Beyond NPS: Next-Gen Metrics for Product-Led Customer Success

Product-led growth (PLG) companies, which rely heavily on self-service adoption and organic expansion, need more than periodic surveys to keep customers engaged. The game has changed—customer success in PLG isn’t about how happy users say they are; it’s about how effectively they use the product and how likely they are to expand their engagement.

So, how do PLG businesses measure what matters most? The answer lies in next-generation metrics that go beyond satisfaction. These metrics provide real-time insights, predict risks, and highlight opportunities for proactive engagement, paving the way for sustainable growth.

Rethinking Traditional Metrics: A Shift from Feedback to Action

Traditional metrics like NPS and customer satisfaction (CSAT) scores are reactive. They rely on customers to provide feedback—assuming they respond—and offer limited insight into real-time engagement. In contrast, product-led businesses thrive on continuous signals from customer behavior: Are users adopting key features? How quickly are they reaching value? Is engagement deepening over time?

“What’s measured drives action.” But measuring the wrong things can steer companies in the wrong direction. For example, a high NPS may not capture usage patterns that signal churn risks. By focusing solely on satisfaction surveys, businesses risk missing out on signals that could have prompted intervention earlier.

The Metrics That Matter: A New Model for Product Success

In a product-led world, the most valuable metrics aren’t retrospective—they predict the future. Let’s dive into some key metrics that modern PLG companies are embracing.

1. Product Usage Score:
This score reflects how actively and comprehensively users engage with the product. Instead of focusing on isolated actions, it aggregates usage patterns over time, providing insight into customer health.

• Example: A collaboration software tracks whether teams use core features like messaging, file sharing, and integrations. Low usage triggers automated reminders, encouraging users to explore more features.

2. Time-to-Value (TTV):
This metric captures how quickly customers experience the benefits of the product. The faster users reach their “aha moment,” the more likely they are to stay engaged.

• Strategy: PLG companies can optimize onboarding workflows by removing friction points that delay value realization. TTV tracking allows teams to monitor each step and intervene when users struggle.

3. Feature Adoption Rate:
Adoption rate reflects how effectively users embrace new features and functionalities. This metric can reveal which customers are primed for upselling and who might need additional support.

Turning Metrics into Real-Time Insights

PLG companies need more than dashboards filled with numbers—they need actionable insights. Real-time monitoring tools play a critical role by surfacing key signals when intervention is required.

The Power of Dashboards

With real-time data, teams no longer need to wait for quarterly reviews to identify at-risk accounts. Dashboards integrated with analytics tools like Mixpanel or Amplitude can provide instant alerts when product engagement dips. By correlating behavior patterns with historical data, these platforms can detect early churn signals.

Predictive Customer Health Scores

Health scores take monitoring to the next level by incorporating behavioral and historical data into predictive models. These scores predict future behavior, such as whether a user is likely to renew, churn, or expand.

Case Study: How Toggl Scaled User Adoption and Boosted Upsells with Predictive Product Metrics

Toggl, a time-tracking and productivity tool, has become an indispensable solution for freelancers, consultants, and remote teams. With products spanning time tracking (Toggl Track), project management (Toggl Plan), and recruitment (Toggl Hire), the company operates in a crowded SaaS market. Although Toggl is smaller than giants like Asana or Monday.com, its strategic approach to product adoption and upselling has positioned it as a successful and profitable SaaS company with over 5 million users worldwide.

However, as the company scaled, it struggled with a common product-led growth (PLG) challenge: high free-to-paid conversion friction. Many users found Toggl’s free tier sufficient, leading to slow conversion to paid tiers. Additionally, while users were actively engaging with time-tracking features, they underutilized Toggl’s premium tools, such as billable hours tracking and team performance reports, limiting upsell opportunities.

To address these pain points, Toggl implemented a predictive metrics model, enabling real-time interventions and hyper-personalized campaigns to increase both user retention and upgrades.

The Problem: High Free-Tier Usage, Low Paid Conversions

Despite Toggl’s broad adoption, only 8% of active users upgraded to paid plans, and users frequently underutilized premium features. Additionally, churn rates among trial users were high, with 60% of trials abandoned within the first week.

