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How is Data Analytics shaping the future of e-commerce?

How is Data Analytics shaping the future of e-commerce

With the emergence of non-banking players in the payments industry like PayPal, Google-Wallet ,Skrill, Bitcoin, PayTM etc. and many innovative vertical specific startups, the e-commerce market is expanding at a rapid pace. Retail ecommerce sales—which include products and services (barring travel, restaurant and event ticket sales) ordered via the internet over any device-will reach $1.915 trillion in 2016, accounting for 8.7% of total retail spending worldwide. While the pace of growth for overall retail sales is subdued, the digital portion of sales continues to expand rapidly, with a 23.7% growth rate forecast for 2016.

The e-commerce business model is basically of two types one is the aggregator based model the other is the Inventory based Model but there are Hybrid Models too. All of these models have a Customer side and a Seller side. For a global online transportation network company like Uber the seller sides are the drivers who are willing to provide service and the Customer side is the commuter who is travelling from place A to place B. Similarly for Amazon, Alibaba, E-bay and other online retail players, it is the vendors who are lining up to sell their goods and the end customer who purchases the goods. The objective of giving these two examples is to delineate that at every stage of the Business value chain there is a tremendous amount of data which is being used to shape the future of e-commerce companies. As per research on Springer, 91 % of Fortune 1000 companies are investing in Big Data Projects, an 85 % increase from the previous year (Kiron et al. 2014a). Another McKinsey report that generated a lot of interest in big data analytics stated that operating margins could increase by over 60 percent by incorporating data analytics at scale across their organizations.

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Various Dimensions of Data

The companies mentioned above are also looking to cross-sell and up-sell their products and services to customers. Customers can be classified into first time buyers and repeat-buyers. Most of the dimensions of Data revolves around these two aspects of the end user and the seller. Some of these dimensions are mentioned below

Pricing Analytics

Price becomes a key differentiator for a customer to buy the product or service from a particular company. In order to identify the best price of the product or the next best price of the product a price comparison needs to be done with the existing competitors. This data is scrapped from competitor websites and is used to benchmark against competition thereby providing intelligence to offer discounts on the SKU (Stock Keeping Unit) or services in order to be the first choice of the consumer.

Customer Segmentation Analytics

Companies employing customer segmentation operate under the assumption that every customer is different and that their marketing efforts would be better served if they are target specific. Customer segmentation relies on identifying key differentiators that divide customers into groups who can then be targeted with specific deals and discounts. Information such as a customer demographics (age, race, religion, gender, family size, ethnicity, income, education level), geography (where they live and work), psychographic (social class, lifestyle and personality characteristics) and behavioral (spending, consumption, usage and desired benefits) tendencies are taken into account when determining customer segmentation practices.

Recommendation Analytics

The purchase pattern of the existing customers is identified based on historical data and based on that customized recommendations are provided on what is his next likely purchase. The website displays a set of likely products the customer might be interested in buying and this is the overall Recommendation System Analytics.

Predictive Analysis

IBM defines predictive analytics as a combination of advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning and more. In Ecommerce some of the major work that revolves around predictive analytics are Fraud management and chargebacks which are an e-retailer’s nightmare. Predictive analytics can lower credit card chargeback rates, reduce overall fraud by analyzing customer behavior and product sales and also by removing products from the assortment that are more susceptible to fraud. The fraud management predictive models identify potential fraud before the customer completes the purchase transaction, resulting in reduced chargebacks and also reduced labor and fees required to process the chargebacks

Supply Chain predictive modelling helps understand consumer demand, to effectively manage the overall supply chain process. This includes planning and forecasting, sourcing, fulfillment, delivery, and returns.

The future of Analytics in E- commerce

According to a report by Staista, the Big Data market size revenue will increase from 23.7 Billion Dollars (US) in 2016 to 92.2 Billion Dollars (US) by 2026. The e-commerce market is set to encounter exponential growth by the year 2021. Big Data Analytics has contributed immensely to enable e-commerce websites to attract a large customer base. Price comparisons and recommendation engines are a result of data analytics and have improved sales and revenue in the e-commerce world. Like many of the industries reaping the benefits of Big Data, ecommerce industry seems to be benefiting the most due to the Volume and footprint of data each customer is providing with each transaction. Even though many of these companies have the ability to handle Terabytes of data but processing the huge volumes of data is still a challenge for these companies.

How do you see Big Data influencing the e-commerce industry? Drop your comments in a line below.

Read more about How Pricing Analytics Can Drive Better Returns

Raghavender Sridhar

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