What is product analytics?
Product analytics is about understanding how users interact with a product or service and how to keep them engaged. Teams can track, visualize, and analyze user engagement and behavior data. Teams can use this data to make their products better. Digital products include many digital assets like websites, mobile apps, kiosks, and specific parts like journeys, funnels, pages, or features.
Several Product analytics tools can help you understand how users are using your product and how it’s performing. These tools include user analysis, which looks at things like cohorts (groups of users), churn (users who stop using your product), and retention (how many users keep using your product over time). Additionally, visualizations like heatmaps (showing where users are clicking or interacting the most), journey analysis (tracking the steps users take within your product), and session replay (watching recordings of user sessions) can also provide valuable insights. Product managers and analysts mainly use product analytics, but UX, CX, marketing, and engineering teams also use them.
How is product analytics different from experience analytics?
Product analytics helps us understand how users interact with our goods or services. This information decides how to enhance the product experience and boost user engagement. Product analytics and experience analytics are two distinct approaches. Product analytics primarily centers around user engagement throughout the customer journey, spanning multiple sessions. Experience analytics tracks interactions in a session using heat maps or session replay. It helps understand users’ struggles or barriers during their interaction and what might prevent them from converting. Product analytics primarily tracks unique users across sessions, while experience analytics focuses on activity within a session.
What’s the difference between product analytics and web analytics?
Product analytics is all about understanding how users interact with your brand across different sessions and devices, like native apps and websites. Web analytics focuses on analyzing anonymous website traffic, including how visitors arrive and how to convert them.
Product analytics is all about understanding how users behave and interact with your product. It looks at how often they use it, whether they return, and how they benefit from it. Marketers use traditional web analytics to measure attribution. It helps them track and analyze the traffic they receive from email or paid marketing campaigns, even if the visitors are anonymous. Companies require more detailed user-level analytics to understand better why users convert or drop out of the funnel.
How do different teams use product analytics?
- Product teams can use data to understand user behavior, make informed decisions, and run experiments to improve activation, conversion, and retention.
- UX/Design teams can gather data on how people navigate feature sets, understand popular and confusing elements, identify challenges, and pinpoint moments of abandonment.
- Engineering teams can identify and resolve issues that cause problems for users. These issues can include bugs, errors, or problems with APIs.
- Analytics teams can use user engagement data to understand and improve business strategies.
- Customer service and support teams can monitor the status of product features in real time. It helps them address customer inquiries and resolve issues faster, leading to a decrease in the number of calls received.
- Marketing teams can use data to determine which programs attract the most visitors, gain insights into how users prefer the product for improved marketing strategies, and discover how users engage with the marketing information they receive.
What KPIs can you improve with product analytics?
Product analytics helps digital product teams improve key performance indicators (KPIs) such as engagement, retention, and customer lifetime value.
1. Engagement
- Customers use the product in different ways. Some features are used more frequently than others. It also tracks how often users return to the product and their actions while using it.
- How to increase brand loyalty among new and existing customers.
2. Retention
- When, how frequently, and by which channels do consumers return?
- What are the factors that influence return and retention, as well as churn?
3. Customer lifetime value
- Determine and classify the most valuable consumers based on demographics, spending, habits, retention, and so on.
- How to convert low-value consumers into high-value customers and so improve company performance.
What is autocapture in product analytics?
Product analytics tools usually need manual data capture, which can be slow and inefficient. To get your data implementation done, you need to know the right questions to ask and wait for engineers to code and configure it manually.
Product analytics teams save time and improve efficiency by focusing on critical interactions. It helps them deliver value more quickly. With a standard software installation, important digital interactions are automatically recorded. It allows for easy monitoring of user behavior from the beginning. You can track links, buttons, taps, swipes, rage clicks, and replay experiences without needing element-level tagging. Sometimes, you may need to track complex or customized metrics and KPIs.
How will your product analytics work with my tech stack?
When choosing a product analytics platform, ensure it works well with your existing technology. It will help you avoid extra work or complications with integrating it. Using the right platform can improve workflows by incorporating tools like Qualtrics for VOC surveys, Salesforce for CRM, Optimizely for experimentation, Salesforce Service Cloud, ServiceNow, or JIRA for service management, and even Google Analytics and Adobe Analytics for traditional analytics. Here are some examples of how you can use it:
1. Voice of Customer feedback
VOC survey solutions help with collecting and understanding customer feedback. Product analytics is made more accessible with the best tool. It allows you to add visual evidence and quantification to your survey responses with just one click. Share replays effortlessly with your digital teams to replicate, resolve, and enhance digital experiences. With just one click, prioritize based on the impact on your business.
2. CX and CRM
Enhance contact center results by accessing real-time customer insights within Salesforce Service Cloud or ServiceNow CSM. The top product analytics embedded replay solution is integrated into agents’ main workflow. It allows agents to easily watch and fix issues with a customer’s digital experience without leaving their CRM.
3. Digital experiments
Using statistical significance to determine the winning A/B test recipe is essential. To get valuable learnings and apply them to future experiments, it’s important to understand why things happen. The product analytics tool lets you connect easily with experimentation solutions like Optimizely. It helps you analyze user behavior through features like heatmaps and session replay. By doing this, you can better understand why certain variations of your product perform better or worse in A/B tests.
4. Data platforms
When it comes to big data, speed is typically not a consideration. The product analytics tool can easily integrate with other data and analytics tools like Adobe Analytics or Looker. It allows you to smoothly transfer behavioral information for purposes like alerting, reporting, or segmentation. With this, you can easily see and find new hidden segments, study customer groups, and make more accurate churn predictions.
Final Words
Product analytics provide the raw truth of how people use the product or a specific feature, but it may be one-dimensional. Combining what you learn from product analytics data with qualitative feedback is essential to build the best product. It can come from customer interviews, concept testing workshops, and sparring. By doing this, you’ll get a complete understanding of what’s happening and be able to create a great product.