playbook-lookalikes

name

playbook-lookalikes

description

Turns one customer case study into the tightest list of companies that would read that case study and see themselves. Decomposes WHY the case-study company resonates into concrete attributes, translates those attributes into database filters, intersects them with a lookalike anchor, then widens with a model judge. Triggers on "find more companies like our best customer", "who would our case study land with", "lookalikes of X", "build a list around this case study", "companies like our happiest client", "expand this list, it is too small". Outputs a filter set plus one merge field naming which case study to cite.

Playbook: Case-Study Lookalikes (filter mining, not raw lookalike)

All rules here are best practice, not law. Override any of them when the campaign calls for it; note the best practice once and move on.

Use when: a client has a case study, a flagship logo, or one obviously happy customer, and you want the list of companies for whom that story is the strongest thing you could say.

Do not use when: you are building a whole market from an ICP sentence with no standout customer to anchor on; or you want to name a customer off the prospect's own case-study page ("saw your work with Intercom") — that is the opposite direction and belongs to playbook-case-study-page.

See also disco-like in this repo for plain seed-domain lookalike discovery. This playbook is the higher-precision version: it mines why the story resonates before it searches.

One-line output: case_study_ref = "attentive" with lookalike_case_study_line = "a marketing platform about your size".

⚠️ Merge-field collision warning

playbook-case-study-page pushes a lead-level field literally named case_study_line, whose value completes a different sentence (Saw your work with Intercom.). This playbook's descriptor is therefore named lookalike_case_study_line. Never rename it back. If both playbooks run on the same campaign, a shared field name silently overwrites and renders "We did this for your work with Intercom."

1. Trigger and scope

A case study only works on someone who recognises themselves in it. "We took an SMS marketing platform from 3 meetings a month to 22" lands hard on another marketing software company with a similar go-to-market, and lands on nobody else. So build the list backwards from the story, not forwards from the client's broadest ICP.

The naive move is to paste the case-study company into a lookalike engine and ship what comes back. That was measured at 40% usable: the vector matched on "is about marketing" and returned agencies, a marketing trade publication, and a Power BI blog alongside real software vendors.

What this playbook does instead: write down the 2 to 5 attributes that are the real reason the story resonates — in the worked example: B2B software vendor, product is marketing technology, US, 50 to 2,000 people — express each as a database filter, intersect those filters with the lookalike anchor, and read the result.

That intersection measured 100% usable. It is also small, which is the point: the intersection is how you learn the filters and train the judge. It is not the final list.

2. Output contract

Inputs

Field	Type	Required?
case_study_domain	string, bare, lowercase	yes
case_study_text	200 to 2,000 words	yes — this is what the decomposer reads
client_offer_summary	3 to 6 lines	yes
geography, headcount_band	strings	yes, always legal pre-filters
domain, company_description	strings	yes, for the labelling half
exclusion_list	CSV of domains	no, but always ask

Output fields

Field	Type	Example	Null?
case_study_ref	slug	attentive	no inside the segment
lookalike_case_study_line	generic descriptor, ≤70 chars	a marketing platform about your size	yes
case_study_match_reason	≤140 chars, QA only, never sent	sells customer engagement software to consumer brands	yes
attribute_card	JSON, one per case study, not per row	see §6	no

Abstain value: "". The gate is qualified: false paired with an empty case_study_ref — the two always agree.

The descriptor is generic on purpose

lookalike_case_study_line is deliberately a generic descriptor, not the case-study company's brand name. Naming a third party's brand in cold copy is a client-facing decision, and the descriptor form costs almost nothing: "we did this for a marketing platform about your size" carries the same recognition as the brand name, and it never creates a permissions problem.

Flip it to brand-naming only when the client explicitly okays it for a publicly published case study.

Coverage expectation

10/10 usable (100%) on the intersection path.

⚠️ Read that number correctly. Coverage is near-total by construction, because the segment is defined by the filters, so every row in it matches the case study.

The metric that actually varies is segment size. The tested intersection returned 21 companies. The number to watch in production is not hit rate — it is whether the widening steps get you to campaign volume without precision falling back to the 70% that unanchored keyword filtering produced.

3. Source chain (cost-tagged)

Steps 1 and 2 fingerprint the case study. Step 3 is the precision core. Steps 4 and 5 are where volume comes from.

