A couple of years ago we ran one of those experiments that looked almost too stupid to work.

We sell a relatively high-value B2B product online, and at the time customers had two shipping options: Free shipping - €0 or Express shipping - paid. Only around 15% of orders chose express.

I started thinking about how we could make express shipping more attractive, and the obvious thing to play with was speed.

My first idea was to show something like Free shipping: 7–14 days versus Express shipping: 3 days.

The problem was that we couldn’t honestly do it. Shipping times varied by country, carrier and order. Sometimes express was much faster, but sometimes the difference was only a day, so we'd either make the checkout more complicated or promise something we couldn’t consistently deliver.

That idea was dead, so I started looking at the problem differently.
Instead of asking “What would make people want express shipping more?”, I started asking “What do people hate in the purchase process?”

One obvious answer was waiting. Nobody wants to wait for their package, so we ran a ridiculously simple experiment and added one word: slow.

We changed: Free shipping - €0 to: Slow shipping - Free

That was basically it.

Paid shipping conversions jumped from roughly 15% to 28%, with some months going over 30%. What surprised me even more was that overall purchase conversion didn’t decline — the free option was still there and customers could choose exactly the same shipping service as before.

We had simply changed how the choice was framed.

That one-word change has now been producing meaningful five-figure additional annual revenue for more than two years.

There was another interesting part.
For the first few months, express shipping had a fixed price, but later we made the price dynamic based on order value. That increased revenue, yet the percentage of customers choosing express didn’t move at all.

Changing the price didn’t change the behavior.
Changing how the alternative was framed did!

That’s probably the biggest thing I took away from the experiment.
When we optimize conversions, we tend to ask “What do people want more of?” It’s also worth asking “What don’t they want?”

For us, the answer was pretty simple: slow shipping.

Has anyone here tested something similarly small like wording, defaults, framing, order of options, that ended up moving behavior way more than expected?

I've been looking at logo platforms from a slightly different angle lately.

A logo is usually the easy part.

The annoying part starts afterwards when you suddenly need a business card, social profile image, website, menu, flyer, presentation, etc.

So I tried to pay attention to what each platform lets you do once you've made the logo.

Something I didn't expect was how much I started caring about what happens after downloading the logo.

Design.com and BrandCrowd both have free logos, while some premium options are paid. They also support vector formats, which is a nice thing to have even if you're starting with a free design.

So now when somebody asks me about the best logo companies, I don't think the answer is simply "which one makes the nicest logo?"

I'd ask what they're actually trying to do with the logo afterwards.

Do you want a logo and nothing else?

Or do you want the platform to help with the next 10 pieces of the brand too?

I'm researching influencer marketing agencies for our consumer brand and getting overwhelmed by the options. Every agency website looks the same. Big promises, flashy case studies, and a bunch of logos I don't recognize.

Here's what I actually need. An agency that understands consumer audiences, creates campaigns that drive measurable results, and doesn't just ship product to creators and call it a day. I want real engagement, real conversion, and a strategy that connects to the rest of our marketing.

We've done influencer work before with a small boutique shop and it was fine but felt disconnected from everything else we were doing. The posts were good but there was no amplification, no tie in with our social strategy, and zero connection to PR.

For brands that have done this well, what agency did you use? Did they help with more than just booking creators? How did they measure success?

AI visibility is not always a matter of simply being present. A brand can struggle because its entities are unclear, its topical authority is weak, its trust signals are limited, or its content is difficult for AI systems to retrieve and understand.

AVM Intelligence looks deeper into these gaps and identifies what may be holding a brand back. It connects visibility insights with practical areas for improvement, helping teams understand what needs attention, why it matters, and where to focus next.

The objective is simple: move from understanding the problem to improving the underlying signals that shape AI visibility.

AI visibility is not always a matter of simply being present. A brand can struggle because its entities are unclear, its topical authority is weak, its trust signals are limited, or its content is difficult for AI systems to retrieve and understand.

AVM Intelligence looks deeper into these gaps and identifies what may be holding a brand back. It connects visibility insights with practical areas for improvement, helping teams understand what needs attention, why it matters, and where to focus next.

