A guide to a product manager's tech stack

Hello everyone, recently some people approached me through benchmarks to understand a little more about the technology stack that the product team at the company where I work uses.

As Product Operations¹, a crucial part of our role in onboarding product teams and day-to-day work is training, maintenance, and evolution of the technologies that the team uses, as well as establishing best practices of governance that involve them. After all, a PM who knows where to find the information they need, what tools are available to them, and how to use them, is a more productive person, isn't it?

Knowledge bases and governance

As teams grow, several challenges begin to escalate, including the need to create a knowledge base that teams can consume and evolve in the safest and most up-to-date way possible. Personally, I have had the opportunity to test three solutions for this challenge: Notion, Coda, and Confluence.

The decisive factor in choosing the tool was the options it offered in terms of automations and integrations. While Confluence has the advantage of being connected to the Atlassian environment and its solutions (including Jira), Notion, on the other hand, has an intuitive interface that is easily inviting with simple commands that allow you to expand your creativity, in addition to having a very interesting company mode.

However, the tool we have been using for a longer time, Coda, has always had very interesting automations and integrations, including with Google Email, Calendar, and Mixpanel, making it easier to create up-to-date documentation and reports.

A spoiler regarding this challenge is that, despite the tool being something fundamental, the culture of the teams in dealing with it is even more important.

Task and Daily Work of the Teams

n the daily work of the teams, we have the traditional Jira for task management. Although it is a tool that delivers what it promises, it becomes even more interesting when you and your team start working with automations within it and combine boards within the same project, creating customized spaces, for example, with design teams.

Web and App Analytics

The "Achilles' heel" of many companies is making data-driven decisions, and behind this desire, there is an unfolding of various teams and tools throughout the process, from the moment data is collected (i.e., user actions on the website or app) to understanding how to direct the product based on that information.

To identify user actions on the website or app, we use Google Analytics 4 and Mixpanel. Both tools are market leaders in their respective segments. GA 4 focuses on how users reach our products, channels, conversions, and more, while Mixpanel focuses on what users do after they start their journey with our products.

An interesting example of usage is how through Mixpanel's Impact² feature, we can identify the statistical impact of new features on specific events. For example, we can determine how much the "share" function in a product has driven purchases.

Since both tools have a pricing model based on the number of events triggered, we have developed a strategy of not duplicating events unnecessarily, except for important e-commerce events that help identify conversions in each journey. It's worth mentioning the use of Microsoft Clarity, which records screen sessions and provides heatmaps, and also integrates with GA 4, further enriching the data about user navigation.

Recently, in addition to the web version of Clarity, an Android version of the tool has also been launched. As for the app, we use UXCam, which offers a free version with a restriction on the number of user sessions for those who are starting out.

Talk Data to me

During the onboarding of a team, I heard the expression "Talk data to me," which, when combined with the song it refers to, I found to be a clever way to break the ice when dealing with data and some paradigms in companies. Throughout my journey, I have used several tools, including Loocker Studio (formerly Google Data Studio), Power BI, Tableau, and Metabase.

It's difficult to choose which one is the best. I believe that despite having similar functional characteristics and ways of working, the environment that the company uses can facilitate handling. For example, when using the Microsoft environment and its solutions like Teams and others, it was very natural for me to connect my databases with Power BI and use my dashboards in presentations and other scenarios.

Currently, we have a source of truth in BigQuery, and we also use the Google environment. Regardless of the number of people and resources, our teams have easy management and access to reports through Metabase.

Experiment, Experiment, Experiment

As Thomas Edison once said, "The true measure of success is the number of experiments that can be completed in 24 hours." And there is no shortage of examples in the market of companies that have invested in an efficient culture of experimentation, such as Microsoft with the case of Bing, and have achieved good returns as a result.

Experimentation is not the responsibility of a single department in a company, not just R&D or a group of specialists. On the contrary, it is the responsibility of all employees, in one way or another, and can be just as important as financial calculations (book: "The Culture of Experimentation" by Stefan H. Thomke). Therefore, the task of raising awareness, documenting, and facilitating experimentation with product teams and stakeholders for A/B testing, multivariate testing, and others becomes a significant challenge.

Today, with the convenience of having the Google environment and using tools like GA 4, it was natural for us to use Google Optimize, which, in turn, has a more affordable paid version compared to other market alternatives, although it is slated to be discontinued.

With this in mind, we embarked on a new journey to discover which experimentation tool could replace Optimize in a way that still empowers our teams to conduct and monitor experiments as autonomously as possible.

The chosen solution was GrowthBook, which is not only open-source but also has various integrations with analytics tools. The team has been investing heavily in new features to take advantage of the market timing compared to Optimize.

Experimentation is a topic that I personally enjoy a lot, so we will definitely be talking more about it here!

Automations here and there, how to improve?

Automations are tools to simplify manual tasks and assist in day-to-day management in some way. As I mentioned earlier, Coda has various integrations that make it easier, for example, to create reports and set up automated weekly triggers. This helps keep stakeholders updated and ensures that tasks are progressing smoothly.

Furthermore, one automation that I have become a fan of is using bots in GChat through ITFF to maintain engagement and, of course, remind our team about their commitments for the week whenever possible! It is effective and simple.

If you've stayed with me until now, enjoyed the article, and want to discuss it further, it would be a pleasure to understand your reality and how we can learn together with mine!

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Take care.