Whilst your organisation may want a roadmap scoped and OKRs set for the next quarter (at least) it is often challenging to understand how you’ll get there and iterate meaningfully as you progress through weeks and months of development and releases. Normally you’ll have carried out some discovery to establish what you think may ladder up to your goals or results. In turn, it is often practice to retrospectively analyse your iterations post major milestone or launch. For example you may ship V3 of your product with the overarching goal of driving up revenue by x %.
Your successes or failures will be examined retrospectively and any pivots needed may simply extend experimentation and eat into your runway.
What if there was a better way? Of being informed almost as you move along your development cycle? Or at least mitigating risk of embarking in the wrong direction.
💡 Enter the concept of leading and lagging indicators!
Making informed decisions is paramount to a product’s success. It’s not just about reacting to past performance; it’s about anticipating trends, addressing potential issues, and continuously enhancing the user experience. This is where leading and lagging indicators can come into play, as they serve as the compass and the rearview mirror of our product strategy, respectively.
By understanding and effectively using these indicators, we can navigate the product development journey with greater precision and agility, ensuring that our decisions are not only well-informed but also aligned with our long-term goals.
Leading indicators are proactive measures that help us predict future outcomes. They provide insights into potential trends and challenges before they become apparent through lagging indicators. Examples of leading indicators include:
Customer Engagement Metrics: Tracking user interactions, such as feature usage, click-through rates, and time spent in the app, can help us anticipate user satisfaction and identify areas for improvement. Tools like Google Analytics or Mixpanel can assist in this.
Customer Surveys and Feedback: Gathering feedback through surveys and customer support interactions can provide early insights into customer sentiment and potential issues. Platforms like SurveyMonkey or Zendesk are useful for this purpose.
A/B Testing: Experimenting with different product variations and measuring user behavior can help us assess the impact of changes before they affect our overall performance. Tools like Optimizely or VWO are commonly used for A/B testing.
Specific examples could be some of these below:
User Engagement Score (UES):
- Definition: A composite score based on user interactions, including feature usage, session duration, and clicks, to gauge overall engagement.
- Measurement: Calculate UES by assigning weights to various engagement metrics and aggregating them. For instance, if a user interacts with a critical feature, assign a higher weight. So for example whilst onboarding is crucial in a fitness app, the critical feature would be the first successful tracking of a goal.
Feature Adoption Rate:
- Definition: Measures how quickly users adopt new product features.
- Measurement: Calculate the percentage of users who have started using the new feature within a specific time frame after its release.
Customer Satisfaction Index (CSI):
- Definition: Reflects user satisfaction through surveys or feedback mechanisms.
- Measurement: Conduct regular surveys using tools like SurveyMonkey or NPS to assess customer sentiment. Calculate the CSI as a weighted average of responses.
Lagging indicators are retrospective measures that indicate the outcome of past actions or decisions. They are typically used to assess the overall performance and success of a product. Examples of lagging indicators include:
- Revenue and Profitability: Financial metrics like revenue growth, profit margins, and customer lifetime value are classic lagging indicators that reflect the overall health of a product.
- Churn Rate: The rate at which customers stop using our product is a lagging indicator that indicates customer satisfaction and product stickiness.
- Net Promoter Score (NPS): NPS measures customer loyalty and is often considered a lagging indicator of overall product satisfaction
Specific examples here could be:
- Monthly Recurring Revenue (MRR):
- Definition: Reflects the predictable and recurring revenue generated from subscriptions.
- Measurement: Sum the monthly subscription fees from all customers.
- Churn Rate:
- Definition: Indicates the rate at which customers leave the product.
- Measurement: Divide the number of customers lost during a specific period by the total number of customers at the beginning of the period.
- Net Promoter Score (NPS):
- Definition: Measures customer loyalty and overall product satisfaction.
- Measurement: Conduct NPS surveys and calculate the score based on the responses (Promoters – Detractors).
Why Leading vs. Lagging Indicators Matter
Understanding the distinction between leading and lagging indicators is crucial for several reasons:
Proactive Decision-Making: Leading indicators allow us to make proactive decisions and take corrective actions before issues escalate, enhancing customer satisfaction and minimising risks.
Product Improvement: Leading indicators help us identify opportunities for product improvement, ensuring that we stay ahead of customer needs and expectations.
Performance Evaluation: Lagging indicators provide an objective way to assess the overall success and financial health of our products, which is vital for strategic planning and resource allocation.
Continuous Monitoring: By monitoring both types of indicators, we create a balanced approach to product management, ensuring a holistic view of our product’s performance.
- But remember these indicators are not only a guide (And can never truly predict the future) but depend on how you inform the very construct of the metric. By weighting your leading indicators properly you stand a better chance of better outcomes and therefore better lagging indictors! Examining leading and lagging indicators should also be woven into your team-wide strategy. Their inputs will often affect the metrics you’re looking at so make sure you get internal feedback too.
Real World Examples
User Engagement Score (UES) – Facebook:
- Facebook tracks user engagement through metrics like daily active users (DAU), monthly active users (MAU), and time spent on the platform. By continuously monitoring these leading indicators, Facebook can anticipate trends and proactively adjust its product to keep users engaged.
Feature Adoption Rate – Slack:
- Slack measures how quickly users adopt new features and integrations. For instance, when Slack introduced the ability to integrate with third-party apps, they closely monitored how many teams started using this feature within a specified timeframe. This leading indicator helped them assess the success of feature launches.
Customer Satisfaction Index (CSI) – Airbnb:
- Airbnb regularly surveys hosts and guests to measure their satisfaction levels. They use leading indicators such as response times, guest reviews, and host ratings to gauge the overall experience on their platform. By tracking these indicators, Airbnb identifies areas for improvement before they become major issues.
Monthly Recurring Revenue (MRR) – Netflix:
- Netflix relies heavily on MRR to assess its financial health. They calculate MRR by summing up the monthly subscription fees from their millions of subscribers. This lagging indicator helps Netflix understand their revenue growth over time.
Churn Rate – Spotify:
- Spotify calculates its churn rate by analysing how many subscribers cancel their premium subscriptions each month. This lagging indicator reflects customer retention and satisfaction. If the churn rate increases, it signals potential problems with the product or service.
Net Promoter Score (NPS) – Apple:
- Apple uses NPS to measure customer loyalty and satisfaction. After making a purchase or using Apple services, customers are often asked to rate their experience. Apple aggregates these scores to calculate its NPS. This lagging indicator helps Apple gauge long-term customer sentiment and loyalty.