How we get customers

Great insight — I've been building in this space too

This matches what I saw running outbound in the Microsoft partner world for years. Lists that fit an ICP on paper converted like cold stone. The deals came from trigger events: someone posting about a migration, a new hire, a budget cycle. Engagement is that same trigger signal applied to LinkedIn, which is why your numbers jumped. One thing I'd push on: not all engagement is equal. A passive like is a weak signal. Someone commenting to describe the actual pain is a strong one, and someone arguing under a competitor's post is the strongest. If you rank-order by engagement type and lead with the hottest, your reply rate probably climbs again. The intent is already in the words people use, not just the click.

The engagement-based targeting insight is underrated. Most cold outreach fails because it's targeting job titles instead of intent signals. Someone who just liked a post about automation pain points is infinitely more likely to buy an automation tool than someone who just fits a demographic profile. The 3-8x reply rate improvement makes complete sense when you think about it that way. This is essentially finding people who are already in the buying mindset.

Acquisition posts in B2B AI usually fall into two camps — either "we did outbound and it worked" or "we did content and it worked." Yours feels more layered than that.

If you had to attribute your first 10 paying customers to one channel each, what would the split actually look like? And which channel surprised you the most — either by working better than expected or by being the one that quietly compounded?

Asking because I think the "one channel until $1M ARR" advice undersells how messy the early mix actually is.

This is actually crazy because I never thought about using engagement as a buying signal like that. It makes so much sense though. Someone who just liked a post about a problem they have is literally telling you they're thinking about it right now.

I just launched something for SaaS founders and distribution is the exact wall everyone hits. Gonna look into IbexAI for sure. How long does it usually take to see results when you first start?

Beyond the metrics, what makes this work is that engaging is a person in a real moment, not a row in a list. Outreach then feels like being noticed, not processed. That felt difference is probably most of the lift.

Great job, I'm definitely going to check ibexAI

Solid results. The 3-8x lift tracks with what I’ve seen when teams move from static ICP filters to live intent signals. Curious what your sweet spot is for engagement recency though? Someone who liked a post this morning vs. 3 weeks ago feels like a very different conversation. Would love to see you write more on how you’ve refined that timing piece.

This makes a lot of sense.

I’m currently building a very early consumer product, and one thing I’m learning is that broad outreach is almost useless when the product needs a specific mindset.

Finding people who recently engaged with the exact problem you’re solving feels much smarter than targeting people only by title or demographics.

For some products, the best signal is not who someone is on paper, but what they just cared enough to react to.

The intent-over-ICP insight is the right one, and your own numbers prove it. The risk I would watch: the leads are only half the value, the other half is the message, and that half is you. You ran an agency and sent 180k DMs, so when you contact a high-intent lead your opener lands. A first-time user pointed at the same lead with a clumsy message gets silence and blames the tool. You already have the fix sitting on your profile, the 50 DM swipe file. Bake those openers into onboarding so a new user's first message is as good as yours on day one. Otherwise activation lags targeting, and people churn convinced the signal does not work when really their copy does not.

Congratulations! AI makes our lives so much easier. It's fantastic to be able to automatically engage more people, thus increasing the likelihood of them becoming our customers. In the future, do you plan to expand the use of IbexAI to other platforms? Or will it only be for use on LinkedIn?

Intent is the right wedge, but 'engaged with topic' covers everything from a buyer to a curious lurker. The signal I'd lean on hardest is comments that ASK something concrete (pricing, integration, switching from X) - those people are already in the decision, not still researching the category. Likes and shares are 10x noisier. Curious how the agent weights different engagement types - is a question-comment worth more than 10 likes in your scoring?

This makes a lot of sense. Targeting based on active intent completely changes the dynamics of cold outreach because you're catching people while they're actually thinking about the problem. Standard ICP scraping usually results in hitting ghost accounts or people who just aren't in buying mode.

One question on the execution: when you target people who engage with a competitor's post, what's your actual hook? Are you calling out the competitor directly, or just leaning heavily into the specific problem discussed in that post?

when I was testing distribution for my sprint planner, LinkedIn engagement signals looked solid but converted poorly. most people liking PM content are in research mode - that jump to paying customer is way longer than the signals suggest.

Will check it out for sure.

Crazy genius.

A $10K MRR solo business with high margins offers a level of freedom that a venture-backed company can never match. Not having a board of investors to answer to means you can actually build for the customer instead of building for the next pitch deck

One thing that helped a lot for Mentiohunt, which I'm building, was treating fresh intent like a perishable asset. If someone commented on a pain point 2 hours ago, we'd reach out that same day with a super specific opener tied to that exact post, and the reply quality was way better than when we waited and sent a more generic pitch.

We also got pickier about what counts as a signal. A random like was usually weak, but a comment with actual context or someone engaging with competitor/problem-aware content was much stronger, so filtering harder gave us fewer leads but more real conversations.

This is genuinely useful. Thanks for breaking it down.

For my situation (selling a SaaS boilerplate), targeting people who just engaged with 'Paddle' or 'Stripe vs Paddle' content is a great filter.They're already in pain, just not asking for a solution yet.

Two questions if you don't mind:

1. What's your reply rate on LinkedIn vs email?I've always thought LinkedIn DMs have lower open rates, but your numbers suggest otherwise.

2. Does this work for founders outside the US/EU? I'm in Nigeria, and LinkedIn outreach here feels different — less trust, more spam. Curious if you've seen regional differences.

For anyone reading: The insight here isn't the tool. It's the targeting signal — recent engagement on relevant content — that's the real gem.

Thanks again.

The engagement-signal wedge is real — targeting people who recently liked or commented on relevant content does measurably outperform blind ICP targeting. 3-8x improvement claim is plausible because demonstrated intent signal works similarly to Meta retargeting.

Two things worth flagging though.

LinkedIn TOS issue. LinkedIn prohibits automated scraping of engagement data and automated outreach based on behavioral signals. Users running IbexAI on their main LinkedIn accounts risk restrictions (limited reach), suspension, or permanent ban. Enforcement tightened in 2024-2026. Similar risk profile to Bazzly on Reddit — works until LinkedIn flags the account, then years of network capital evaporate.

Competitor density also worth knowing. Trigify, Surfe, Aware, Heyreach, Expandi, Dripify all target overlapping wedges with engagement detection or LinkedIn automation. Sales Navigator advanced filters surface similar signals natively. The wedge isn't proprietary — execution quality and risk tolerance is where competition sits.

The "33% of signups from own tool" data point is more credible than typical vanity claims because it's bounded and verifiable. But selection bias — founder with established LinkedIn network using tool is different baseline than new user with no credibility. Worth showing conversion data for accounts without founder-level audience too.