How Great Product Managers Predict Success Before It Happens

Everyone wants to be a Product Manager till their new launch is bleeding revenue and customers are flooding their NPS feedback channels with angry insults.

Product management isn’t for the faint-hearted. You either predict success early or end up explaining why everything tanked.

Smart product managers know this game and use leading indicators — metrics that give you a glimpse of future success — to make sure they’re never blindsided. In this piece, I’m going to break down leading indicators versus lagging indicators, and share some juicy real-world stories of companies that nailed it by keeping an eye on the right metrics.

What Are Leading Indicators and Why Do They Matter?

Simply put, leading indicators are metrics that help predict a product’s future performance. They give you the ability to take action based on early signs, rather than waiting for corporate poop to hit the fan.

For example, keeping an eye on user engagement metrics like daily active users (DAUs) or feature adoption rates can give an early signal of whether your new product is clicking with users, letting you make decisions that help improve features, enhance user experience and boost product performance before major problems start affecting more serious outcomes.

Leading vs. Lagging Indicators: Which Metrics Are Actually Worth Tracking?

Both leading and lagging indicators are important, but they serve different purposes. Think of leading indicators as weather forecasts — tracking them can help you prepare for what’s coming. Lagging indicators, on the other hand, are like the temperature gauge: they tell you what just happened.

Leading indicators can help you take early action rather than reacting after it’s too late.

Examples of leading indicators include:

• User engagement metrics like Daily Active Users (DAU)
• Feature adoption rates to see if users are connecting with new features
• User activation rate — the percentage of new users who take key actions that predict retention

In contrast, lagging indicators show results after they’ve happened. They’re useful for long-term planning but come too late to drive immediate action. For example:

• Monthly Recurring Revenue (MRR) shows revenue trends but reflects past behavior
• Customer churn tells you the percentage of users who’ve already left

While lagging indicators help assess long-term outcomes, leading indicators allow you to make real-time adjustments to enhance user experience, drive engagement, and ensure growth.

How Top Companies Use Leading Indicators to Crush It

Slack’s Secret Sauce: How Slack’s Focus on Daily Users Sparked Explosive Growth

Slack’s growth was fueled by focusing on engagement and product-led growth. One of their key metrics was DAUs (Daily Active Users). Slack realized that users who actively engaged with the platform — especially those who sent lots of messages and used integrations — were more likely to stick around, making DAU a critical metric for predicting success.

But just tracking DAUs wasn’t enough. Slack also made moves to reduce adoption friction and drive early engagement. For example, they offered a rolling 10,000-message history on the free plan, which was a huge improvement over competitors who often limited history to just 100 messages. This allowed users to keep accessing their conversations without feeling pressured to upgrade too soon.

Slack also designed an intuitive onboarding process that encouraged users to invite their colleagues and get involved quickly, leveraging the multiplayer effect — a phenomenon where more users joining increases the value for everyone, making the platform stickier and more engaging. Slack found that teams with three or more active users sending 50+ messages in the first few days had much higher retention rates.

By encouraging early collaboration, Slack ensured that teams got the full value of the platform early on — leading to long-term engagement and customer retention.

Another smart move was Slack’s fair billing policy, which prorated billing for inactive users. This helped sustain trust and build goodwill, which paid off big time in their user retention and conversion.

All these moves combined led to over 200,000 paid companies out of their customer base of 750,000 organizations (in 2024)— a 27% free-to-paid conversion rate, way above the industry average of 10–20% for most B2B SaaS products.

Spotify’s Secret Playbook: How Early User Habits Reveal Long-Term Success

Spotify’s growth strategy is built around their North Star Metric, “Time Spent Listening” (TSL), a key leading indicator of engagement and satisfaction. TSL works well as a predictor of success because the more time users spend on the platform, the more likely they are to stick around and upgrade from free to premium.

To boost TSL, Spotify identified specific early behaviors — like creating playlists or saving songs, as indicators of future retention.

They then introduced features like Discover Weekly, collaborative playlists, and Spotify Wrapped, all designed to make the experience personal and keep users coming back.

Spotify’s freemium model also helps boost TSL. They use direct reminders (like limited skips on ads) and subtle nudges (like offering offline listening) to entice users to upgrade without feeling pressured.

Their free-to-premium conversion rate of nearly 44% is proof that this approach works, especially when combined with a 30-day premium trial that lets users experience the benefits before committing.

Spotify also expanded beyond music into podcasts with acquisitions like Gimlet and Anchor, and even entered the metaverse with “Spotify Island” on Roblox. By staying true to their North Star Metric, Spotify keeps attracting new audiences and strengthening their brand.

Picking the Right Leading Indicators

If you want to leverage leading indicators in your product management strategy, the key is to align these metrics with your product goals.

For example, if your goal is increasing user engagement, track metrics like Daily Active Users (DAU) or average session length. If your goal is driving early feature adoption, consider Dropbox’s approach — tracking how often users completed the onboarding flow and adopted features like file sharing.

By aligning metrics like these, you can better ensure your product goals are on track for success. Start by identifying behaviors that closely correlate with success, like early feature adoption, engagement, or activation rates.

