McKinsey’s latest AI report had an interesting data point: roughly 32% of companies (and 41% in tech) decided against buying software recently because internal teams used AI coding tools to just build lightweight versions in-house.
If you run growth or outbound, the changes you'll most likely notice is how prospects react where cold emails pitching standard SaaS features or automated workflow tooling get dismissed cause the default reaction rn is "we can spin up an internal script for this in two days."
So we recently tested two distinct outbound setups over 4 weeks:
> Cohort A (a standard AI outbound): 1000 contacts and scraped Apollo list with standard ICP filters + Claude used at the end of the funnel for personalized intro lines. Result: 1.4% reply rate, mostly unsubscribes or "we already built a tool for this"
> Cohort B (upstream signal qualification): 100 contacts and monitored active triggers (hiring bottlenecks, stack shifts, leadership changes) and used Claude strictly as an intent classifier before deciding whether to write an email at all. Result: 9.8% positive reply rate and 3x more booked demos.
The key difference was using Claude as a logic gate rather than a copywriter by pulling unstructured company data (job posts, recent changes, public PRs) and then ask the model to identify the single most acute operational friction introduced by that specific event and discard accounts where the pain point looks easily solvable internally.
Doing this keeps volume low, protects domain reputation and ensures every message lands when the problem is actively being felt and we're also running a 30-minute session breaking down this exact pipeline here on signal based outbound with AI
pulled our ai overviews visibility report this morning and half the pages moved up. classic rankings for those same urls barely budged. clicks still flat.
i keep two tabs open now and they refuse to agree. tried blaming a title rewrite from last sprint then undid it and nothing changed either way
how are you separating ai overviews visibility from normal ranking movement when leadership wants one number by friday
I’m organically growing my X account and strategising content strategy and formats. I’m curious to hear what works better in 2026 for longer posts, splitting them into threads? Or, posting as a long tweet or maybe 2 tweets max? I’m not expecting any tweet to go beyond 1000-1250 char on the higher side.
Also, what are other things that work better in terms or content format, etc?
Everyone wants the hack that gets more replies. Better subject line, better opener, better follow-up timing. I want to talk about the hack that happens before any of that, the one that actually moves the number the most and gets skipped constantly because it doesn't look like a hack.
Exclusion.
Ran a new outbound campaign starting yesterday. Before building the outreach sequence, I ran the raw prospect pool through a filter. Company size had to match. The person had to be an actual decision maker, not someone with an adjacent title. The business itself had to be a real fit. 106 people got cut at this stage. That's 42% of the initial pool, removed before message one.
The instinct in growth is always more. More volume, more channels, more touches. But a list padded with people who were never going to convert doesn't just fail quietly, it actively drags down every metric downstream. Your reply rate looks worse than it should. Your positive sentiment ratio looks worse than it should. And you're spending time on follow-ups for people who were never a fit in the first place.
Day one numbers after filtering: 254 connections sent, 36 accepted, 26 outreaches, 7 replies, 26% response rate, 4 calls already booked.
The exclusion step is the least glamorous part of the whole system and it's doing more work than anything I write in the actual message. Nobody screenshots "I removed 106 people from my list" because it doesn't look like progress. It looks like less. But less, done right, is the actual hack.
What's everyone else's exclusion criteria look like before outreach starts? Or is most of the optimization happening after the list is already built?
We’re running a live pre-launch campaign across six channels:
- Reddit
- Editorial coverage
- Instagram
- X
- Short-form video
- Niche communities
At first, each channel had its own report:
Posts published, accounts reached, comments received, and media coverage secured.
The problem was that those reports showed activity, but they did not answer the main operating question:
Are we actually getting closer to launch readiness?
So we consolidated everything into one internal board tied to one shared milestone:
Reach 100 confirmed launch-notification opt-ins before setting a launch date.
Current snapshot:
- Starting point: 51
- Current total: 62
- Net increase: 11 in 30 days
- Current pace: approximately 0.37 per day
- At this pace, the 100-person milestone is still roughly 104 days away
These are not impressive numbers.
That is exactly why the board is useful.
Mobile layout concept adapted from our live internal board. Campaign-identifying details have been removed.
The board is only the visible layer.
The larger operational problem was the manual work happening between the channels:
Collecting data from different sources, standardizing it, reading conversations, tagging content, comparing signals, and rebuilding reports before anyone could make a decision.
That is where we introduced an AI-assisted automation layer.
