Do you think it’s always good to have many options? In 2004, an American psychologist Barry Schwartz wrote a book called “The Paradox of Choice,” where Barry argues that eliminating consumer choices can significantly reduce anxiety for shoppers. So today, we would like to talk about product recommendations, their benefits, types of recommendations, and which brands use them to increase their digital front.
Any situation can be stressful, from shopping for jeans to selecting university courses, and those who finally choose a thing will eventually worry that their choice wasn’t good enough. Whether you’re considering choices between cereals, movies, career choices, pension plans, or even partners, the number of options out there is undoubtedly overwhelming. In the modern age, however, the freedom to decide is mandatory.
While Indian supermarkets usually carry about 20000 items, the eCommerce stores arent limited in terms of shelf spaces. But how can these online shoppers find precisely what they need as in regular stores? They have salespersons recommending the products. But in any online store, the shoppers do not have any such option. Instead, they will have to rely on a search and filtering mechanism or a recommender system to integrate with the digital store. Recommender systems are algorithms that suggest particular products to individuals, which plays pretty much the same role as salespersons at a supermarket. But before we get into recommender systems, we need to break down what personalization is and how it works so well in the eCommerce industry.
What is Personalisation?
According to Gartner, personalization is a “process that creates a relevant, individualized interaction between two parties designed to enhance the experience of the recipient.”
But on a more Digital level, personalization refers to a practice of creating personal experiences on an eCommerce platform by dynamically showing media, product, content, or even recommendations based on their browsing behavior, purchase data, and demographics.
eCommerce platforms know that there is no better way to make their customers feel special than by offering personalized shopping experiences. Once the customer feels that they connect with a brand or platform, his loyalty increases, and so does the revenue for the organization. Almost 90% of consumers say that they have been influenced by eCommerce personalization, and almost 90% of marketers say that eCommerce personalization has boosted revenues (source).
These stats are enough to determine the importance of personalization in eCommerce recommendations and why it is the need of the hour.
Benefits of personalization product recommendations for eCommerce companies
1. Enhanced Customer Satisfaction:
Product recommendation engines perform an excellent job developing a sense of satisfaction among users during and after their search. When they enter specific keywords to look for their desired products, the algorithms will analyze based on several criteria, such as keywords, previous purchases, etc., to determine customer tastes and preferences. Therefore, visitors can receive appropriate suggestions during their shopping period. Even after making a purchase, shop admins can send recommendations to shoppers via their emails or their subsequent method of communication. The appropriate suggestions at the right time and right place make it easier for browsers to find out what they want. This way, product recommendation engines convert and retain most visitors.
2. Increase revenue and Conversion:
Businesses that simplify and speed up product search for users also increase revenue and conversion. For example, Salesforce discovered a dependency between recommendations, income, and user activity. Only 7% of all visitors to eCommerce sites had clicked on personalized offers but, this 7% had accounted for 24 percent of all orders and 26 percent of revenue.
“Customers who use search and click on recommendations convert four times than customers who only use search.”- Smart Insights
Types and Tricks to more sales using Product Recommendations
Product recommendations come in many different flavors. Some of them are simple and non-personalized, and some quite different from each other. Consider each customer’s purchase history, preferences, behavior, etc. For example, a platform can show various categories and brands on a home page, so a seller can quickly attract a visitors’ attention. If many people buy it, it increases curiosity and makes them check the product out.
Bestsellers are majorly non-personalized, based on the current preferences of shoppers in a particular demographic. For Example, Amazon updates its bestseller list every hour.
Another way to increase sales is to show your visitors complimentary items on product pages or during checkout. Usually, recommended products belong to different categories or brands, and this approach is also known as Market Basket Analysis. Amazon calls this feature “frequently bought together” and uses it for cross-selling. For example, a customer who adds a smartphone to his cart automatically gets a screen protector suggested before checkout, or a person buying a laptop gets suggested a laptop bag automatically.
Even though this method is beneficial for customers and companies, there is very little to no personalization involved here. These product recommendations don’t require Machine Learning, and suggestions are based on general purchasing statistics, and individual customer preferences are not considered.
Some platforms have integrated similar search history-based items on their home page, like Flipkart. For example, when you open the homepage of Flipkart, it shows you the things that you checked out based on your past search history. It will also curate specific deals on the home page, urging the customer to make the purchase. By applying the homepage feature based on search history, eCommerce sellers have witnessed a 248% increase in conversion rate.
Another fantastic feature is using personalized email recommendations. Email marketing has stood the test of time and has provided sales and revenue investment conversion as high as 4300%. Customers open personalized emails with offers, and it has an 85% higher open rate.
This is enough to prove how vital product recommendations are and why every e-commerce company has them.
We at Upshot.ai have seen how personalization can convert and retain customers, and that’s why personalized product recommendations are an essential addition to any store with a digital front.
As marketers, you will have to look for new ways to drive performance. For example, the personalization of products with a bit of automation will help you drive performance. If done successfully, your website will edge ahead of your competitors, increase average order values, and ultimately drive more revenue.
Upshot.ai is an omnichannel, user engagement, and gamification platform that helps digital product owners and marketers improve their product adoption and conversions. Fortune 1000 companies such as GE, UHG, Puma, Sony, ITC, Tenet healthcare are using Upshot.ai and observed a massive increase in product adoption YoY increment in revenues.
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