What is product analytics?
Companies use product analytics to analyze their users and to improve their customer experiences. Analytics makes tracking users easy because it automates the collection and management of data. Product leaders, designers, and developers use this data to guide their decisions and studies show that companies who rely on product analytics are far more profitable than their peers.
Why do companies use product analytics?
Product analytics allows companies to fully understand how users engage with what they build. It is especially useful for technology products where teams can track users’ digital footprints step-by-step to see what they like or dislike and what leads them to engage, return, or churn. Analytics is a critical piece of modern product management because most apps and websites aren’t designed to run detailed reports on themselves. Without analytics, the data they collect is often inconsistent and improperly formatted (known as unstructured data). Product analytics makes that data useful again by integrating all data sources into one single organized view.
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Why is product analytics important?
Product analytics shows companies what their users really do, not just what they say they do. These are known as revealed behaviors and they’re highly telling. Users aren’t very adept at predicting their own futures (consider any given new year’s resolution) and having analytics allows product teams to dig deeper than human-error prone surveys and user interviews. Hyper-detailed data leads to more profitable decisions. According to McKinsey, “Companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not.” To see these returns, companies must learn how to use their product analytics.
How to use product analytics
All product analytics platforms are built around two core functions which help companies answer questions about users:
- Tracking data: Capturing visits, events, and actions
- Analyzing data: Visualizing data through dashboards and reports
With data that’s been tracked, captured, and organized, companies are free to ask questions such as:
- What are our user demographics?
- What is the typical behavior flow that users take through our site or app?
- What opportunities do we have to reduce churn?
And more. The answers they receive arm them with statistically valid facts upon which to base marketing and product decisions. Common goals with product analytics:
- Improve product retention
- Segment the most profitable users
- Decide where to invest marketing dollars
- Understand how people are using the site or app
- Uncover user pain points
- Reduce churn
From there, teams typically graduate to proving or disproving hypotheses such as “Will adding a pop-up increase subscribers by more than 30 percent?” or “By adding icons, will customers find what they’re looking for 20 percent faster?” Over time, teams build up a repository of data-backed evidence which allows them to create positive feedback loops. That is, the more data teams get back from product analytics, the more they can iterate their marketing and product development. More iteration leads to more data, more tests, and more improvements in a virtuous cycle. Products that emerge from this cycle often achieve a high degree of usability.
What tools does product analytics provide?
The tools that teams actually use to uncover insights vary from platform to platform. The following are the most common (and useful) features. Common product analytics features:
- Track users: Automatically track events inside your site or app.
- Profile and segment users: Find out who users are and segment them by device, time, region, and behavior.
- Send notifications: Communicate with users and send alerts to the product team.
- View a marketing funnel: View customer journeys and conversions.
- A/B Test: Test variations on messaging or features.
- Display dashboards: Visualize data with templated or custom reports.
- Measure: Measure engagement by feature.
How to implement product analytics
Implementation begins with choosing a product analytics vendor. Depending on your budget, there are three options: free, paid, or DIY.
There are three options for product analytics
|Free analytics||Build an analytics system yourself (DIY)||Paid analytics|
|Pro||Free||Custom designed||Feature-rich, supported, scalable, secure, accessible|
|Con||Feature poor, unsupported||Shown to be 8x more expensive than buying, difficult to support||Costs money|
Ensure that at minimum, your product analytics vendor can support:
- Integrations to all sites and applications you plan to track. If not, you’ll need more than one analytics platform.
- An intuitive UI that’s accessible to the whole team. Otherwise, you’ll develop an insights bottleneck as everyone in the organization relies on the one power user who understands the platform.
- Data security and reasonable uptime. Without security and uptime, your product’s success will hinge on an unprotected point of failure.
The actual implementation of product analytics will come down to integrating it with your sites or apps. For websites, there’s typically a snippet of code that must be added to the site’s header and for apps, a software development kit (SDK), which serves a similar function. If your analytics vendor supports codeless implementations, it will speed up this process dramatically. With a product analytics system in place, you’ll be ready to begin ingesting data and analyzing it for invaluable insights.
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