When Optimizing Campaigns on 'Free Trials Sign-Ups' Isn't Resulting In Revenue...
- Brady Hancock
- Oct 29
- 7 min read
Conversion Signal Engineering for Advertising Success
The Misalignment Problem
If you run a subscription-based business and invest in paid ads, there’s a good chance you’re optimizing for the wrong signals. It’s not your fault—most platforms make it easy to optimize for top-funnel actions like free trial starts, sign-ups, or installs. But if those actions don’t correlate with actual revenue or retention, you’re effectively training the algorithm to bring you more low-value users.
Over time, a gap can form between what the ad platform thinks "success" looks like and what actually drives your business forward. When this occurs, we need to course-correct the algorithm through signal engineering.
What Is Signal Engineering?
Signal engineering is the intentional design and feedback of conversion events that reflect real business value. Instead of sending every micro-conversion back to Meta or Google, you define and send signals that correlate with high-value outcomes like retention, Average Revenue Per User (ARPU), or Lifetime Value (LTV).
The goal? To train the algorithm not just to get you the cheapest trial users—but to find the right users that are more likely to provide long-term revenue.
🧠 Signs It Might Be Time to Explore Signal Engineering
You’re getting a high volume of free trials, but low subscriber conversion.
→ If trial signups are strong but few users stick around or convert to paid, your campaigns may be optimizing for the wrong outcome.
Your CAC is rising, even though your targeting and creatives haven’t changed.
→ This could mean your signals are no longer helping the algorithm find quality users—it’s just finding cheap ones.
LTV hasn’t budged despite increased ad spend.
→ If you’re investing more in paid but seeing no lift in subscriber value, the gap between what platforms optimize for and what matters to your business is growing.
You’re relying on “Trial Started” or “Install” as your main optimization event.
→ These events are too shallow and broad. They often correlate poorly with retention and monetization.
You’re hitting scale ceilings or saturation quickly.
→ If your best campaigns stop working after a few weeks, you may be targeting too narrow of a lookalike audience—signal refinement can help refresh the pool.
You can’t explain why some users convert and others don’t.
→ A signal audit and regression analysis can uncover behavioral patterns that predict conversion and retention.
You have strong engagement metrics but poor ROAS.
→ The platform might be optimizing for soft actions (clicks, installs, low-barrier trials), not meaningful engagement.
Your team is building great creatives, but performance remains flat.
→ Even the best creative can’t fix a weak or misaligned optimization signal.
You have meaningful in-app events but aren’t using them in ad optimization.
→ If you know that quiz completions, profile setup, or onboarding milestones correlate with LTV, you should be leveraging them.
You’ve never updated your conversion events since launch.
→ If your signals haven’t evolved with your funnel or product, chances are they’re outdated—and holding you back.
Why Subscription Brands Need Signal Engineering to Advertise Successfully
Subscription businesses live and die by retention. But platforms like Meta and Google are built to optimize around short-term, easy-to-measure events. If you tell Meta that a free trial is your success event, that’s what it will chase—regardless of whether those users ever become paying customers.
Signal engineering helps bridge that gap by:
Aligning optimization goals with real business outcomes
Improving user quality instead of just volume
Helping ad platforms learn faster using predictive behaviors
The key to signal engineering is identifying the best signal to optimize campaigns with. But how does one go about that?
What Makes a Good Signal?
Good signals help your campaigns scale smarter, not just cheaper. Based on field experience and industry benchmarks, strong signals typically include:
High correlation to long-term business value – The behavior should reasonably predict future revenue or retention.
Early enough to fit within conversion windows – Preferably Day 0 to Day 2. The fresher, the better.
Enough volume to optimize – Aim for 10+ signals per day.
Low noise – Minimizes false positives and negatives.
Clean and transferable – Can be passed via SDK, MMP, or CAPI without excessive complexity.
Adaptable – Can be edited or tuned by thresholds (e.g. quiz score > 80%)
Monitorable – You can measure how often it fires and its downstream impact
What Signal Engineering Looks Like in Practice
Identifying Qualified Trial Indicators
Not every trial user is created equal. To move beyond vanity metrics and build signals that reflect real value, brands need to identify which early behaviors predict retention.
Here are a few ways subscription brands can qualify trial users more intelligently by using one or a combination of these tactics:
1. Behavioral Indicators
Completed Onboarding Flows: Signals the user has taken time to explore core product features.
Activated Key Features: Look for engagement with your "aha" moment—e.g. uploading a file, building a project, creating a playlist.
