Data driven product management
When I started this blog on Product management, I wanted to provide succinct information on topics. The idea was to get someone quickly upto speed on a topic and inspire them to read up more and deep dive on areas specific to their interests.
On that note, let me introduce the topic of the day - Data driven product management
This is a term that you may have seen in blogs, technical articles, job postings for product roles. Let’s break it down - Data driven product management, DDPM, (I love acronyms and I’m too lazy to type whole words!) is a style/technique of product management that uses data extensively in all stages of the PM journey. So what does that mean exactly?
Think about different aspects of the PM function
What to build? - A product manager determines what to build by analyzing market needs, usage patterns, and customer pain points. This involves identifying gaps and unmet needs, ensuring the product delivers value and aligns with the company’s strategic objectives.
Prioritization & roadmap - Prioritization balances three key inputs: business goals, customer impact, and technical feasibility. The PM gathers insights across teams to create a roadmap that reflects immediate priorities and long-term opportunities while considering resource constraints.
Measure & Iterate- After launch, the PM tracks key metrics like adoption and engagement to assess product performance. This data is essential for achieving product-market fit, improving retention, and guiding future iterations.
Some of the key benefits of DDPM include
Customer centricity - Since all stages of product management rely on qualitative and quantitative customer data and patterns, it aligns product features with real customer pain points.
Better ROI - The PM ensures resources are allocated to the right features and initiatives, maximizing impact for both internal and external stakeholders.
Objectivity - With data supporting every product decision, there's less room for bias and reduced risk of wasted effort on unvalidated initiatives.
Agility - A PM can quickly pivot and explore alternative approaches when data doesn't align with expectations, allowing for agile adjustments that keep the product on track and responsive to user needs and market changes.
