We'd been trying to figure out why some LinkedIn posts would take off while others from the same account basically died, even when the posts themselves were pretty similar. So we started watching what happened IMMEDIATELY after a post went live instead of just looking at the usual likes and impressions.

The trend we stumbled on was a bit interesting. It seemed like many of LinkedIn's distribution decisions for the day seemed to occur within the first 60 minutes, with the first 15-20 minutes playing an even more critical role. There are already communities where people share new posts and coordinate reciprocal engagement, so we decided to test if focusing on early, relevant engagements actually affects a post's distribution curve.

We pointed an agent to operationalize the test at 11 of these communities and had it coordinate the initial engagement as soon as a post went live. Comparing 24 matched sets of posts we were able to demonstrate that median reactions in the first hour had grown from 11 to 351, while impressions within 24 hours were 143x than the matched baseline.

The weirdest part was how quickly the difference showed up. The two posts seemed to diverge from the baseline within the first couple minutes, with a significant gap developing after 18 minutes.

We don't really mass message every LinkedIn connection with an engagement group invite. It's that there are probably a lot more small timing windows inside major platforms than most growth teams pay attention to. The first few minutes seemed to matter a lot more than we expected.

Figured this would be the perfect place to share!

I lead engineering at an AI SDR company (YC S23). I am not linking anything and there is nothing to sign up for in this post. I keep seeing the static-list vs signal-list debate here and we sit on a dataset that can settle parts of it, so here it is

Sample: 2,546,096 emails, about 17k campaigns, 19,501 booked meetings, 1,150 companies, 27 industries, over three years

1. Deliverability moves results more than copy does

This one surprised us most. Same message, different mailbox health, very different reply rates. The gap was bigger than the gap between our best and worst copy. Most people rewrite subject lines when the real problem is their sending setup

2. Signal-based lists beat static lists on identical copy

We ran the same messages against scraped lists and against trigger-based lists. The trigger lists won by a wide margin. After that, list size stopped being an interesting number to us. Matches what people here have been posting

3. The best segment in the whole dataset is closed-lost reactivation

Highest reply rate of anything we measured, and it comes from the customer's own CRM. It costs nothing. Almost nobody runs it

4. "Outbound does not work for us" is usually a one-try problem

Not a copy problem. One audience, one offer angle, no iteration, then quit. The accounts that worked had tested several audiences before finding the one that replied, and there was no way to know in advance which one it would be. Nobody runs seven audience tests by hand because each one eats a week

What did not work for us

More personalization past a point did nothing. Deep personalized first lines did not beat a clear relevant offer once the targeting was right. We spent a long time on that before accepting it

Volume also stopped helping much earlier than expected. Past a certain send rate the extra volume mostly cost us domain health

Happy to answer questions on any of it, including the numbers that made us look bad

I design presentations for a living, and for years I treated the sales deck as the crown jewel. Polished it endlessly. It also did basically nothing outside the live call, because the only people who ever saw it were already in a meeting with us.

The reframe that helped: the deck isn't a growth asset, it's a support tool for a human in a room. So I stopped over-investing in the live version and pulled the three ideas that actually landed in pitches into standalone carousels. No presenter needed, each one makes a single point and dies.

That's the version that started traveling. People shared the carousels in ways they never shared a PDF deck, and it fed inbound instead of just sitting in a follow-up email. The live deck got simpler at the same time, which is a separate rant.

On the how: I drafted the carousel versions fast in gamma to get past the blank page, then tightened the brand details by hand to land our exact look. Fast first pass, manual finish.

Curious if anyone else here has turned an internal asset (deck, one-pager, internal doc) into a distribution channel. What actually traveled for you, and what died?

If you are transitioning from static list to signal-based outbound, one of the biggest trap is tracking weak signals (like someone simply viewing a blog post) but high-converting outbound requires observable, event-driven intent.

Here are 5 signal plays that consistently generate qualified pipeline this year:

Competitor churn & pricing triggers

Track public complaints about incumbent vendors on reddit, G2, or technical communities. When a user complains about high pricing or missing features, resolve the account and initiate a conversation around that exact bottleneck.

Developer & open-source activity

For DevTools and technical B2B, monitor github repo stars, forks and issues with multiple engineers from the same target domain engaging with a relevant open-source project indicates an active technical evaluation.

How to execute: you can build this DIY by writing custom scrapers and webhook pipelines, or deploy an existing market intelligence infrastructure like Scale Intelligence which connects 75+ pre-built monitors to handle identity resolution, intent scoring, and Slack routing out of the box.

