Analytics for E-commerce From Market Info to Market Leadership

E-Commerce drives much of the sales revenue in most industry segments today, and continues to grow at a rapid pace. According to statista.com, digital buyers, that is, those who used an ecommerce website or app to make online purchases have grown steadily worldwide. While in 2014, 49.6% of Internet users and 22.2% of the worldwide population made one or more online purchase, in 2019, these numbers are expected to be 57.6% and 32.8%.

Needless to say, business organizations are investing significant time and resources to increase their sales revenues from e-commerce stores which are a lot more cost-effective than brick-and-mortar stores. This has resulted in more and more Analytics for E-commerce. That is, business houses are analyzing tons of product, customer and sales data – to derive insights which can help them increase sales.

Accordingly, such Analytics are of two types: retrospective analytics and predictive analytics. Retrospective analytics is the older of the two and involves deriving patterns and insights from existing data, in order to make informed decisions which will increase sales. Predictive analytics, which is more recent, is an evolutionary step that takes the utility of the data to the next level. In addition to deriving patterns from existing data, predictive analytics goes a step further to predicts buyer behaviour around various aspects of the buying experience.

So what are the areas of E-commerce that can be positively impacted by retrospective or predictive analytics?

  • Supply and Distribution Chain Management: Analytics about hiccups in the supply chain and distribution chain can help resolve these issues quickly and streamline flow of goods to and from warehouses, suppliers and distributors.
  • Fraud Detection at merchant and customer side: Merchants may not dispatch the purchased goods at times, while some customers fake address or other information with malicious intent. Analytics can help predict such instances and prevent them in advance.
    • Analytics of Merchant Information: Analytics of what merchants must stock, why their sales are low and other information can help E-commerce companies and merchants collaborate better and create a win-win situation for both.
    • Recommendation algorithms: Recommendations on what the prospective buyer or customer may be interested in are displayed to the buyer/customer before he signs out. Such a measure helps improve cross-sales, upsales, and elicit better customer loyalty when the recommendations are appreciated by the buyer.
    • Product-related analytics: Analytics about why a product is selling or not selling well could detect plus points or issues in product performance, pricing, features, packaging, etc.
    • Online Marketing Analytics: Analysis on which are the ads, or where are the ads that customers are clicking on helps the e-commerce company bid on Google or other ads in a more informed manner.
    • User Experience Analytics: Analytics help E-commerce companies track the minutest deals around how customers arrive at the shopping cart, how they navigate through the steps, how much time they spent, etc, in order to correct those steps which may be spoiling the customer experience.

From Customer Engagement to Customer Delight, Customer Loyalty and Customer Experience, Analytics goes all the way in transforming the bottom-line for E-commerce companies. By engaging the services of a data scientist, they can arrive at effective data models that will maximize the ROI from their Analytics spend.

 

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