How Product Managers Build A Data Story

Editor’s note: the following was written by a guest blogger. If you would like to contribute to the blog, please review the Product Blog contribution guidelines and contact [email protected]

As businesses operate more digitally with less brick and mortar locations, Product Managers have the potential to play a more strategic role in driving revenue growth for their companies. Leading a cross functional team to ship features is what most PM’s do. But the role can and should be much more strategic. 

PM’s have access to data that gives insight into how customers are engaging their organization’s products. They should facilitate the entire organization to build institutional knowledge that it can build a strategy on top of. The PM should teach the organization what actually provides value to the customer and what does not. The PM must bring more than their opinions. They need a data story. 

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Exploring readily available data

The data story of product performance is the most important story a product manager will tell on behalf of their organization. It’s a comprehensive overview that keeps all parties across the organization updated on their product’s high-level progress. Product managers are constantly evaluating how to improve the customer experience. For example, Get Fit owns a streaming exercise video service that has recently grown to over 7 million subscribers. They are currently monitoring these key measures to gauge performance:

• Metrics to forecast Product Success such as Monthly Recurring Revenue (MRR)

• Metrics to grow User Engagement that includes Daily Active User (DAU)

• Metrics to analyze Customer Retention like Customer Churn Rate (CR)

• Metrics to measure Product and Feature Popularity like Number of Sessions per User.

• Metrics to evaluate User Satisfaction such as Net Promoter Score (NPS)

With these metrics Get Fit has been able to tell stories like these: 

• Get Fit is growing despite losing some customers each month.

• Get Fit has maintained their ARPU at $10 which is inline with their monthly subscription fee. This can be an opportunity to increase ARPU with additional services.

• The product appears to be less sticky due to the MAU/DAU ratio but we have not seen an impact on retention. This is something to keep an eye on.

• Get Fit’s customer retention rate has been in the high 80’s range the last two months. 

Bringing in relevant data

This data story about product performance, however, shouldn’t be limited to just product analytics. PM’s should broaden the data story beyond customer engagement and user satisfaction. Get Fit is exploring a partnership with Happy Times Media (HTM). HTM would like to offer new customers a six-month membership to Get Fit’s fitness video library as part of signing on with them. A comprehensive analysis that incorporates customer segmentation and revenue impact is needed to fully analyze this deal.

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With these additional metrics from separate domains they can now tell a story like this:

• Get Fit has had a 30% increase in MRR over the past 6 months averaging at $70M

• Millennials make up 30% of Get Fit’s customer base with a MRR of $17M.

• Gen X make up 35% of Get Fit’s customer base with a MRR of $30M 

The data point that is key to adding dimensions to the data story is the customer id. This unique identifier is the common thread that joins product, customer segments and financial data. The customer id is usually captured when the customer logs into a website or mobile app. This is how we begin to collect information about where and how a customer engages with the product. The engagement data can then be tied back to additional information we have about the customer including demographics, geographic location and lifetime value score. 

Segmenting the data

In one of my previous marketing roles, I stood up a dashboard that helped track the monthly enrollments in our rewards program and what rewards were being redeemed. In order to begin understanding what our customers valued about our service offerings, we had to look beyond the number of enrollments. We needed to know who those members were and what types of services they currently had. Understanding the rewards they were redeeming could help consumer marketing brainstorm on particular brand partnerships and discounts. Identifying the right product bundles for the right audience helps us to better understand our customer value proposition. 

Stories that incorporate data from multiple sources can help identify customer value propositions and begin to help everyone across the organization begin to see the big picture of their business and how each area impacts the business. As we begin to identify customer value propositions, it will be helpful to segment or group our customers by different categories. That’s because overall stats that describe your entire customer base can be misleading. They do not show nuanced patterns about the underlying data. 

Building a compelling data story

According to Aristotle, making an effective argument requires three things: credibility (ethos), emotion (pathos) and logic (logos). Balancing these three attributes leads you down a path of framing stories and arguments that will influence team members and help the organization get to know their customers. The Get Fit project team found interesting insights analyzing average revenue per user (ARPU). As they looked at ARPU by age group they observed the following:

Get Fit has an overall ARPU of $10. This coincides with their monthly base subscription fee. For $10 you have access to Get Fit’s extensive fitness video library. If a customer wants to view live workout sessions with a group instructor they can add this premium for an additional $5 per month. Based on the table above it appears that Generation X is more inclined towards improving and maintaining their health. We can begin to build this data story using Aristotle’s rhetorical triangle.

Ethos: Using data and reputable sources, we can begin to establish that Gen X consumes more health conscious content. Get Fit’s marketing department cited Mobile Marketer when looking to establish customer profiles. They learned that this age group’s online time is mostly driven by purpose.

Logos: Based on the data collected, Get Fit’s Gen X customers make up the largest share in their customer base at 35%. In addition, they bring a MRR of $30M. An even more telling insight lies with ARPU. They have the highest ARPU at $12.00 which over indexes against the total customer base. Gen X customers are purchasing premium content which indicates their dedication to fitness.

Pathos: When communicating with Gen X, Get Fit can begin to tell a story that appeals to the busy professional balancing work and family. The tagline can be “Get Fit is a way to squeeze workouts into a busy schedule.”  

Creating this data story supported by these rhetorical pillars helps communicate the impact the Gen X customer segment is having on the Get Fit business. It also helps the company make decisions on growing additional customer segments. Based on this analysis, the Get Fit project team feels more confident about moving forward with their partnership deal with HTM. 

Choosing a Robust Platform

It’s important to choose a data visualization platform that is robust and can join different data sources to broaden the customer story and filter and segment those “aha” moments. It’s even more important to allow various departments in the organization to have access to analyzing the data to collaborate on cross-functional teams. As we saw in our Get Fit example, different members of the organization brought a context and perspective to the table that added additional insights to the product performance story. Product managers can take the lead on building the knowledge framework needed to guide strategy for the organization. In summary, PM’s should:

1. Explore readily available data. Become a subject matter expert on your products and familiarize yourself with the underlying data. Doing so will help you begin to build a comprehensive understanding of what is important to the customer and how to convey those needs to the broader organization backed with data.

2. Broaden your knowledge with additional data. As you begin to build a holistic story, expand your knowledge of the metrics beyond product engagement. If you’re currently not tying in how your product’s performance impacts revenue or who your customers are demographically, then it’s time to analyze those data sets. The more you know about your customers and how they impact your organization’s growth, the better.

3. Filter and segment the data. As you expand your data set that helps understand the customer base, this is a great time to start segmenting. Grouping customers by location, product usage, revenue, etc begins to reveal trends that might not have been as obvious before.

4. Tell a compelling story. How your findings are communicated is key. When delivering insights or proposing an idea, it needs to be backed by data and resonate with the appropriate audience.

Meet the Author

Allen Hillery serves as part time faculty at Columbia University’s Applied Analytics program. He has spent the greater part of his career being an ambassador for business teams and championing the voice of the customer. Allen is very passionate about data literacy and curates an article series that focuses on the importance of creating data narratives and spotlighting notable figures on how their use of storytelling made major impacts on society.