How the e-commerce industry can use analytics and AI to be ubiquitous

The retail industry has undergone monumental changes over the past few years. Today’s e-commerce sites, supermarkets, and other retail brands no longer bother with creating and delivering marketing campaigns to sell products and services. Instead, modern businesses/traders now spend considerable time collecting data and analyzing it to make business decisions based on data intelligence. The onset of the COVID-19 pandemic has further accelerated the change in business mindset and accelerated the e-commerce industry’s transition to advanced digital tools like analytics and artificial intelligence (AI).

When companies leverage these innovative, demand-driven technologies in their operations, they reap unprecedented benefits. For example, AI is transforming the online shopping experience of the entire e-commerce industry. An AI-based engine tracks customer interests, preferences, browsing history and purchases and helps companies recommend products and services to shoppers accordingly. Besides these benefits, AI supports e-commerce businesses in several ways to help them become more competitive and ubiquitous. Let’s take a brief look at them.

Personalization at its peak

Personalization has become the top priority for AI in e-commerce marketing. AI predicts shopping habits based on what products online shoppers plan to buy at specific times. For example, when online customers purchase a particular brand’s product (such as rice) each week, AI helps businesses provide customers with personalized, machine-learning-based recommendations for complementary products that match their needs. pair well with rice dishes.

However, according to the retailers surveyed, only 15% say they have implemented personalization across all channels, which shouldn’t be the case. Businesses need to stand out with more personalized messaging and one-on-one conversations with their customers. This is because advancements in AI and machine learning enable deep personalization techniques to personalize content by users. By analyzing big data from purchase histories and other customer interactions, businesses can focus on what their customers are asking for and deliver the messages that resonate the most.

Increase in customer loyalty

Customer loyalty is no doubt not an easy task. But the available data pool makes the job really convenient and easy for retailers. For example, effective engagement with chatbots dramatically improves customer engagement and retention by seamlessly resolving customer issues. Smart chatbots largely eliminate the need for other customer inquiries like emails and calls and also inform them about new personalized products, services, offers, events, etc.

Thus, creating and delivering personalized and targeted marketing and advertising messages to customers can significantly increase customer retention. With this in mind, McKinsey’s omnichannel personalization research indicates the potential for a 10-15% increase in revenue and retention for omnichannel personalization strategies. He further states that a critical part of personalization is creating better customer data and insights, something that also generates additional value throughout the value chain.

Seamless Automation

The purpose of automation is to accomplish tasks with minimal human intervention. AI plays a crucial role in helping retailers automate repetitive tasks that make their online stores run better. Using AI, businesses 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 lot of data about their customers and automating abandoned cart follow-up requests. Additionally, businesses can help customers move through the funnel by engaging them with chatbots for simple questions or by automating their stores.

Use cases for AI in e-commerce

The industry is witnessing countless prominent use cases for AI that help develop innovative solutions and seamless customer experiences in e-commerce. These include personalized product recommendations, better price optimization, improved and efficient customer service, accurate customer segmentation, smart logistics and accurate analytics based on forecasting sales and demand.

Conclusion

The aforementioned factors indicate how e-commerce businesses can increasingly leverage AI (including predictive analytics and machine learning) to deliver tailored solutions such as personalized customer services and become ubiquitous. AI-powered solutions help retailers turn data into actionable insights that enable faster and more accurate decisions. So, if companies want to lead the industry through exceptional brand credibility and visibility and customer loyalty, technological innovations like AI are their one-stop solution.

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