Product analytics is the process of gathering and transforming user-level data into insights that reveal how customers interact with specific products. Product analytics enable an organization to track and analyze its users' journeys -- from user activation through all other phases of use -- to understand what makes them engage with and return to the product. This also helps organizations understand the value that the product provides to customers.
Usage data gathered through product analytics includes information such as the most popular features of a product, the average time users spend on a specific action, which marketing channels are generating the best users and how many users return to the product in daily, weekly or monthly increments. With this information, companies can analyze how users interact with what they build and use these insights to improve their user experiences (UX).
Product analytics is typically used by technology companies that provide web and mobile applications; however, it is also used by e-commerce companies whose primary revenue comes from digital properties. Within these companies, product leaders, designers and developers use the data gathered through product analysis to inform their decisions. Many market leaders, including Uber, Spotify and Netflix, use product analytics to improve their services.
Product analytics is an important part of a company's product management practices because most apps and websites are not designed to produce detailed reports on themselves. Without analytics, usage data is often improperly formatted and inconsistent. Product analytics software makes this unstructured data beneficial by integrating all data sources into a single, organized view.
Importance of product analytics
Product analytics shows companies their customers' behaviors -- specifically what users actually do instead of what they say they do. Understanding the customer and their needs is essential to building effective and beneficial products. Product managers can use the hyper-detailed information gathered through product analytics to empathize with customers and make more informed and profitable product decisions.
Qualitative data provided by customer surveys, discussions and interviews is not always accurate and often doesn't tell the full story of the user experience. In contrast, product analytics provides objective and definitive customer data that has been gathered by software tracking real user behavior with the product. Therefore, product analytics allows product teams to conduct a deeper analysis of certified information than they would have been able to with human-error prone interviews, surveys and discussions.
While implementing product analytics is beneficial for companies looking to understand how their customers use existing product features, it is also valuable for testing new features and gauging the user experience. If a team has a goal for how much a new feature should be used, then they can use the data provided by product analytics to work toward that goal.
In recent years, tech companies focusing on application development have found product analytics to be one of the most efficient ways to increase user retention and optimize the company's position within a competitive market.
How to implement product analytics
Product analytics should only be implemented after a product has reached a set minimum number of users. If the customer base is too small then the data gathered through product analytics will not be enough to provide a meaningful sample that can guide organizational decisions. Qualitative feedback collection practices -- such as surveys and customer interviews -- are recommended until the product has reached its lowest user benchmark.
The product analytics implementation process involves:
- Connecting customer data to business goals. Specific business objectives should be outlined in relation to the data that is being gathered. For example, the goal could be converting more free trial participants into paying users.
- Building a tracking plan for the data. Product analytics data is broken down into units called events. An event is an action taken by a user with the product. Detailed tracking plans should be created, including an identification of all events that should be tracked while customers interact with the product. Failing to create an effective event tracking plan could distract from important insights into how users engage with the product.
- Choosing the best product analytics tool. Every tool is different; no single tool performs all the product analytics tasks or generates every type of report. A business must research available tools in the product analytics market -- such as Google Analytics or Mixpanel -- to determine which ones best fit the organization's needs. It is common for multiple tools to be implemented to better serve a company's product analytics strategy.
How to use product analytics
Product analytics combines business intelligence (BI) with analytical software that gathers customer feedback, product returns, service reports, warranties and data from embedded sensors. The process is used to help businesses identify product defects, evaluate usage or capacity patterns, discover opportunities for improvement and link all this information to the user.
Product analytics software is built around two core functions that help companies understand their customers:
- Tracking data -- capturing visits and events
- Analyzing data -- visualizing the information in dashboards and reports
The data that is gathered and organized by product analytics software enables companies to ask a variety of questions, including:
- What are the user demographics?
- How can the churn -- or user turnover -- rate be reduced?
- What is the typical user journey flow through the website or app?
By answering questions such as these, product managers and their teams aim to improve their products and user experiences by:
- understanding how customers are using the site or app;
- discovering points of user dissatisfaction, error or pain;
- segmenting the most profitable users;
- decreasing customer turnover;
- increasing product retention rates; and
- identifying where their marketing budget should be invested.
Product analytics is also beneficial for teams looking to test new product features. This process often involves:
- setting a clear prediction for a product change -- for example, the amount of replies is expected to increase by 10% after increasing the size of the reply button;
- preparing the most cost-effective implementation of the change, including any analytics events needed to test the predictions;
- implementing the change for a subset of customers using an A/B testing technique; and
- breaking down the results once they come in to decide if the change was successful.
Over time, as a company continues to use product analytics, the product team will collect a repository of evidence based on data that allows them to make positive feedback loops. A positive feedback loop means that as a team accumulates more data from product analytics, the more they can use it to improve and iterate their marketing and product development strategies. More iterations will also lead to more data and more tests, which will ultimately improve the overall product lifecycle.
Product analytics tools
Product analytics tools enable businesses to step-by-step track their users' journeys. Features such as segmentation, funnels and cohorts are essential to helping companies understand their customers and the decisions they make.
Product analytics software also needs to operate in real time so it can alert product teams to service and replacement needs or suggest preventative actions. The tool should also be able to route service requests to the appropriate individuals or automate the service using machine learning (ML).
Common features of product analytics tools include:
- User tracking. The ability to trace customer actions inside the app or website.
- Measuring. User engagement should be measured by each product feature.
- Segmentation. Discover who users are and separate them into smaller groups -- or cohorts -- based on shared factors, such as demographic, age or job.
- Cohort analytics. Users are separated into cohorts and businesses track information -- such as the number of days it takes a user to complete their second action -- and visualize it in a chart.
- Notifications.The tool should allow users to communicate with each other, as well as send alerts to the product team.
- Funnel analytics. A funnel is a visual representation of the users' journey. It can help companies identify where users are getting stuck, where they leave and when they complete the final step. Businesses practicing funnel analysis can use the information to track where customers are lost and assess different ways to reduce churn at those points.
- A/B testing. Also known as split testing, A/B testing is a technique that allows businesses to identify which version of their product achieves the best results or meets the business goal most effectively.
- Display dashboards. This user-facing feature allows companies to visualize their collected data with templated or customer reports.
Some of the best product analytics tools available include:
- Google Analytics
- Heap Analytics
Product analytics vs. marketing analytics
While both product analytics and marketing analytics are used to help organizations achieve their business goals, the two processes vary greatly in how they do so.
Product analytics focuses only on the company's product. The software collects customer event data from multiple sources. The goal of the process is to provide decision-makers with insights that reveal the big picture and improve product decision-making efforts.
In contrast, marketing analytics looks specifically at the success of marketing activities. The process involves collecting data from multiple marketing channels. The goal of marketing analytics is to provide marketers with the opportunity to see where they can make improvements to their campaigns.
Product analytics is also more complex than marketing analytics. For example, creating a funnel for marketing analytics is easier because the customer journey consists of less steps than that displayed in a funnel for product analytics. Furthermore, marketing analytics only looks at the earlier stages of the customer journey -- such as conversion and acquisition -- while product analytics considers all the different phases of the journey.
In addition, product analytics operates with confidential customer information while marketing analytics uses public data. The sensitive data involved in product analytics includes customer names, surnames, email addresses, physical addresses and phone numbers. On the other hand, data collected through marketing analytics is openly available to the public.