What is growth marketing?

Growth marketing is a long-term, data-driven system that compounds results across the full customer funnel.

What is growth hacking?

Growth hacking is a set of rapid, low-cost experiments designed to generate quick spikes in key metrics.

Can I combine growth marketing and growth hacking?

Yes—use growth hacks for early traction, then apply growth-marketing rigour to retain and monetise users.

What data do I need to predict churn?

You need billing status, last activity date, feature-use counts, plan tier and support ticket counts.

Which no-code AI tools work best for churn prediction?

Obviously.ai, Akkio and Google Vertex AI's GUI let you upload a CSV and create a churn model without coding.

How accurate are these models?

With six or more months of data and at least 200 churned users, expect 75–90% ROC-AUC accuracy.

Why include a stat in every LinkedIn post?

Numbers anchor credibility and increase reshares by approximately 25%.

How long should a viral LinkedIn post be?

Aim for 180–220 words; it's short enough to read but long enough to include story and CTA.

Which AI model works best for copy?

GPT-4o and Claude Opus consistently produce the highest-engagement B2B hooks in benchmark testing.

How can AI predict churn in SaaS users with zero code?

Youssef Moutik
Jun 1, 2025
2 min read

AI can predict SaaS churn without a single line of code by feeding your billing + product-usage data into a no-code AutoML tool (e.g., Obviously.ai, Google Vertex “Tabular” with the GUI, or Akkio). In under 30 minutes you’ll get a probability score for every user, which you can pipe into email or in-app win-back campaigns—no developer required.

TL;DR

Export Stripe + event logs ➜ upload to no-code AutoML ➜ get churn scores ➜ trigger retention flows. Accuracy ≈ 85 % in real-world tests.

Why predict churn at all?

Retention = growth: a 5 % churn drop can lift MRR 25 – 95 % over a year.
Cheaper than acquisition: saving a customer costs 5–7× less than buying a new one.
Segmentation unlocks personalisation: you can message “at-risk” users differently from power users.

The zero-code, 5-step workflow

Step	Action	Typical time
1. Export data	From Stripe (billing) + Mixpanel/Amplitude (usage) as CSV. You need user_id, last_login, events_30d, plan, status.	10 min
2. Clean & merge	In Google Sheets, VLOOKUP on user_id to combine billing + usage. Fill blanks with 0.	5 min
3. Upload to AutoML	Sign in to Obviously.ai (or Akkio) → Create Model → select “Churn” column as target.	5 min
4. Train & evaluate	Click Train. The tool tests dozens of algorithms and shows accuracy, ROC-AUC, top drivers (e.g., “events_30d”).	5 min
5. Deploy & act	Download the scored CSV or connect a Zap to HubSpot/Customer.io. Users with >0.6 churn probability go into a save-flow.	5 min

Total ≈ 30 minutes, all GUI clicks.

Deep dive: which features matter most?

Last login gap – days since last session.
Feature breadth – # unique features used this month.
Support tickets – unresolved tickets correlate with churn.
Plan type – promo plans churn 1.4× more.(AutoML surfaces these for you in “Top Predictors.”)

Mini-case: InboxHero (email SaaS)

Problem: 11 % monthly churn.
Action: Ran the above workflow in Obviously.ai with 5,200 accounts.
Result: Model AUC = 0.84, flagged 620 high-risk users. Win-back email sequence saved 173 of them → churn fell to 7.8 % in one month—worth $13.4 K ARR.

Common mistakes to avoid

Too few churn rows: ensure at least 200 churned users for solid training.
Using personally identifiable info: remove names/emails; you only need IDs.
Ignoring class imbalance: if churn = 5 % of rows, tick “balance dataset” in the tool, or it will predict “no churn” for everyone.