Price Discovery Techniques for e-commerce Stores & R Google Analytics

April 22, 2015

Beginner Level


CMO, CEO, Director of Web Analytics, Sr. Web Analyst, Digital Marketing Manager

About This Webinar

E-commerce as a business has four main drivers: users , conversion rate, avg. product price & quantity.

Traditionally, most of the focus has been on getting more users to site or to some extent converting them to buy something. This leads to huge opportunity that exist for optimization of Price component within the avg. order value.

This presentation is a walk through of techniques of conjoint analysis & regression analysis to help e-commerce store to identify products for which the price could be improved.

Key Takeaway Points

In this 45 minute informative session you’ll learn how to:

Introduction to 4 Different Quantitative Revenue Drivers
Why Focus on Pricing as a Lever to Drive More Revenue
How to Identify Products which Consumers are willing to pay higher prices using Conjoint Analysis & Product Demand Index
Why Use R to solve complex business problems and 3 Real Life Case Studies


Carve out your “best customers” from the pool of transacting customers via Predictive Machine Learning and remarket only to them and improve your Remarketing ROI and Conversions by leaps and bounds using Predictive Analysis!


Ravi Pathak

Ravi Pathak

Ravi is the co-founder of Tatvic and an expert at managing different web analytics tools. He actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He is also an expert at R, SPSS & predictive modeling for web analytics data. He has worked intensively on Omniture & Google Analytics to help clients generate value out of the web analytics data.

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