What is product management analytics?
Product management analytics can measure users’ needs so teams can build more effective products. This guide covers the data, metrics, and tactics teams need to generate insights and make informed decisions.
Product analytics is a type of software that harvests data from digital products to reveal how consumers use those services. At the most granular level, analytics track users and the actions they take—known as events—and make that information accessible to product teams by summarizing it in reports. With access to precise information on user segments, cohorts, and funnels, product teams can make informed decisions about what features to alter and how to develop the product to increase its usefulness.
Simply having an analytics platform, however, doesn’t guarantee greater insight. Teams still have to ask the right questions and track the right product metrics.
Tracking a product launch? Read the Mixpanel Product Launch Guide.
Why is analytics important to product managers?
Product analytics allow teams to study the past in an attempt to predict the future. As they predict and test, they build knowledge. If the team behind a weather app, for example, finds that app usage spikes before every big storm, they can begin to forecast usage just as they do the wind. The team can run tests to establish links of causality between app events, such as sending marketing push-notifications and spikes in social sharing, and use those learnings to nudge users.
User behaviors aren’t always obvious, and analytics help shine a light on points of leverage to influence their behavior. The children’s coding tool CodeSpark, for instance, didn’t know exactly how its freemium in-school coding app would lead to purchases. It’s players—young children—were reliant on their parents to pay for a subscription. By using analytics to track players, however, the team saw that when their designers added narratives to CodeSpark games, kids were more likely to take the game home and play it there. When parents saw kids playing at home, they were more likely to get involved and make a purchase. Analytics revealed the relationship between adding stories and increased revenue.
Because most analytics platforms are on-demand, teams don’t need to conduct a formal study to answer questions. If several of a music app’s users call support asking for the ability to create more than 100 playlists, the product team can quickly log into their analytics to see precisely how many playlists users create before deciding whether it warrants a feature change.
Teams can also use analytics to measure their progress toward goals. If the leadership at a media company asks its team to increase the number of comments on articles, teams can use analytics to benchmark the site’s current performance and measure whether bigger comment boxes or brighter text help them meet that goal.
How do you measure media success? Download Mixpanel’s 2018 Media & Entertainment Benchmarks Report.
How to choose metrics for product management analytics
Though often used interchangeably, goals, key performance indicators (KPIs), and metrics are not the same. Rather, they sit in a hierarchy, with goals on top. Goals are a company’s highest level priority, such as driving revenue. KPIs measure progress toward goals, and metrics measure progress toward KPIs.
Once teams select their goals, they can determine the KPIs and metrics that support them. No two companies are the same and there’s no consensus around the “correct” set of KPIs for any given business. Each team must learn for itself.
Product management KPIs and metrics can be divided into two categories: those that measure engagement and those that measure transactions. Ad-supported companies like media sites tend to use engagement KPIs and metrics because those businesses make money off users’ time and attention. Purchase-driven companies like e-commerce stores tend to use transaction KPIs and metrics that measure direct revenue. Both types are valuable, but their uses vary.
To select product management metrics and KPIs, teams must first look at what data is available to track. There are, generally, five categories of data, available either through analytics or through data providers:
- Behavioral: A user’s actions such as visits, clicks, time on site, and conversions
- Demographic: A user’s social data, gender, birthday, language, location, or income
- Firmographic: Like demographics, but for businesses: age, employee count, revenue, industry, and business model (B2B or B2C)
- Technographic: Technologies a company uses, such as CRM provider
- Psychographic: A user’s interests, beliefs, and affiliations
When teams have identified the depth, quantity, and quality of their data sources, they can begin selecting their product metrics and KPIs.
How to track the right metrics for product management analytics
Every team should select only the handful of metrics that are deterministic of their product’s success. Tracking too many metrics often proves unmanageable—like a car dashboard with too many gauges.
Every metric that’s chosen should be simple, and practical to collect and roll up into KPIs and goals. Teams can either track their metrics on their analytics dashboard or, at a minimum, record them in an Excel spreadsheet.
Common product management metrics:
- Monthly recurring revenue (MRR)
- Average order value (AOV)
- Gross merchandise volume (GMV)
- Customer lifetime value (CLV)
- Ad click-through rate (CTR)
- Ad cost per million (CMP)
- Cost per acquisition (CPA)
- Average daily active users (ADAU)
- Net promoter score (NPS)
- Customer satisfaction (CSAT)
- Retention or churn
- Event frequency
- Product searches
- Shopping cart or checkout abandonment
- New versus repeat customers
- Time on site or app
- Bounce rates
- Pages viewed
- Social shares
- Conversion rate per visitor
- Percentage of active users
- Session per user
Once teams are using analytics to track their product metrics and KPIs, they can begin their analysis and turn data into useful insights.
