The 2024 edition of Product Camp delivered transformative content for professionals in product, data, martech, and growth. One of the highlights at the Minders Aquarium stage was the talk by Lucian Fialho, co-founder and CTO of Métricas Boss, who provided a practical approach to structuring and implementing data measurement plans.
In this blog post, we’ve gathered key insights from his session, including strategies for collecting relevant data, avoiding common pitfalls, and building an effective data-driven culture.
If you work with data and are looking for ways to make your analyses more efficient and valuable, keep reading!
Why Measuring User Behavior Is Essential
Lucian opened his talk with a crucial point: many teams struggle with poorly structured or irrelevant data. The impact of this issue is significant—unnecessary costs on tools and immense difficulty in making decisions based on reliable data.
Key takeaways from his talk:
- Less is more – It’s crucial to measure only what adds value to the business. Collecting excessive data increases costs and makes analysis harder.
- Maximizing collected data – Well-organized data helps teams understand user behavior and optimize experiences.
- Facilitating decision-making – Consistent data allows teams to identify trends and solve problems more quickly.
Lucian emphasized: «It’s not about measuring everything, but measuring what matters.»
Step-by-Step Guide to an Effective Measurement Plan
A good measurement plan starts with the end goal: the user journey’s macro-conversion—such as completing a purchase or subscribing to a service. From there, the key metrics to track at each stage of the journey are defined.
1. Understand the User Journey
To measure effectively, you need to know the steps users take on your website or app. This includes:
🔹 Mapping the most common interactions.
🔹 Identifying key friction points and drop-offs.
🔹 Prioritizing critical stages for business success.
2. Define Events and Parameters
Events represent user actions (e.g., clicks, form submissions), while parameters add details (e.g., device type, product category).
For example:
- Event: «Add to Cart»
- Parameters: Product name, product ID, category, price
3. Use a Consistent Taxonomy
Maintaining consistency in naming events and parameters is essential to prevent errors and data duplication. Lucian recommended using camel case or English-based naming conventions, as many global tools adopt these standards.
4. Foster Cross-Team Collaboration
A successful measurement plan involves product, marketing, design, and tech teams. Diverse perspectives ensure all critical aspects are covered.
«A diverse team creates more relevant events and prevents resource waste.»
Real-World Cases and Lessons Learned
Lucian shared real-world stories that illustrate how an efficient measurement plan can transform business results.
Taxonomy Error in E-Commerce
An e-commerce client believed they had a solid measurement plan. However, simple mistakes—such as using both “AddToCart” (uppercase «A») and “addToCart” (lowercase «a»)—split the data, making analysis difficult. After fixing these inconsistencies, the company unified its data and optimized marketing campaigns.
Boosting Conversions with Data
A client of Métricas Boss conducted its first data-driven technology sprint. The team prioritized fixing key bugs identified across different devices. As a result, the conversion rate significantly improved in a critical user flow.
Quantitative vs. Qualitative: Striking the Right Balance
Lucian highlighted the importance of combining quantitative and qualitative analyses:
Quantitative: Answers “How many users clicked button X?”
Qualitative: Explores “Why did users abandon the form at step Y?”
This combination provides a holistic understanding of user behavior, delivering richer insights for decision-making.
Data Culture: A Competitive Advantage
Building a data-driven culture goes beyond technical implementation—it’s about empowering teams to use data in daily decision-making and ensuring information is accessible to everyone.
Tips to Strengthen a Data Culture:
- Democratize data – Provide clear dashboards and reports for all teams.
- Offer training – Teach teams how to interpret and apply data effectively.
- Align objectives – Ensure everyone understands the importance of measurement for business success.