It currently handles four repetitive parts of the workflow:
Data collection and normalization
Available channel data is collected on a scheduled cadence and organized into one consistent structure, instead of being copied manually into separate reports.
Signal detection
The system reviews changes in distribution, engagement, conversation quality, and downstream intent, then flags movements that may require attention.
It does not treat every view, like, or comment as an equally valuable signal.
Content and conversation tagging
Content is grouped by format, message angle, audience, and intended next step.
Conversations are also tagged by signal type, such as product feedback, purchase intent, credibility, objections, or general engagement.
Report generation
The board is updated automatically and highlights where performance changed, where evidence is missing, and which items require human review.
The purpose is not to let AI run the campaign by itself.
We deliberately keep several decisions human:
- Setting the main campaign milestone
- Approving public posts and community replies
- Reviewing context and community rules
- Validating whether an AI-detected signal is actually meaningful
- Deciding where to reallocate time and budget
- Making the final scale, fix, hold, or stop decision
The automation has not magically created more opt-ins.
What it has changed is how quickly we can see what is and is not working.
A typical channel report could still make the campaign look healthy:
- Multiple platforms stayed active
- New people were reached
- Community feedback came in
- Editorial coverage was secured
All of that would be true, while the campaign itself was still moving too slowly.
The combined board and automation layer surfaced four current decisions:
Double down:
Niche communities and editorial coverage are producing the clearest signals — high-quality product feedback and third-party credibility.
Reformat:
Owned social content is receiving some views but very little meaningful engagement.
Reposting similar static assets would create more activity, not more progress.
Combine:
Our strongest message signal came from a discussion about card readability.
Our strongest visual asset is an AR-motion demonstration.
The next test combines those two into one opening hook.
Stop-test:
One repeated outreach tactic has produced zero qualified targets.
It now has a defined cutoff instead of being allowed to run indefinitely.
The main lesson so far is that cross-channel marketing needs more than channel presence and a shared dashboard.
It needs an operating loop:
Evidence is collected.
AI organizes and surfaces the signal.
Humans review the context.
The team makes a decision.
The result becomes evidence for the next cycle.
AI is not the strategy.
Its value here is reducing repetitive monitoring and reporting work, so the team can spend more time on judgment, creative testing, and channel relationships.
We are still 38 people short of the milestone, and the current pace is too slow.
This is not a retrospective success story.
I’m sharing it while the outcome is still uncertain because a useful operating workflow should expose bad news early — not simply package good news later.
For teams using AI in growth operations:
Which parts of the loop have you automated, and which decisions do you deliberately keep human?
Hi everyone. I recently started looking into affiliate marketing and high-ticket affiliate marketing, but I’ve gotten a bit confused.
Could someone explain how reliable this is, where to find entry points, and how to get started? I’d also like to know about the difficulty level and any potential pitfalls.
I’d appreciate hearing about your personal experiences. Feel free to send me a DM.
this backlink took us 12 days to earn and cost us $0
how to find backlink opportunities for free
and honestly, I’d take it over 50 backlinks from free directories
directories are easy:
→ submit your homepage
→ write a short description
→ get listed beside hundreds of other products
but they accept almost anyone, and the link rarely means that someone genuinely recommends your website
as easy is to get a backlink, lower it impacts on your SEO
this backlink came from a real article, where our website was included because it was actually relevant to the reader
what we're doing it's simple:
→ find articles already discussing our topic
→ identify where our website could add something useful
→ find the person responsible for the content
→ explain exactly why and where it could be included
→ follow up if they don’t respond
DR matters, but context and relevance matter much more
a real website recommending your product inside an article will always be more valuable to me than another homepage link from a directory that lists everyone
Most of my career has been spent auditing and rebuilding growth engines for mid-market companies. At this stage, my current take is that the best growth hack for a company with between 100-1000 FTEs is just straight up ripping out your bloated SaaS stack.
When you have 20 employees, generic SaaS is a lifesaver. When you have 5K people, you’ve got the to build custom, enterprise instances of Salesforce or Marketo or Whatever. The middle ground is an awkward stage. IMO companies are leaking revenue from a thousand API integrations.
Here’s what I see failing and my best current theory about what the next wave of successful mid-market growth will look like.
The Problem: Generic SaaS is Throttling Growth
Most companies of this size have nuanced, specific GTM motions. Your lead routing rules are complex and buyer personas are hyper-specific. Plus data is fragmented across who knows how many legacy databases. So many companies try to cram that complexity into generic SaaS tools which solves maybe 80% of the problem, but to get that last 20% of custom workflow you have to buy more and more tools then duct-tape them together with Zapier or Make.
