Data-Informed Product Building

As the world changes and ecosystems evolve, companies are growing faster and products are easier to create than ever before. As a result, products are creating more data — and strategically collecting and analyzing that data has never been more important. Analytics and data science are now must-haves, not afterthoughts.

But despite the indispensable nature of data science, there is a scarcity of literature that explains how to conduct useful product analysis. We intend to help fill this knowledge gap with a series of posts on how to build both data-informed products and world-class data science teams (with particular attention to the consumer space). This introduction is a living document; the table of contents below will be updated as new posts are published.

We hope you find these articles useful, and we welcome your feedback: data-science@sequoiacap.com.

Table of Contents

1. Evolution of a Product: Understand the characteristics of successful products from conception to maturity.
2. Measuring Product Health: Metrics to diagnose and analyze product health.
3. Defining Product Success: Metrics and Goals Setting the right goals and metrics is imperative to product success.
4. Retention: Techniques to improve user retention and drive growth.
5. Sustainable Product Growth: Learn about growth pitfalls that can limit long-term success.
6. Frameworks for Product Success: Understand the need for frameworks by exploring product-focused examples.
7. Analyzing Metric Changes: Learn how to diagnose shifts in metrics and develop an action plan for monitoring changes in your product.
8. Analyzing Metric Changes Part II: Product Changes: Understand how to diagnose shifts in metrics resulting from product changes.
9. Analyzing Metric Changes Part III: Seasonal Factors: Consider how behavioral changes can affect metrics.
10. Analyzing Metric Changes Part IV: Competition and Other External Factors: Consider how external factors can affect metrics.
11. Analyzing Metric Changes Part V: Mix Shift: Learn how mix shift can drive metric changes and the techniques used to analyze its effects.
12. Analyzing Metric Changes VI: Data Quality: Consider how to ensure consistent data quality that enables effective analysis.
13. Analyzing Metric Changes Part VII: Action Plan: Develop an action plan for monitoring shifts in your metrics.
14. Leveraging Data To Build Consumer Products: Our story so far.
15. Engagement Drives Stickiness Drives Retention Drives Growth:Understand the connection between engagement, stickiness, retention and growth.
16. Engagement: Engaging experiences provide value.
17. Engagement Part I: Introduction to Activity Feeds: Engagement is the earliest indicator of product market fit.
18. Engagement Part II: Content Production: Content production is the single most important factor that influences engagement.
19. Engagement Part III: Connections and Inventory: Connecting people with the right content will drive greater relevant inventory
20. Engagement Part IV: Activity Feed Ranking: Activity Feed Ranking is critical for driving Engagement in high inventory situations.
21. Engagement Part V: Consumption: Delightful consumption of stories leads to higher engagement and ultimately to stickiness, retention and growth.
22. Engagement Part VI: Feedback: Understanding the various types feedback of feedback and the role feedback plays in building an engaging product.
23. Engagement Part VII: Summary and Product Implications: Driving a sustainable highly engaging product requires careful considerations.
24. Engagement: Professional Content Part 1: Content Production:Producing evergreen content is the most important lever for engagement.
25. Engagement: Professional Content Part 2: Recommendations: For a platform offering professional content, recommendations are the primary means of highlighting the content users will find most relevant.
26. Engagement: Professional Content Part 3: Content Consumption:Consumption of content is strongly affected by device and connectivity.
27. Two-Sided Marketplaces and Engagement: Designing a thoughtful framework is valuable to understand engagement.
28. The Building Blocks of a Data-Informed Company: Successful data-informed companies do two things well — focus on impact and build a data-informed culture.
29. Why Data Science Matters: The world will become more data-driven over time but data-informed decision making will continue to be impactful.
30. Building World-Class Teams: People, culture, and process are critical to a company’s success in the long term.
31. Role of a Data Scientist: The role of a data scientist is to leverage insights from data analysis to help drive product decisions. There are six different types of data scientists, and they all share five core skills.
32. How a Data Organization Evolves: Products evolve. How does infrastructure, data organizations and teams evolve with it? In this next post in our data science series, we’ll walk you through the top characteristics shared by elite data teams and their evolution as they grow.
33. Hiring a Data Scientist: Analysis case, applied analysis, programming, quantitative and problem formulation skills need to be assessed during a data science interview.
34. Progression Of A Data Scientist: The career trajectory of a data scientists depends chiefly on how much impact they are able to have. This is achieved through four levels of scope: Project, Product, Domain, and Company.

Check back next week for more updates!

This work is a product of Sequoia Capital’s Data Science team and originally published at www.sequoiacap.com. Jamie Cuffe, Avanika Narayan, Chandra Narayanan, Hem Wadhar and Jenny Wang contributed to this post. Please email data-science@sequoiacap.com with questions, comments and other feedback.