Session Depth or Frequency: Returning more than once within 24-48 hours can be a strong signal of intent.
2. Declarative Indicators
Quiz Funnel Responses: If you ask users about their use case, experience level, or goals during sign-up, certain answers may correlate strongly with subscription likelihood.
Self-identification (e.g. SMB owner): If your product serves specific personas, ask qualifying questions directly.
3. Predictive Scoring
Regression Models: Use historical data to run regression analysis on which combinations of early behaviors (e.g. device, country, feature use, onboarding quiz) drive LTV.
Lead Scores: Assign a score based on a weighted formula (e.g. quiz score + time on platform + feature use).
4. Post-Trial Conversion Signals
Upgraded Without Discount: Users who convert to paid without a promo often signal strong intent.
Early Plan Expansion: Users who add seats, features, or integrations in the first 7 days can be excellent targets.
Of course, all of this depends on having detailed tracking in place across your trial sign-up flow and product engagement. Without reliable data infrastructure, it’s difficult to identify patterns, qualify users, or build meaningful signals in the first place.
At Measure Marketing Pro, we use GA4 as the foundation for meeting our clients’ tracking and reporting needs for signal engineering. GA4’s flexibility and customizable event model make it a game-changer for piecing together engagement and user data to form a holistic, full-funnel picture. Whether you’re analyzing onboarding flows, activation events, or post-trial conversions, GA4 helps us capture the nuance required for accurate signal engineering.
Expected Outcomes of Signal Engineering
When brands shift from generic signals like "Trial Start" to more qualified, behavior-based events like "Onboarding Completed" or "Qualified Trial," the results can be substantial.
Lower Cost Per Trial: One test showed a 48% drop in cost per trial after switching the optimization event from "Trial Started" to "Onboarding Completed" and mapping it to a standard Purchase event. With further iteration (no creative changes), cost per trial dropped as much as 64%.
Higher ROAS: In a separate test, optimizing for a Qualified Trial event instead of a standard install drove a 31% improvement in ROAS. This was achieved by attaching a predicted value, delaying postback to capture early engagement, and feeding cleaner signals into the ad platforms.
How to Measure Success
Once you begin feeding new signals into your campaigns, your traditional KPIs may not tell the whole story. You might see costs per lead or trial go up—and that’s not necessarily a bad thing. Stronger signals usually surface more qualified users, and those users often cost more to reach.
The real question is whether those users are delivering more value.
Rather than fixating on CAC or CTR, shift your focus to cohort behavior:
Are your LTV or ARPU numbers rising?
Are users sticking around beyond the first billing cycle?
Are you seeing stronger retention or more product engagement from signal-qualified users?
Don't rely solely on modeled conversions or platform-side metrics. Make sure you’re backing your decisions with real performance data and tracking how each signal-qualified cohort behaves over time.
Having a centralized reporting hub makes this dramatically easier. At Measure Marketing Pro, we use Looker Studio to bring together data from ad platforms, GA4, and CRM tools. This unified view gives us the ability to quickly & confidently check in on performance cross-channel & individually to see how our efforts are panning out.
Do’s & Don’ts of Signal Engineering
✅ Do:
Look at your own data before choosing a signal. Use your analytics to understand what behaviors tend to lead to paying, long-term users.
Focus on signals that happen early in the user journey. The sooner the platform gets feedback, the better it can optimize.
Map your best-quality events to standard conversion types (like Purchase or Lead) that platforms know how to optimize for.
Keep an eye on signal health and volume. If your signal drops below 10/day per ad set, the platform might struggle to learn.
Run multiple campaign types in parallel—for example, value-based and event-based—to find what works best.
❌ Don’t:
Assume more data is better. Sending too many irrelevant signals can be worse than sending none.
Let one optimization event define everything. There’s no silver bullet.
Delay your signal too far beyond the trial. If it comes too late, it won’t impact ad delivery as effectively.
Try to be overly clever too early. Especially if you're just getting off the ground, keep it simple.
Use signal engineering as a patch for bad creative or an unproven offer. This is about amplifying what's already working—not rescuing what's broken.
Final Thoughts
Signal engineering isn’t a growth hack. It’s a strategic shift that realigns your media buying with your business goals. It works best when paired with strong creative and clear positioning—but if you’re scaling on paid and struggling to see that growth reflected in actual subscribers, it might be the most important lever you haven’t pulled yet.
Want to explore what signal engineering project for your brand? Let’s talk.
Comments