This is the single biggest GTM mistake
by•
Not building lookalike list.
All of us must be having some customers. Some customers came inbound, few via referrals, few via word of mouth- it does not matter.
What matters is that these customers are able to solve their pains with your product.
Hence, once they are your customers- you should be "finding more of these".
This is by far the most underrated GTM hack I have come across.
There are few lookalike tools you can try- Ocean, Jesse (my product).
While using these tools, you should be able to find out why certain new customers were surfaced- what were the logic behind these new suggestions, and then run a focussed outbound/ABM campaign on those list.
127 views
Replies
Agreed. Too many teams optimize for more leads instead of more customers that resemble their highest LTV accounts. That's where GTM efficiency really comes from.
Jesse
@gautham_madhavan Exactly. In fact, this has been the highest lever of growth for us
Lookalike-from-existing-customers is one of those things that's so obvious in hindsight that it's embarrassing how many teams skip it. Most outbound campaigns get built from ICP guesses instead of paying customer reality.
I'm running cold email to ~160 manually sourced DTC beauty leads right now, and the only reason the list isn't garbage is that I built it backward from 3 brands whose use case I'd already validated. The signal compounds, every reply teaches me what the next 50 leads should look like.
The piece that's hard isn't finding lookalikes. It's defining the "alike" axis correctly. Two brands with identical Shopify revenue, traffic, and ad spend can be completely different customers if their creative cycle is 1 week vs 8 weeks. Surface-level firmographics miss the operational reality, which is usually where the actual pattern lives.
Curious how Jesse handles that, does the matching logic let you weight operational signals (cadence, team size, tool stack) against the obvious ones (revenue, industry)?
Jesse
@elias_motionfy So, let's say you have two existing customers from student debt financing sector: Juno and Lending Tree.
This is how you can configure Jesse to find more similar companies.
@sudipta_biswas4 That's a clean walkthrough, the rationale column is the part that would actually make me trust the output. "Closed $45M Series C, expanded to graduate students" is way more useful than a generic "in similar industry" tag.
The piece I'm still curious about is whether the weighting under the hood is tunable. In your example, Earnest and Sallie Mae both show up at relevance 10, they're both in student debt financing so the surface-level match is obvious. But operationally one is a fintech startup and the other is a $40B legacy lender. If I'm an outbound team selling, say, modern marketing tooling, those are different sales motions entirely even though the firmographic match looks identical.
Does the rationale logic surface operational differences like that, or does Jesse treat them as equally relevant until the human reads the rationale column and infers the difference? Asking because the rationale column is doing a lot of work in your UI and I'm trying to figure out if the ranking respects it or if it's just display layer.
Finding more of your existing customers is underrated GTM.
Jesse
@ritesh2503 The crazy part is most people ignore it.
Strong take and definitely underrated.
One layer I'd add: lookalikes are only as good as the signal they're built on. Most teams optimize for firmographic similarity (same industry, company size, etc.), when the real advantage comes from pain similarity, finding companies experiencing the same problems your best customers had before they converted.
Logo lookalikes fill a list. Pain lookalikes fill a pipeline.
I'm Curious, @sudipta_biswas4 does Jesse let you weight prospects based on why customers converted, rather than simply who they resemble?
Jesse
@somesh_putatunda Yes you can absolutely do that.
There are two ways to achieve this:
You can put a compressed summary of the pain points of the closed won deals in Jesse prompt while going for lookalikes
(a bit advanced)- you can use our API/n8n workflow :
use an n8n node to get on closed won deals from CRM
Use an LLM to compress the pain points from the won deals
plug those into the prompt of Jesse n8n node/API for lookalike search
I agree with this. Most teams spend too much time chasing new ICP theories and not enough time studying the customers who already proved the product works.
The important part is what you said about explaining why a lookalike account was surfaced. A list by itself is not enough. The team needs to see the pattern: what trigger, pain, industry motion, hiring signal, or tech stack makes the account similar. That turns lookalikes from a prospecting shortcut into a learning loop for GTM.
Jesse
@rahulbhavsar can’t agree with you more
@gautham_madhavan hello, this is one of those GTM ideas that sounds obvious only after someone says it. Most teams keep inventing new ICP theories, but the best signal is usually already sitting inside their happiest customers. I also like the point about understanding why an account was suggested — that turns the list into learning, not just more leads.