Your product engineering team needs to make data-driven decisions. Which analytics tools will you choose?

Yes, I think this is good advice. Before you worry about a tool, you should spend some time defining the most important questions you are looking to answer. Then you can use that as criteria for tool selection. Tha other important thing to keep in mind is that your product engagement data is not only used for making product decisions (especially in a product-led environment) - it is also used to drive key GTM actions and decisions. This means you should collaborate with your GTM teams (CS, Sales, Marketing) to understand what data they will need to power their work. This will help you make a strategic plan for tracking your product data so you do it right and do it well.

Sounds a bit silly given I've been in the data world my entire career, but when I first started gathering Product Usage data, I was so keen on just collecting info that I overlooked setting clear goals and KPIs. I was all about 'knowing stuff'. This led to two things: 1- I had to go back and add new features to capture missing data. 2- Once I got the data, I hadn't set any benchmarks for success, so it felt a bit pointless initially.

In my experience, defining product goals and metrics is foundational to successful product engineering. It's crucial to establish metrics that are not only aligned with business objectives but also resonate with customer satisfaction and retention. For instance, at Timechamp, we focused on user engagement metrics that directly correlated with feature adoption rates, which in turn informed our product development cycle. By leveraging tools like Hubspot Analytics and Tableau, we could visualize data trends and make adjustments in real-time, ensuring our product evolution was always in step with user needs and market demands.

It depends on the requirement of the Product and platform-specific but currently available tools such as Google Analytics, Mixpanel, Amplitude, Heap, Tableau, Looker and Power BI are good to satisfy the requirement.

É fundamental escolher ferramentas de análise que estejam alinhadas com as metas e métricas da equipe de engenharia de produto. Isso pode incluir ferramentas como Google Analytics, Mixpanel, ou Amplitude, que oferecem insights sobre o comportamento dos usuários, desempenho do produto e outras métricas relevantes. Ao conhecer suas metas específicas e as métricas que melhor refletem o progresso em direção a essas metas, você pode selecionar as ferramentas de análise mais apropriadas para monitorar e avaliar o desempenho do produto de forma eficaz.

Identificar as fontes de dados mais relevantes para suas metas e métricas específicas, garantindo que as informações coletadas sejam precisas, atualizadas e abrangentes. Além disso, é importante considerar a capacidade das ferramentas de análise em integrar-se facilmente com outras plataformas e sistemas utilizados pela equipe de engenharia de produto.

Selecting analytics tools depends on your specific needs. Popular choices include Google Analytics, Mixpanel, and Adobe Analytics. Consider factors like ease of integration, features, scalability, and cost to make an informed decision based on your team's requirements.

Avaliar a capacidade das ferramentas de análise em gerar insights significativos a partir dos dados coletados, bem como sua capacidade de apresentar esses insights de maneira clara e compreensível por meio de visualizações eficazes. Considere recursos como painéis personalizáveis, gráficos interativos, relatórios automatizados e capacidade de segmentação de dados.

Priorize ferramentas intuitivas que não exijam treinamento extensivo e possam ser facilmente adotadas pela equipe. Além disso, certifique-se de que as ferramentas possam lidar eficientemente com grandes volumes de dados e ofereçam recursos de expansão conforme necessário para acompanhar o crescimento das necessidades da equipe ao longo do tempo.

You want a solution that require little to no training. Otherwise you won't use it. We ended up changing one solution providing usage data that was super easy to setup but quite hard to use. As always, be careful when selecting your solutions. Most if not all will be SaaS and therefore make sure you understand the cost: usage, volume, number of users, etc.

In a recent project, our product engineering team needed to improve the onboarding process for our SaaS platform. We decided to implement Segment to collect and unify user data from our website, mobile app, and backend systems. By analyzing this data using Tableau, we identified key drop-off points in the onboarding flow and iterated on the user interface to streamline the process. As a result, we saw a significant increase in user engagement and conversion rates, demonstrating the effectiveness of our data-driven approach. Segment is a customer data platform (CDP) that helps product engineering teams collect, clean, and unify customer data from various sources.

DWH: Google BigQuery, Amazon Redshift, or Snowflake to store & analyze big data Data Processing: Apache Hadoop & Apache Spark open-source frameworks to process & analyze big data Data Visualization: Tableau, Power BI, & Looker to easily understand & interpret Statistical Analysis: R, Python, & SAS ML: TensorFlow, PyTorch, & scikit-learn for ML models Data Collection & Cleaning: Kafka for real-time data collection & Talend for data integration BI: Microsoft Power BI, Tableau, & QlikView for converting raw data into meaningful insights It's also important to consider factors like cost, scalability, ease of use, and integration with existing systems. It's often beneficial to use a combination of tools to cover all aspects of data analytics.

For Roadmap prioritization, I now use the newly released Jira Product Discovery tool, basing decisions on a variety of metrics like goals, value, effort, and insights to calculate an impact score for each idea. For Product Usage analysis, I leverage Segment to send data into Snowflake, which I then analyze using Tableau.