INTRODUCTION:  E-commerce or electronic commerce is an industry where products are purchased and sold over electronic systems such as the Internet or other computer networks. It is generally considered as the sales aspect of an e-business.

Data analytics deals with extracting useful information from data. Big data analytics is the process by which predictive analysis is done on a huge amount of data to create meaningful insights. Big data analytics is one among the fastest growing fields today. Various sectors are adopting big data analytics to improve their efficiencies and outputs. E-commerce companies have become one pf the fastest adopters of Big Data Analytics.

Data can be classified into 2 types – structured data and unstructured data. In the field of e-commerce, name, sex, preferences, address, etc. would be considered as structured data and the unstructured data would comprise of social media entities such as videos, tweets, clicks, etc. Structured data is generally stored in fixed fields within databases whereas unstructured data cannot be accessed by merchants from databases.

Collecting data is a relatively easier task when compared to the process of storing, organizing and analysing it. Sources range from the company’s website to Facebook, Twitter, AdWords, CRM software, etc. From this seemingly useless tangle of data, analytical tools help to extract meaningful information by revealing hidden patterns and correlations which are otherwise not easily discernible.

TYPES OF DATA ANALYTICS

Some of the common uses of data analytics are analysing visitor browsing patterns, login counts, past purchase behaviour, etc. Companies can also record responses to promotions in order to determine if the promotions are working or not. With the help of such analytics, it is possible for retailers to decide which promotion and discount schemes to offer for maximum impact.

This form is known as clickstream analytics. This involves collecting, analysing and reporting aggregate data about which pages visitors visit in what order, depending on the succession of mouse clicks (clickstream). E-commerce based clickstream analytics can be used to determine the effectiveness of the website as a channel-to-market by quantifying the customer’s behaviour. Pre-programmed applications can be used to interpret the data and generate specific reports.

Another form of data analytics is association rule learning. In the field of e-commerce, it related with shopping cart analysis. By this, it is possible to figure out which products are purchased together. This information can be of immense use to the companies, as they can evaluate and re-design their marketing mixes in alignment with these data.

According to a report, digital data is to double every two years. However, majority of the growth in data will be in the unstructured category. The biggest challenge facing organizations today is how to use and interpret all these unstructured data to increase conversions and thus profits for the organization.

USING BIG DATA ANALYTICS IN E-COMMERCE

Personalization

A major thing that attracts customers to make purchases is personalized content. Big data can help e-commerce companies in this aspect by enabling them to figure out and notify customers by providing personalized content by matching their likes and past purchases with current fashion. Personalization is said to deliver 5 to 8 times the ROI (Return on Investment) on marketing spend and thus, increase sales by more than 10%. It is also possible for the retailers to extract more purchases from the customers by providing visual access to complementary items to what they have already purchased. This is one of the biggest advantages of using shopping cart analysis. Customers tend to be attracted to items that are shown to be as trending with respect to sales or discounts. Presenting them with products that are shown to be complementary with already purchased products will lead to a customer pull, with high chances of resulting in another sale.

Improving Customer Experience

The experience of a customer in the buying process is a critical factor for their conversion. Keeping in mind all the varied choices available to a customer today, e-commerce companies need to put in a lot of efforts to ensure a smooth seamless flow for the customer in order to retain them and ensure that they make a sale. Data analytics can be of huge help in this regard as it can help in detecting and deciphering customer behaviour. With data analytics, it is possible to track customer movements across the webpage and thus find out where customers drop out of the site. The sole aim of businesses is to convert as many customers as possible – i.e. get as many sales as possible. Knowing where customers drop out can help retailers determine where changes have to be made in order to retain the customers at those stages.

Pricing

The word competition is almost synonymous with the e-commerce industry today. Companies should constantly strive to offer the best deals and experiences to the customers in order to improve conversions and retain them. In the world of e-commerce, pricing is one among the most important items in a customer’s list. Companies have to compete with each other on a continuous basis to offer the best relative prices. For this, companies have to be constantly aware of the industry prices and deals offered by competitors as well as of the demand for various products. Without data analytics, it cannot be possible to constantly update prices of millions of products, thus leading to loss of customers to competitors. Analytics can also help in making retailers decide the pricing strategy to use, whether it is necessary to include sales promotions or whether the product is priced higher or lower than expectations.

Predictive Analysis

In determining what to offer and what not to offer, e-commerce companies need to be aware of customer needs and expectations. In order to get the maximum conversions, companies have to offer what the customers want. It should be possible to predict customer behaviour to achieve results. Today, e-commerce is not just based on individual retailers’ marketing abilities but also on their abilities to use analytics to predict what their customers are likely to purchase. Predictive analytics can also help companies to forecast external events and thus develop abilities to adapt to it.

Managing supply chain

E-commerce companies have to manage a huge supply chain. They deal with a large number of vendors, warehouses, etc. and have to efficiently manage logistics, deliveries, returns and a lot more. Without proper management of the supply chain, the business would not be able to survive in the long run. Companies are increasingly turning to analytics in order to re-define and re-invent their supply chain processes. Data analytics promises a much more efficient and effective management system than the traditional system.

Enhancing inventory management

Data analytics can enable efficient inventory management by helping retailers eliminate slow-moving products and increase supply of fast-moving items. Handling inventory is a major task for e-commerce companies and it is essential to have a robust, manageable system in order to have the business under control.

Planning sales promotions

At any given point of time, customers are bombarded with plenty of sales promotions, offers, discount coupons and many more. Although sales promotions work to a company’s advantage, it might not be the same in every case. There can be numerous instances when promotions fall flat, resulting in losses rather than profits. It is of utmost importance for the retailer to know and understand which promotions succeed and which fail. Ineffective merchandising efforts have to be removed immediately and products with stagnant sales also need to be replaced or altered accordingly. Analytics provides the companies with such information, enabling them to remain alert to the various dimensions of their business. Identifying successful sales can also help in increasing conversion rates.

FEASIBILITY

Launching a Big Data initiative does not have to be an expensive process. The required analytical tools and data could be procured for a nominal price. In the current scenario, cloud platforms are gaining unmatched precedence over all others. In the same regard, cloud data storage can be utilized for data analytics. Using the cloud is recommended because it is relatively inexpensive and is highly scalable. With the predictions of exponential growth of data, cloud storage can offer a huge advantage in terms of flexibility and expansion.

CONCLUSION

E-commerce industry is one where profit margins are extremely thin. In such industries, data-driven decision making is highly crucial. As more and more Big Data solutions appear in the market and as more and more online merchants start adopting Big Data, it will soon become a necessity for all e-commerce businesses. It is, therefore, important for merchants to understand and realise the value of Big Data and start incorporating it into their businesses as soon as possible to gain and maintain a competitive advantage.

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