Advanced E-commerce Data Science Methods

Data science (also referred to as data-driven science) is a field of science that combines several disciplines, processes, algorithms, and systems to get knowledge or in-depth understanding from data in different forms; both structured and unstructured. It is almost like data mining. Data is one of the greatest assets that any company or entity possesses as it can be put to use to make well-informed and futuristic decisions. To be able to use data for decision-making, entities require that data to be analyzed well to provide the needed information. Due to the complex process of analyzing data while drawing conclusions from it, data science consulting companies come in to offer their services. They are experts in the field and have accumulated a wealth of experience which they offer to companies.

Integrating data science with e-commerce is a great step. It enables a better understanding of the customer, through the capturing of information about the customer’s behavior on the web, the factors that lead to the decision to buy a certain product, and others things. So, what are some of the data science methods employed in e-commerce? Read on for a discussion on the issue.

Predicting customer lifetime value

Customer lifetime value (CLV) is the sum of all the gains that a customer brings into your company by interacting with your business over their lifetime. This is done using algorithms and equations that have been designed for the task. The main ways of calculating CLV are:

Historic CRV – it is the total of the gross profit from all past purchases from a particular customer

Predictive CRV – this is a forecast analysis that takes into account past transaction history and several behavioral pointers predicting the lifetime value of a customer. With the equation being accurate, the CLV will become more precise with every time the customer interacts with the company and buys more products or services.

Acquisition of new customers is harder than retention of existing ones. Hence, there is the need to work on how to extend the CLV, as it is crucial for a healthy business model. The models used include the Gamma-Gamma models and the hidden Markov chains models.

Wallet share estimation

This is the proportion of a customer’s total money expended in a category that goes to a company. This is crucial to identify possible ways in which the company or business can sell more sophisticated items to the customer, or more of the items they buy (that is, upselling). In addition, the business can look at ways of selling related commodities to what the customer buys (cross-selling).  The models used to do this analysis include the Quantile nearest neighbor and quantile regression.

Customer segmentation

This deals with clustering customers with similar buying patterns from past purchased commodities. These can be targeted with specific products, offers, and communication media. The models that can be used for customer segmentation are Non-supervised learning algorithms, for example, k-means.

Affinity analysis

This is analyzed to identify the items or set of items that are usually bought together. A priori algorithm can be used to accomplish this analysis.

Replenishment

This is an analysis to predict the precise time that a customer is likely to place a subsequent order for a product. Time Series analysis, probabilistic models, and Monte Carlo Markov chains are some models that can be used for this analysis.

Data science has wide application in e-commerce. Without it, e-commerce would not be a success.