Below are some known leading metrics that often correlate with product success:

• Daily Active Users (DAU)
• User Activation Rate (e.g., completing key onboarding steps)
• Average Session Length
• Time Spent in App (exclude idle time where possible)
• Feature Adoption Rate (e.g., new feature usage within a specific timeframe)
• Net Promoter Score (NPS) or Customer Satisfaction Score (CSAT)
• Churn Prediction Metrics (e.g., reduced engagement over time)
• Conversion Rate from Free to Paid over time
• Retention Rate (especially during early usage periods)
• Referral Rate (e.g., users inviting others to join)

Using these metrics in the context of your goals, strategy and product is key to getting it right. I’ll write about this in a separate article, but for now I’ll show you how you can effectively track important metrics without spending your entire week looking at your dashboard.

The Definitive Guide to Leveraging Leading Indicators

Step 1: Pick the Right Metrics

You can start by choosing metrics that reflect early user behavior. Metrics like DAUs, conversion rates, or Net Promoter Score (NPS) are often strong predictors of success.

For example, if you’re building a social app, tracking DAU can tell you how sticky your product is. Similarly, an NPS survey could reveal early signs of user satisfaction or dissatisfaction that may impact retention down the line.

Step 2: Get Dashboards Ready

Now that you’ve got the metrics, you need to see them in action — set up dashboards for real-time tracking. Use tools like Amplitude or Mixpanel to monitor these leading indicators in real time and adjust strategies as needed.

Creating and monitoring dashboards effectively requires several important considerations.

By automating monitoring, generating meaningful insights, and ensuring clear follow-up actions, you can make the most of your dashboards and use them better than 90% of teams that monitor dashboards manually.

To learn more about using data tools effectively, I’d recommend checking out Aakash Gupta’s publication on Product Analytics Market Overview .

Step 3: Test, Tweak, and Repeat

You can’t just assume your current strategy will always work — test it! Run experiments and A/B tests by making small changes to onboarding, pricing, or features, and see how they affect leading indicators. For example, if you launch a new feature, A/B test it by showing different versions to two groups of users. Track which version drives better activation rates.

Use cohort or channel reports to track how different user groups respond to changes and identify which segments benefit the most. If your new onboarding process boosts activation by 20%, keep it!

Proceed with caution! Leading indicators can sometimes give a false sense of security. There are edge cases where leading indicators can be positive, but key outcomes still fall short. This often happens because there is still a user journey between things like activation and engagement (leading indicators) and actual subscriptions or purchases. If the product experience falls short of your customer’s expectations after initial engagement, leading indicators won’t tell you the full story. To get a more comprehensive understanding, you should run A/B tests that measure not just initial actions but complete outcomes as well.

Step 4: Zero in on Your Key Users

Your product shouldn’t be everything to everybody. Identify key user groups and figure out what drives their success. You can use cohort analysis and segmentation to identify these groups.

For example, if you notice users coming from a specific marketing channel tend to have higher engagement, consider doubling down on that channel.

Analyzing cohorts helps you tailor strategies to specific segments. If users from email campaigns are performing better, you could adjust your marketing focus to prioritize emails over other channels.

Step 5: Stay Alert!

You need to know when things are going wrong — right away. Set up alerts for when key metrics drop below a certain level. Integrate Amplitude with tools like Slack to receive alerts directly in your workspace — Slack, email, or spreadsheets (if you’re into that kinda thing). If DAU drops suddenly, you’ll know immediately and can dig into the root cause before the issue starts hurting lagging indicators like revenue.

Step 6: Keep It Fresh

Finally, you need to make sure your metrics are relevant as your product evolves. For example, if your product adds new features but your dashboard doesn’t reflect these changes, you may miss key drops in new feature engagement, leading to misguided decisions and potential user churn.

Three years ago, our team was pumped about a big product revamp. But we dropped the ball — we forgot to update our dashboard after launch. We got fooled by positive DAUs and completely missed a dip in new feature usage. End result? The ROI on the revamp was outright embarrassing. Two months later, we were scratching our heads over stagnant growth — all because our outdated dashboard had us looking in the wrong direction.

If your product launches a new feature, add its adoption metrics to your dashboard. Keep an eye on these metrics to evaluate how well the new feature is resonating with users. Metrics evolve just like products do, so your dashboard should too!

What Does It All Mean?

Leading indicators are essential to predicting product success early. Keeping a close eye on metrics like engagement, activation, and feature adoption can give you an advantage before bigger issues arise. Companies like Slack and Spotify have shown that focusing on metrics that represented early engagement for them, like DAUs and Time Spent Listening helped drive growth while staying ahead of potential churn.

Leading indicators are metrics that focus on early actions, giving insights into future outcomes.

• Metrics like DAU, TSL, and user activation rates are strong predictors of success.
• Use tools like Amplitude or Mixpanel to set up dashboards for real-time tracking.
• A/B testing can help validate the impact of new features on leading indicators.
• Implement alerts and real-time monitoring for key metrics to stay proactive.
• Update your dashboards with every new product release. Make adding events, adoption and engagement metrics to your dashboards a part of the product release process.

By staying proactive and adjusting your strategies based on real-time data, you can get ahead of potential challenges and drive product success more effectively.

Ready to Start Predicting Success?

Pick one leading indicator that aligns with your product goals and start tracking it today. Whether it’s DAU, user activation, or TSL, monitor its impact over time, and watch how early insights can make a big difference. If you want to go deeper, stay tuned for more on leveraging data to drive success in product management.