Data for every step of the journey
Searching for the right data to take your company to the next step
Digital transformation of the 2020s: the time when you hear companies that you associate with the 1990s suddenly and frantically turn to data to find growth. And when “data is the new oil” cliche is sprinkled over every other Gartner presentation.
Interestingly, many companies commence data-driven initiatives without thorough understanding of what exactly they need and how it will help them in their decision-making. A rather common approach is to collect easy data points (e.g. user logins) and produce vanity metrics that look good on a PowerPoint slide.
So what data do you really need? A simple answer is that it varies. Here is one way of thinking about it.
Level 1: Just starting
The majority of companies are solving a problem that the founders experienced first-hand. As such, the only data that you need at that stage is the knowledge of the founding team to get you to an MVP as quickly as possible. As you begin validating your product/market fit with your first users and thinking about what’s next, that’s a good time to consolidate the product and messaging feedback from the users. The best data will come from direct interviews. Some - from analyzing your subscriber data (demo- and firmographics), acquisition channels, mailing list subscriptions etc. The key here is to be listening to your early customers.
Level 2: North Star
After you launched or are about to launch your first version, your first users will become your new best friends. You want to know as much as you can about them and their problems. It is important to build infrastructure that will allow to collect basic usage data from your first customers. Many product leaders at this stage target vanity metrics like MAU/DAU or count meaningless events (“button clicks"). Instead, you must focus on one thing that really matters at this point: the North Star. Specifically, how many people get to the North Star Metric (NSM) and how quickly.
To do it right, start with “What’s the ultimate value the product delivers?” and “What functionality does the product require to get users to value quickly?” Products like Amplitude and MixPanel are a great help here.
Any data point comes with a cost (and often a missed opportunity cost is even higher). Minimize effort by focusing on only those metrics that matter. Skip on everything else (I mean it). And learn to segment your data correctly: cohorts give a great way of looking at your value/feature progression over time and your product/market fit for different acquisition channels.
Level 3: Validate Hypotheses
As your product complexity and usage increase, your product managers will focus more on experimentation and optimization. Knowing about your users and their activity will help you drive hypotheses ideation and validate them. Build a well-oiled machine to produce output signals in response to various experiments and to use those signals to feed new experiments. An experimentation flywheel.
The flywheel can be used to carry out specific experiments, including monetization, and overall market positioning and strategy. Either way, keep an eye on your NSM: getting users to value quickly and frequently is the ultimate goal of any experiment and optimization.
Other Notes
When at Levels 2-3, make sure to mingle product usage with your GTM data. Using your buyer/user path to value, you can test additional use cases, market segments, and acquisition channels.
Build messaging to engagement attribution. Product/message fit is important, and your product usage data can be invaluable in figuring out what messaging drives the best users.
You can start with Level 3 if your product is already mature or you’re considering a large launch.