Lighthouses provide a beacon to guide ships at sea, without them disasters happen, and lives are lost. While most people think of lighthouses as simple mechanisms, they are sophisticated engineering feats that require a high degree of trust.
Similarly, your product analytics provides you a beacon to monitor your customer experience, poor implementation or lack of quality leads to poor decisions that impact your business. If you're not measuring the right metrics or don't have a shared understanding of the goals for your business, then chances are you're sailing fast towards disaster.
Teams who operate with a feature factory mentality, shipping feature after feature without taking the time to understand the levers of their business and how their work is laddering up to their overall goals, often create product bloat and subsequently increase customer churn.
While most organizations want to leverage data to inform their decisions, they're unsure of what to track and what a good metric looks like. Often we see teams looking at vanity metrics or celebrating success theater, and this is because they don't practice good data literacy or have proper instrumentation in place.
If teams want the insights, they need to be willing to put in the work required to document their goals and metrics. This is the only way you'll know what data you should be capturing in the first place and ensure that you're not reading tea leaves.
Encourage your team to focus on outcomes over outputs by defining clear hypotheses that are tied to metrics. Using the template, 'We believe... will result in... because...' helps teams capture hypotheses consistently and makes it easier to prioritize based off of the expected outcome. It's helpful to visualize the relationship between your goals, metrics, and hypotheses.
It's easy enough to follow up afterword to validate the hypothesis. Did shipping a free plan increase the number of evaluations by 45%, and if not why? This exercise encourages teams to obsess over the learnings because shipping is no longer good enough, they need to follow through and measure the impact of what they've shipped.
1. Keep it simple
Metrics should be easily understood by everyone in the organization. Great metrics should inspire action, teams should know how to react when they change. Is the 'Sign up' event triggered after the customer clicks submit, when the email is verified, or when the account is provisioned in the database?
2. Show ratios & compare over time
Avoid looking at absolutes and instead, compare ratios or rates. There is a big difference in the amount of information conveyed when looking at '6000 sign ups this week' versus '+15% in sign ups WoW'.
3. Create realistic forecasts & tie metrics to OKRs
Is +15% good or bad? How do you know if you're on track to hitting your goals? Forecasts should align with your OKRs and help your team prioritize their work. When they've hit their desired result move on to the next most crucial thing.
4. Make metrics accessible
Your metrics should be accessible to everyone in the team, else how do you expect them to change behavior? Metrics set a common goal for the team to rally around.
5. Assign an owner
As mentioned in What happens when you can't trust your data? metrics should have a single owner who is empowered to drive results. This helps ensure accountability and teams stepping on top of each other.
This is an iterative process, and it will take some time to adopt with your team. The best thing to do is start small and continue to improve over time.
The number one advantage for SaaS companies is creating a data-informed culture that is obsessed with learning. To foster a culture of learning you need to invest in data-literacy and empower your team to take ownership of metrics. What are some ways that you promote data literacy and build a culture of learning within your organization?
If you're actively working on improving your product analytics, let's chat.