.jpeg)
Product-Market Fit (PMF) is both the holy grail and the bare minimum for any successful company.
How and why?
It's the holy grail because it's a point of resonance with a significant market segment, leading to growth and potential scalability. Yet, it's also the bare minimum, a make-or-break milestone on the challenging startup journey.
Although it can definitely feel that way sometimes, this journey isn't just about intuition or luck; it's about careful measurement and insightful analysis. The compass for this journey? Metrics.
In this article, we'll discuss why setting up the right metrics is crucial just before you attain PMF. Moreover, given how important iteration is for reaching there (and the right metrics are to guide iteration), about how the right metrics are essential in actually attaining PMF too. We'll explore the 'smile curve' framework, and how the decision making structure that can be learnt from it can help understand how to look at other buckets of frameworks.
Metrics, in the context of a startup, are quantifiable measures used to assess the status of specific business processes. They're the heartbeat of your venture, the pulse that indicates whether you're moving towards health or heading towards decline.
Why are they so crucial? They offer an objective way to track progress, pinpoint areas that need improvement, and make data-driven decisions. Just as a doctor uses vital signs to assess your health, startups ought to use metrics to gauge their condition and direct their actions.
As one approaches Product-Market Fit, setting up the right metrics becomes even more critical, both because metrics help get you closer to PMF, and because they help you ascertain if you’ve attained it.
Not all metrics are created equal. On one hand, there’s vanity metrics—those that may look impressive but don't contribute much to your core business objectives. This includes metrics like total users, pageviews (which should have conversions to be valuable) and downloads (if active usage is low). On the other hand, there’s more tangible and actionable ones—those that provide valuable insights and directly influence your business goals. This includes your MAU, CAC, and Churn Rate.
The key is to focus on the latter as you try to get closer to PMF.
So, how do you ensure you're focusing on the right metrics and not being misguided by vanity ones? To establish a sort of deliberative framework, it’d help to analyze a method used for driving retention-the Smile Curve.
The ‘smile curve’ in product analytics is an effective way to understand user retention and engagement over time. This curve, much like its namesake, has a characteristic 'U' shape—initially high, then dipping, and finally rising again. It's a visual representation of a product's user engagement across different stages of the user lifecycle, and it's an indispensable tool for anyone on their journey to attaining PMF.
Why is it crucial? The smile curve enables you to assess how well your product is growing overall, and how specific features are performing amongst different subsets of users.
Now, how does the smile curve connect to user retention? When a user first interacts with a product, their engagement is typically high (the initial peak of the curve). It then tends to dip as the novelty wears off, before rising again as the user discovers more value in the product, thereby forming the 'smile'.
The beauty of the smile curve is its predictive power for future retention trends. Regularly monitoring this curve can help spot potential issues before they become significant problems and adjust their strategies to improve user retention.
As a result, the smile curve can also help identify areas of the product that need improvement. For instance, if the curve doesn't rise again after the initial dip, it could indicate that users aren't finding enough value to continue engaging with the product. This insight can be a powerful prompt to reassess the product's features, enhance its 'magical moments', and reduce any friction that might be hindering user retention. Facebook’s famous growth hack of pushing users to add 7 friends in 10 days helps drive an uptick in the right side of the curve.
Furthermore, retention is a crucial element with regards to both attaining PMF in the first place, and then even to ascertain one actually has. Metrics you'd consider when optimizing for retention would include user stickiness, engagement, referrals, and feedback.
Now, retention is just one of the facets of your product you’d want the right metrics for, but of course, there are others as well. How do you optimally select the right metrics for those?
Your first checkpoint on this roadmap is relevance. A metric is only as useful as the insights it can provide into your business. Can it answer key questions about your users or product? Does it tie back to your company’s strategic goals? If the metric doesn't serve a clear, direct purpose, it might be a vanity metric, and might not be as useful for your journey towards PMF.
Consider you're running an online educational platform and are tracking the number of new sign-ups. However, if the number of course completions is very low, those sign-ups don't seem to translate into engaged, learning users.
In this scenario, the number of course completions might be a more relevant metric than mere sign-ups. It reflects not only the users' engagement but also the effectiveness of the course content and teaching methods, aligning more closely with your company's goal of providing impactful education, which is ultimately what would lead to the path to profitability.
So, while new sign-ups can give an inflated sense of growth (vanity metric), course completions would provide more actionable insights that would help you achieve PMF.
The next checkpoint is actionability. It's not enough for a metric to be relevant—it should also provide insights that can be translated into action. If a metric highlights a problem or an opportunity, you should be able to respond with a meaningful change in your product or strategy.
Suppose your e-commerce website's customer acquisition cost (CAC) via Google Adwords is increasing, outpacing your profits. Here are the action points this metric could lead to:
Experiment with different ad copy, targeting, or bidding strategies to reduce the cost per click.
Enhance website design, user experience, or checkout process to convert more visitors into customers, thus offsetting the rising costs.
If AdWords is getting pricier, try alternative channels like SEO, social media marketing, or email marketing.
Consistency in measurement is the final checkpoint. The way you define and measure a metric should remain constant over time. This enables accurate comparisons and helps you track your progress more effectively.
Let’s think about Session Duration to understand this better.
Session Duration refers to the amount of time a user spends on your app during a single use. Suppose you initially define a 'session' as the time from when a user opens the app until they close it, with no activity for 30 minutes marking the end of a session.
Six months down the line, you decide to redefine a 'session,' changing the inactivity period to 10 minutes instead of the original 30 minutes. This change will likely increase the number of sessions and decrease the session durations recorded, distorting your data.
If you compare these shorter session durations to your historical data, it might seem like users are spending less time on your app when in fact, you've just changed the measurement criteria. This inconsistency in measurement can create confusion and mask the true user engagement trends.
The journey towards PMF is a challenging but obviously crucial venture. Guided by well-chosen metrics and frameworks such as the smile curve, you're equipped to navigate this path.
The right metrics, reflecting user interaction and company growth, are more than data points. They offer relevance, actionability, and consistency in your strategic decisions. Combined with the smile curve's snapshot of user behavior, you have a potent tool to get closer to PMF, especially when considering the fundamental reasons you could learn from how a smile curve might be applied to other domains of metrics.
Remember, the path to PMF isn't a straight line, but that doesn’t mean it’s not navigable.
At Crunch, our goal is to help you navigate that path.
See how we do so-signup for our waitlist here!
