How to get started with Product Analytics? Session 1

This session was hosted by Mathew Brandt as a part of the cohort course Product Analytics 101 hosted by The Product Folks.

What is Product Analytics?

It is about applying the best practices in data analytics to get insights about users, product usage and business in order to make key decisions.

Let’s see each of the definition components:

Best Practices:

• Standardised and agreed definitions. For instance, a customer can be referred to as a prospect.
• Reliable and clear how the data is collected, processed and used within the data stack.

Users:

• Ignoring a user is dangerous. But if we treat all the users the same, one can lose value. For instance: No two user in Netflix receives the same recommendations.
• In the meantime, if we end up with all user data and their journeys, the data will be huge to comprehend. So basically, it’s about the balance!

Product Usage:

It is all about, how the actions are taken in the product, by the users. What the users are trying to achieve? And what path do the users take to achieve the goal?

Business:

Any changes made to the product will always have an impact on the business, be it trivial or a major change.

One has to understand which changes will have what impact. A PM cannot have his own thinking on how the users will feel about the change.

Product Analytics Use Cases:

1. Understanding how to prioritise upcoming feature releases- Based on the cohort analysis.
2. Deciding on which product features to remove.
3. Evaluating which activities are more effective for user retention.
4. Testing which version of a product feature is most accepted and used.

With all the definition break down given above, there comes a question,

Who benefits most from Product Analytics?

The user!

Product Analytics is a discipline that is integrated across the Data Stack — Collect, Process, Store, Model and Output.

The data to be analysed is collected, processed, stored, modelled using modelling techniques and the final output is derived. The final output could be as simple as an email to the customer and arriving at a cohort analysis!

Why Do we need Product Analytics?

1. User Acquisition is far costlier than User Retention, with respect to your product.
2. Designing features in the product based on gut feeling is dangerous and risky.
3. One should assess the financial impact on the product decisions as it determines the longevity of their organisation.

More to come on Session 2 Next week!