For any large or small online retailer, achieving customer satisfaction is the key towards staying ahead of the competition. Along with providing unique shopping experiences for the online customer, data analytics has been utilized in online retail to shift from the broad-based marketing strategy to a more effective and personalized approach for each shopper.

While data analytics tools can provide valuable insights for retailers to understand their customer needs based on their digital footprint and online purchase history, Ecommerce companies are constantly looking for innovative technology tools that can enable them to convert more customers and increase their market share among their competitors.

As a result, predictive analytics is increasingly being adopted by online retailers to help them take proactive strategies based on real-time customer data and predictions. This article evaluates how predictive analytics can benefit online retailers and provide them with a competitive advantage.

What is Predictive Analytics?

In simple terms, predictive analytics is a technology-enabled tool used to extract valuable information from business data and determining futuristic patterns and trends. While big data models provide business enterprises with loads of real-time customer data, predictive models, based on predictive analytics, can analyse this historical data and customer insights to predict future trends with a high degree of accuracy.

For Ecommerce companies, predictive analytics provides multiple benefits, including:

  • Determine the product line that customers are likely to buy, along with the price that the shopper will agree to pay for it.
  • Target product recommendations and promotions to the right customer.
  • Implement better product pricing strategies.
  • Improve the supply chain management.
  • Improve the sales revenue.

The following sections details some of the major benefits of predictive analytics for Ecommerce companies.

Audience targeting and engagement:

Predictive analytics tools enable online retailers to make micro-level predictions (based on a specific individual or targeted audience) rather than based on broad-level averages. Using predictive analytics models, audience data can be categorized and sorted for actionable customer insights. Without predictive analytics, business would face many challenges including:

  • Generating audience-specific information instead of general product-based information
  • Futility of sharing valuable customer information across multiple channels and brands
  • Delivering an enhanced customer experience across channels

Sales Performance and Forecasting:

Sales forecasts are an important tool for any business enterprise (including Ecommerce retailers) to plan their sales budgets and initiatives. Instead of basing sales forecasts and revenues on historical data of shoppers (which could include single-time purchases), predictive analytics provides a more accurate sales forecast based on buying trends of customers. Models based on predictive analytics can analyse data patterns, based on historical and transaction data, and identify both risks and opportunities for the future. Based on this assessment, sales teams can improve and manage their sales effectiveness by targeting the right opportunities.

Marketing campaigns:

Traditional forms of marketing campaigns can cost a lot and have limited impact on product reach. Personalized marketing campaigns, as demonstrated by relevant digital ads for Facebook or Instagram users, are more effective in customer assessment and conversion. AI-based personal incentives is among the various means to personalize marketing using predictive analytics tools. This can, in turn, improve the ROI on the campaigns and build better customer loyalty.

Anticipating customer needs:

With the large amount of data generated from each customer transaction, online retailers are looking to convert single-time shoppers into loyal customers. Combining customer-provided insights such as search histories and shopping preferences with predictive analytics can help retailers to foresee customer needs and encourage them with a more personalized experience.

Additionally, improving the in-store experience for online shoppers through product recommendations by integrating digital and predictive analytics can help retailers build a long-term relationship between the brand and the customer.


This article aims to highlight some of the benefits accruing from the deployment of predictive analytics in the competitive world of online retail. Knowing what the online shopper wants and is willing to pay can enable retailers to use predictive analytics to get the desired customer engagement.

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