How product managers can analyze and report on data
For analytics to provide answers, teams must ask questions and perform tests. That means recording predictions for how the team thinks everything will fit together and then testing how it holds up in reality.
Teams should write their plan down early on. Nothing is set in stone—they can and should revise the plan as they learn more, but a documented strategy provides a benchmark so they know if they’re making progress.
The personal finance app Mint, for instance, wanted to increase its revenue by tweaking fees for credit card and debit card payments. The team hypothesized that higher fees would increase overall revenue, but wrote down their expectations and tested multiple variants to be sure. The team was surprised to learn that lower fees actually led to so many more transactions that they earned more revenue, even with fee cut. In the end, the team did the counterintuitive thing and cut fees.
Product teams can use a variety of reports to understand their metrics. Here are a few of the most useful product analytics reports:
Segmentation: A report that allows teams to divide users by the characteristics they share, such as behavior, signup date, or marketing source. Data-driven product teams can compare the metrics and KPIs of different groups and draw distinctions between them. For instance, users who came from a particular marketing source might be 3x more valuable than the average user. Or users under 25 years old might be twice as engaged.
Want to try it for yourself? Here’s how to perform Segmentation in Mixpanel.
Cohort analysis: A cohort is a user segment that has been named and saved for future comparison. For example, a news site might save two cohorts: one for it’s paying subscribers and one for its free visitors. Each has different behaviors and interests, and the team can cater to each without offending the other.
There are two types of cohorts: relative and absolute. Relative cohorts track a shifting group of users, such as those who signed up within the past 30 days, while absolute cohorts track a fixed group of users, such as those that signed up during the week of a particular conference.
Retention: Retention reports measure how well digital products keep users coming back. Every company measures retention differently, but it’s typically tied to repeat actions. A free social media app might define retention as any user coming back to like a piece of content within seven days. An enterprise security software might define it as a user renewing their subscription after one year.
Funnel analysis: Funnels measure a series of steps users take toward a desired outcome such as a purchase. Funnels help reveal the health of processes like onboarding, and show teams where users drop off or get lost.
The international peer-to-peer shopping app Grabr, for instance, used Mixpanel Funnels to track how newly referred users progressed toward becoming paid users, or “shoppers.” When the Grabr team identified a drop off in conversions between the referral and signup stages, it discovered that users were confused by its signup landing page. The team A/B tested new versions of the page and increased their conversions from referrals.
Once teams have collected statistical insights, they need to translate the data into user stories so that they’re useful to the rest of the company. Simply knowing that eight-week user retention is 20 percent, for instance, doesn’t mean anything on its own. But knowing that eight-week user retention doubled to 20 percent after the company offered its users discounts is a story that everyone from the customer success to the sales team can understand and use.
Once teams have stories, they can move from diagnosing what’s happening to find out what they can do about it. In Grabr’s case, the team graduated from funnel analysis to A/B testing where they tested several different variants of a landing page until they identified one that doubled their conversions.
Where possible, teams should test cheaply. An analytics platform with the ability to run A/B tests and to send multi-channel messaging via email, SMS, push notifications, and in-app messages can enable teams to quickly identify what works, what doesn’t, and put their newfound user knowledge into practice.
How to choose the right product management analytics tool
The best product management analytics tools are ones that teams can grow into. Companies that scale quickly typically outgrow free analytics platforms, and switching from one platform to another can be a technical nightmare. So can trying to build your own DIY version.
Here are five factors for your team to consider when evaluating analytics platforms:
- Breadth of tracking: Can it track the right operating systems, channels, and events?
- Integration with other tools: Does it offer lots of pre-built integrations and flexible APIs?
- Resources needed to implement: What expertise is needed and how much will it cost in terms of development resources?
- Price: Does the price match the product’s business model? Are there price breaks at scale?
- Support: Does the vendor provide lots hand-holding? Is that because the service is difficult to use?
Once teams purchase, they should take implementation seriously as poor implementation make analytics hard to use. Development, design, product, and marketing teams need to form a committee and all partake in the deployment so that the right user events get tagged with a sensible nomenclature that can outlast the current team.