Getting started with the top eCommerce use cases

Nowadays, almost all the projects in eCommerce companies are data-dependent and everyone wants to leverage data science techniques to mine as much information as they can from that data. From tracking their customer’s shopping behavior to recommending them what to buy, from finding new leads for their market to calculating their lifetime value, from improving customer experience to increase their profitability. When we navigate through any website, we leave our traces and companies track these touchpoints to get insights about how we behave online. Companies sometimes have different landing pages based on the gender of the user.

This post will be focused on some of the use cases in marketing which are gaining attention over the past few years. I have been associated with different eCommerce companies as a data science consultant.

Upcoming months has a lot to offer as I will be writing blogs about the following use cases:

  1. Multi-touch attribution: A data-driven approach
  2. Introduction to Recommendation engines
  3. How Important is Customer Lifetime Value?
  4. Customer Segmentation
  5. Dynamic Pricing

 

If you are interested in reading the success story for the Multi-touch attribution project you can find it here.

Aakash Chugh

Data Scientist with 4+ years of experience implementing advanced data-driven solutions to complex business problems. Grounded knowledge of building classic machine learning algorithms in R and Python, inferential statistics and modern development tools ( Docker, etc.).

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