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Leveraging Data To Build Consumer Products

Data Science Team

To date, we have discussed growth, retention and frameworks as they relate to building consumer products. We thought it would be good to step back to put all the concepts we have discussed in context and motivate the next part our our journey.

The combination of more products, more Internet-connected devices, and increased time spent online has caused a spike in the volume of user interaction data. Simultaneously, the virtuous cycle of more A/B testing and experimentation → faster product iteration → accelerated development releases → compounding product growth, has fueled internal demand for unlocking insights from the growing amounts of data generated. A company’s ability to compete and innovate on a product is increasingly driven by how successfully it applies analytics to vast, unstructured data sets across disparate sources.

Figure 1

The world’s greatest companies are built on the foundations of a data-informed culture. Take Amazon and Facebook as examples, both companies have a strong “test and learn” culture where they use data to help drive product decisions. Analytics is invaluable not only for “counting numbers” and building dashboards, but for helping define goals, roadmaps and strategies.

“Our success at Amazon is a function of how many experiments we do per year, per month, per week, per day”?—?Jeff Bezos

“One of the things I’m most proud of that is really key to our success is this testing framework … At any given point in time, there isn’t just one version of Facebook running. There are probably 10,000.”?—?Mark Zuckerberg

With the increased focus on driving data-informed product decisions, companies generally focus on three outcomes that analytics helps drive.

  • Evaluate the health of the business by building dashboards/reports
  • Ship the right products and features using A/B testing
  • Set roadmap and strategy for the product by deep exploratory analysis

These outcomes are achieved by defining key north star metrics and goals for products; creating a scalable data-informed organizational structure; and deeply understanding how to drive sustainable growth, retention, stickiness, engagement and monetization for products. We have published a series of medium posts that cover some of the topics aboce. In this article, we connect each of the different topics covered and provide a flavor of the future articles that we plan to publish.

Goals and Metrics

A set of of Northstar metrics and goals are a must-have. Identifying a top-line metric which best encapsulates the vision for your product and mission for your company is critical. This Northstar metric is one that is a manifestation of usership, is easily measurable, and is associated with the drivers of your business. Closely tied with this metric is the goal of your organization.

Goals are important in defining and monitoring success. When a goal is in place, the destination is clear?—?even though the route may change. Goals help connect your mission to your strategy, roadmaps, initiatives and tactics by tying the single metric you care about most with a target and a time frame during which it can be achieved. More than anything else, this process will help your team define success.

Organizational Structure

For any company to become truly data-driven, the organizational structure plays an important role in the success. Generally speaking, the vision and mission of a company could potentially drive the organizational structure of the company. By understanding how different parts of your company are inter-related, we could construct a formula for the entire company. At Facebook the formula was: # users * TS/ # users * $$$ / TS = $$$. Get everyone to join Facebook, keep them deeply engaged with the product and monetize every minute of time that they spend on site.

Figure 2

This formula defined the focus for each each team, the growth team would focus on the number of users, the engagement team on the time spent per user, and the ads team on dollars per time spent. In that way, every team had a clear Northstar metric that aligned to drive company-level outcomes. Under each of these core business units, you can have a number of small teams underneath each of which have their own formulas that align to drive the business-level outcomes. For example, under growth might be the Internationalization team that focus on growth in users overseas. Under internationalization, there could be a series of teams for each country each with their own set of metrics. This results in a set of ripple down formulas that make it clear to everyone how their work connects to the business at large. This alignment around goals and metrics is the foundation of the bottoms up data-informed execution culture that separates the truly great companies from just the good.

Product Evolution, Health and Sustainability

With the organizational structure, key goals and metrics established, we explore multiple dimensions of healthy products with a particular focus on how consumer companies should measure aspects such as growth, retention, stickiness and engagement.

Products evolve over time; the characteristics of an early-stage product are quite different from those of a mature one. For strong products, the phases of growth usually resemble an S-curve: in the Early phase, growth is modest and shallow. Then it accelerates as the slope arches upward during the Growth phase. This is followed by a Hyper Growth phase?—?a period of exponential growth. After maximum growth is reached, growth tapers off and the product begins its Mature phase, during which there is little to no growth. We describe the dynamics of each phase to help you set expectations for your product.

To succeed in the long term, a product must create real value. When it is deeply engaging and has high adoption, users will return to it frequently of their own volition. Most successful, healthy products can grow sustainably for long periods of time because they address fundamental problems. Sustainable growth is growth that can be maintained without exhausting future resources. There is a trade-off between rapid growth today and growth in the future; rapid growth today may exhaust the addressable market and make it impossible to sustain your product over a longer period of time. We discuss how to grow sustainably here and show that it depends on two key factors: product-market fit and positive net growth. Retention is the best way to measure product-market fit and by far the best lever for product growth. Without retention, a growing product will eventually be left with no users.

It is essential to monitor metrics to achieve product success. Having established a top-line metric moving that metric in the right direction becomes the top priority. Metric changes are almost always due to one or more of the following factors: product changes, seasonal factors, competition, mix shift, and data quality.

Upcoming

In previous posts, we have spent significant time discussing growth and retention as it relates to product building for consumer products. A critical component that has not yet been discussed in-depth is engagement.

Engagement reflects whether users truly love a product and find value from it, it is the earliest indicator of product-market fit. It is also the most important driver of both retention and sustainable growth. Therefore, a deep understanding of the drivers of engagement is critical to product success. On the most fundamental level, engagement drives both growth and retention. Without an engaged audience a product is rendered useless.

We look forward to introducing the concept of engagement in our upcoming posts and illustrating how it relates to retention and growth. Please email us at data-science@sequoiacap.com or comment below with any thoughts and comments on our material so far.

This work is a product of Sequoia Capital’s Data Science team. 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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This work is a product of Sequoia Capital's Data Science team. 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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