Toggl identified two main issues:

1. Feature Blind Spots: Many users didn’t realize the value of advanced features, such as custom reports or integration with payroll tools.
2. Delayed Time-to-Value (TTV): Trial users failed to set up workflows and often didn’t experience the product’s full value before their trial expired.

Without intervention, the company risked revenue stagnation and churn among trial and freemium users.

Deploying Predictive Metrics to Improve Adoption and Retention

In 2022, Toggl integrated Amplitude and Pendo to track user behavior and implemented predictive product health scores to forecast churn and upsell readiness. The company began tracking two key behavioral metrics that correlated with long-term retention and conversion:

1. Workflow Completion Rate: Measuring whether users completed essential setup tasks, such as creating projects, inviting team members, and setting up billable hours.
2. Feature Exploration Index: Tracking how frequently users engaged with premium features (e.g., detailed reports, custom tagging) during their trial period.

Toggl’s new predictive model flagged users at risk of churn if they failed to complete these actions within the first three days of signing up. The health scores also identified power users who interacted with key features frequently, marking them as prime candidates for upsell campaigns.

Real-Time Engagement and Automation Strategies

With predictive scores in place, Toggl launched several automated engagement strategies tailored to specific user behaviors:

• Trial Rescue Campaigns: If a user stalled during onboarding, Toggl triggered personalized in-app messages with step-by-step guides, such as “Here’s how to track your first billable hour”.
• Feature Nudge Workflows: Users who ignored advanced features received targeted in-app nudges like, “Discover the power of custom reports—track performance with ease!”
• Upsell Incentive Offers: For users with high engagement scores, Toggl’s CRM automatically sent discounted upgrade offers toward the end of their trial, emphasizing the benefits of premium tools aligned with their current use case.

Additionally, Toggl integrated its health scoring model into customer success workflows. Customer success managers (CSMs) received alerts about high-potential accounts that hadn’t yet converted, prompting one-on-one outreach to offer personalized help.

Key Takeaways from the Data:

• Free-to-paid conversion rate more than doubled, reaching 18% within six months.
• Trial abandonment decreased by 25%, driven by timely nudges and onboarding improvements.
• Users experienced value two days faster on average, increasing the likelihood of retention.
• Custom report adoption—a key premium feature—rose by 28%, driving upsell opportunities.
• Monthly churn decreased by 37%, indicating greater product stickiness among paid users.

Why This Case is Relevant to PLG SaaS Companies

Toggl’s success provides valuable lessons for niche SaaS companies that rely on freemium models and self-service onboarding. The company demonstrated that with the right predictive metrics and targeted interventions, even users who seem content with a free plan can be converted into paying customers.

For SaaS companies facing similar conversion bottlenecks, Toggl’s approach offers three critical insights:

1. Identify Key Actions that Predict Success: Tracking workflow completion and feature adoption enabled Toggl to focus on behaviors that mattered most to retention and conversion.
2. Act Early with Real-Time Interventions: Automated nudges and personalized campaigns helped users overcome friction points before disengaging.
3. Align Product and Success Teams with Metrics: Predictive health scores allowed Toggl’s customer success team to prioritize outreach to high-potential accounts, maximizing impact with minimal resources.

By using data-driven engagement strategies, Toggl turned product signals into meaningful customer interactions, driving sustainable growth without expanding its sales force.

This case shows that even smaller SaaS companies can compete effectively by leveraging predictive analytics and personalized automation. For companies navigating the complexities of PLG, Toggl’s success offers a playbook for accelerating user adoption, reducing churn, and unlocking new revenue streams.

Beyond Tracking: Metrics as Engagement Drivers

Tracking metrics isn’t about passive observation—it’s a way to catalyze action at precisely the right moments. Successful PLG companies know that metrics should not only measure the present but also shape the future by identifying touchpoints for deeper engagement.

