How can the E-Commerce Industry Use Analytics and AI to be Omnipresent
How can the E-Commerce Industry Use Analytics and AI to be Omnipresent

The retail industry has undergone monumental changes over the past few years. Today's e-commerce sites, supermarket stores, and other retail brands no longer worry about creating and delivering marketing campaigns for selling products and services. Instead, modern businesses/merchants now spend considerable time collecting data and analyze it to make data intelligence-based business decisions. The onset of the COVID-19 pandemic has further accelerated the business mindset shift and fastened the transition of the e-commerce industry to advanced digital tools like Analytics and Artificial Intelligence (AI).

When businesses leverage such innovative demand-driven technologies in their operations, they enjoy unparalleled benefits. For instance, AI transforms the entire e-commerce industry's online shopping experience. An AI-based engine spots customer interests, preferences, past browsing history, and purchases and helps companies recommend products and services to shoppers accordingly. Apart from these benefits, AI supports e-commerce companies in several ways to help them become more competitive and omnipresent. Let’s take a brief look at them.

Personalization at its Prime

Personalization has emerged as the top priority of AI in e-commerce marketing. AI predicts shopping patterns based on those products that online shoppers consider for buying at specific times. For example, when online customers purchase a particular brand's product (like rice) every week, AI helps businesses to offer personalized and machine learning-enabled recommendations to customers for supplementary products that align well with rice dishes.

However, according to surveyed retailers, only 15% say they have implemented personalization across channels, which should not be the case. Businesses should stand out from the crowd with more personalized messages and conduct one-to-one conversations with their customers. It is because advances in AI and Machine Learning enable deep personalization techniques to customize content by the users. By analyzing big data from purchase histories and other customer interactions, companies can zero in on what their customers demand and deliver the messages that resonate the most.

Increased Customer Retention

Customer retention is undoubtedly not an easy task. But the available data pool makes the job truly practical and easy for retailers. For instance, effective engagement with chatbots significantly enhances customer engagement and retention by seamlessly resolving customer issues. Intelligent chatbots majorly eliminate the need for other customer inquiries like emails & calls and also inform them about new products, services, offers, events, etc., personalized.

Thus, creating and delivering personalized, targeted marketing and advertising messages for customers can significantly increase customer retention. In line with this, McKinsey’s omnichannel personalization research states a 10-15% uplift potential in revenue and retention from omnichannel personalization strategies. It further states that a critical element of personalization is building better data and insights on customers, an asset that also generates additional value across the value chain.

Seamless Automation

The goal of automation is to accomplish tasks with minor human intervention. AI plays a crucial role in helping retailers automate the repetitive tasks that improve their online stores' functioning. With AI, companies can automate various business areas, including product recommendations, loyalty discounts, low-level support, social media marketing, and many more.

Efficient Sales Process

By embracing AI, retailers can create a more efficient sales process by gathering a large pool of data about their customers and automating follow-up abandoned cart inquiries. Moreover, businesses can help move customers through the funnel by engaging them with chatbots for simple questions or automating their stores.

AI Use Cases in E-Commerce

The industry witnesses innumerable leading use cases for AI that help develop innovative solutions and seamless customer experiences in e-commerce. These include personalized product recommendations, better pricing optimization, enhanced & effective customer service, accurate customer segmentation, smart logistics, and sales & demand forecasting-based accurate analytics.


The abovementioned factors indicate how e-commerce businesses can ever-increasingly leverage AI (including Predictive Analytics and Machine Learning) to offer custom-tailored solutions like personalized customer services and become omnipresent. AI-empowered solutions assist retailers in churning data into actionable insights that enable faster and more accurate decisions. So, if businesses wish to lead the industry through exceptional brand credibility & visibility, and customer loyalty, technological innovations like AI is their one-stop solution. 

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