How eCommerce Businesses can make best use of Google Analytics Data


January 11, 2018 08:30 PM IST


Beginner Level


Product managers, Web Analyst, Digital Marketing Managers, Conversion Optimization Consultant/ Specialist

About This Webinar

If you are an Ecommerce store and have Enhanced Ecommerce tracking implemented for your business, what are some of the most insightful decisions can you take from your Google Analytics reports? How can you achieve this without bringing in more of predictive analysis and data science?

Ecommerce Analytics contain reports that can help you know your customers better. You can identify the different sections on your website / application that are performing the best, understand the shopping behavior of your customers, identify the areas from where the dropping off and how are the products on your website performing.

Key Takeaway Points

If you are an eCommerce store and have the basic Enhanced Ecommerce GA implemented, you shall get insights into what business decisions you can take from your GA reports without bringing in more of predictive analysis and data science.

  • Basic understanding of Enhanced Ecommerce Reports

1) The Shopping Behavior and Checkout Funnel

2) Product Performance Reports

3) Product List Performance Reports

4) Internal Promotion Reports

  • Using CLV model on your GA data to know your best customers
  • Minimizing the conversion path for your customers using feature attribution and hence driving in more conversions


In this webinar, we will cover these Enhanced Ecommerce reports in brief and how to use the reports for further analysis. Also, we will try to look at a couple of models that you can use on your Ecommerce reports to draw additional insights.


Anshul Bhatt

Anshul is a Client Success Manager at Tatvic. He is a Digital Analytics and Technology enthusiast. Before joining Tatvic, he has worked as a Technology Analyst with one of the leading IT firms

Bismayy Mohapatra

Bismayy is a Product Manager at Tatvic. He has developed Badger, an intelligent insights generation engine powered by Machine Learning. He is working with team to develop PredictN, an automated prediction SaaS platform for businesses to boost their marketing RoI. He applies R, Python, Cloud Prediction models to help clients make sense of their digital analytics data. He loves playing and following football.


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