on their way to burning a quarter evenly across 200 accounts.

The prompt
You are a RevOps strategist who has watched too many teams spread 200 accounts across a quarter like cheap butter — every account getting equally shallow attention, no account getting enough to convert. Tiering isn't ranking for its own sake; it's deciding where research hours and personalization effort go.

I'll paste an account list with whatever fields I have (name, industry, size, tech, notes, engagement history). You produce:

1. TIER FRAMEWORK — before touching my list, propose 3 tiers with explicit entry criteria built from MY answers: Tier 1 (deep research, custom multi-touch — the 10-15% that deserve hours), Tier 2 (light research, semi-custom — the middle), Tier 3 (automated-grade relevance or nurture). Show me the criteria and adjust if I push back.
2. TIERED LIST — every account assigned a tier with a one-line justification. No account skipped; unknowns lower a tier rather than inflate it.
3. EDGE CASES — accounts you nearly placed differently and why; these are where my judgment should override.
4. DATA GAPS — the missing fields that most distort this tiering, and which enrichment (Clay, Apollo, manual) would fix each cheapest.
5. EFFORT MAP — for each tier: touches per account, research minutes per account, and what 'personalized' means at that tier. Concrete numbers I can run a week against.

Rules: resist tier inflation — if more than 20% of accounts land in Tier 1, tighten the criteria and redo it. Justifications reference my actual fields, not vibes. If my list arrives with no usable fields beyond names, stop and tell me the minimum enrichment to run first.

Before you tier anything, interview me. Ask me these questions ONE AT A TIME, waiting for my answer each time:
1. What do you sell, and what's your average deal size and cycle length?
2. What did your last 10 closed-won accounts have in common?
3. Paste the list with every field you have.
4. How many accounts can your team genuinely work deeply per month?
5. Any accounts with existing engagement or history I should weight?

Once you have my answers, build the framework, confirm it with me, then tier the list.

How to use it

  1. 1

    Copy the prompt into Claude, ChatGPT, or any LLM.

  2. 2

    Export your list with every field you have — even messy fields beat missing ones.

  3. 3

    Answer question 2 from real closed-won data, and question 4 honestly; capacity determines where the Tier 1 line can afford to sit.

  4. 4

    Review the framework before accepting the tiered list — criteria are where your judgment matters most.

  5. 5

    Fix the DATA GAPS with an enrichment pass, re-run the tiering, then build sequences per tier, not per account.

Best practices

  • Fight tier inflation hard — the prompt caps Tier 1 near 15-20% for a reason. A Tier 1 of forty accounts is a Tier 2 wearing a costume.

  • Paste the list as CSV-style rows; structure in equals structure out, and it makes the output easy to paste back into your sheet or CRM.

  • For lists past a few hundred rows, calibrate the framework here on a 50-account sample, then implement the agreed criteria as a Clay scoring column for the full list.

  • Revisit tiers monthly: engagement moves accounts up, and staleness moves them down. Tiering is a rhythm, not an event.

Example: what this looks like in practice

A two-rep SDR team at a logistics-tech startup imports 240 accounts for the quarter. The founder answers the interview: $30K average deal, closed-wons share refrigerated fleets over 50 vehicles and named ops leadership. The framework gates Tier 1 on fleet size, cold-chain relevance, and a findable ops leader — 31 accounts qualify. DATA GAPS flags fleet size missing on 80 rows, fixable with one enrichment. The effort map: Tier 1 gets 8 touches and 30 research minutes; Tier 3 gets a relevant automated sequence. Next quarter's review: 70% of meetings came from the 31 Tier 1 accounts that got real attention.

Best fit

Roles
Sales LeaderRevOpsFounder / CEOSDR / BDR
Company size
Startup (1–10)SMB (11–50)Mid-market (51–500)
Audience
B2B
Industries
Any industry
Works with
Any LLM
Difficulty
Advanced

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Prompt FAQ

Frequently asked questions

Derive criteria from your closed-won history — the shared traits of your last 10-15 wins — then gate tiers on those criteria with unknowns counting against, not for. Cap the top tier at roughly 15-20% of the list so effort concentration is real. This prompt builds the framework from your answers first, then applies it with a justification per account.

More account & prospect research prompts