Been researching predictive analytics tools for marketing teams, and one thing stood out:

Most analytics platforms are still great at telling you what already happened.

But the bigger opportunity is predicting what happens next.

The useful questions are becoming:

• Which customers are likely to churn?
• Which leads are most likely to convert?
• What users show buying intent?
• Who has the highest future value?
• What action should marketing take next?

Some tools I found interesting:

• Salesforce Marketing Cloud → enterprise marketing automation with Einstein AI predictions
• Intempt → predictive attributes, customer scoring, AI segmentation, and activating insights through campaigns
• Adobe Customer Journey Analytics → complex customer journey analysis and enterprise insights
• Power BI → forecasting and predictive dashboards for teams already in the Microsoft ecosystem
• Tableau → AI-powered analytics, forecasting, and executive reporting
• ThoughtSpot → conversational AI analytics and self-service insights
• Improvado → marketing data aggregation and AI-powered forecasting

The biggest difference I noticed is whether a tool only gives you a prediction, or whether it helps you actually do something with it.

A churn score is interesting. A churn segment that automatically triggers a retention campaign is much more useful.

Curious what teams are actually using today. Are predictive analytics part of your marketing workflow yet, or are most people still relying on dashboards?

I’m going to be real with you. When I started blogging 3 years ago, I was drowning. Gurus told me to write 5,000 word pillars. Others told me to just "use AI to scale."

Result? 0 traffic. 0 engagement. Just digital noise.

I learned the hard way through trial and error. The problem wasn't my writing. It was my strategy.

Here is the exact workflow that saved my blog. I wish someone had drawn this out for me on day one.

The 2026 "Question-First" Workflow (Copy this)

Step 1: The Seed
Pick a broad seed (ex: chatgpt or claude).

Step 2: The Filter (This is where you win)
Plug the seed into SEMrush or Ahrefs. Do not look at the generic keywords.

Step 3: The Goldmine (The "Question" Filter)

• Filter #1: Questions only (Who, What, Why, How, When).
• Filter #2: KD (Keyword Difficulty) Max 29. (Don't touch KD 30+ as a new blog).
• Filter #3: SV (Search Volume) >500. (Ignore the 10 volume vanity metrics).

Step 4: The Secret Sauce (INTENT)
This is the most important part of 2026.
Look at the "Intent" column. If you ignore this, you lose.

• Informational Intent = Write a Listicle or "Ultimate Guide."
• Commercial Intent = Write a Comparison or "Vs" post.
• Transactional = Write a Review (But wait until you have traffic).

Step 5: Cluster & Clean
Select your target keywords. Cluster them by topic. Remove duplicates. You should have a list of 10-20 specific questions.

Step 6: The "Content Gap" Assassination
Before you write a single word, Google the top 3 results for that question.
Ask three brutal questions:

1. Are they actually answering the question? (Most don't).
2. What is missing? (Date? Screenshot? Specific example?).
3. Is the info outdated? (If the post is from 2023, you win immediately).

Step 7: The Brief (Don't skip this)
Write the outline + brief before you open the editor. Headings, sub-headings, data points needed.

Step 8: The Write
Write like you are talking to one human friend. Not a bot. Not Google.

Step 9: Edit & On-Page Ops
Run it through the SEMrush Writing Assistant (or SurferSEO).
Fix readability. Add internal links. Optimize the meta description.

Step 10: Publish & Walk Away (The hard part)

The "Reality Check" You need to hear

>

You are going to write a masterpiece and get 0 views for 3 months. That is normal.

If you pick the wrong question (too hard, too broad), the post dies. That is not your fault. Just move to the next question.

The Mantra that saved my sanity

Slow is smooth. Smooth is fast.

Take it slow. Target the right questions (literally the "how" and "why" queries). Refine your briefs. Write as much as quality allows.

Do not burn out trying to post 5x a week. Post 1x a week with this system. In 6 months, you will overtake the guy posting AI slop every day.

curious how people are measuring activation for ai products.

with traditional saas, it's usually straightforward:

• created a project
• invited a teammate
• connected a data source

but with ai products, a user can send 50 prompts and still never come back.

is activation:

• first successful outcome?
• first repeat session?
• first workflow completed?
• first team member invited?

i've been looking at tools like Mixpanel, Amplitude, PostHog, and Intempt, and it feels like the industry is moving away from measuring clicks and events toward measuring outcomes and engagement quality.

for teams building ai products, what's the single event you track that best predicts long-term retention?

