How to become more insights driven
In this post I will be sharing key concepts and activities to get more value from your product analytics
The ironic thing about product teams trying to become more data driven is that most teams I have worked with over the years actually had no shortage of analytics that track user behaviour. Yet the overwhelming majority of those teams complained that they are not gaining any insights from all those metrics.
While the natural response is to track even more when we feel like we’re not getting much value from our data, it usually does exactly the opposite: the team gets overwhelmed, quality suffers and the understanding and trust in metrics slowly diminishes. I wrote about this in one of my previous articles on the importance of tracking less to create more focus and avoid KPI overload.
I have learnt that focus is the first fundamental step to getting more insights from our data. While it doesn’t automatically translate to getting more insights from your data, it does create more time and space to spend on activities that will then make you more insights driven.
Instead of maintaining endless events lists and dashboard reports, tracking less allows us to dive deeper into the core metrics that actually matter and spend more time on learning about our customers’ behaviour. The ultimate goal to become more insights driven is to then use those learnings to drive future product and business decisions.
There is huge difference between teams that just go through the motions of tracking metrics, versus the ones that actually learn and create insights from their data. Let’s get into the activities that you should spend more time on.
Key activities to becoming more insights driven
0️⃣ Track less and create focus: this is the fundamental step I recommend to tackle first - it’s amazing how less cluttered dashboards can help us uncover essential problems or opportunities. Most teams I have worked with have defined their key metrics around four core areas: their customer acquisition, activation, engagement (e.g. your monthly or daily active users) and your long term retention / churn. You can read more about those standard key metrics here.
1️⃣ Improving the quality and actionability of your metrics: avoid the use of vanity metrics if you want to actually learn from your metrics, and make them easy to understand and actionable by using more comparative metrics. For example, a graph showing you how something compares to other time periods or how your sign ups compare to your acquisition spend is always more useful than a single total number in isolation. I’ve written about this in more detail in one of my previous articles if you want to learn more.
2️⃣ Digging deeper into the why: layer in qualitative data effectively and combine it with your quantitative data to not only understand what is happening, but also why something is happening. If you see a big drop in your week 4 retention, try to gather more feedback by sending a quick survey or personal email follow ups to those customers to find out why they stopped using your product.
3️⃣ Setting intentional goals for learning and experimentation: rather than tracking random experiments and hope for the insights to come to you later, we need to set a clear intention and objectives of what we’re trying to learn, before we start experimenting. Define the biggest assumptions or decisions you need to make, plan out how you will test and measure those experiments, and most importantly how different results will impact your ultimate decision making.
4️⃣ Connecting the findings and creating data narratives to share with the organisation: just having good data is still not enough, we also need to make sure we connect the pieces of information in a meaningful way and form a strong narrative of what we have learnt. This is as much of a self reflection exercise to potentially uncover any gaps in our thinking, as well as a powerful tool for sharing insights with stakeholders and team members. A good data narrative should include what you set out to learn in the first place, how you tested, what the findings were, and how this may impact the product roadmap.
Especially the final point of sharing those insights effectively is often forgotten about. I want to highlight that this is a key step for teams to get stakeholder and team buy-in so the organisation can start to really include data insights more into their ways of working.
To sum it all up, the core idea behind this approach is that you go back to the essentials first and define the metrics you should really care about. You can then double down on those few key metrics and spend more time on making them more actionable, extracting and sharing more learnings to then drive product decision making that is based on real customer data insights. 😃✌🏼
Want to read more about data practises?
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