If you have 20k to spend on social, with a goal being trafic to your website, what would you do ?
This budget must integrate everything needed.

Things I personally consider and will test independently:

1- regular influencer marketing. I guess I can have a big influencer (300-500k followers) sharing on both IG and TikTok.

2- creatives + ads: I guess an agency can make 5-10 creatives for 4-5k and I can spend the rest on a trafic campaign.

3- UGC at scale + reuse of creatives for TikTok/insta boost. I guess I can have 100-200 real ugc posted on ig+ tiktok for 2-3k$, have some organic growth and re-use the best creatives for ads with the 17-18k budget

4. Try to make it with a full organic strategy on my company page. I would pay a freelancer to manage my accounts and create videos. I can may be post 2-3 videos a week for 3-4 months with a 20k$ budget

Any other idea ?

I will try and compare all these tactics in the coming weeks and post my learnings here.

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The recommend a friend feature has been sitting untouched at the bottom of the post purchase email since launch and it shows. Starting to evaluate options this week with the goal of having something properly set up before the next acquisition push. What were the things that mattered most when you were choosing a platform and did the results come through faster than you expected?

Most early-stage B2B SaaS teams are working on the wrong problem.

Not because they're bad at their jobs — but because symptoms look like the problem.

"We need more pipeline" → they hire an SDR.

"Conversion is low" → they A/B test landing pages.

"CAC is too high" → they pause paid and blame the channel.

These are symptoms. The actual constraint is usually (at least) one layer below.

Here's the sequence I use to diagnose what's actually broken:

Foundation → Strategy → Execution

0. Problem + ICP (before everything) Is the problem urgent AND important? Both, not one. If customers are managing fine with a spreadsheet and some willpower, you don't have a distribution problem - you have a switching cost problem.

Clayton Christensen's JTBD and SPICED framework are useful here: what's the trigger that makes someone switch from their current workaround to you? That trigger is your real positioning hook.

Narrower ICP also means lower CAC and faster iteration cycles. It feels like a constraint. It's actually a multiplier.

1. Positioning April Dunford's framework is the clearest I've seen: who are you best for, compared to what alternative, and why does it matter to them? The "alternative" is usually a spreadsheet or an internal process - not a SaaS competitor. That reframe changes everything about how you message.

FletchPMM's message architecture adds the layer most people miss: a hierarchy of messages by use case and buyer role, not a single positioning statement.

2. Product & PMF Stop looking at sign-ups. Look at retention curves, organic referral rate, and feature adoption depth. Those are the signals.

Itamar Gilad's Total Impact Matrix is worth a look - balances customer value against business value so you're not optimizing one at the expense of the other.

Pre-define your success, guardrail, deterioration and quality metrics before you run experiments. (Spotify's model for this is the best real-life example.)

3. Pricing & Packaging Flows from positioning - not market comps. Price the unit of value your ICP actually experiences (usage, seat, outcome). If you're pricing based on what a comparable Series A company charges, you've skipped the thinking.

4. GTM Motion Deal size + buyer complexity + business model → motion. Not trends.

$5K ACV + single buyer = PLG or low-touch.

$50K ACV + buying committee = sales-assisted.

Copying a competitor's GTM without their stage, resources, and timing is how you burn runway while feeling busy.

5. Channel Strategy Last. Not first.

Three questions before spending: Does search demand exist for your category? Can you reach your ICP at viable unit economics? Do you have the conversion infrastructure to make a test meaningful?

The diagnostic I actually use:

🔴 Can't get meetings → Problem/ICP layer

🔴 Meetings don't convert → Positioning/Pricing layer

🔴 90-day churn → PMF layer

🔴 CAC unsustainable → GTM/Channel layer

🔴 "Channels don't work" → Unit Economics layer

Most of the time, the team is working one or two layers above the real constraint.

What layer is your company currently stuck on?

B2B Saas Growth Diagnostic: Dependency Map

I’m building Fanverse, a football community platform where fans discuss matches, predictions and World Cup debates.

We’ve started getting early traction but I’m struggling with one thing: distribution.

The product is getting better, but I’m not confident about how to consistently reach football fans across platforms without feeling spammy.

If you’ve built or marketed something in sports or community spaces:
• what actually worked for you early on?
• what would you avoid doing again?

Would really appreciate honest feedback.

We were on Ahrefs for about two years. When they moved to the credit system last year and bumped the price up, our renewal came in noticeably higher for the same usage we had before. That was the moment I started actually looking at what we were using it for day to day.