I'll go first, here are some tools that surprised me recently:

• Clay for enrichment workflows
• Perplexity for research
• Descript for editing
• Intempt for customer analytics/journey visibility

I work in SaaS marketing and was looking for tools that could handle more than just pipeline management.

Mainly wanted better visibility into:

• customer behavior
• segmentation
• journeys
• enrichment
• retention workflows

A few newer platforms I genuinely found interesting:

1. Attio Probably the cleanest UX out of all of them. Very flexible and much less rigid than traditional CRMs.
2. Clay Honestly feels more like a GTM engine than a CRM. Amazing for enrichment and outbound workflows, but definitely has a learning curve.
3. Folk Really lightweight and collaborative. Felt good for smaller teams that don’t want enterprise complexity.
4. Close Fast, simple, and still one of the better systems for outbound-focused teams.
5. Intempt Interesting because it combines CRM-style workflows with analytics, journeys, segmentation, and personalization instead of treating them as separate tools. Still early, but I liked the direction.

Curious what newer CRM platforms others here are actually using lately.

Feels like the category is changing pretty quickly.

I used to write using ChatGPT mainly and saw some good results in SEO. But lately I've been experimenting with Claude and the ones that it pro in terms of rankings and clicks.

I even ran the /intel skill for the project intempt i've been working on.

I was wondering if anyone else experimented the same. If yes, could you please share me your findings.

Also which tool do you use for writing High quality content?

I kept watching the usual stuff:

• session frequency
• onboarding completion
• CTRs
• feature usage

But one pattern ended up mattering way more than I expected:

users who stopped exploring the product early almost always churned later.

Not immediately.
Usually weeks later.

What confused me was that a lot of these users still looked “healthy” in dashboards:

• still logging in
• still active
• sometimes even clicking emails

But they stopped:

• trying new features
• exploring workflows
• customizing anything
• going beyond their initial use case

The product basically became a static utility instead of something they were discovering value from.

And weirdly, that predicted churn better than most of the metrics I was tracking.

I started noticing this more clearly after spending time across tools like GA4, Mixpanel, PostHog, and Intempt’s analytics because each one exposed different parts of the user journey.

The frustrating part though was how fragmented everything still felt:

• acquisition data in one place
• product behavior somewhere else
• retention analysis in another dashboard

Curious if others have seen similar patterns.

What’s the earliest churn signal you trust the most?

https://preview.redd.it/48dkzvfz623h1.png?width=1424&format=png&auto=webp&s=8db44ff9e6f8c63f7b4174d898937fefcfdb7106

I tried reverse-engineering RAG using Perplexity and Claude to understand how AI retrieves and shows results to users.

Here’s a simple breakdown of the entire AEO process:

I asked:

>“What are the best Mailchimp alternatives in 2026?”

To answer that properly, the AI first needs to figure out:

• what tools actually exist
• which ones are still relevant in 2026
• pricing and feature differences
• which tools fit specific use cases (ecommerce, automation, creators, etc.)

So the goal becomes:

>“Find realistic Mailchimp alternatives people actually use in 2026.”

Then the search process starts.

Step 1: AI searches the web

The AI turns your question into search-style queries like:

• “best Mailchimp alternatives 2026”
• “email marketing platforms for ecommerce”
• “Mailchimp competitors pricing”

It sends those to a search backend (basically similar to search engine APIs).

The search system returns results like:

• URL
• page title
• snippet/description
• publish date
• metadata

Example:

Title:
“11 Best Free & Paid Mailchimp Alternatives Compared (2026)”

URL:
brevo.com/blog/mailchimp-alternatives

Snippet:
“Discover the best Mailchimp alternatives with better pricing, automation, and features.”

Published:
2026-02-16

At this point, AI has not “trusted” the page yet.

It only has candidate sources.

Step 2: The filtering process starts

Now the AI evaluates whether the source is worth using.

1. Domain authority & publisher quality

The system checks things like:

• Is this a real company?
• Is this site known in the SaaS/marketing space?
• Does the website consistently publish content about this topic?

For example, Brevo is a real email marketing platform.

