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Become truly data-driven 

Do not just write data-driven on your CV, be truly data-driven. Being data-driven means you are using data to base your decisions unlike making your opinion. So you may ask what is wrong with using experience and opinion to base our decision? Well, the problem is that every individual’s opinion differs and when that happens, you have no clue which of the 8 Billion + population’s opinion makes an impact. 
When your company is chasing people, they look at data. How many people were acquired? How many people Signed for our service or product? How many people visited our site daily? What action were taken by the visitors?

You see in the above example not only just numbers but qualitiative impact was also looked upon. So it is not all about numbers, it is also about what people do and what they think that matters. Specializing in this skill will boost your product management capability. You will be valued since not many people are as data-driven as they should be. 

Following links, will you give you the best idea around data-driven product management. This will also involve purely data books, courses etc. to ascertain that you know all about data such as where it comes from, how to best collect, analyse and base your decisions. 

Best part about the below topics are that they are based on the keywords extracted from Job descriptions. So, you know you are learning somehting which will be used in your interviews and jobs.

  1. Data-Driven: Business and Strategic Data: you must understand business data to comprehend business requirements. If business goal is to increae customers, retain them better or increase monetization, then you would want to drill down and see where this problem comes from, why is it a problem, what is the data that suggests we can optimize the funnel to reach our company goals through product. Key things to consider are:
    • Understanding key business metrics (revenue, profit, market share)
    • Analyzing industry trends and market data
    • Interpreting competitive analysis reports
    • Forecasting market potential using historical and predictive data
  2. Data-driven: Customer Insights and Market Data: Look at what customers want: This is one of the most important data-driven part of the product managemnt. This is where it all starts of what the product should be.
    • Analyzing customer segmentation data
    • Interpreting customer journey analytics
    • Using Net Promoter Score (NPS) and customer satisfaction data
    • Analyzing churn rates and customer lifetime value (CLV)
    • Conducting market research and analyzing findings
    • Understanding customer acquisition costs (CAC) and ROI
  3. Data-Driven: Product Development and Experimentation: Now that you have collected the survey data and asked people about what problems they have to achieve their goals, it is time to put out solution as an experiment to see how you can make a difference and if customers will use your product to solve their problems.
    • Using data to inform long-term vision and strategy
    • Aligning product metrics with overall business goals- OKR
    • Tracking sprint velocity and team capacity
    • Analyzing agile metrics (burndown charts, cycle time)
    • Using data to estimate and refine story points
    • Monitoring quality metrics (bugs, rework, test coverage)
    • Designing and analyzing product experiments
    • Using data to validate or invalidate hypotheses
    • Analyzing data from minimum viable products (MVPs)
    • Tracking and categorizing feature requests
    • Using data to prioritize product backlog
  4. Data-Driven: Product Performance and User Behavior: Now that you have collected user data around what is their problems and developed a solution. It is time to understand how they are using your product. These are the points to take care in product performance and user behavior measurement.
    • Analyzing user engagement metrics (DAU, MAU, session length)
    • Tracking feature usage and adoption rates
    • Monitoring product health metrics (uptime, load times, errors)
    • Analyzing funnel metrics (conversion rates, drop-offs)
    • Interpreting heat maps and session recordings
    • Using cohort analysis to understand user segments
    • Conducting and analyzing A/B and multivariate tests
  5. Data-Driven: Advanced Analytics and Decision Making: If you are interested in exploring its big brother that is data science and data concepts around database, then this section is for you. As a product manager you can manage data products or ML products with a better understanding of these concepts. This, however, is just a basic version of the advanced ML or data science. This section is good to create interest and then look for practical experience to learn further concepts and knowledge.
    • Basic understanding of statistical methods (regression, significance)
    • Knowledge of data visualization tools (Tableau, Power BI)
    • Familiarity with SQL for data querying
    • Basic knowledge of machine learning concepts (classification, clustering)
    • Understanding of data privacy and governance
  6. Data-Driven: Growth and Marketing Data: Both growth and marketing are crucial in bringing in customers to the product. Its like without knowing that your product solves a specific problem, people may not be able to find your product, without which you cannot grow your product and make it profitable. Following topics cover your growth and marketing cases.
    • Analyzing user acquisition channels (CAC by channel)
    • Understanding viral coefficients and growth loops
    • Using attribution models to understand conversion paths
    • Analyzing pricing data (elasticity, tiering effectiveness)