Turned out most of our team was using it for three things. Keyword volume checks, rank tracking for client reports, and the occasional backlink lookup. That's it. We weren't touching half the features we were paying for.

So I looked into what it would cost to just pull that data ourselves via API instead. DataForSEO came up a few times in the research. Pay as you go, no monthly minimum, you top up and use what you need.

Spent a couple of weekends building something basic. Nothing fancy, just an internal dashboard that pulls the data we actually need and formats it the way we want for client reports. The keyword volume data comes from DataForSEO, rank tracking same thing.

Three months in, and our monthly data cost is a fraction of what we were paying Ahrefs. The main difference is we only pay for what we actually pull now instead of a flat fee for a tool we were using at maybe 20 percent capacity.

The thing I didn't expect was how much easier the reporting side got once we could control the output format ourselves instead of exporting from someone else's dashboard and reformatting everything.

Not saying this makes sense for everyone. If you use Ahrefs heavily for backlinks and competitor research the depth is probably worth it. For us, the use case was narrow enough that it stopped making sense.

Anyone else gone down this route after the pricing changes?

Hot take maybe but I think the CRO tool market has a massive blind spot with mobile. Almost everything out there was designed for web first and then kinda adapted for mobile, which means heatmaps that don't capture swipe gestures, funnel analysis that misses mobile specific interactions, and A/B testing that can't handle the reality of app store release cycles.

Running the mobile CRO workflow through uxcam exposed things our old web focused tools never caught. The first week turned up three conversion killers nobody had spotted. autofill broken on samsung keyboards, the main CTA getting swallowed by the software keyboard on smaller screens, and a confirmation dialog rendering behind a custom modal on certain android versions that made the whole app look frozen

Every single one of these is a mobile specific problem that desktop focused heatmaps and funnels would never surface. If your primary conversion flow lives in a native app and you're still relying on web CRO tools, you're almost certainly missing issues like these.

We are in a very fast-moving niche (AI dev tools), and new competitors are popping up every week. I’m looking for ai seo services that can monitor the SERPs in real-time and alert us when a new domain starts ranking for our core keywords.

I want to be able to analyze their content strategy automatically and find gaps that we can exploit. My challenge is that traditional tools are too slow to update. Does anyone use an AI-powered seo service to stay ahead of the curve?

Chat GPT has launched its new ad platform after testing with select partners for months.

I’m curious to hear from anyone who has been through the signup process - how long is it taking to get approved? What are your early impressions of onboarding and the tool itself?

I’m really interested to see and test the targeting options considering the different ways people interact with LLMs vs search engines.

A third-party review site is hijacking our brand search traffic. They bid on "[Our Brand] Reviews," use a fake 5-star graphic to get the click, and then route our prospects to a page full of negative UGC.

Legal won't send a cease and desist because using our name to host reviews is "descriptive use."

I need to bypass Legal and hit them through Google Ads policy violations (misrepresentation/deceptive tactics).

How are you tracking, documenting, and successfully reporting these leeches at scale? What is your playbook for defending your brand SERP when Legal won't help?

I do not think AI UGC replaces good creators.

But I do think it changes when you should hire them.

Before, the workflow was:

Brief creator → wait → revise → launch → hope it works.

Now the workflow is:

Generate 10–20 AI UGC videos → test hooks → find signal → hire creators to remake winners.

That makes way more sense economically.

I use Instant-UGC for this: https://instant-ugc.com

The point is not to make the most polished video in the world. The point is to learn which message deserves polish.

Talk about how:

• strategy still matters
• messaging still matters
• but production speed changed completely
• landing pages, carousels, decks, reporting assets now take far less time

Very natural place for future workflow discussions.

A lot of founders I talk to treat affiliate marketing as a "later" thing like something you set up once you've got budget to spare and a team to manage it. I used to think the same way. But I think that's backwards.

The core reason, it's the only acquisition channel where you literally cannot lose money on a conversion. You pay after the sale happens. No wasted ad spend, no traffic that bounces. Compare that to paid ads where you're gambling on conversion rates with real money upfront.

A few things that changed my thinking:

On CAC: Affiliate CAC can look high in month 1-2. But affiliates aren't like ads, they don't stop working when you stop paying. A good review or recommendation keeps sending traffic for months. Paid ads stop the second your budget does.

On commissions: Get this wrong and the whole thing falls apart. For SaaS, 15-30% recurring is pretty standard. The math only works if you actually know your LTV and churn. If retention is strong, be generous. If churn is high, add caps or tiers.

