35% of Amazon’s revenue is generated by its recommendation engine. Are you recommending the right products to your customers?
While it has become imperative for most brands to be online, the information era demands a lot more. It is essential for e-commerce websites to have recommendation engines that provide valuable suggestions to consumers. After all, while customers may have arrived at the website looking for one thing, thoughtful suggestions can ensure that they leave with a lot more.
In brick and mortar stores, the sales assistant can do this. Perceiving and understanding a customer’s needs, they can steer them towards several products. Online, however, this is the work of a recommendation engine.
Understanding consumer psychology, e-commerce platforms use several different kinds of recommendation engines to improve the efficiency and effectiveness of suggestions. Let’s talk about a few of the popular ones:
Beginning the journey across the website with a homepage curated specifically to each consumer, a website builds a rapport with the individual.The suggestions are based on previous searches and purchases to increase their chances of relevance, and the unique experience improves consumer patronage.
Ideal for : Returning Users on any and all E-commerce Websites.
Complementary Product Recommendations
While most e-commerce websites recommend similar products to present the buyer with a range of choices, it is the complementary recommendation engines that take the cake.These work by anticipating consumer needs even before they do.
This is done by using the meta-data input to determine which products complement one another.
For example, upon adding a new cell phone in your cart, you may notice the option to search for phone cases and covers. These complementary needs increase the chances of an additional purchase, while also improving user experience.
Ideal for : Electronics e-commerce websites.
Best Selling Product Recommendations
Promoting your most sold products is one of the most common recommendation-type by e-commerce websites. Since most people are buying a particular product, recommending it to the rest of your customers that fall into similar profile can be a good bet. The customer profile can be based on several factors spanning age, gender, location, past purchases, current search etc.
Ideal for : Grocery Ecommerce companies as they have a fixed list of fast selling items.
Identifying similar product viewing and purchase patterns, recommendations based on customer bundling often show you results for ‘customers who bought this also bought’. These increase the chances of suggesting relevant products that may interest a prospective consumer, thereby fueling conversions.
Ideal for : Any e-commerce website
Similar recommendations provided to customers can be divided into two segments:
1. Similar Recommendations Based on Meta-Data
These offer products that match the customers’ search inquiries based on the meta-data input in the catalog. For example, if a customer is searching for a black dress with a unique pattern, the results will reveal products that match only the keywords entered. This could include a vast range of black dresses, overwhelming the consumer before they have time to browse through and find the product they are looking for.
Consequently, it isn’t the most effective for products sold on the basis of aesthetic appeal.However, it may prove useful for consumers looking for a specific book instead.
Ideal for : E-commerce platforms selling electronics and grocery
2. Visually Similar Recommendations
On the other hand, visually similar recommendations analyze an image to understand its contents. Without feeding in specific keywords, the AI presents results that are similar to the image uploaded.
This is especially crucial when users are looking to find a black dress with a particular pattern or a unique design. Not only does it reduce the hassle of browsing through irrelevant results, but it is also significantly easier.
It works like a virtual shop assistant by highlighting products which “look similar” to the product in which the customer is interested. It helps retailers to suggest products with similar pattern, shape and color from their entire catalogue. These recommendations are style-based and popularity agnostic.
Ideal for : E-commerce websites selling fashion and lifestyle categories or home and furniture categories.
In both these categories, the aesthetic elements are at the very core of every purchase, and descriptors often lack those. Moreover, several aesthetic elements are difficult to put into words.
By providing additional tabs to set the price, brand, style, and any other parameters, customers can receive focused results that are not only similar looking but also in line with their choice factors. Additionally, the recommendation engine will also take the consumers’ tastes and style into account, generating each next set of suggestions based on the innate characteristics for optimum appeal.This increased ease and relevance of search results improves consumer loyalty, spikes conversions, and boosts company sales!
Today product recommendation engines have come a long way using deep learning capabilities to understand the user search preferences, giving individuals focused recommendations.
Top retailers of the world who are taking the omni-channel approach to growth are also rapidly implementing similar solutions(e.g. ASOS, Macy’s, Target etc.)and have seen a significant increase in CTRs, impromptu purchases, as well as consumer loyalty.So if you have been sitting on the fence, it’s time to take the leap.
“Turing Analytics is a machine learning company that delivers intelligent solutions to Retailers helping them improve product discovery, customer engagement and boost conversion rates. They expertise in building Visual Search & recommendation solutions, which enable retailers to recommend visually similar products to their customers and search products by uploading a picture.”