#	Source	Cost	What it does	Stop rule
1	An internal company database, the case-study company's own record	FREE	read its description and offerings	always run. found: 0 just means the decomposer works from the case-study text alone
2	A company-search API on the case-study domain itself	FREE	this is how the database tags it, which is the taxonomy your filters must speak	always run, then run the industry-enum bake-off below. The returned industry is a candidate, never automatically the filter
3	The lookalike anchor intersected with the mined attribute card. THE RECOMMENDED CALL	FREE	company_lookalike + company_industry + company_keywords + headcount + location	10/10 usable, 21 companies. Advance whenever the count is below campaign volume, which is almost always
4	The same attribute card with the lookalike anchor removed, then every row through the model judge	CHEAP	paginate to completion, judge each row	7/10 raw, ~95% after the gate. Always run when step 3 is short. Never skip the judge — the raw 70% is what makes unanchored filters dangerous
5	Snowball: re-run the step 3 call once per confirmed member as a new seed, union, repeat	FREE	adds 30 to 100% on top of step 4	stop when net-new drops below 2 to 3% per round
6	An entity-search API	METERED	last resort	only if 3 to 5 are still short, or the category is genuinely absent. ⚠️ These pad to the match limit with junk, so never trust one without the judge, and they often return profile URLs rather than domains

The industry-enum bake-off (do not skip)

A database's industry tag on the case-study company is frequently NOT the enum you want to filter on.

Measured: one marketing-software vendor is tagged Advertising Services — but the winning 10/10 call filters on Software Development. Advertising Services is the bucket the agencies live in, and it is exactly the bucket that produced the 40% failure this playbook exists to avoid.

So the database's own tag is a candidate, never the answer. The procedure is free, because a page-1 total count costs nothing:

1. Collect candidates: the case-study company's own industry, plus the industries assigned to the 3 to 5 companies in the raw lookalike page an operator reads as obviously right.
2. For each candidate, run the step 3 call once with only that enum swapped in. Record the total count and eyeball the first 10 names and descriptions.
3. Keep the candidate whose sample is dominated by companies of the right TYPE — product vendor vs agency vs publisher. Break ties towards the larger count.
4. If two enums both look right, include both. It is a union.

Write the chosen enum, and the ones you rejected and why, into the run notes. That one sentence is what stops the next operator repeating the bake-off.

4. Verification

VERDICT: PASS 10/10 on the intersection path. The unanchored arm measured 40% usable, and keyword-only filtering without the judge measured 70%.

⚠️ Cost is an estimate, not a measurement: the live test made 3 database calls and zero model calls. Comparable prompts in sibling playbooks measured $0.05 to $0.27 per 1k. Treat any figure under that as a floor and measure on a 50-row batch before quoting it.

5. Clay implementation

• clay-table.md — the labelling half as columns.
• clay-workflow.md — the CLI-buildable version.

6. Locked prompts

Two prompts. Prompt A runs once per case study. Prompt B runs per row.

This prompt is input-heavy, so pick the model with the cheapest input rate at standard pricing. Params: max_completion_tokens=300, never temperature, flex tier for batch.

Prompt A — the decomposer (once per case study)

STATIC PREFIX (byte-identical across calls, keep first)

You are a B2B list-building analyst. You will be given one customer case study, a profile
of the company the case study is about, and a summary of what the seller offers. Your job
is to explain why this story would resonate with a company, in attributes that a company
database can filter on.

Return JSON only:
{"resonance_reasons": ["..."], "industry_enum_candidates": ["..."], "keywords_include": ["..."],
 "keywords_exclude": ["..."], "headcount_min": 0, "headcount_max": 0, "geography": "...",
 "descriptor": "...", "confidence": "high|low"}

Rules:
- resonance_reasons: 2 to 5 short reasons, each one testable against a company description.
  "sells software to marketers" is testable. "is innovative" is not. Drop untestable ones.
- industry_enum_candidates: 2 to 4 CANDIDATE enum strings, ranked best first, for a human to
  bake off against total_count. Include the exact industry string the database assigns to the
  case-study company, but do NOT assume it is correct: databases often tag a software vendor
  by who it sells to. Rank first the enum that names what the company IS, then the database's
  own tag, then at most 2 adjacent ones.
- keywords_include: 3 to 6 phrases that name what the product IS. Prefer 2 and 3 word
  phrases. Never include a word so common in the category that every company carries it.
- keywords_exclude: words that pull in agencies, publishers, marketplaces, and staffing
  firms in the same topic space.
- descriptor: how to describe the case-study company in cold copy WITHOUT naming its brand,
  4 to 10 words, lowercase, no trailing period. It must read correctly inside this
  sentence: "We did this for DESCRIPTOR."
- Never invent a fact. If the case study does not say it, it is not a reason.
- No em dashes.