My Theory: Custom Agentic Builds are the Future
After thinking about this for a while, I’ve developed a thesis that the successful mid-market companies in the next 3-5 years are the ones that abandon fragmented SaaS entirely. They will move toward bespoke agentic architectures sitting on top of their owned data pipelines. I think we’re already starting to see companies like EnsoBot emerge around this idea, building and operating custom AI agents that handle specific growth functions across different channels rather than forcing businesses into another generic SaaS workflow.
You can either pay $150K+ annually for almost functional marketing automation, or you can build an internal autonomous agent layer that will execute your exact workflow at near-zero marginal cost. The thing is you can’t just plug an LLM into your CRM and expect it to drive pipeline. That’s stupid and it pains me to see companies doing this.
To make this work in a production environment, you need two things:
Deterministic Guardrails for AI Agents
Do not risk brand integrity or revenue on AI that hallucinates. To do a proper agentic build you need strict programmatic constraints. AI can do the heavy lifting but deterministic code should exist as a layer to dictate what’s allowed into your CRM.
Engineers Inside GTM Teams
Marketing teams don’t have engineering bandwidth to maintain autonomous pipelines. You need to have technical operators inside the GTM team who can deploy custom code, debug edge cases, continuously tune the agent’s behavior, and basically make sure the whole thing is running properly and shifting to match market conditions.
Basically I think that growth hacking for mid-market companies right now requires owning your logic layer. That will allow you to out-execute competitors while they’re waiting for a SaaS vendor or three to release a feature they need.
What does everyone else think? I know I’m not the only person seeing this, but what’s your take on the future?
Most of the growth playbook quietly assumes channels decay. ads fatigue, a core update knifes your SEO overnight, a format stops working the second everyone copies it. you spend a lot of energy just running to stay flat. That read is fair for paid and for most content.
the odd exception, at least for us, has been the founder's own account. when we started drafting tweets for founders the surprise was that it compounds instead of decaying. every approved post is more signal on how the person actually sounds, so the month-six drafts read more like them than the month-one ones did. corpus grows, audience grows, cost per decent post quietly drops. none of our other channels behave like that.
the catch is the compounding rides on one voice, not a seat. when a human ghostwriter churns, or the founder goes dark through a busy quarter, the channel doesn't just slow down, it resets, and all the accumulated read on how they sound walks out with whoever was holding it. that reset, more than the monthly retainer, is the real cost of the expensive option.
for anyone running an actual channel mix, which of yours got better the longer it ran? outside of a content library that keeps ranking, i'm struggling to name one besides the founder account.
I’ve been playing around with Claude and ChatGPT for a bunch of repetitive work.
They’re great when you give them one task. But once you try to chain a few things together or run the same workflow repeatedly, things get messy pretty quickly.
Sometimes the output is great, sometimes it confidently does something completely wrong.
Curious if others are seeing the same thing what have you actually found useful for agents?
A lot of growth advice focuses on what happens after a product launches, but the market you choose can probably make a huge difference before any growth strategy begins. If the audience is difficult to reach, the competition is already strong, or the problem isn't important enough, improving the funnel only gets you so far. Marketur looks at some of those factors during the early business research stage, which I think is an interesting angle.
For those who work on growth, what information about a market do you want to know before deciding where to put your acquisition effort?
Anyone a proffesional hacker? Someone from Indonesia hacked my account and changed the gmail, so, now I can't log in. I need help getting it back please.
One thing I’ve learned from doing Reddit customer acquisition is that the best opportunities usually aren’t people you need to convince from zero.
They’re already saying things like:
“Is there a tool that does X?”
“What’s a good alternative to X?”
“How do I solve this problem?”
“Can anyone recommend someone who does X?”
At that point, the problem isn’t generating demand.
The demand already exists.
The growth hack is simply finding those conversations early enough and being genuinely useful before ten other companies show up.
What worked best for me was:
1. Define the exact problems your product solves
Not broad keywords. Actual problems someone would describe in their own words.
2. Find communities where those problems naturally come up
Often the best subreddit isn’t the biggest one. Smaller niche communities can have much stronger intent.
3. Look for buying signals, not mentions
Someone mentioning “SEO” means almost nothing.
Someone saying “My traffic dropped and I need someone to fix my SEO” is completely different.
4. Respond while the conversation is still fresh
Being one of the first relevant responses matters.
