Personalized flow for eCommerce

Ever wonder how Amazon manages to keep you so engaged and impulsive on their site? On just about every level 2 things are true about them: Every user interacts with the same basic components and structure & every user sees different products at different times. In retail, this would be the equivalent of walking into a Gap and have the room shift in front of you – but online it’s expected

Having the ability to introduce different content to individuals at different times offers a choose your own adventure browsing experience that builds its own narrative. Centered around search and recommendations dynamic discovery is a lot more fluid than sifting a static catalogue. This customer centered approach creates the opportunity for a continuous flow of information each and with each find, a wee hit of dopamine. It’s no wonder amazon is responsible for about 40% of all US eCommerce sales in 2018. 

Recommendations are the foundation of commerce, and are commonplace on the retail floor. Sales associates are great at making informed judgements on the fly, with very little effort. On the other hand rudimentary recommendation systems that occupy many websites are static afterthoughts. Thinking about the a typical high street store, it’s stylists are the bedrock of the customer experience, their recommendations and helpful suggestions guide the right people to the right things at the right time. 

Training a website to be as reactive and intuitive to interact with can be challenging. Truly personalized eCommerce stores thrive on the data we all leave behind. Their secret sauce is the associations they make about each product and user. Collectively this vast array of data uncovers patterns in buying behavior that can be used to represent the identity of a customer segment.

The assumption is those in the same segment have similar interests. This is great at the macro, and with recent advancements in AI and machine learning generating hyper personal recommendations is becoming immensely accurate.

Common interfaces like “You might also like” suggestions and “Frequently bought together” bundles help users discover more content and spend more and advancements in AI is making the sort of recommendations we receive a lot more accurate.

The assumption is that this technology has typically been out of reach to independent retailers and required significant investment to get up and running. 

The reality is that frameworks like Shopify and their App Store partners are flipping that on its head. Now it’s possible for anyone to access advanced ecommerce infrastructure is as easy as signing up for an email account and implementing AI powered recommendation software is as easy as one click. companies like Scopemedia using advanced AI and computer vision in our efforts to bring improved personalization to the platform. 

Last year eCommerce revenue accounted for just about half of all retail growth. As the balance shifts away from the high street, finding ways to provide a similar level of personalization as your local shopkeeper may be the only way to compete in the landscape.

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