The Pivotal Role Of Big Data In E-Commerce
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The global market for Big Data in e-commerce is set to grow from $2.5 billion in 2018 to $6.2 billion by 2025. Big data is instrumental in identifying and evaluating the target audience and their preferences and demands. All business organizations need valuable data and insights for the functioning of their business operations.
The anticipation and the identification of the customer needs is beneficial for the enterprises for their business and revenue goals, since analyzing the customer patterns leads to higher traffic and sales. The crucial information—such as delivery information, inventory, payment data and sales data—are necessary for effective operation of an e-commerce business.
The integration of big data aids in getting access to voluminous data, which further helps in maximizing revenue generation and acquiring the aforementioned crucial information. As a result, there has been an increase in the adoption of Big Data by companies.
What is Big Data Analytics?
Big Data analytics means the process of harnessing these large data sets to reveal hidden patterns, market trends, customer preferences, etc. With the help of big data analytics, business owners are empowered to derive values from information and make optimal business decisions.
Big Data in E-commerce
The analytical capabilities of big data have had a positive impact across industries, including the e-commerce industry. Online vendors engage in developing services to link Big Data analytical tools to their businesses. The use of Big Data simplifies and improves business performances by enabling companies to analyze historic trends and current consumer behavioral patterns and thereby offer better and more customized products.
The application of Big Data enables e-commerce businesses to get access to huge volumes of data that they can use to reshape their operations and maximize revenue generation. Companies these days are already actively using Big Data to study customer purchase patterns and preferences and to reorganize their offerings to drive up sales.
The growing popularity of the e-commerce industry is expected to require huge amounts of data, which will in turn propel the growth of the market.
Rapid development and technology advancements in the e-commerce field are likely to offer potential opportunities for big data application. One of the upcoming trends in this industry is contextual and programmatic advertising, which is expected to use huge amounts of data sets to identify target customers.
Social media sites are in the process of revamping designs to cater to this trend. In addition, the significant influence of social media such as Facebook, Twitter and WhatsApp is encouraging e-retailers to introduce groups and pages to showcase their products to expand their visibility to larger consumer bases.
Consumers' changing preferences require continuous product modifications and customizations. This scenario demands the application of Big Data to understand customer behavioral patterns, which will in turn enable e-retailers to customize their product offerings and recommendations and thereby provide enhanced interactive customer experiences.
For instance, coupon offers, promotional campaigns and discounts based on previous spending records are helping online retailers draw huge customer traffic and generate profitable returns. The increasing use of big data is expected to allow e-retailers to recommend products and remind customers of pending purchases, thereby increasing sales as well as customer satisfaction.
Impact of Data in 2020
Personalized stores: Merging search and purchase history of customers and lookalike visitors will create a much more personalized shopping experience. This will translate to higher conversion rates and more cross-sell opportunities.
Personalized marketing: Marketing will become increasingly sophisticated. Merchants will send multiple email variations based on customer segments. For example, if a customer buys only t-shirts, sending him an offer for pants will likely be ineffective. Similarly, customers who buy only discounted goods will presumably not respond to a full-priced offer. Marketing to both customer types requires collecting and segmenting the data.
Increased automation: Automating repetitive tasks not only saves human resources. It also improves the customer experience. An example is using chatbots for customer service, which can improve accuracy and response time. Find ways to automate by asking each employee to describe repeated tasks.
More cross-border sales: Automated language and currency translation streamlined shipping and local payment options will help merchants penetrate global markets with little investment. Even human translators (such as on Fiver) are becoming less expensive. And shipping platforms and plugins can calculate at checkout the exact worldwide transit cost.
Better forecasting: Business intelligence tools can now forecast sales, optimize prices, and predict demand—in detail. The result is lower inventory quantities and targeted promotions based on a product’s demand. Businesses can move faster without spending a lot of money. To start, merchants can acquire an intelligence platform or hire a machine learning expert who can forecast in R or Python.
Research with social media: Marketers will focus on understanding the customer and her behavior leveraging the massive, public data on social media sites. Retailers will shift from using net promoter scores and surveys to analyzing qualitative and quantitative info. Merchants can start by manually categorizing the opinions of customers and prospects around products, product types and the business overall. Over time this data can be aggregated for ongoing insights.
More privacy laws: Governments worldwide are imposing strict privacy laws on the collection and use of consumer data. Examples include Europe, Korea and California. More will undoubtedly come. Merchants will spend money on legal fees, employees such as data compliance officers and consultants. Marketing capabilities will presumably decrease, as will customer experiences.