Its blog regularly publishes:

• email marketing guides
• automation tutorials
• platform comparisons
• Mailchimp alternatives
• pricing breakdowns

That makes it more trustworthy than some random blog posting about 50 unrelated topics.

2. Content structure

The AI also checks whether the page is structured like a useful buyer guide.

For example:

• does it compare multiple tools?
• does it explain features?
• pricing?
• “best for” use cases?
• pros/cons?

Pages titled things like:

• “Best Email Marketing Platforms”
• “Top Mailchimp Alternatives”
• “Best Ecommerce Email Tools”

usually score well because they directly match the user’s intent.

A random opinion article usually scores lower.

3. Recency

This matters a lot.

Email marketing platforms change pricing and features constantly.

So a 2026 article is more useful than a 2021 article.

The system checks:

• publish date
• “2026” in title
• updated timestamps

Fresh content gets prioritized.

4. Cross-checking with other websites

This is the important part most people miss.

The AI does NOT blindly trust one source.

Instead, it compares multiple sources together.

Example:

Brevo’s article mentions:

• ActiveCampaign
• HubSpot
• Omnisend
• Moosend
• GetResponse

Then independent review sites mention many of the same tools.

When multiple unrelated sources keep mentioning the same platforms, confidence increases.

That overlap matters a lot.

Step 3: Building confidence

The AI starts combining signals.

If a tool appears in:

• Brevo’s blog
• independent reviewers
• SaaS comparison sites

then it’s probably a legitimate market option.

At this stage, the system starts extracting facts like:

• pricing
• automation features
• ecommerce support
• SMS/email capabilities
• Shopify integrations
• best-fit use cases

Vendor-specific claims are treated differently though.

For example:

If Brevo says:
“We have the best automation.”

The AI does NOT treat that as objective truth.

But if Brevo says:
“Our Business plan includes automation and transactional email.”

That’s treated more like primary-source information.

Step 4: Ranking the final recommendations

After gathering data from multiple sources, the AI narrows things down.

Usually based on:

• relevance to your use case
• frequency across trusted sources
• recency
• feature fit
• pricing fit

So platforms like:

• Omnisend
• Brevo
• ActiveCampaign
• Klaviyo
• HubSpot
• GetResponse

bubble up because they repeatedly appear across high-quality sources.

What “fetching snippets and metadata” actually means

When people hear this, it sounds complicated.

It’s basically just structured search result data.

Something like:

{
  "title": "11 Best Mailchimp Alternatives Compared (2026)",
  "url": "https://www.brevo.com/blog/mailchimp-alternatives/",
  "snippet": "Compare pricing, automation, and features.",
  "date": "2026-02-16",
  "type": "comparison_guide"
}

From just this small block, the AI already learns:

• topic relevance
• freshness
• likely content quality
• search intent match

Then it decides whether the page is worth fully reading.

The easiest way to think about it

Honestly, the AI process is very similar to how experienced marketers do research manually.

You would probably:

1. Google “best Mailchimp alternatives”
2. Open a few comparison articles
3. Compare overlapping recommendations
4. Ignore obvious garbage
5. Shortlist tools
6. Test them yourself

AI basically automates that workflow.

It:

• finds candidate pages
• filters weak sources
• compares overlapping recommendations
• extracts structured facts
• summarizes the useful parts

So when AI says:

>“Brevo’s blog is a valid source”

it usually means the page passed multiple checks:

• relevant topic
• strong domain
• fresh content
• structured comparisons
• aligned with other independent sources

Not because the AI blindly trusted one random blog.

What to do next?

• Focus on publishing new, valuable, relevant and updated content.
• Match the search intent as much as possible.
• Make sure to add the search-style queries with the relevant year in the intro, and in Meta tags. (You can just provide a prompt that the user will type in and ask Perplexity for the exact search query it used to retrieve results)
• If you are using Claude - create a skill using this information and run it to optimize any content on final edit.

Hi,

I'm not a designer, I am currently working on a side project building my own SaaS tool. I'm a marketer by default - I tried all these AI vibe designing stuff.

but none come close to a human designer.

I dont' want to use framer or other templates either. I want to learn how to properly design a landing page, so that it has character.

So if I can get some resources, to learn design that would be awesome!

Thanks in advance!

I created a new account from a different profile, then I shared a post URL on a community, and I got banned immediately.

Did reddit become super strict now?