• Strategic Use of Product Signals: When customers exceed specific usage thresholds—such as interacting with five core features or inviting more team members—it’s an opportunity to suggest value-adding upgrades. For instance, a CRM might automatically suggest a data analytics add-on when users hit a reporting limit, creating a seamless upsell experience within the product.
• Nudging Dormant Users with Precision: Metrics around inactivity provide subtle cues for re-engagement campaigns. If a trial user hasn’t completed a setup task after 48 hours, automated in-app prompts or emails can guide them back on track. The most effective nudges aren’t generic reminders but are tied to specific actions, such as “Haven’t created your first dashboard yet? Let’s get started.”
• Triggering Proactive Human Engagement: Not all engagement can or should be automated. Strategic metrics allow customer success teams to intervene with high-value users when signals indicate friction. For instance, a predictive model could alert the team when a high-revenue customer’s usage dips below normal, signaling the need for personalized outreach before the customer considers churn.

By leveraging real-time data, PLG companies create proactive engagement loops that enhance the customer experience, leading to higher retention and expansion. Metrics thus act as bridges—turning data points into meaningful interactions that continuously deliver value.

Unified Metrics: Aligning Product, Marketing, and Customer Success

In PLG companies, alignment across departments isn’t just a nice-to-have—it’s essential for sustainable growth. Metrics must be unified across product, marketing, and customer success teams to ensure everyone is working toward shared objectives. When these teams operate in silos, missed signals and disjointed efforts can undermine growth.

• Creating a Cross-Functional Dashboard: A unified dashboard that pulls in key performance indicators (KPIs) from product analytics, marketing campaigns, and customer success platforms allows teams to have a single source of truth. For example, tracking feature adoption alongside campaign-driven leads gives marketing teams insights into which features resonate most with specific customer segments.
• Embedding Feedback Loops Across Teams: Unified metrics encourage bi-directional communication between departments. For instance, if marketing notices a surge in engagement with specific features, this insight can inform product teams about areas to improve or expand. Similarly, customer success teams can use product insights to identify upsell opportunities for accounts showing high feature engagement.
• Aligning Incentives Across Teams: Many organizations struggle with alignment because each team is measured by different—and sometimes conflicting—KPIs. By unifying metrics like TTV, feature adoption, and upsell success into shared goals, PLG companies encourage collaboration. For example, marketing and product teams might jointly focus on reducing TTV, with product improving onboarding workflows and marketing running targeted campaigns to guide new users toward value faster.

Alignment through shared metrics ensures that every team has visibility into the same data and is motivated to pull in the same direction. This cohesion drives faster decision-making, reduces redundancy, and ensures a smooth customer journey from acquisition to retention and expansion.

Implementing Next-Gen Success Metrics: A Practical Playbook

Successfully adopting next-gen metrics isn’t a plug-and-play exercise—it requires careful planning, cross-functional collaboration, and iterative refinement. Below is a detailed roadmap for embedding these metrics within a PLG framework.

1. Choose Metrics that Directly Impact Business Outcomes
Rather than tracking every available metric, PLG companies should focus on those with the greatest influence on growth.

• Strategy Tip: Prioritize metrics like feature adoption, usage frequency, and expansion opportunities that align with revenue goals. For instance, SaaS businesses targeting enterprise clients might emphasize seat expansion and advanced feature usage as critical metrics over standard engagement rates.

2. Integrate Systems for Real-Time Data Flow
Data must flow seamlessly between analytics tools, customer success platforms, and CRMs to provide real-time visibility. Disconnected systems create blind spots and slow down decision-making.

• Pro Tip: Use middleware solutions or APIs to ensure data is synchronized across tools. This enables dashboards to reflect the latest customer behavior, helping teams act without delay.

3. Automate Engagement Based on Behavior-Based Triggers
Automation is essential for scaling engagement without overwhelming teams. Define key behavioral triggers—such as completing a core action or abandoning onboarding—that initiate targeted interventions.

• Example: When users hit 80% of their storage limit in a SaaS platform, they receive an automated in-app offer for an upgrade. Meanwhile, customers who abandon onboarding receive tutorial videos tailored to their progress.

4. Foster a Culture of Continuous Experimentation and Iteration
Metrics should evolve with the product and customer behavior. Regular A/B testing and iterative improvements are crucial to ensuring metrics remain relevant and effective.

• Actionable Insight: Teams should test different onboarding flows to see which results in the lowest TTV. By tracking feature adoption in these experiments, businesses can determine which interventions are most effective and refine them over time.