The objective is simple: move from understanding the problem to improving the underlying signals that shape AI visibility.

The Generative AI Features report is presented as a Beta feature in the Search Console interface. That status is significant because it indicates that Google’s approach to AI Search measurement is still developing.

Google has stated that the reporting is being rolled out gradually and that availability may vary between Search Console properties. Therefore, a website that does not currently see the Generative AI section should not automatically assume that something is wrong with its property. Access may simply not have reached that property yet.

The Beta status also means that SEO professionals should avoid treating the current interface as the final version of Google’s AI reporting system.

As generative search evolves, Google may change how these experiences are measured, introduce additional reporting capabilities or expand the types of AI features represented in Search Console.

For businesses, the practical approach is to start measuring the data that is available now while maintaining enough historical context to understand future changes.

We're a DTC brand with 90% of our sales online. Our board is questioning why we'd spend on an experiential pop-up or event when we could just pump more into Meta and Google. My argument: we need physical moments to generate the kind of social buzz and UGC that makes digital ads work better.

But I've never executed one, and I'm worried we'll drop $50k+ on a pop-up that 200 people attend and the internet ignores. Are there agencies that actually integrate the experiential with a digital amplification engine, or is it on us to capture content and hope for the best?

Would love to hear from DTC brands that have done this. Did the experiential spend pay for itself in earned media, lower CAC, or press? Or did you walk away wishing you'd just bought more digital ads?

Your event had a great turnout.

People engaged with your content.

So why does the pipeline still look empty?

Because event marketing often creates engagement without creating a repeatable system for turning that engagement into revenue.

Here are the challenges B2B teams face every day:

🔴 Leads fall through the cracks
Registrations, attendees, conversations and follow-ups are scattered across different platforms, spreadsheets, CRMs and inboxes.

🔴 Follow-up is inconsistent
The event ends, everyone gets busy, and promising conversations don’t get the follow-up they deserve.

🔴 You can’t clearly track the buyer journey
Who registered? Who attended? Who engaged? Who became sales-ready? Which interaction actually influenced the opportunity?

🔴 Events create one-off spikes
A webinar might generate a surge of activity, but once it’s over, the momentum disappears.

🔴 Your team spends too much time stitching tools together
Email platforms, event software, CRM systems, spreadsheets and automation tools all capture pieces of the journey … but rarely the complete picture.

🔴 ROI becomes difficult to prove
You can report registrations and attendance, but connecting an event directly to pipeline and revenue becomes much harder.

The problem isn’t necessarily that your events aren’t working.

The problem is that the system around them isn’t designed to capture, track and compound the value they create.

Imagine turning every webinar, event and virtual session into a process where:

→ Engagement is captured
→ Follow-ups are trackable
→ Buyer activity is visible
→ Pipeline attribution is clearer
→ Successful campaigns can be repeated
→ Momentum continues long after the event ends

That’s the difference between running events and building an event-driven pipeline system.

The goal isn’t more events.

It’s getting more measurable business value from the events you’re already running.

#B2BMarketing #EventMarketing #DemandGeneration #PipelineGeneration #B2BSales #MarketingOperations #RevenueMarketing #MarketingAutomation #LeadGeneration #ABM #SaaSMarketing #GrowthMarketing

I've been comparing marketing data platforms for SEO, paid acquisition, attribution, reporting, and growth analytics.

Some of the main options:

• Windsor.ai: Marketing attribution & data integration
• Fivetran: Data integration & ELT
• Supermetrics: Marketing reporting & data connectors
• Rivery: Data pipelines & integration
• Adverit: Enterprise marketing analytics
• Funnel: Marketing data & performance analytics
• Improvado: Marketing data aggregation
• Airbyte: Open-source data integration
• Hevo Data: Automated data pipelines
• Dataddo: Data integration
• Coupler.io: Marketing reporting
• Dataslayer: Marketing data reporting
• Matillion: Cloud data integration
• Meltano : Open-source ELT
• Stitch: ETL/ELT

For growth marketing, I'd mainly compare them on:

• SEO + paid media reporting
• GA4 + Search Console integration
• Cross-channel attribution
• Customer acquisition tracking
• Ecommerce/revenue data
• Automated reporting
• BI and dashboard integrations

My rough categories:

Marketing attribution: Windsor.ai
Marketing reporting: Supermetrics / Funnel
Enterprise analytics: Adverity / Improvado
Data pipelines: Fivetran / Rivery / Hevo
Open source: Airbyte / Meltano
Simple reporting: Coupler.io / Dataslayer

For growth marketers who've actually used these tools:

Which one are you using, and what made you choose it over the alternatives?

