AI-powered insights are bringing more curated and personalized suggestions to customers online than simple automation can, such as displaying similar items on the product page. Figures from Deloitte’s Consumer Review on Mass Personalization reveal that 22% of consumers are happy to receive a more personalized service in return for sharing some of their data and 25% are even willing to pay more for such services.
Personalization in today’s e-commerce world is a lot more than simply adding someone’s name to their shopping cart or account. It’s about forming a deeper understanding of their preferences, behavior and the context of each unique website visitor. How is that possible on a mass scale? The whole recommendation system is based on e-store and customer data, such as product feed, history of page views, transaction history including different channels, periodic transaction updates or online data stream.Here are four ways how AI is delivering a personal shopping experience to millions of online customers.
Suggestions for similar products are not a new phenomenon. However, the analysis and connections of data on ecommerce sites are becoming more sophisticated. For example, in the retail food industry, a customer who bought organic bananas in the past will not, as some might assume, have other sorts of non-organic bananas displayed as similar items. Instead, the algorithm understands that similar products that will interest a visitor will mean showing other non-banana items that are organic, healthy and wholesome i.e. muesli. Therefore, it is not product characteristics that define similarity but in fact buying contexts.
Personalized product recommendations based on purchase preferences and behavior
Thanks to algorithms, customers no longer have to think about what else would be useful to have when buying a specific product. For example, when buying a phone, let the power of AI do all the thinking for you. It can suggest phone covers, headphones and other useful things needed – everything bundled into one transaction. The perfect time-saving hack!
Recommend a customer complementary products to the one currently viewed to increase order size.
We’ve all seen that message ‘people who bought this also bought these products’ but AI is taking this up a notch and making suggestions more tailored and customer-centric. Let’s say a customer is looking for a red flower-patterned dress. Advanced visual algorithms will not only display similar red dresses with floral prints but even know what cut of dress you prefer. Personalized product recommendations are based on data such as browser history, page visits, visit frequency or content viewed.
Shopping cart recommendations suggest what you may want to additionally buy through acquiring tastes based on everything you have put into your cart. It is possible to choose whether to recommend on the basis of product similarity, popularity or recommendation variety. For example, if a customer only buys silver electronic products from a certain brand then cart recommendations will be likely show mainly products from that brand that are of the same color – in this case silver.
What online customers want is not a guessing game anymore
It’s not only consumers that are drawn to more personalized experiences and product offerings. Businesses, too, see it as an opportunity to engage users, boost sales and promote customer loyalty. Highly accurate recommendations and suggestions enhance the whole shopping experience and reduce searching and decision-making time.
Adding personal touches and customized paths to millions of daily online transactions would be an impossible task without the help of AI. Thanks to advancements in neural networks and algorithms, tailor-made recommendations will continue to advance in the future.
P.S. At Synerise, we can implement recommendations in less then 12 days. If you want to learn more about this module in our platform, check out AI Recommendations section in our Knowledge Base!