By Michael Jaszczyk, CEO, GK Americas
Preventing lost sales is a main priority for e-commerce retailers, as the average shopping cart abandonment rate is a staggering 70%. While it’s harder to convert online shoppers, it is essential: online shopping rose due to the COVID-19 pandemic.
Mobile, in particular, has seen a boost, with sales growing 41.4% in 2020. But the smaller screen limits the number of items retailers can display and demands them to provide customers with relevant, personalized results to minimize abandoned carts and reduce lost sales.
Image similarity, or artificial intelligence-driven product recommendations based on patterns between images, can improve and influence the shopping experience on mobile, as well as desktop. Let’s explore three ways retailers can use image similarity to boost sales and customer satisfaction.
1. Display Relevant Items On Product Webpages
When a shopper finds an item that catches their eye, they usually click on the specific product listing. There, they can learn more information about the product, view reviews, and explore images and videos. These product pages are also a perfect place to feature recommendations calculated on similarity analysis. For example, if a shopper is looking for a cotton dress, a retailer can suggest dresses that are similar in terms of material, color, or size – or all of the above – using image similarity.
Seventy-one percent of consumers expect companies to deliver personalized interactions, and 76% get frustrated when this doesn’t happen. Image similarity ensures that shoppers find the exact product they’re looking for, every time.
2. Eliminate Disappointment From Out-Of-Stock Items
In a perfect world, once a shopper decides to buy an item, they place it in their online cart and immediately proceed to purchase. However, we know this doesn’t always happen. The path to purchase may involve the customer opening new tabs, comparison shopping, or even stepping away from their device for a length of time. As a result, they might return to their shopping cart to find the item is sold out.
This is growing more common as retailers face several ongoing supply chain challenges. In fact, it’s predicted that supply chain disruption could cost North American apparel and footwear brands between $9 billion and $17 billion in lost profit this year.
To counteract lost sales and cart abandonment caused by out-of-stock items, retailers can display products that are visually similar to sold-out items. Retailers using AI-enabled image similarity can better understand customer behaviors and take a personalized approach to define filter criteria to best meet their needs. For example, they can ensure that items displayed are the same size as the out-of-stock item.
3. Upsell Through Wish Lists
A wish list or “favorite” feature makes it easy for shoppers to save products to buy later. This offers a great opportunity for retailers to upsell, as it’s an early stage in the purchase process. Shoppers save items but might not be committed to adding them to their cart and are open to product recommendations.
Using image similarity, retailers can analyze which recommendations are accepted by customers and added to wish lists – as well as shopping carts – to better inform future merchandising decisions. This might look like narrowing down recommendations based on a price range, rather than the color of the product that was originally saved to a wish list.
As e-Commerce Grows, Image Similarity Can Reduce Cart Abandonment
In 2021, e-commerce comprised 46% of all apparel sales. As shopping habits from the pandemic continue to solidify, it’s time for retailers to ensure they have a robust e-commerce strategy. Improving the shopper experience from the first click to the point of purchase is essential. Image similarity creates more accurate product recommendations that ultimately reduce cart abandonment and achieve higher sales.