Impact of Analytics on Pricing Decisions

Abstract

Pricing strategy is a fundamental component of the marketing process. Pricing is a process where businesses decide the price at which the products or services should be sold. Some of the objectives of pricing are profit maximization, price stability and to prevent the competition. With the use of available data such as transactional data, sales data, these strategies can be implemented effectively. Pricing with analytics helps to leverage the data to increase the profit.

This paper explores approaches for price bundling and price forecasting strategies using machine learning algorithms. Bundling is about grouping products or services with the aim of offering a discount. The study uses the K-means clustering algorithm to determine the price of each bundle. Then the results are compared with the traditional approach of bundling where MS Excel Solver is used. Revenue is increased significantly using the new approach.

Further, it discusses an approach to forecast the prices based on qualitative data such as customer reviews, brand name, item condition. Natural Language Processing (NLP) methods are used to read and manipulate the text data and then prices are predicted using machine learning algorithms such linear regression, K-Means clustering. This method shows how textual data can be leveraged to make pricing decisions more efficient.