Product Management
Product Analytics
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Learn Product Analytics with the Practica AI Coach
The Practica AI Coach helps you improve in Product Analytics by using your current work challenges as opportunities to improve. The AI Coach will ask you questions, instruct you on concepts and tactics, and give you feedback as you make progress.Intro to Product Analytics
Product Analytics is the process of collecting and analyzing data to understand how users interact with a product, and how to improve it.
What every product manager needs to know about product analyticsSam provides a good introduction to product Analytics, explaining core ideas like setting up event tracking, looking at data for existing products, predicting metric changes from new features, and how to integrate product Analytics with Customer Interviews.
Common Product Analytics Challenges
These are common challenges people face when gaining expertise in product analytics. Tackling these challenges head-on can help you learn this skill quicker.
I'm a product manager at a startup and have been asked to work on our product analytics. This is a new area for me, so I'm not sure what are the right metrics to track. Can you give me some ideas of the most impactful metrics to track and how I should get started tracking them?I'm a senior PM at a mid-sized tech company, and I'm responsible for product analytics. I'm encountering some problems with data quality. The data I receive is often incomplete or inconsistent, making it difficult to draw meaningful conclusions and insights that we can pass to the product team. What steps should I take with our team to improve the situation?Work on your own challenge with the Practica AI CoachWhat Metrics to Track
Metrics to track depend on the product's goals and can include user acquisition, retention, engagement, and revenue.
Selecting the Right User MetricSequoia explains the value of a north-star metric (such as DAU, GMV, gross sends), and how to pick it by looking at the vision for a product, its usage, and competitive benchmarks.
The Only Metric That MattersJosh builds on the idea of selecting a north-star metric by explaining that vanity metrics (e.g. DAU) should be replaced by it with a metric that shows the user is getting value and that they are using a core value-based feature of the product.
The North Star PlaybookThis is a 7-chapter playbook that covers what is a north star, how to run a workshop to establish one with your team, and case studies on north stars with Netflix and Amplitude.
Critical Metrics Every Product Manager Must TrackEvgeny gives 11 actual examples of metrics to track (which would be outside of your North Star Metric), across User Engagement, business metrics, and Customer Service, with benchmarks.
How to Set Up Product Analytics
Setting up product Analytics involves identifying the right tools, defining events to track, and integrating with the product.
The Startup Founder’s Guide to AnalyticsTristan walks you through how your startup should be doing Analytics at five different stages: Founding Stage (0 to 10 employees) Very Early Stage (10 to 20 employees) Early Stage (20 to 50 employees) Mid-Stage (50 to 150 employees) Growth Stage (150 to 500 employees)
Data Science for Startups: Tracking DataBen dives deeper than Andrew's post into how to track data, covering what type of data to collect about product usage, how to send data to a server for analysis, issues when building a tracking API, and some concerns to consider when tracking user behavior.
Closing the gap between data and product developmentFlora explains how product Analytics data is collected as events, which are needed to aggregate into patterns of use. Intercom had 350 events with confusing names, making Analytics difficult, so they switched to a naming structure with Actions, Objects, Places, and Owners in order to democratize Analytics.
Pitfalls of Working With Data
Pitfalls of working with data include misinterpreting results, biased data collection, and privacy concerns.
The Agony and Ecstasy of Building with DataJulie provides 3 pitfalls when working with data: 1. Picking the wrong metric to optimize for 2. Over-pivoting towards what’s measurable 3. Biasing towards the short-term. She also provides 4 pitfalls when performing a/b tests: 1. Spending too long perfecting tests. 2. Shipping successful tests right away. 3. Running too many tests on details that don’t matter. 4. Relying on A/B tests to do anything innovative or large or multi-faceted.
Product Analytics Case Studies
Product Analytics case studies highlight how companies have used data to drive product decisions.
Product Analytics at SquareFan explains how the product Analytics team is structured at Square, and then provides a case study on segmenting user types and digging into signup friction to increase conversion.
The 27 Metrics in Pinterest’s Internal Growth DashboardJohn shows the actual metrics used in Pinterest's primary growth dashboard, and why they selected each one.
