User behavior is the set of actions and patterns that users demonstrate when interacting with your product. Tracking and analyzing user behavior will help you evaluate what users find value in, and enable you to improve their experience.
It’s also important to note that user behavior is not marketing behavior—what you analyze in website analytics tools such as Google Analytics. Web analytics tools give you data focused on acquisition and marketing interactions that happened before the person became a user of your product. User behavior is about people who are already active users.
User behavior focuses on metrics such as signups, activation rates, feature usage and impact, funnel drop-off for in-app purchases, and retention rates. For example, you can gain insight on the effect of pricing changes on retention or the popularity of a feature in a cohort of users.
Acting on user behavior data will help your team become product-led and customer experience-driven. You’ll make product development and product marketing decisions based on actionable data, rather than guesswork.
- Tracking and analyzing user behavior helps you improve the product experience and product strategy.
- Experience is everything. Leveraging user behavior data helps you make experience into your competitive advantage.
- User behavior is the key to feedback loops and making decisions based on actionable data.
- Feature usage and impact, stickiness, retention, activation rate, and funnel drop-offs are examples of user behavior metrics.
- Journeys, cohorts, conversion paths, engagement matrices, and anomaly tracking are among the methods used in user behavior analysis.
- Case studies from a number of companies such as Under Armour, Calm, DoorDash, and Babel show that user behavior can uncover opportunities for improvement that lead to large increases of retention or activation.
- Common mistakes in optimizing for user behavior include shipping too much at once, not instrumenting properly, or overdoing the number of tracked events.
Why is user behavior important for companies?
Customer experience is everything, and user behavior helps you understand, prioritize, and improve the experience. Having this mindset is essential for creating product-centric companies like Netflix, Airbnb, Slack, and Peloton. These brands entered saturated markets in their respective categories, but they offered unique experiences that they kept improving based on user behavior. As a result, they succeeded in standing out from the crowd.
Another product company known for its experience is DoorDash, which offers an app and a web version. For most companies like DoorDash, the goal is to streamline the way people add to their cart and simplify the buying process. They could improve their apps and websites separately—which is a common approach—but DoorDash is striving for a more cohesive experience for their customers. Users can go to the DoorDash website, place orders, and get real-time updates via the app. Offering this level of ease can be the company’s selling point, especially since 83% of consumers cite convenience as a priority.
Creating a seamless omnichannel experience doesn’t happen effortlessly. You need to connect the dots for your users and understand how they behave across platforms. With this insight, you can use conscious design and experimentation to create the most rewarding experiences.
Benefits of analyzing user behavior
The following benefits can make major contributions to a brand’s success:
- Create an experience that becomes your differentiator and growth engine. Jumbo was able to grow and get customer experience right thanks to personalizing experiences without triggering privacy concerns.
- Develop and innovate at scale. After using curated charts to track the impact of product changes, Babbel was able to release new content and product updates faster.
- Mitigate the risk of investing in the wrong product initiatives and features. Under Armour Connected Fitness, the brand’s digital product division, leveraged behavioral insights to launch a new feature following a successful test of a product update.
- Increase conversion, retention, and revenue. Calm used behavioral cohorting to compare users who used their Daily Reminder feature with those who didn’t. After highlighting this feature and making it more visible to other users, retention went up.
Key metrics for collecting behavioral data
User behavior metrics can help provide a holistic view of the customer experience and indicate opportunities for improvement. We’re intentionally not including marketing metrics, which focus on the path to signing up and becoming a user.
- Feature usage: Knowing what features users want can help you decide what features to build, add, or remove.
- Element clicks and interactions: The more clicks an element (e.g., a button) gets, the more helpful it is for users.
- Funnel drop-off during activation: Seeing a clear pattern of people abandoning your product can help you resolve any issues.
- Stickiness ratio: Stickiness gives you insight into how many of your customers are returning to your product or feature.
- Sessions per user: The number of instances a user used your product is an indicator of engagement.
- Adoption rate: The percentage of users who make use of a new feature you shipped.
- Activation rate: The rate at which your onboarding is successful in getting new users to perform the first key actions in your product, such as setting up the first chart in a data visualization tool.
- Referral rate: Indicates how well your product or its features are able to motivate users to introduce it to others.
- Churn rate: The percentage of users you lost during a time period. Losing users is common, but can be minimized when managed right.
- MRR and ARR: The monthly and annual revenue your product generates.
- Retention rate: Measures how many users return to your platform—and with the right behavioral data, also reveals why.
Example of Amplitude’s Retention Analysis chart
By considering two or three metrics, you can gain a more complete understanding of how to improve experiences. For example, the retention rate shows how many people keep paying for your product. If you combine it with the stickiness of a feature, you may be able to see which of your features help users want to keep paying you.
At Amplitude, we also advise companies to define their North Star Metric (NSM). This metric defines your product’s value proposition along with contributing inputs. It allows you to link your customers’ problems to your company’s revenue target, allowing you to chart a course that benefits both. Explore the NSM framework in our North Star Playbook to find your North Star.
