The role of big data analytics in e-commerce: A review of untouched realms of statistics and analytics

The role of big data analytics in e-commerce: A review of untouched realms of statistics and analytics

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Introduction

The role of big data Analytics in e-commerce analytics has increased in the recent past. That said, the theoretical and practical research in the domain of e-commerce vis-à-vis big data is not greatly explored. We aim to explore the growing role of big data analytics in e-commerce by exploring various aspects and characteristics of big data and its relevance to the digital industry. We strive to discuss the broader aspects of the role of big data in e-commerce analytics.

A review of statistics

A study by the BSA Software Alliance concluded that big data analytics has the potential to expand the sales of the e-commerce industry by more than 66 percent. According to this study, the e-commerce firms that use analytics improve their value chain experience with more productivity levels than the ones that shy away from analytics. These productivity levels are in the range of 9 to 10 percent. Therefore, it is not a coincidence that the top 90 percent of the Fortune 1000 companies are investing in big data analytics and witnessing higher levels of growth.

Analytics and the E-commerce ecosystem

The e-commerce industry must deal with both structured data and unstructured data in the long run. It is easy to derive insights from structured data due to its relative categorization. Structured data is classified based on age, gender, preferences, demography, and the like. Unstructured data, on the other hand, deals with the real-time processing of incoming streams of data with the involvement of hidden elements of noise and a high degree of randomness.

The landscape of e-commerce is flooded by big data, and customer analytics is the unique answer to finding solutions to the most pressing business problems. Customer analytics can act as a savior for the e-commerce industry because of its cost-effective processing capacity and a wide range of analytical tools.

Various realms of big data in the e-commerce industry

There are various categories of big data that we use in the e-commerce industry. The first among these categories is the transaction activity data. We can generate this data due to the exchanges between customers and the company over a period. This data is usually structured in nature and gives a picture of the sales transactions for a particular period. It is believed that among the various realms of big data, this realm of big data is indispensable for deriving numerous benefits and supplementing business revenues in the long run.

The second category is that of clickstream data. This data is related to online advertisements and digital content like posts and blogs. This data is useful for promotional strategies and aids tactical decisions related to customer analytics. This data is a hotbed of research in the domains of customer preferences and taste. Digital giants like Netflix heavily rely on this type of analytics.

The third category is the availability of video data, which can be in the form of live images. However, it has been observed that the e-commerce industry is not keen to capture this type of data as it becomes relatively difficult to process. The last type of data is in the form of voice signals. This data has great significance for the business process outsourcing industry.

The personalization factors

Various studies have been conducted to find the impact that personalization can bring to various ecommerce firms. In these studies, it was found that personalization strategies help ecommerce startups to segregate old customers from new customers. Such strategies also help in the effective channelization of promotional campaigns. Personalization strategies can bring up the levels of investment on market expenditures by great amounts.

The dynamic pricing model

The dynamic pricing models help a new e-commerce firm to compete with the established technological giants in the market. A functional dynamic pricing system, often developed by an experienced IT software development company, keeps an eye on the competing values and alerts the company in a stipulated time. This helps in increasing the revenue of an e-commerce firm by giving it the ease to update its prices. Companies like Amazon have witnessed a rise of more than 33 percent in their sales by roping in the dynamic pricing model.

Customer grievance redressal mechanism

Ecommerce firms can make great use of big data analytics in addressing the grievances of the customers. After making a purchase, a customer is prompted to rate various services of the company, and a low rating can serve as an alarm to get back to the customer and earn his trust. This is called the proactive maintenance protocol, which many digital firms make use of.

In conclusion

Big data analytics has the potential to drive the e-commerce industry to new levels of growth and innovation. The need of the hour is to effectively utilize various processes, technologies, and systems that are offered by big data analytics at a nominal cost.

As one of the trusted Data Consulting service providers, Algoscale helps e-commerce businesses transform raw data into actionable intelligence enabling smarter decisions, personalised customer experiences, and sustainable competitive advantage. Transform your e-commerce business with data-driven insights. Learn more about our Expert Big Data Consulting Services or reach out to us at askus@algoscale.com.


Also Read: Big data as the prime driver of e-commerce analytics: A review of trend prediction, price optimization and demand forecasting

Neeraj Agarwal

Founder, Algoscale

16+ years in data engineering and analytics. Has led enterprise data warehouse and lakehouse builds for retail, fintech, and manufacturing clients including Walmart and Capital One.

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