Predictive analysis in ecommerce

March 31, 2014
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(This article was first published on Pingax » R, and kindly contributed to R-bloggers)

predictive-analytics

Welcome to the blog post! We all know the predictive analysis is very hot topic now days. Everyone is looking for how the power of predictive analysis can be used in their business and get their business questions solved.  Recently, I was doing study on the predictive analysis in ecommerce. I found many interesting things during my study.  Let me talk about it, I hope you may like it. So In this post I will discuss on what are the business questions in ecommerce? How the business questions get solved using the power of predictive analysis? And what data can be used to solve those problems.

E-Commerce

Let me start with the business questions in ecommerce.  Now days, ecommerce industry is rising drastically and ecommerce business is growing with significant rate. Online retailers are in competition of acquiring more customers as much as possible. Also online retailers are making too much effort to target their visitors through the campaigns. But online retailers are facing the issue of how to convert their visitors into customers. They are trying to target potential visitors who are likely to buy.

Then major concern is how to identify which visitors are likely to buy.  This leads to do several analyses.  Online retailers are interested into knowing

  1. Who will revisit their sites in next couple of days?
  2. Who are likely to buy?

Let me explain each question little bit in detail. First question aims to identify the visitors who are likely to revisit site in few days. What happens many visitors visit site in a day, among them few visitors visit site again to continue the journey of purchase. So the major concern is can we identify in advance at the end of the day who will revisit site in next few days.  If it is possible then we can target these visitors in better way in their revisit and tailored approach can be used to help them in their buying process.

Second question aims to identify visitors who are like to buy. Here the idea is to learn the characteristics of visitors who are likely to buy.  Many times users initiate their buying process in their particular visit and end up with buying nothing because of some reasons, but their affinity of purchase is higher.  So can we identify the visitors whose affinity of purchase is higher? In other words can we score the visitors based on the likelihood of purchase.  This would help to take appropriate actions or create strategy to target those visitors.

Now, if we are able to solve these problems then it would be helpful in business and definitely impact the bottom line. But the question is “Is there any way to solve these questions?”  If “Yes” then “How?”

Here the predictive analysis comes into the picture to solve these questions. The power of predictive analysis will be helpful to solve these questions.   In next post I will discuss more on which data points we can obtain in order to do analysis and methodologies to perform predictive analysis. I will also discuss how we can carry out this analysis using R

Stay tuned and post your comments!

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