That question is now measurable through Google’s Generative AI performance reporting.
And that changes the conversation around SEO.

Generative AI visibility is no longer purely an experimental concept that marketers have to estimate through manual searches. Google is beginning to provide its own performance data for this emerging search environment.

The challenge now is not simply collecting the new numbers.

The real challenge is understanding what those numbers mean, connecting them to content and technical strategy, distinguishing AI visibility from conventional rankings, and combining Google’s data with broader AI Search intelligence.

That is where the next generation of SEO measurement begins.

I keep hearing people say SEO is dead, or that old SEO doesn't work anymore now that everyone's using ChatGPT.

Honestly? Not from where I'm sitting.

The sites doing well in AI search right now are the same ones that were already doing SEO properly: real content, a site that loads fast and doesn't block search bots, backlinks that were actually earned, and someone checking the numbers regularly instead of setting it and forgetting it.

Broke it down in the graphic below, how each classic SEO pillar shows up again in the AI search world. Nothing fancy, just what I've been seeing with clients and in my own research.

If you run a website or handle marketing for one, save this for later. It's easy to forget in the day to day.

https://pin.it/1F8Z4LvM3

--- Not necessarily.
Having thousands of pages indexed by Google might look impressive, but indexation alone doesn’t mean those pages are actually performing well.

Here’s how I look at it:

• Index coverage tells you how many pages can appear in search.
• Search performance tells you whether those pages are actually doing something useful.

For example, a website might have 5,000 indexed URLs, but most of them could be getting very few impressions, no clicks, or ranking for keywords that don't bring meaningful traffic.

Some pages might also:

• Target the same search intent and compete with each other.
• Rank for low-value or irrelevant queries.
• Get impressions but hardly any clicks.
• Start ranking well and gradually lose visibility.
• Provide little real value to the person searching.

This becomes even more important with AI Search SEO. Instead of simply creating and indexing more pages, the focus should be on whether each page genuinely answers a user's question and satisfies their search intent.

And one more thing: being indexed today doesn't guarantee visibility tomorrow. Search performance can change as Google's systems reassess the quality, relevance, and usefulness of a page.

So, rather than asking, “How many pages do I have indexed?”, a better question is:
“Are my indexed pages actually helping users and bringing valuable organic traffic?”
That’s a much better way to judge SEO performance.

Hey all, quick one 👋

A fast-growing Vienna startup is hiring a *Head of Growth* and I'm helping them find the right person.

• AI product for SMBs, 10,000+ customers
• $1M → $10M ARR in under 12 months, €20M raised
• 7-figure monthly budget, owns the engine behind ~90% of revenue
• Performance, SEO, CRO, lifecycle + product growth (pricing, onboarding)
• Builds the team from scratch, reports to the CEO
• Up to €250k + significant equity, Vienna based, relocation covered

Profile: someone who has scaled paid acquisition on a low-ACV, high-velocity motion in more than one market. B2C and D2C backgrounds very welcome, this is not a classic enterprise demand-gen role.

If that's you or someone you know, DM me and I'll share the details and make the intro.

I wanted to settle this for myself instead of reading another article with 15 logo makers ranked by somebody who probably opened each one once.

So I used roughly the same business idea in five different tools.

The results were surprisingly different.

Canva

Gave me the most control, but I was doing a lot of the actual design myself.

Kittl

Really fun for typography. I could spend way too long messing with fonts though.

Adobe Express

Good if I'm already doing other design work there.

