1. Focusing on one “north star” metric
Ranking the different data you are collecting is an efficient strategy. It allows to analyze the chosen data in accordance with the company’s objectives. If we compare conversions or revenue metrics with the Facebook followers metrics, the first is certainly more important for your business.
But choosing the top metric or so-called a “north star” one can lead to neglecting the other less crucial metrics that can be important for your business in some way.
To ensure that your strategy works, focus on the core metrics, but don’t forget to check the other, less crucial ones. It will help you to see the bigger picture and avoid missing information that matters.
2. Data-driven approach leads to analysis paralysis
Maksym Babych, a Saas products expert notes that sometimes product managers can collect to much data that worth analyzing and studying. Trying to be as data-driven as possible, they can end up being trapped inside this vicious circle, when analyzing one piece of data leads to studying another one. They afraid to miss something valuable and are not ready to make any decisions, since they are sure somewhere could be another piece of data crucial for their business.
3. Cognitive bias may result in missed insights or wrong conclusions
People tend to subconsciously follow cognitive bias even when it is time to make decisions or choose business strategy. One of them is confirmation bias, which can lead to looking for confirmation of our own theories, without taking into account an existence of other possibilities. Here is an example. Let’s say you are in a head of a B2B SaaS product. You decide to review usage data from your installed base and find out that customers don’t use a specific feature you thought to be very important for for their workflow. You and your team start building a list of assumptions:
- The feature we have built is really valuable, but the way it has been built doesn’t work for our users
- The feature we have built isn’t valuable. My team and I were wrong
- The feature we have built is really valuable and the way it has been built works for our users, but it lacks findability. Our users just fail to find it within the application
4. Inefficient way of collecting data
There are two main reason the data-driven SaaS product managers can collect data inefficiently — technology issues or personal processes and comfort level. While personal issues may vary, technology ones are now easy to resolve. Collecting data is now very easy, since there plenty of tools to track and analyze data. Product managers today don’t need a team of developers to build them from scratch.
5. Conclusions built on over-weighted piece of data
Sometimes data-driven PMs overweight particular piece of data and make the decisions, taking it as a starting point. Try to answer the following questions before making conclusions:
- What percentage of those respondents are end-users and what percentages are actually the buyers or decision-makers for their companies?
- How close were you to launching your product when you sent out that survey? If you were months away, can you reliably assume those numbers will still be accurate on launch-day?
- What percentage of your survey respondents actually responded? And what, if anything, do these numbers tell you about the people who chose not to answer your survey?
Bio
- Maksym Babych, serial entrepreneur and SaaS professional.
- Founder and CEO SpdLoad