Big Data In E-Commerce: Spotting, Analyzing, Acting
The process of applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data is Data Analysis. By brining order and structure to mass data, qualitative analysis is derived, that helps establish consumer and consumption patterns. It enables prediction, personalization and planning.
Data has often been touted as a knowledge mine for businesses. In the internet-enabled business of e-commerce, each step, right from customer acquisition to product delivery is tracked. And thus, the e-commerce industry has access to a goldmine of data, upon which analysis can be leveraged to transform the very core aspects of business decisions across strategy and planning, customer, marketing and supply chain.
For instance; to determine the evolution of demand for Digital Cameras and finding the top selling products during the summer of last year, will resulting in billions of rows and columns; and, manually scanning through them and using excel to dig the data would be a tiresome approach. So how does one validate conclusions (or a hypothesis) that states ‘DSLRs are more popular during summer.’
This is where analytical tools such as Tableau and Google Analytics come into picture. With powerful software packages, web and data analytics is made simpler and more efficient than it ever was. Efficient software’s, bundled together can be a great way to make internal reports with effortless collaboration. Once the raw data is processed, the task is to further create tables and charts that show clear trends to the viewer, these could be trends pertaining to the purchase patterns, search time, most popular camera brands, most popular type of cameras etc.
Data, when analysed, can tell a powerful story.
Data analysis and the derived trends help the ecommerce companies in formulating strategies across verticals. Not only does it help them understand more about the audience; such as their location, their affinity to certain categories etc. but also facilitates real-time price adoption based on customer demand and competitive pricing. With the analysis of large amounts of product data and consumer likes, purchase patterns and product reviews, the portfolio of products can be optimised for each user or group of users based on similar clusters or segments. Analytics also help predict what customer needs and assist in recommending the products.
Recognising trends backed by strong data shows potential ways to optimise and earn more revenue. Aided by the right tools and minds, these data sheets are well optimized to support and further grow varied functions across the e-commerce industry. For e.g. if the data reflects an up-surge in the demand for a particular brand or product from a certain region, a quick reflex would be to enhance listings and supply chain management and stock up at specific locations to be able to meet these demands.
Why Data Analysis?
A recent Gartner report estimated that e-commerce in India was expected to grow at the rate of 60 to 70 per cent year-on-year. The dynamics of this industry in India and lean operating margins, further serve as a motivation for quicker and efficient adoption of data analytics.
Online shopping has been growing steadily in popularity over the last decade in India, and many consumers now use the internet to find great deals and offers to seek out prices that beat in-store offers. This mass-growth rides on industry competiveness. As e-commerce sites are all playing in a harshly competitively landscape, it becomes almost imperative for these players to adapt to and efficiently use data analysis. The convergence of marketing technology and big data is now preparing the ground for target-oriented practices in e-commerce.
Whether it is pricing, product recommendations or SEO, effective use of big data can as well translate into higher conversion rates for online retailers and help pull bigger revenues.
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