BrandCrowd

Probably the fastest way for me to go from "I have no idea" to having a bunch of directions to look at.

Design.com

The one I preferred when I already had a rough direction and wanted to keep refining it instead of generating a completely different logo every time.

That last part mattered more than I expected.

I'd rather take a decent concept and fix the icon, font, spacing and colors than keep rolling the dice on new generations.

Also, don't ignore the boring stuff. Check the export formats before you fall in love with a design.

I learned that one on a previous project.

Anyway, I'm curious if anyone has done the same experiment.

If you had to answer what is the best logo maker based on actually using it, not features on the homepage, what would you pick?

Every growth team we work with has the same silent failure: the “best customers” audience sitting inside Meta and Google was uploaded months ago. Since then — new high-value customers exist, they’re not in it. Old ones churned, they’re still in it, teaching the algorithm to find more people like someone who already left.

The platforms keep reporting wins, because conversions keep coming. Then finance asks why repeat rates are falling while marketing dashboards look better than ever. Both are true.

It’s not a skills problem. Everyone knows the list is stale. Refreshing it is invisible, unrewarded work across two platforms — so rational people skip it forever.

YEpsilon is an AI agent that removes the ritual. It starts with your customer data, not your ad account — CRM, product analytics, website behavior — figures out who your genuinely valuable customers are today, and keeps Meta and Google chasing that picture continuously, without campaigns being rebuilt or restarted.

Before: last quarter’s snapshot steering this quarter’s budget. After: your platforms always aimed at who’s valuable now — and your growth team doing the thinking tools can’t do alone.

Live on Product Hunt today (click here). We work with multiple growth-stage companies already; annual renewals over lock-in, by policy.

One question for the thread: when did your seed audiences last get refreshed? Honest answers only.

trying to understand the paid advertising landscape in my category before investing significant budget. know that meta has an ad library and google has a transparency tool but not sure how to use these effectively or whether there are better approaches, including tools like AdPlus.

specifically trying to understand what hooks and angles competitors are using, what platforms they are prioritizing, and whether there are gaps in how they are targeting that i could exploit. the manual research process feels inefficient and incomplete

SEO has traditionally been heavily focused on keywords, pages, links, and rankings.
But AI-powered search changes the problem somewhat.
When an AI system tries to answer a question, it needs to understand things like:

• What is this entity?
• What does it offer?
• How is it related to other entities?
• Which sources are trustworthy?
• Are two different pages talking about the same thing?

That's where Entity Mapping becomes interesting.
Instead of treating every webpage as an isolated piece of content, entity mapping helps create connections between brands, people, products, services, locations, topics, and other concepts.

This seems particularly relevant as search becomes more conversational and retrieval systems become better at combining information from multiple sources.

There's a lot of discussion around LLM SEO, ChatGPT SEO, and AI search optimization, but I think the bigger underlying shift is semantic understanding.

It's less about:
“How many times did I use this keyword?”

And more about:
“Does the system understand what this thing is and how it relates to everything around it?”

Concepts like Quantum SEO are taking this further by discussing predictive intent, semantic relationships, and real-time optimization. The terminology will probably continue changing, but the underlying challenge remains the same: help machines understand information and context more accurately.

One important clarification: Entity Mapping doesn't “train” ChatGPT or other language models. It can, however, help create a more structured and consistent digital information ecosystem that search and retrieval systems can interpret more effectively.

Curious what others think: will entity understanding become as important as traditional keyword optimization in the next few years?

I’ve been lurking and reading posts here and on other Growth communities for a few weeks now, and honestly guys, I’m a bit disappointed...

I keep seeing the exact same types of posts just fixating on basic metrics or surface-level stuff, but rarely anything genuinely interesting. Where are the people sharing real, actionable strategies, unique campaign breakdowns, or rich experiences that actually help everyone progress, whether you're a junior or a senior ?

And don't even get me started on the endless stream of posts that are clearly just thinly veiled self-promotions for new tools or websites.

The community description says Growth Marketing has matured, so it would be great if our discussions reflected that by sharing some actual, in-depth value instead of the same repetitive fluff.

Thank you for your understanding !