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Intro to Product AnalyticsProduct Analytics is the process of collecting and analyzing data to understand how users interact with a product, and how to improve it.
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?Add your own to track your progress and inspire others
What Metrics to TrackMetrics 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.
How to Set Up Product AnalyticsSetting 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)
- The first 6 steps to homegrowing basic startup analytics
- 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.
- How to Set Up a Bottoms-Up SaaS Product Analytics Stack
Pitfalls of Working With DataPitfalls 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 StudiesProduct Analytics case studies highlight how companies have used data to drive product decisions.