What are Product Analytics?
The term product analytics refers to capturing and analyzing quantitative data through embedded tools that record how users interact with a product.
This type of usage data can include the most frequently accessed features of a product, the average time users spend taking a specific action, and a map of each user’s journey through the product.
Why are Product Analytics Important?
Most traditional methods of gathering product feedback— such as surveys and customer interviews—require self-reporting from users and are based on their memories and perceptions.
When their product feedback comes only from survey forms or discussions with users about their experiences, product managers can come to the wrong conclusions. Customers often don’t know or can’t articulate exactly why they interacted with a product in a specific way, or what they find most valuable about a product. As a result, this feedback is not always accurate or at least doesn’t tell the complete story.
Product analytics, by contrast, represents definitive and objective data because the organization is tracking users’ actual behavior within the product. It can uncover important insights that help organizations design better, more effective products.
How are Product Analytics Used?
As the marketing institute CXL explains, a product team should implement product analytics only after its product has reached some minimum number of users or customers.
When the product’s user base is still small (fewer than 100 B2B customers or fewer than 2,000 B2C users) the data you derive from product analytics won’t be a large enough sample to give your organization meaningful guidance on what to do with your product. So until a product reaches that type of benchmark, the institute recommends that product teams use qualitative product feedback such as surveys and customer interviews.
But when the product has enough of a user base to warrant it, gather and analyze quantitative metrics to help make your product even better. The best way to gather these metrics will be through product analytics.
The CXL Institute suggests this type of product analytics implementation plan:
1. First, connect your data to business goals.
The institute suggests to first outline specific business objectives for the data you plan to capture. That way you avoid any time and resources wasted on gathering data your organization won’t be able to put to productive use. This could mean, for example, figuring out how to convert more free trial downloads to paid subscribers.
2. Create a tracking plan for your data.
Product analytics data is typically broken down into units called events. An event describes an action your user takes with your product. For example, they access a feature, open a new screen, send a message, close the app, etc.
The CXL Institute suggests creating a detailed tracking plan, using a spreadsheet, listing all events (user actions) that you’ll want to track while users are interacting with your product. This step is critical because if you leave out any steps in a user’s journey, you could lose important insight into how they’re engaging with the product.
3. Choose the right product analytics tools.
Finally, the institute suggests researching the product analytics tools available such as, Kissmetrics and Google Analytics.
Because no single tool performs all of the tasks or generates every type of report your team will want, you’ll likely need to sign up for a couple of these solutions to implement your unique product analytics strategy.
What are Examples of Product Analytics Tools?
There are many product analytics tools on the market today. Among the most popular are:
- Google Analytics
- Heap Analytics
It’s important to keep in mind that each of these solutions will excel at different aspects of product analytics.
Product Analytics: An Invaluable Source of Business Intelligence
For products newer to the market and still finding their customer base, qualitative data derived from talking with customers is often sufficient for helping guide the product team’s priorities.
But when that product reaches a certain threshold of users, enough to yield statistically significant data, then it will be time to implement product analytics. This quantitative, real-world usage data can provide the most useful type of business intelligence available to a product team.