A Focused, Yet Flexible Plan Around Product Analytics: Insights from a Product Manager’s Perspective 

As a product manager with a decade of experience building and launching digital products, I’ve seen a few things change in that time. I know what you’re thinking, “of course, technology has changed over that timeframe,” and of course, you are correct. But one of these things that deserves a deeper conversation is: 

• How to shift to becoming more data-driven in your product strategy 

Organizations are indeed becoming more data-driven. But before data can shape your roadmap, where should you start when it comes to looking at your own product data? This doesn’t just happen overnight. How do you begin to make that shift? What should you measure? What are you trying to learn?  

Aligning on Key Metrics: Stay Focused 

In the past, analytics was often an afterthought – sure, we wanted to increase downloads, page views, and active users, but we were happy to celebrate the launch of some shiny new UI and were pleased when nothing broke. Today, the marketplace is crowded with highly functional digital experiences (across industries), and particularly over the last two years, there has been a significant shift in how analytics for a given product or feature is used. 

Data is now the key driver of what’s on the product roadmap and digitally mature organizations are measuring how each and every feature may be moving the needle. But when it comes to product analytics, there is no shortage of data to look at. Where should you start? 

First, you don’t have to measure everything! One strategy that consistently fails is adding event tags to each interaction in a given application, with no real plan on how to synthesize the data. The product launches, and you don’t know what to look at. Theresa Torres, in her book “Continuous Discovery,” emphasizes the significance of focusing on a select few critical metrics rather than overwhelming ourselves with an abundance of data. This approach helps product teams stay focused on what’s most important.  

Instead of indiscriminately applying tags to every interaction within the app or website, it’s crucial to identify the most critical aspects of functionality that require measurement. By selecting three to five key metrics that align with your business objectives, you can avoid data overload and focus on the most meaningful insights. This approach ensures clarity and facilitates effective post-launch analysis as we know exactly which data points to evaluate. Ask yourself (and your team) the following questions to get the conversation started: What are our top-level business objectives? 

• What aspects of these objectives can our product influence? 
• What are some leading indicators of success (not revenue) that may tell us if we’re succeeding or failing? 
• Can we measure that? If not, how might we measure it in the future? 

The Context of the Data Matters: Be Adaptable 

Aligning on what to measure is an important part of considering what shapes your roadmap, but the context of that data matters too. Be willing to accept that how users are using your product may be different than you assumed. In a lot of my work across the loyalty and restaurant space, ordering conversion rates and revenue reign supreme. There are two problems with just focusing on conversion rate and app-generated revenue:  

1. It’s a short-sighted approach 
2. Revenue is a lagging indicator 

Over time, Product usage will tell you much more about your user than you knew when you started, specifically around intent. Here’s an example: 

A restaurant ordering mobile application designed to optimize the user’s ordering of food to be delivered to their home. After a while, you start to see that each new release isn’t really increasing conversion rate as you’d hoped. However, app usage is up, and in-store revenue is up. Eventually, you discover a sizeable segment of users using the store locator feature to check hours and get directions to a store. Maybe they even engage frequently with your engagement campaigns. This is a simple example, but the point remains: just because one high-level business metric isn’t being achieved, your product could still be moving your business forward! You need to understand more than just direct revenue. Once you have a significant learning like the one in the example above, you need to be agile enough to adapt your product strategy. 

In summary, start with a small yet focused KPI plan for your product while also being prepared to learn from the data about the context and intent of your user. By keeping your list of KPI’s small and focused, you don’t lose sight of what’s most important to your business, and you don’t get overwhelmed by data overload. Pair that with an understanding that you will learn new information about your user’s intent and context when they use the product, which will shift your priorities. With this focused and adaptable mindset, you’ll be well-positioned to become more data-driven in your product strategy and roadmap for the future.