While the futuristic notion of a world filled with humanoid-robots scurrying to perform their fleshy-master’s commands is still a distant dream, pragmatic artificial intelligence (AI) is moving full steam ahead – offering forward-thinking e-commerce organisations the opportunity to deliver real results.
Retailers across the globe are profiting from the very real benefits of machine learning: improving the online shopping experience through enhanced personalisation, all the while busily gathering data in the background to help them better understand shopper preferences and consumer behaviour.
Having access to a service that continually learns and adapts to market trends as they occur is priceless. Add to the mix an ability to improve site-search and increase revenue, and a winning combination is formed.
How Machine Learning Improves Site-Search
In e-commerce, the sheer value of search is incontestable. Your customers cannot buy what they cannot find and if your products fail to appear in front of visitors, how will they ever know you exist?
Site-search is a critical element of e-commerce success, and machine learning algorithms can dramatically enhance product search results – boosting click rates, customer ratings and conversions.
Machine learning enables a search function to forge new relationships between relevant products, producing an intelligent, self-optimising list of 'related results'. These recommendations are completely unlike those provided by outdated recommender systems, but can be targeted, relevant, and individually tailored to the needs of the shopper.
Even in situations where no pre-existing shopper data is available, with greater insight into shopper preferences and consumer behaviour, a machine learning powered engine can create an “average user” experience – eliminating the cold start problem.
How Machine Learning Improves Revenue
Relevant site-search increases sales. That’s the bottom line. If a visitor finds exactly what they want, the first time they ask, then you've removed a key opportunity for them to change their mind.
The biggest obstacle to e-commerce sales is doubt: is the product too expensive? Do I really need it? Is this the best place to get it? The challenge of retailers is to override this internal questioning with targeted, personalised, relevant products. That’s the key to converting visitors into customers, and machine learning offers this opportunity.
With machine learning engaged strategically in the search process, search results become more meaningful and geared towards the shopper’s precise needs. By tapping into specific facets of an individual’s buying habits – colours, styles, budget etc – a machine learning powered engine will prioritise the products that bring in the most revenue or generate incremental sales – thus increasing conversions and improving revenue.
In addition, the implementation of machine learning enables retailers to automate many of their resource-intensive and costly manual processes, such as tweaking and redirecting search results or adding related products on the individual product level. This frees up valuable staff time, allowing them to concentrate on more core responsibilities and continue to deliver savings year on year.
The Loop54 Machine Learning Algorithm
Advances in AI – and machine learning specifically – are having transformational impact on site-search and the e-commerce industry as a whole. Machine learning is becoming much smarter in adapting to customer needs – enhancing personalisation and helping retailers better understand shopper preferences and consumer behaviour. Therefore, companies that haven't adopted AI technology at scale or in a core part of their business, risk being left behind.
If you're one of those retailers and feel the time has come to embrace the AI revolution, a great place to start is with your site-search and navigation. Our free guide provides a short list of the 8 best tips for designing a frictionless search and navigation experience – download it here for free.