Understanding Google Analytics 4 (GA4) isn’t optional – it’s essential for running high-performing PPC campaigns.
The interface? Confusing.
The modeling? New.
The terminology? Often unclear.
But predictive analytics? That’s where things start to make a bit more sense.
This article is going to walk you through:
- What each predictive metric means (as simply as possible).
- Where to find them in GA4.
- How to activate and use them for real campaign outcomes.
Whether you’re looking to increase ROAS, trim wasted spend, or personalize messaging with greater precision, predictive analytics helps you do it faster and smarter.
What GA4 predictive metrics are – and why they matter for PPC
Powered by Google’s machine learning models, GA4’s predictive metrics translate raw behavioral data into forward-looking insights.
Instead of just reporting on what happened, you get a glimpse into what’s likely to happen next.
Think of them as a link between data science and media buying.
They shift your focus from interpretation to prioritization, so you know:
- Who to target.
- When to target.
- With what message.
For PPC pros managing tight budgets and strict ROAS goals, that’s a competitive edge we should all have in our arsenal.
Getting started: Where to find predictive metrics in GA4
Predictive metrics are accessible in two key places:
Audience Builder
GA4’s Audience Builder lets you create custom segments of users based on:
- Behavior.
- Predictive metrics.
- Demographics.
- Or any combination of conditions.
These audiences can be used to analyze performance inside GA4 or exported to Google Ads for targeting, exclusion, or bidding strategies.
Use predictive templates to create dynamic audience segments such as:
- “Users likely to purchase in the next 7 days.”
- “Users likely to churn in the next 7 days.”
Once the audience is created, it can be exported to your linked Google Ads account and used in campaigns that support audience targeting, such as:
- Search.
- Performance Max.
- YouTube.
In most cases, they’re added as audience signals, helping guide the system toward higher-value users rather than acting as strict targeting filters.
Dig deeper: How to leverage Google Analytics 4 and Google Ads for better audience targeting
Explorations > User Lifetime report
Part of GA4’s Explorations toolset, the User Lifetime report allows for deeper, custom analysis.
It helps you see how users behave over time, from their first visit to their latest session, including how likely they are to purchase or churn (if predictive metrics are available).
It’s ideal for identifying patterns across:
- Acquisition sources.
- Engagement depth.
- Conversion timing.
Use this for deeper insight into user-level behavior, pairing purchase/churn probability with:
- Engagement metrics.
- Channel acquisition.
- Funnel stage performance.
GA4 separates users into:
- Those with predictions (enough training data exists).
- Those without (insufficient data).
Activation requirements (a.k.a. the fine print)
GA4 doesn’t just hand out predictive metrics. Your property must meet strict eligibility thresholds:
Minimum Event Volume (rolling 7-day window in the last 28 days):
- 1,000 users who completed the relevant event (e.g., purchase).
- 1,000 users who didn’t.
Correct event setup:
- Send purchase or ecommerce_purchase events.
- Include both value and currency parameters.
If your data doesn’t meet quality/volume thresholds, the model won’t run or may pause later if data drops below standards.
To check eligibility, head to Audience Builder > Suggested Audiences. If predictive templates are missing, your property hasn’t qualified yet.
Dig deeper: How to combine GA4 and Google Ads for powerful paid search results
Tactical use cases for PPC campaigns
Once you’ve activated predictive audiences and synced them to Google Ads, here’s how to turn that insight into performance.
Remarketing to ‘likely to purchase’ users
Goal: Push high-intent users over the conversion line.
Steps:
- Create a “Likely 7-day purchasers” audience in GA4.
- Add filters like product page views or session value.
- Sync to Google Ads.
Activate in:
- Search Campaigns: If you’re running a Search campaign, you can layer “likely to purchase” audiences to customize ad messaging or monitor performance. However, with smart bidding now standard, predictive audiences serve best as signals, not guaranteed targeting layers.
- YouTube/Display: Use urgency-driven creative like “Only a few left!” or “Don’t forget your cart.”
Excluding ‘likely to churn’ users
Goal: Save budget by avoiding users who are unlikely to return.
Steps:
- Create an audience using “Likely 7-day churners.”
- Combine with inactivity filters (e.g., last transaction > 30 days).
Apply in:
- Performance Max / Display / YouTube: Add as an excluded audience.
- CRM/email (optional): While predictive audiences can’t be exported directly from GA4 to your CRM or email platform due to privacy limitations, you can mirror the churn logic in your own database (e.g., users inactive for 30+ days) and trigger low-cost winback flows like “We miss you” emails or loyalty perks.
Lifecycle-based segmentation
Goal: Serve the right message at the right moment.
GA4 allows you to create combined predictive audiences (e.g., high purchase probability + low churn risk) to target users at different lifecycle stages.
These segments can be used in Google Ads campaigns with tailored creative, from gentle brand recall to time-sensitive offers, based on user readiness.
Segment by:
- High purchase + low churn: Loyal, conversion-ready.
- High churn + low purchase: At-risk, disengaged.
Campaign flow:
- Phase 1 (Reminder): Short, brand recall ads.
- Phase 2 (Incentive): Limited-time offers.
- Phase 3 (Push): Countdown or last-chance messages.
Where GA4’s predictive metrics fail and what to do about it
Like any machine learning tool, GA4’s predictive metrics come with caveats:
- Minimum thresholds apply: If you don’t meet the 1,000 returning users who triggered the relevant conditions and 1,000 returning users who did not trigger the conditions split, the model won’t run.
- Daily refresh lag: Predictions update once every 24 hours, not in real-time.
- Use in context: Predictive segments work best with other targeting (remember they act as signals, not guaranteed targeting) layers, like CRM data.
- Model fragility: Volatile traffic or seasonality can degrade prediction quality. If ROAS dips, revisit your segment logic and scale cautiously.
Drive Google Ads efficiency with GA4 predictive audiences
When I cracked open my Dell laptop 16 years ago, determined to run my first Google Ads campaign, I didn’t expect that data science would eventually become part of the job. But here we are.
GA4’s predictive metrics aren’t perfect, but they’re a step forward.
They help shift your focus from reporting on what happened to anticipating what’s likely to happen next:
- Who’s likely to buy.
- Who’s likely to churn.
- Where your ad dollars will work hardest.
You also don’t feel like you need an advanced degree to use them.
With clean event tracking, enough data, and a clear use case, predictive metrics can become a practical part of your PPC strategy.
- Start small.
- Test one predictive segment.
- Monitor the lift.
- Then scale what works.
Dig deeper: Your guide to Google Analytics 4 attribution