Data science is a key part of product managers’ day-to-day job. Playing the most customer-centric role in their organisation, product managers have a fundamental need to understand their customers in every possible way, but using gut feeling is no more enough, given the velocity, volume and variety of product data.
Key data challenges for product managers include:
- Decide on what they want to achieve with data (e.g. prioritise product development, finding the overall market-product fit)
- Design a core set of “health” metrics to focus
on. These can be:
- Customer Acquisition Cost (CAC)
- Customer Conversion Rate (CCR)
- Repurchase Rate (RR)
- Daily Active Users (DAU)
- Feature Usage (FU, yes that’s really the abbreviation)
- User Churn (UC)
- Net Promoter Score (NPS)
- Customer Satisfaction (CSAT)
- Customer Lifetime Value (CLV)
- Capturing data from different channels
- Defining where and how to structure data (e.g. 5-star rating or thumb up/down system)
- Blending different types (e.g. structured vs unstructured) to create datasets
- Analyse data (self-serving and/or hiring data scientists)
- Extract actionable insights for insights
The above stated, it is far from certain that big data are always the right data that will lead to meaningful insights. Qualitative methods based on human interactions (e.g. user testing) as well as instinct have can play a key role in product development. Human intuition and interactions are still instrumental in today’s world, more so when they are backed by evidence.
Sourced from a great piece by Luciano Pesi:
https://towardsdatascience.com/becoming-a-data-driven-product-manager-f320d0182a9c