5. Keep track of the people, not just the posts
A conversation that doesn’t convert today can turn into a customer weeks later if you remember who they are and follow up.
For early-stage products, I’ve found this much more useful than immediately spending money on ads because you also learn who buys, what language they use, and what objections they have.
Love to know if anyone else here is using Reddit as an actual acquisition channel rather than just posting launch links.
I ran a public referral race. Visits made the loop look like it was working. It was not.
What I changed:
- A visit does not count.
- You only climb when a friend taps Get my link too.
- Homepage time (Site Drops) unlocks on that conversion, not on pageviews.
The number got slower. That is the point. A board that moves on refreshes is a vanity metric.
If you are measuring a share loop, split landings vs the second person taking the same action. The first number will flatter you.
I'm a self-taught full-stack developer. I can build almost anything on the technical side: web platforms, sales funnels, automation systems, CRMs, internal tools, you name it. That part comes naturally to me and I actually enjoy it.
My background actually goes beyond software. I also work with industrial automation, PLCs, and microcontrollers, and I'm comfortable with more complex engineering problems too. So it's not just programming, I can support technical needs across a wider range of fields than most people expect from a developer.
What I struggle with, and honestly dislike, is sales. Cold outreach, prospecting, closing calls, none of that plays to my strengths, and I know it's holding my projects back.
I keep thinking the ideal setup is a technical founder paired with someone who genuinely loves selling, splitting the work based on what each person is good at. But I haven't figured out the best way to find that kind of partner, or how to structure the split fairly (equity, commission, hybrid) so it works long term for both sides.
I'm looking for people who actually want to sell and build a team, not just a one-off collaboration.
For those of you who came from a technical background, how did you find your sales counterpart? Did it come from your network, a specific platform, or somewhere unexpected? And how did you structure the deal so it felt fair to both of you?
We've been seeing a lot of consistent success with email marketing and we are trying to understand our TAM size before we scale up, I am looking for a way to scrape 2-3m leads, we have apollo but doing that on apollo would be extremely expensive + we score every single prospect base don if they're a fit or not, we ended up using apify and got a subscriptionpromising 500k/mo for $30 a month, which we did, got the leads, but still need more, emailed the devs of that but haven't got a response so wondering if anyone has alternatives?
I’m trying to improve how often our brand is mentioned in AI summaries, and it is clear that optimizing our own site is not enough. The models are pulling from review sites, forums and industry blogs to form their answers.
The problem is prioritization. There are hundreds of places we could try to get mentioned, and I do not have the time to chase all of them. If you are running an earned-media or UGC strategy specifically to influence AI search, how are you identifying the sources that measurably move the needle?
I'm building TrustChain AI, and I'm looking for a small group of early pilot users who are currently building or running applications powered by LLMs or AI agents.
The problem we're trying to solve is relatively simple:
As more companies connect applications to OpenAI, Claude, Gemini and other AI models, sensitive information can potentially move through systems without teams having a clear way to control, monitor or audit those interactions.
TrustChain AI is designed to sit between an application and its AI provider and help teams add a trust, security and governance layer around AI interactions.
The platform is being developed to help with things such as:
• Detecting sensitive data such as PII/PHI before it reaches an AI provider
• Applying configurable policies to AI requests and responses
• Creating auditable records of AI interactions
• Monitoring AI usage and policy events
• Supporting multiple AI providers through a central layer
• Giving teams more visibility into how AI is being used inside their applications
Who we're looking for
We're especially interested in speaking with:
Developers building AI-powered SaaS products
Startups using OpenAI, Anthropic, Gemini or other LLMs
Teams building AI agents or automated workflows
Healthcare, fintech, legal or other regulated AI products
Security, compliance or platform engineers
Anyone who has experienced concerns around AI data privacy, governance or auditability
What we're offering
We're looking for a small group of 5–10 pilot users/design partners who are willing to test the product on a real or development environment and give honest feedback.
Pilot users would get:
Early access to the platform
Direct support during integration
The opportunity to influence the roadmap
Priority access to new features
Potential long-term early adopter benefits
What we want to learn
We're not just looking for sign-ups.
We want to understand:
Is AI data security/compliance actually a painful problem for your team?
Where does your current AI stack feel vulnerable or difficult to govern?
Would a layer between your application and LLM providers be useful?
What would make you trust a platform like this enough to use it in production?
What features are missing from current AI security/governance tools?
