The Need For Product Analytics

posted on 11 Mar 2023

Note : I’m currently taking a Product Analytics course and this blog post is just me collating the notes that I took during the first module of this course so that I can refer to these later.

As a PM, if you want to build a successful product, you require a profound understanding of user behaviour, their needs and problems. You then have to drive revenue impact by figuring out solutions that would serve the users by addressing their problems and needs. This means, first seeking answers to questions like -

• How do we build solutions that add value to customers?
• How do we reduce friction in the experience so that customers get value faster?
• How do we measure the value added ?

In the past, product teams relied on qualitative data gathered from conversations with a handful of customers and gut instincts to make decisions. However, this approach has limitations, as the conversations may be biased or the loudest voice may win in terms of what to prioritise. To improve decision-making, product analytics is the solution.

Product analytics is the act of capturing and analysing data about how users interact with your product allowing product teams to be more data informed and rely reliant on gut instincts. Product analytics can answer questions such as whether users are using the product in the intended manner, whether they are seamlessly getting to the core value-generating action, who the power users are, and what features they use the most.

Here are a few other concrete questions that product analytics helps you answer :

Baseline usage questions:

• How many active users do I have today, last week, last month?
• What is a typical customer journey of usage of the product?
• What features do they discover first ?
• Are users finding key features and parts easily?
• What features are most used?
• What parts do users spend most of the time on?
• How soon do users get to the core action?

Retention questions:

• How frequently are active users coming back? (Retention at the entire application level)
• How many users continue using product in the first few months of usage?
• How any users who interact with a key feature come back? (Retention at a feature level)

How can different functions use product analytics to their advantage?

• Engineering teams can use product analytics to monitor the quality of the product. Analytics can help engineering teams uncover quality issues around user experiences and bugs so that they can make better prioritisation decisions about the issues to address first.
• Marketing teams can use product analytics to identify power users so that they can be reached out for a review or for more collaboration through interview, webinars, case study etc.
• CS teams can use product analytics to check whether the customer journey is as expected and identify customers who are stuck or having trouble achieving a specific task and can intervene. They could also use product analytics to show customers valuable metrics to prove ROI during QBRs, renewals etc.
• Sales teams can identify users who are primed up for additional purchases. For e.g If the product has a freemium model and a customer signs up on a free plan and completes a core action in the product and adds other team members , it is a good sign that the customer has found value in the product and sales team can engage with such customers to further pitch other value additions.