5. Link Metrics to Financial Impact for Stakeholder Buy-In
Linking product metrics to financial outcomes ensures that stakeholders understand their value. Show how TTV reductions correlate with higher renewal rates, or demonstrate the impact of feature adoption on upsell success.

• Insight: Presenting dashboards that tie product metrics to key financial outcomes—such as lifetime value (LTV) or revenue per user—creates alignment with executive priorities and ensures continued investment in metric-driven strategies.

Implementing next-gen metrics is a dynamic process that requires thoughtful alignment between strategy, technology, and people. With the right approach, PLG companies can embed these metrics into the heart of their operations, creating a feedback loop that drives continuous growth.

Redefining Success in a Product-Led World

The future of customer success lies beyond traditional satisfaction metrics. In a PLG environment, success is not just about measuring how happy users say they are—it’s about anticipating their needs and guiding them toward deeper engagement.

Companies that adopt predictive metrics and align teams around real-time insights will unlock sustainable growth. As the landscape continues to evolve, the winners will be those who see beyond satisfaction and embrace the power of proactive success. Are you ready to look beyond the numbers—and into the future?

FAQ: 

Why are traditional metrics like NPS insufficient for product-led growth (PLG) companies?

While NPS (Net Promoter Score) measures overall customer satisfaction and likelihood to recommend, it only offers retrospective feedback—giving companies a delayed view of potential issues (Smith, 2023). In PLG models, customer success relies heavily on real-time product usage data, adoption metrics, and proactive engagement to prevent churn and increase expansion opportunities. Next-gen metrics like Time-to-Value (TTV) and feature adoption rates provide actionable insights that allow businesses to intervene early and ensure continuous product engagement (Gainsight, 2023).

What are the most important metrics for tracking product-led customer success?

PLG companies should focus on behavioral and predictive metrics that reflect customer engagement and value delivery. Key metrics include:Product Usage Score: Tracks how frequently and deeply customers engage with core product features Time-to-Value (TTV): Measures the speed at which users derive tangible benefits from the product. Feature Adoption Rate: Monitors the usage of advanced or critical features. Predictive Customer Health Scores: Forecasts potential churn or upsell opportunities based on historical and current behavior patterns. These metrics help drive proactive customer success efforts and optimize for long-term growth (Userpilot, 2024).

How do predictive metrics reduce churn and increase conversions?

Predictive metrics analyze patterns in user behavior to anticipate future outcomes—such as customers at risk of churn or users ready to upgrade. For example, when a customer fails to complete onboarding tasks or doesn’t engage with critical features, automated alerts can trigger personalized interventions like targeted emails, in-app messages, or sales outreach (Userpilot, 2024). This proactive engagement reduces churn by resolving issues early and accelerates conversions by identifying high-potential users for upsell offers.

What role does automation play in product-led customer success strategies?

Automation is crucial for scaling customer success efforts without overwhelming internal teams. Automated triggers—such as a nudge when a user abandons onboarding—ensure immediate engagement based on user actions (or lack thereof). CRM integrations and real-time dashboards help product, marketing, and customer success teams align around shared metrics, making data-driven decisions faster. By automating repetitive workflows, companies can reserve human resources for high-value interactions, such as personalized outreach to at-risk accounts or strategic upsell conversations (WDCweb, 2024).

Reference:

Gainsight (2023). The Future of Customer Success in a Product-Led World. Available at: https://gainsight.com

Komodo Technologies (2024). Applying Predictive Analytics to Improve SaaS Churn Reduction. Available at: https://www.komodotech.io 

Raaft.io (2024). 5 Churn Prediction and Prevention Strategies for SaaS Teams. Available at: https://www.raaft.io 

Smith, J. (2023). The Death of NPS? Why Product-Led Growth Needs New Metrics. Harvard Business Review. Available at: https://hbr.org
Userpilot (2024). B2B SaaS Funnel Conversion Benchmarks to Know in 2024. Available at: https://userpilot.com

WDCweb (2024). How to Calculate and Optimize SaaS Conversion Rates: Industry Benchmarks & Tips. Available at: https://wdcweb.com 

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