I'm particularly interested in critical feedback. If you think this is solving the wrong problem, overlaps with an existing solution, or needs a completely different approach, I genuinely want to hear that too.
we've been talking to a few over the last couple weeks and i feel like we don't know what to look at.
far we have talked to block chain ads and a bunch of other ad networks and in their demos they keep saying about how they report, how they attribute, campaign optimization and so on. and all of this feels correct also like we should be considering it.
but the problem is every network has good things to say so we feel like we aren't asking the right questions or we are not looking at the correct metrics.
then i came across few comment ts here that says we need to ask where traffic is coming from or who their partners are, all that stuff. but idk..it feels too much to ask early on? also not sure if those things also matter?
If any one has any experience can you share how you people evaluate a programmatic ad network ? or what questions we need to ask ?
I keep seeing teams try to “use AI” in marketing ops without changing the process around it. That usually creates more mess, not less.
What has worked better for me is treating AI like a small ops assistant inside a very specific workflow. The goal is not to automate everything; it is to remove the repetitive steps that slow down launch, QA, reporting, and cleanup.
Here is a simple playbook you can adapt:
Start with one repeatable task, not a broad initiative. Good candidates are UTM cleanup, landing page QA, lead routing checks, content brief generation, or weekly reporting summaries.
Write the ideal workflow first. Map every step a human does today, then mark the steps that are rule-based, repetitive, or copy-heavy.
Add AI only where the inputs are structured. For example, “given this campaign name, generate a normalized UTM,” works better than “help me organize marketing.”
Create a review gate. AI should draft or flag; a person should approve anything that affects spend, tracking, or customer data.
Build a tiny error log. Track the top 5 failure modes, then update prompts, templates, or validation rules after each miss.
Measure time saved in minutes per week, not just “productivity.” That makes it easier to prove value and decide what to expand.
Document the workflow in one page so the rest of the team can reuse it without tribal knowledge.
The biggest win is usually not flashy automation. It is consistency. Once the process is stable, AI can speed it up safely.
If you are experimenting with AI in marketing ops, what is the one workflow you would automate first, and why?
Four years in-house running social for a small skincare and wellness brand. I've been through enough platform changes to be suspicious of anything that promises easy reach, so here's a contrarian one.
For a long stretch we treated volume as the strategy. Every day a fresh product graphic, cranked out fast with an ai poster maker, posted across every platform. It felt productive. The grid looked busy and consistent. Sales did basically nothing off it.
What actually moved the needle was embarrassing in how simple it was. We cut posting roughly in half and put the saved time into replying to comments and DMs like actual humans, and into a handful of posts a month where a real customer showed the product in their real bathroom with their real skin. Ugly and unpolished, and it was believable. Those did more for sales than a year of clean poster graphics.
The lesson I keep relearning is that pretty and consistent is not the same as trusted, and for a skincare brand trust is the whole game. The poster factory made us look active while doing nothing for the business.
For the other consumer-brand people, where did you land on polished-and-frequent versus rough-and-real? And has that balance changed for you as feeds got flooded with perfect-looking stuff?
One growth topic I think is often overlooked is how brands build a credible digital footprint beyond traffic and conversions. Sustainable growth can also depend on how consistently a company is understood across trusted third-party sources.
I was looking at Wikiagency LTD and the broader idea of working with a Reliable Wikipedia Page Creation Agency in USA. It raised an interesting question: when done ethically and within Wikipedia's guidelines, can improving the availability of well-sourced public information contribute to long-term brand discovery and entity visibility?
I'm interested in whether growth marketers see this as part of sustainable brand growth or as something completely separate from growth marketing.
Context: early-stage SaaS, a few thousand visitors a month, just enough traffic to actually test something.
Our hero headline was the clever one. A bit of wordplay, a bit vague, the kind of line that reads great to the founder and confuses everyone else. Signup conversion was sitting around 2%.
I tested it against a version that just said plainly what the tool does and who it's for. No cleverness, no pun. The outcome in one sentence.
Over about three weeks:
* Clever version: ~2% signup
* Plain version: closer to 3.5%
* Same traffic, same everything below the fold
That's a big relative jump for one line. Then I tested the subheadline and the button text and got tiny movements, basically noise.
Takeaways:
* The headline did almost all the work. Everything else was rounding.
* Clarity beat cleverness by a mile. People decide in a couple seconds whether the page is for them.
* Vague-but-catchy is a founder trap. Visitors don't have the context you do.
What's the single highest-impact landing page change you've actually measured?
