User Analytics OverviewLast edited: Aug 24, 2022
User analytics is a method for analyzing the users of digital products such as apps or software. A user analytics platform, or product analytics, is a type of software which performs user analysis and surfaces deeper insights than products can typically provide on their own. This helps teams build products users love which makes them more profitable.
Why are user analytics vital?
Without user analytics, product teams are left flying blind. Within any given app, thousands, millions, and perhaps even billions of events fire every day as users navigate the interface to find what matters to them. If product teams don’t implement user analytics to collect, store, analyze, and act upon this treasure trove, they’re left guessing about how to improve the product. Product teams that rely too heavily on intuition and user research can run into problems. Product managers and marketers are human, after all, and they’re subject to common cognitive errors such as confirmation bias, which leads product teams to hunt for data that only confirm their preconceptions. This is a serious problem in user interviews when users are already predisposed to telling product teams what they want to hear. It leads to a gap of understanding between what product teams think users do and what they actually do. It’s only with user analytics that product teams can reveal the full picture of user behaviors in the wild. This allows them to:
- Understand user behavior and demographics
- Make more profitable product decisions
- Empower all teams with data
How do user analytics work?
User analytics act as a hub which connects many data sources like spokes on a wheel. It’s one central repository which pulls data from the app or service, hosting platforms, advertising partners, app stores, and internal databases to make it all accessible. User analytics platforms excel at making data accessible because they’re often created by software providers who specialize in analytics. Few apps or software that require analysis are created with these sort of in-depth self-diagnostic capabilities from the get-go. Teams that attempt to build their own analytics functionality after the fact rarely anticipate how costly and time-consuming it can be. For reasons both functional and financial, most companies buy best in class pre-built user analytics.
Pre-built user analytics platforms have three primary capabilities:
Data collection and storage
Good user analytics platforms automate data collection. Through integrations, they pull data from many sources monthly, weekly, and perhaps even in real-time. Platforms store this data in a simple, secure, and scalable way so that it’s easily accessed for analysis.
Analysis and recommendation
Most product teams get to know their user analytics through their web-based interfaces. These provide extensive tools for reporting on data and building dashboards to keep employees organized across teams and business units. Product teams use analytics to ask questions like:
- Who are our users?
- What actions do our users take?
- What is our ideal user journey?
- What is our retention rate?
- How does our marketing perform?
- How does our platform perform based on a variety of metrics?
For more app metrics, read What Are Mobile App Metrics?
However, for even mid-sized organizations, the hoards of data and flexible tools make it tough to stay on-track. Some product teams try to boil the ocean by tracking and measuring everything while others do too little. To help them strike the right balance and spend less time analyzing and more time testing hypotheses and improving their product, some platforms provide recommendation engines. Mixpanel, for example, uses machine learning to mine users’ data for answers to questions product teams hadn’t thought to ask. It can alert product teams of anomalies, recommend feature tweaks, and guard against human errors.
Testing and marketing
Modern products need to evolve at the pace of users’ tastes, and user analytics platforms help product teams do that. They offer tools for A/B and multivariate testing to analyze hypotheses and streamline the user journey by testing new forms, text, images, and workflows. They also automate the testing so that the product self-optimizes. Some user analytics platforms include marketing automation capabilities so that product teams and developers can message individuals via in-app messages, push notifications, or email to help them discover value, learn the product, buy more, and remain customers.
How to evaluate a user analytics platform
Product teams that are interested in user analytics should look for a platform that can both meet their current needs and grow with them. Switching platforms can be cumbersome, costly, and have long-lasting negative effects. While many platforms allow users to export data, they rarely allow users to keep proprietary data. If a product team begins with an entry-level or free platform based on their budget, but must switch to a different, more effective provider as they grow, the team could incur a catastrophic loss of historical data. If, however, they begin with a free version of a platform and then upgrade to its more advanced paid version, there is no data loss. For many teams, it makes the most sense to find one platform that they plan to grow into. Here are five things teams should look for in a user analytics platform:
- Integrations: Does the platform unify data from various systems of record?
- Automation: Does it save time and make the team more efficient?
- Scale: Does the platform suit needs now, but also scale to the enterprise level?
- Optimization: Is the tool designed for product teams?
- Accessibility: Is it easy enough for everyone across all teams to use it?