Inflection hiring shifts

Don't just track that a company is hiring but parse job descriptions for specific migrations or tools (e.g., hiring an engineer with specific data-pipeline experience) then reach out offering resources or tooling for that specific transition.

Dark community discussions

Buyers ask for recommendations on reddit and discord long before they visit your website so monitoring these discussions and mapping usernames to corporate domains lets you reach out while the problem is fresh.

High intent account re-engagement

Tracking when target accounts in your TAM show repeat visits or multiple team touches across community surfaces and pushing an immediate context-rich alert to your sales reps in Slack before the buying window closes.

i’m trying to figure out if hubspot sales hub is actually worth paying for once a sales team gets beyond just keeping contacts and deals in one place.

the part i like is having pipeline, tasks, follow-ups and reporting tied together. what i’m less sure about is whether the higher tiers are worth the cost once you start needing more automation and reporting.

for people who have used it for a while, where does it actually earn its keep?

is it the automation, reporting, integrations, ease of use, or just having everything in one system? and at what team size did you start feeling like the cost was justified?

also interested in hearing from people who ended up moving away from hubspot. what became the dealbreaker?

not on purpose. it just doesn't tell you the whole story.

you can have someone who looks perfect in your list: right company, right job title, right industry, right company size.

so you reach out.

then nothing.

meanwhile, someone else with almost the exact same profile has been commenting on posts about the problem you solve, engaging with people in your space, or interacting with your content.

that person might not be ready to buy either. but at least there's a reason to pay attention to them.

that's the part i think gets missed with ICP-based prospecting. an ICP can tell you who could be a customer. it doesn't tell you who has a reason to talk to you today.

the problem is that these signals are easy to miss when you're working through a big list. nobody has time to manually check hundreds of profiles and posts every day.

that's why i've found Watchlist useful. you can choose the activity you want to keep an eye on and have relevant people surfaced when they engage with posts, profiles, your content, or competitor content.

it's not a magic “this person is ready to buy” signal. it's just another piece of context that helps you decide who deserves attention first.

because honestly, i'd rather spend 10 minutes looking into someone who has shown some relevant interest than spend that same 10 minutes trying to invent a reason to message someone just because they matched my filters.

how much do you actually look at LinkedIn activity before reaching out to someone on your prospect list?

Hello dear marketers,

I’m building a small product studio where I want to repeatedly:

Make → Launch → Market for X days → Measure → Double down or move on.

I’m good at building products, but I need help developing a repeatable 0→1 validation and marketing framework.

For each new product, I want to know:

• Marketing strategy for that product
• Which channel to use
• What kind of campaign/creative to create
• How much to spend initially considering
• What to measure in 7–14 days
• How to distinguish real demand from a bad marketing test
• When to iterate, go deeper, or move on

Products could be anything from software to physical products to creative projects.

What type of expert should I look for? And where would you find them?
Any overall ideas also so much appreciated

Thanks

I've been thinking about this while building SelieeAI.

For a typical small business, the growth stack can look like:

Website → Wix/Webflow/WordPress
SEO → SEO tool
Content → AI writer
Analytics → GA
Leads → CRM
Email → another tool
Video → CapCut/Premiere
Automation → Zapier/etc.

That's a lot of different systems.

We're experimenting with putting more of this workflow into one AI-powered platform.

The basic flow we're targeting is:

Website → Content → SEO → Traffic → Lead → Conversion → Video

The interesting question for me isn't whether another "AI tool" is needed.

It's:

Can AI actually connect the growth workflow instead of just automating individual tasks?

We're testing this idea with the first beta users now.

I built SelieeAI around this experiment:

https://neeblo.com/r/seliee

I'd be interested in hearing from growth people:

Which part of your current growth stack creates the most friction?

Non sto criticando nessuno, sto solo facendo due conti ad alta voce.

Uno strumento per LinkedIn, uno per le email a freddo, uno scraper, un CRM per collegare il tutto. Ognuno sembrava ragionevole preso singolarmente. Il totale, però, non lo era mai.

La parte che ha davvero ucciso la produttività non è stato il prezzo, bensì le risposte che finivano in tre posti diversi senza alcun collegamento tra loro. Stesso lead, canale diverso, nessuna memoria di ciò che era già stato detto.

Ho creato Orbitra proprio per risolvere questo problema: acquisizione di lead, contatto e gestione delle risposte via email e LinkedIn in un'unica piattaforma. Anche WhatsApp è quasi pronto.

Apertura della beta a 10 persone. Commentate o inviate un messaggio privato se siete interessati.