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Turn one customer win into conversations with lookalikes

Post the customer's result with them, tagged and approved. Then find 20 companies that look like that customer, in size, industry and stage, connect with the buyer at each and, once connected, message them the story with one question about their own situation. Proof from a company like theirs does more than any pitch. The Imagine AI agent finds the lookalikes and drafts the post, notes and messages for approval.

Last updated
Oct 20, 2026
Written by
Sky Yang, Co-founder and CEO
How we wrote this
Written from how the Imagine AI agent runs this play on LinkedIn, with approval.
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When it runs

A customer just got a result worth talking about.

One prompt. The agent runs the play.

The agent has its own computer and browser, so it finds the people and the news itself. It drafts every comment, connection note and message, and you approve what goes out. Use it when:

  • A customer just got a result they're happy to talk about.
  • You sell best into one clear segment.
  • Prospects ask for proof from companies like theirs.
Lookalikes of our best customer
Fig. 01: Best customer lookalikes, run by the agent. You approve before anything goes out.

The skills behind this play.

The agent has its own computer and browser, so it does the legwork itself. Every skill is on for every play; these are the ones this play leans on.

[01]Used in this play

Writes in each person's voice

Drafts for the company page, founders, executives and employees from each person's best posts. Imagine AI started as the operating system for ghostwriting teams, so sounding like you is the core of the product.

[02]Used in this play

Designs the graphics

Multi-image posts, infographics and visuals from your brand kit, made in its own sandbox, with the right people and companies tagged.

[03]Used in this play

Has its own computer and browser

It does the legwork itself: finds the people, checks job changes, launches and funding news, reads the sources and fact-checks claims before anything ships.

[04]Used in this play

Engages for you

Comments, connection requests and messages on LinkedIn, aimed at the people your posts pull in who match your ICP.

[05]Used in this play

Finds the buyers in your audience

Matches everyone who engages to your ICP and hands sales a worked list every week.

[06]On

Runs one calendar for the team

Plan launches, sequence posts, schedule comments and edit together. Approvals sit with each person or route through one owner.

[07]On

Works from your content filesystem

Positioning, brand kit, decks, campaigns and compliance rules in one place it reads before every draft, remembered across threads.

[08]On

Turns what you have into posts

Events, podcast episodes, videos, decks and call notes become a week of posts across the team.

[09]On

Knows what works

Reads the public benchmark of 1,000+ B2B companies and tracks your competitors, so post types and formats follow your industry's data.

[10]On

Maps content to revenue

Connects to HubSpot or Salesforce and traces each closed deal back to its first content touch: post, person, deal stage moves.

[11]On

Plugs into your stack

Engager data over API and webhooks, and the benchmark over MCP for your own agents.

What the data says

Customer Highlight beats the author's typical post in 0 of 13 industries, median 0.86x.

  • Voice & Media AI5.1% of posts0.91x
  • Other B2B SaaS5.9% of posts0.91x
  • Security & Compliance3.7% of posts0.9x
  • HR & Payroll5.5% of posts0.9x
  • Data & Analytics3.3% of posts0.88x
  • Venture Capital0.8% of posts0.87x
  • AI Infrastructure4.3% of posts0.86x
  • Customer Support AI8% of posts0.86x
  • Sales & Marketing Tech5.7% of posts0.85x
  • Dev Tools & Infrastructure5.5% of posts0.83x
  • Fintech5.5% of posts0.83x
  • Tax & Accounting4.6% of posts0.83x
  • Healthcare Tech5.3% of posts0.76x
Fig. 02: Customer Highlight by industry, dashed line at 1x. Source: Imagine AI Benchmark (benchmark.useimagine.ai), public aggregates, data through Oct 20, 2026. Lift = a post type's reactions as a multiple of each author's own typical post (geometric mean), so big accounts do not skew it. CC BY 4.0.

Step by step

The playbook lives as a file the agent reads. Edit it once and every run follows it.

What you need
  • The customer story, approved by the customer.
  • What makes that customer a good fit: size, stage, industry.
  • The title you want to reach at each lookalike.
Files/Imagine AISend to agent

customer-lookalikes.md

Save

Step 1 · Post the story

  • The result in the customer's words, with them tagged and their approval. Their team usually engages, which widens the reach.

Step 2 · Find lookalikes

  • 20 companies that match the customer on size, stage and industry, and the right buyer at each.

Step 3 · Connect

  • A short connection note that mentions their segment, no pitch.

Step 4 · Message with the story

  • Once connected, the story and one question about how they do it today.

Fig. 02: The playbook as a file in your workspace.

Anatomy of the lookalike message

The outline, what to avoid and how to measure it, in the file the agent drafts from.

Files/Imagine AISend to agent

lookalike-message.md

Save

Outline

  • 1Thanks for connecting.
  • 2One result from a company like theirs, in numbers.
  • 3One question about how they do it today.

Example from the run above

  • Hi Sarah, we work with a few Series B fintech teams on LinkedIn. Would like to connect.
  • Marcus, saw your post on building growth from zero at a payments company. Would like to follow along.
  • Thanks for connecting, Priya! A team your size went from 2 to 9 posts a week here. How does your team handle LinkedIn today?

Avoid

  • Posting the story without the customer's sign-off.
  • Lookalikes that only match on industry.
  • Leading with the product instead of the result.

Measure

  • Acceptance rate from lookalike buyers.
  • Replies to the story message.
  • Engagement on the post from the customer's team.

Fig. 03: The outline, part by part.

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