Examples:
Input: case study about an SMS and email marketing platform selling to consumer brands,
1,500 people, US; seller runs cold email campaigns for B2B software companies.
Output: {"resonance_reasons":["sells marketing technology as a product, not as a service","sells to marketing leaders at consumer brands","US company big enough to have a dedicated demand generation team"],"industry_enum_candidates":["Software Development","Advertising Services","Marketing Services"],"keywords_include":["marketing platform","marketing automation","customer engagement platform"],"keywords_exclude":["agency","staffing","conference"],"headcount_min":50,"headcount_max":2000,"geography":"United States","descriptor":"a marketing platform about your size","confidence":"high"}

PER-CASE-STUDY DATA (appended last)
Case study text: <case study text>
Case study company profile: <profile>
Seller offer summary: <offer summary>

Prompt B — the row judge and labeller (per row)

STATIC PREFIX (byte-identical across calls, keep first)

You are grading whether one company would recognise itself in a specific customer story.
You will be given the attribute card that defines the story's audience, and one company's
name and description.

Return JSON only:
{"qualified": true|false, "case_study_ref": "...", "case_study_match_reason": "...", "confidence": "high|low"}

Rules:
- qualified is the gate. Set qualified true and return the case_study_ref given in the
  attribute card ONLY if the company satisfies EVERY resonance reason in the card.
  Otherwise set qualified false and return "" for case_study_ref. These two always agree:
  qualified false always pairs with an empty case_study_ref, never with a non-empty one.
- A company in the right industry with the wrong product does not match. A services firm,
  agency, publisher, marketplace, or community does not match a software product story.
- case_study_match_reason: under 140 characters, quote the part of the description that
  decided it. Always populate it, on qualified false rows too.
  This is internal QA text, never sent to anyone.
- Never invent a fact about the company. Judge only what the description says. A thin or
  empty description means qualified false, case_study_ref "", and confidence "low".
- No em dashes.

Examples:
Input card reasons: ["sells marketing technology as a product","sells to marketing leaders"]. Company: Customer.io, "Create personalized customer journeys that engage and convert with our versatile customer engagement platform."
Output: {"qualified":true,"case_study_ref":"attentive","case_study_match_reason":"customer engagement platform sold as a product to marketing teams","confidence":"high"}
Input card reasons: ["sells marketing technology as a product","sells to marketing leaders"]. Company: Fivetran, "Fivetran, the global leader in data movement, helps customers use their data to power everything from AI applications to analytics."
Output: {"qualified":false,"case_study_ref":"","case_study_match_reason":"data movement infrastructure, not marketing technology","confidence":"high"}

PER-ROW DATA (appended last)
Attribute card: <attribute card>
Company: <company name>
Domain: <domain>
Description: <company description>

The verifier pass you DO need: liveness

Not needed for fact hallucination — prompt B only judges text it was handed and returns the reason it used.

What is needed is a liveness pass, because company databases happily describe companies that no longer trade. Fetch each live homepage, classify dead / parked / live, and re-judge live sites on their current content.

Always run it. Database descriptions qualify dead companies otherwise.

7. Edge cases and failure modes

Symptom	Cause	Fix
The list is full of agencies, publishers and blogs	You pasted the case-study company into a lookalike engine and shipped the output. Measured 40% usable	Mine the attributes first and intersect with the anchor
The right industry filter returns the wrong kind of company	The database tags a software vendor by who it sells to	The industry-enum bake-off. The database's own tag is a candidate, never the answer
The segment is tiny	Expected. The tested intersection returned 21 companies	Widen by dropping the lookalike anchor and judging every row — never by loosening the attributes
Precision collapses when you widen	Unanchored keyword filtering measured 70% raw	Never skip the judge. ~95% after the gate
An entity-search API returns exactly the number you asked for	These pad to the match limit with junk	Never trust one without the judge
Dead companies qualify	Database descriptions outlive the business	The liveness pass
Copy renders "We did this for your work with Intercom"	Two playbooks pushed a field with the same name	Keep lookalike_case_study_line distinct from case_study_line
The judge qualifies a thin row	An empty description is not evidence	The prompt sets qualified: false, confidence: "low" on thin descriptions. Keep that rule