The Covid-19 pandemic presented a series of challenges for retailers and customers, online and offline. For retail brands, it was about either adopting the online model asap or managing the suddenly increased demand to drive business online.
On the other hand, consumers were expecting in-store-like shopping experiences from online stores.
However, markets globally were not prepared for the rapid shift in consumer behavior. Even with a large amount of actionable customer data, except for some, most retail brands were not able to capitalize on the opportunities to deliver personalized shopping experiences.
One key finding from the same report revealed that just 6% of consumers think online shopping experiences are always relevant.
It means online businesses are still struggling to match up with customer expectations for relevant shopping experiences.
This ‘Experience Gap’ creates a vacuum between online businesses and customers, negatively affecting customer engagements and conversions.
With personalized product recommendations, powered by AI, businesses can create better relevancy between modern customers and products.
An AI-powered recommendation engine can combine customer data, intent, and design tools to help the brands better understand customers and determine which of the products from product catalogs will drive sales performance.
Also, it can help online brands cater to individual preferences and build better customer-to-product relationships.
By implementing a personalization strategy, eCommerce businesses can expect a significant increase in revenue, conversion rates, and customer lifetime value while reducing abandonment rates.
With online becoming a primary medium of buying, online brands have to continuously evolve and adopt a data-centric personalization approach to get customers to return to their stores again and again.