Product analytics is the process of collecting and analyzing data on how users interact with your digital products or services.
Today’s digital landscape is crowded with customers who have high expectations from the products they interact with in terms of form and functionality. This is true for hardware and digital products, but for this article, the focus will only be on digital products. Hence, crafting top-notch user experiences (UX) that solve user needs in a delightful manner is not just a goal but a necessity to keep an edge in the ever-evolving competition.
Product analytics can help solve this challenge by offering invaluable insights that enable product teams to understand user behavior patterns, identify friction points, and optimize customer journeys within their products.
By thoroughly analyzing data points, such as feature adoption and usage, conversion and dropout rates, and user navigation patterns, product teams gain deep insights into what users like about their products and what aspects of their products require enhancements. Some of the key benefits of products include, but are not limited to:
Now that we know some benefits of using product analytics in products, the question arises: how do we incorporate it into our products? Luckily, there is a framework that could guide product teams toward implementing analytics into their products:
Define your product analytics strategy
Before measuring insights or implementing a product analytics tool, product teams must do a (or many) huddle to define their analytics strategy. A solid product analytics strategy must answer the following questions:
Setup Data Infrastructure
Before data collection can be started, product teams must choose the right tool for the job. There are many off-the-shelf options, but sometimes you need something very specific. In that case, teams can create an internal tool to fit their product’s unique needs.
This is where teams must also think about how they could best leverage the existing data in the form of an existing data warehouse.
Data Acquisition
Once the data infrastructure is in place, data collection can be undertaken. On the front end, there are several off-the-shelf tools available to perform data collection automatically, such as session recordings, custom event tracking, heat maps highlighting user engagement, and user path funnels.
Other data sources, such as backups of operational database systems, can also uncover some interesting usage patterns about how users are interacting with your product. This can specifically come in handy when analyzing revenue across multiple sales channels. Teams might also be interested, for example, in analyzing the number of orders placed and units sold through the mobile app as compared to the web app.
Looking for patterns and actionable insights
Now, it's time to collate all the data captured through your analytics tool and measure it to make the most sense. It is also a great time to ask questions about the data, such as:
Reporting and Visualisation
After gaining insights from the data, it is time to present the data as a report or a dashboard. This ensures everyone on the product team is on the same page, with easy access to key information about your product. This shared information allows the product team to align on a common understanding of the product, enabling them to collaborate more effectively.
Plan and Take Action
With everyone on the same page thanks to shared insights, teams must plan their next steps. For example, if the data reveals that the cart abandonment rates are high, it might indicate that the checkout process needs to be streamlined to reduce friction for users to complete their purchases. By utilizing these insights, product teams can continuously optimize their products and deliver a seamless user experience.
Measure progress
Product teams that implement tweaks on the back of the data need to continuously monitor the outcomes of these changes. There is a likelihood that the users did not like the changes made to the checkout process we discussed above and the drop-off rates are even higher or vice versa. Hence, measuring the changes is equally important as making them in the first place.
Pivot if necessary
As noted earlier, if a feature isn't delivering the user experience customers expect, it's time to pivot and re-imagine what can be achieved. This is where analytics-driven UX shines. By constantly evaluating and iterating based on user insights, product teams can tweak UX until the customer is happy and, in turn, adopting your product. It's an ongoing process revolving around learning and iterating until your UX reaches its desired state.
Define Data Governance, Compliance, and Privacy policies
Having a transparent data governance and privacy policy is critical while working with user data to avoid penalties in case of a data breach or a security lapse. Depending on the industry your product targets, product teams might need to incorporate certain legal requirements into their policy. Failing to meet legal and minimum security requirements can wreak havoc on the product’s reputation and its success prospects.
Product analytics starts with the development of the product. Teams must measure how their alpha and beta testers are interacting with their products. This can uncover some valuable insights before launching your product. Remember, your real-world customers have a limited attention span. Hence, you want to launch your product to the mass market in a state that is as refined and polished as possible.
Product analytics empowers product teams to enhance digital user experiences through automated tracking, measurement, and actionable insights. Leveraging these insights effectively, teams can optimize user experiences to meet your customer's needs and prioritize new features for their products.
Ready to elevate your digital user experience? Learn how Ippon can help you deliver exciting and delightful digital experiences to your customers.