How to analyze user behavior
Analyzing user behavior can reveal insights for developing a better product. Ideas for what to analyze include:
- Behavioral cohorts involve segmenting users based on the actions taken in your product in order to facilitate comparisons and reveal trends.
- Journeys show paths toward conversion or away from it. Knowing where your users experience friction allows you to modify your product, messaging, or strategy. You can also correlate these patterns with behavioral cohorts for future analysis.
- Anomalies and monitoring for them systematically helps you uncover current and potential bugs in your product and pinpoint the causes.
- Engagement matrix allows you to gain insight into how well certain features are received by your users, so you can either highlight those features or improve them.
- Stickiness shows you what differentiates your most active users from average ones. The idea is to find what brings them back to your product again and again.
- Funnel A/B analysis rooted in event tracking allows you to analyze where users drop off from the flow, so you can remove the hurdles.
- Conversion behaviors are actionable especially when you find what preceded them. Knowing what customer behaviors lead to conversion can help you determine which product development ideas are worth pursuing.
- Impact analysis is about tracking how the first use of a feature affects a user’s overall journey. This allows you to discern which features can lead to a better user experience.
- Revenue and LTV is the end game. But remember that it’s not straightforward to optimize it because you usually have to optimize all the stepping stones first.
When analyzing user behavior, the focus is on actions taken within your product (starting a game, opening the app) or related user activity (push notifications, making a purchase). We call this wealth of information ‘events.’
The good news? You have full access to the show since the events are happening in your product. The key to leveraging them lies in knowing where to look. Follow this 10-step user behavior analysis process to get started:
- Set business and analytics goals.
- Determine which events (user actions) support these goals.
- Set up a taxonomy of event categories and product properties.
- Identify users to attribute anonymous events to their rightful owners.
- Decide if you need cross-platform behavioral analytics.
- Identify app metrics that reflect your business goals and analytics.
- Track events crucial to onboarding, conversion, and retention.
- Set user and event properties to gain deeper insight into how your customers interact with your app.
- Examine whether user behavior events are being tracked correctly.
- Analyze user behavior.
When you’re all set up, you can use user behavior insights to refine your product and customer experience.
Examples of behavioral data analysis
Revenue is the goal of every business, but focusing on it singularly tends to yield short-term results. Actively leveraging user behavior analysis is key to a sustainable growth strategy. By doing so, you can identify areas for improvement, know what users want at scale, understand what core metrics can contribute to long-term growth, and gain a competitive advantage.
Under Armour Connected Fitness
Under Armour’s performance after leveraging user behavior analytics.
Under Armour wanted to know how their mobile experience helped users meet their fitness goals. But their product analysts were subjected to a time-consuming process that required multiple iterations. By leveraging user behavior analytics, the team could quickly test assumptions, access data, and respond.
They soon discovered that their race training plans were low on user engagement. So, to turn it around, they revamped the plans to introduce a wider variety of goals, from running basics to cardiovascular fitness. The changes delighted users, increasing conversions from free to paid, and improving retention. The training plans feature tripled in use among paid users.
Babbel is one of the most popular language-learning apps.
Language learning app Babbel created a Product Performance team to generate high-quality content faster. To do that, they looked at how their learning activities and product changes affected users.
Using Amplitude Templates, the team could keep track of the impact of product updates using curated charts immediately. This gave them valuable data that shortened their release cycles and allowed them to create more content.
Common mistakes when analyzing user behavior
Avoid these common pitfalls when thinking through your behavioral analysis:
- Having a vanity metric as a North Star. Businesses need to achieve goals and KPIs to improve profitability. But setting a vanity metric like increased revenue doesn’t provide a clear path forward.
- Launching too many new features simultaneously. You end up creating far too much work for yourself. Monitoring one feature alone takes seven steps to evaluate success. Launching too much at one time could muddle the whole picture or make the analysis ineffective.
- Improperly instrumenting events and properties. When you instrument events, your company must establish and enforce a set of data governance rules. In user behavior tracking, good implementation is the cornerstone of success.
- Initially tracking too many events with your analytics tool. Focus only on key events at the beginning, so you’ll see which ones make more impact. Our recommendation is to instrument 20 to 30 events in the initial pass. If more events come up, you can always add them later on.
- Not involving every team in analytics usage. Product analytics is not just for product teams and data scientists. Data democratization removes bottlenecks that prevent other teams (UX/UI design, marketing, sales, support, leadership) from contributing to the user journey. Customer-facing teams, for instance, are often excluded.
- Analyzing data with auto-tracking or marketing tools. Studying surface-level marketing interactions doesn’t help in the long term. With user analytics tools, the focus is on your product experience and buyer’s journey to promote sustainable growth. Learn more about the differences with our Google Analytics vs. Amplitude comparison.
- Not using best-in-breed tools across your tech stack. Technology evolves quickly. You don’t want to end up with new tools that don’t integrate with your old ones, creating data silos, which can happen a lot to legacy companies.
User behavior leads to better product initiatives
As you tune into your existing user behavior, it becomes easier to determine what they need. You can use your findings to build or improve products (and features) that not only satisfy current users, but also attract new ones at a growing rate.
To see what user behavior looks like in a product analytics tool, explore our test data in this free self-service demo.