On operations: The hidden cost nobody talks about is manual management. Spreadsheets + manual payouts + debugging attribution = your "free" channel suddenly costs 10 hours a week of engineering and ops time. Use software that integrates with your payment processor from day one.

On timeline: Don't judge it like paid acquisition. Month 1 will be quiet. Month 6 is where it gets interesting when affiliate content starts ranking and partners optimize their own funnels.

The real unlock for early-stage companies is that you probably already have the ingredients, customers who love the product, a content team, power users. An affiliate program just formalizes that into a measurable revenue channel.

Has anyone here launched an early-stage? What worked, what didn't?

Hey folks. I write about GTM and AI for a living. I talk to 5+ SaaS growth leaders a week (CMO, CRO, CEO), sometimes more, and I'm picking up on a pretty big shift. If I have this right, the buyer's journey is closer to being captive to LLMs than I suspected just a month ago.

I'm trying to understand trust - the trust people have in LLMs. Not trust as in "it writes decent copy." Trust as in "I bring it a problem and I actually believe the output enough to act on it."

If that's true, it changes everything about how buyers make decisions. If growth leaders trust AI as a genuine thought partner, their buyers probably do too. And that rewrites the buyer journey in ways I need to account for when I write. It can also change what I build / develop to help growth leaders get some grip on the shift.

Blabh blah blah... I'm curious where you actually are with all of this.

Some questions:

Trust: When you're working through a real strategic problem do you bring it to Claude or ChatGPT (aka, is an LLM a thought partner for you)? Do you trust the output enough to use it, or do you still treat it like a rough draft that needs heavy editing? Don't care about which LLMs your using per say....

Building: Anyone here moved past prompting into actually building things? I fell down the rabbit hole and have been cranking out agents and web apps galore. I feel like my community is doing the same but I surround myself with marketing geeks and freaks. Are you using Claude Code or Cursor or similar tools to create internal workflows, apps, automations? Agents?

Prompt libraries vs. agents: This one's more specific. Six months ago everyone was sharing prompt libraries. Do those still feel useful to you, or have you moved past that? If you've built trust with an LLM, you've mastered prompt engineering, whether you realize it or not. Claude 4.5 likely killed the structured prompt (again, geek but I'm seeing less demand for deeply structured prompts since then). If someone handed you a purpose-built agent or web app that solved a specific sales or marketing problem (identifying which companies in your market are showing signs of a problem you solve) would that feel more valuable than another prompt template? Or does that still feel like sci-fi?

Cheers

I’ve been experimenting with Answer Engine Optimization (AEO), especially for tools like Perplexity, and noticed a few consistent patterns that actually improve visibility and citation rates.
Here’s a simple breakdown of what seems to matter most right now:

1. Rely on credible, verifiable sources
2. If your content isn’t grounded in authority, it gets ignored or de-prioritized.

• Prefer primary sources (research papers, official docs, government sites)
• Use well-known publications when needed (not random blogs)
• Always ensure claims can be traced back

Perplexity and similar systems heavily favor content that can be cross-verified quickly.

2. Be information-dense (but not noisy)
   Thin content gets skipped. But fluff hurts even more.
   What works:

• Concrete facts
• Definitions + examples
• Numbers, dates, comparisons

What doesn’t:

• Repetition
• Vague marketing language
• Over-explaining obvious things

Think: “Can this answer survive being cited out of context?”

3. Structure matters more than style
   AEO systems extract meaning based on clarity.
   Best-performing formats:

• Short sections with clear headers
• Bullet points for multi-part answers
• Direct Q&A style when possible

Avoid:

• Long unbroken paragraphs
• Ambiguous headings
• Hidden conclusions

1. Always align with user intent
2. This is where most content fails.
3. Ask:

• What is the user actually trying to solve?
• Are they looking for a definition, comparison, or action steps?

Then answer that directly in the first few lines, not the end.

5. Freshness is a ranking signal (more than people think)
   Outdated info gets quietly filtered out.
   To stay relevant:

• Update stats and references regularly
• Include “last updated” context when relevant
• Prefer recent sources when available

We tried running audits across a few hundred URLs and quickly ran into issues.

Results fluctuate a lot between runs, and it’s hard to tell what actually changed vs what’s just noise.

Tried a few things like limiting runs, scripting batches, focusing on key pages, but nothing feels very reliable so far.

Curious how others deal with this in real setups?

Do you just ignore small changes, average results, or track something else entirely?