Data-Informed Product Building

Image Source: Sequoia Capital

As the world changes and ecosystems evolve, companies are growing faster and products are easier to create than ever before. The time it takes a product to reach 100M monthly active users has shortened dramatically (see Figure 1) — and continues to shrink today.

(From Multiple Sources)

The barrier to entry in creating software is decreasing exponentially as more people learn to code. Cloud services are eliminating the need for tedious development and infrastructure maintenance. As predicted by Moore’s law, the cost of computation is on the decline. Consumer purchasing power continues to grow. Platforms such as Google, Facebook and Amazon are making it easier to reach target audiences, and app stores make distribution a cinch.

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. They are invaluable not only for “counting numbers” and building dashboards, but for helping define goals, roadmaps and strategies. The success of a company is increasingly dependent on the strength of its data science team.

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).

Our goal is to give you an understanding of how a product evolves from infancy to maturity; a holistic sense of the product metric ecosystem of growth, engagement and monetization; a framework to define goals for your company; and a toolkit you can use to analyze your product’s performance against those goals.

We will offer guidance on the analytical tools, approaches and methods required to build data-informed products. In future posts, we will also cover exploratory analysis, forecasting techniques and machine learning methods — all of which are indispensable in creating product roadmaps and strategies.

In addition, we will provide context on building world-class data science organizations: What is the role of a data scientist? When should you hire them? What skills should they have?

We plan to share several posts per month for the foreseeable future. This introduction is a living document; the table of contents below will be updated as new posts are published.

In a future series, we will discuss similar themes in the context of marketplaces and enterprise companies.

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. Diagnose Product Health: Learn how to diagnose shifts in metrics and develop an action plan for monitoring changes in your product.
  8. Effect of Product Changes: Understand how to diagnose shifts in metrics resulting from product changes.
  9. Seasonal Impact on Product: Consider how behavioral changes can affect metrics.
  10. Competitive Pressure on Product: Consider how external factors can affect metrics.
  11. Effect on Behavioral Changes on Product: Learn how mix shift can drive metric changes and the techniques used to analyze its effects.
  12. Data Quality Can Mislead You: Consider how to ensure consistent data quality that enables effective analysis.
  13. Action Plan for Diagnosing Product Health: 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. Driving Engagement on News Feed: Engagement is the earliest indicator of product market fit.
  18. Content Product Drives Engagement: Content production is the single most important factor that influences engagement.
  19. Relevant Inventory Drives Engagement: Connecting people with the right content will drive greater relevant inventory
  20. News Feed Ranking Drives Engagement: Activity Feed Ranking is critical for driving Engagement in high inventory situations.
  21. Delightful Consumption Drives Engagement: Delightful consumption of stories leads to higher engagement and ultimately to stickiness, retention and growth.
  22. Feedback and Mimicry Drive Engagement: Understanding the various types feedback of feedback and the role feedback plays in building an engaging product.
  23. Building a Sustainable, Engaging Product: Driving a sustainable highly engaging product requires careful considerations.
  24. Drive Engagement on Professional Content: Producing evergreen content is the most important lever for engagement.
  25. Recommendations Drive Engagement: For a platform offering professional content, recommendations are the primary means of highlighting the content users will find most relevant.
  26. Professional 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. 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.
  32. Five Core Skills 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.
  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.
  35. Role of a Data Science Manager: Data team managers need to build strong, stable teams in a nascent and rapidly evolving field that achieve high quality outcomes. To be successful, data managers must: drive impact and build world class teams.
  36. Making Data Science Work: A data-informed culture that drives success begins with company leadership emphasizing the right value of data and hiring and developing top data scientists.
  37. Understanding Products Through Storytelling: Storytelling is a powerful technique for building data-informed products.
  38. Selecting the Right User Metric: Selecting the right metric for success requires thoughtful exploration.
  39. The laws of nature strongly influence product behavior: Laws of nature strongly influence the behavior of users and ultimately the success of a product.

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.

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