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Introduction to Product Analytics

Desktop shows different parameters and graphs of product analytics

In this blog, we will talk about product analytics 101. Product analytics is a robust set of tools that allow teams to assess the performance of the digital experiences they build.

Below is an excerpt from the book Behind Every Good Decision – How Anyone Can Use Business Analytics to Turn Data into Profitable Insight.

If you are in Product or part of a Business Unit (BU)

The Chief Product Officer (CPO) or head of BU has similar challenges while leading the product organization or a Business Unit. Again, he or she can use many of the same techniques and strategies. 

 

1. Identify new products and features for the various customer segments—understand consumer needs per segment and deliver targeted products:
  • Divide the base to understand differences in needs (based on past product usage, demographics, etc.)
Simple Segmentation
  • Identify different products and features across multiple segments
Testing
  • Prioritize new product ideas or features
Sizing and Estimation
2. Prioritize which product features to include. This can be  determined by understanding the expected business impact Sizing and Estimation
3. Optimize the customer experience to increase product usage by motivating consumers to take action like buying a product or signing up
  • Identify friction points that prevent users from using your product well
Testing

Correlation Analysis

In the future, in the next set of series beyond the 101, we will talk about predictive analytics, machine learning, and deep learning for product analytics. But it’s important to note, that is not where you begin. 

In the next blog in this 101 series, we will look at Customer Service and Operations data science 101. (https://aryng.com/blog/all-things-about-customer-analytics/)

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