Artificial intelligence is revolutionizing so many aspects of our lives. From the way we operate appliances in our homes to the way that children play with toys. Also, is starting to have a significant effect on the way e-shops attract and retain customers.
New algorithms, powerful new computer technologies, and access to massive amounts of digital information have created significant opportunities for the rapid growth and adoption of machine learning techniques all around us. E-commerce has been an early adopter, though it is still very early days, and the possibilities to delight customers with these technologies are endless.
In fact, a recent survey predicts that by 2020, 85% of a client’s relationship with a business will be managed without interacting with any human. For a better understanding of how this type of technology works, check out some applications below.
An image can do wonders!
In the past, most customers have used a text-based search in a search field to look for an object the want to purchase. Today, however, e-commerce businesses are taking advantage of visual search – a technology that uses AI to analyze a photo that a shopper submits, then find products that match that image.
For example, there are companies that use visual search technology to allow app users to take photos of objects in the real world, then locate similar ones in their catalog.
Tell me what you want!
In addition to using images to search for products they want to buy, shoppers are now adopting voice search. It is the ability to search for items using speech. Voice search uses AI to understand what is spoken and to get better at recognizing voices and phrases.
Voice search was popularized with voice assistants like Alexa and Siri, which has made it necessary for ecommerce retailers to re-optimize their pages so they can handle voice-based searches. A recent study predicts that by 2020, at least 50% of all web searches will be conducted by voice.
Personal shoppers are no longer just a luxury service for high-end consumers. Today, thanks to artificial intelligence, consumers can take advantage of virtual ones. Online companies can leverage AI technology to recommend and curate items a shopper would like, without having to do any of the work themselves.
There are several online personal shopper technologies that use big data, collected in real time, to “learn” users’ shopping habits and personal taste.
Machine Learning for Better Search Results
At least 30% of online shoppers will use an e-commerce retailer’s search function. For that reason, getting search results just right can pay off big for retailers – which is where AI comes in.
Retailers are now turning to machine learning to improve results for consumers who use search. Machine learning can improve search results each time a user shops on a site. It can also generate a search ranking, which allows the site to sort the results by relevance, instead of keyword matching.
Artificial Neural Networks are a type of artificial intelligence that tries to recreate how the human brain works. It’s the science behind things like self-driving cars and it’s not just for complex machinery.
Online companies will soon be able to use them to take the data they receive based on ad performance, then actually understand why those ads aren’t working.
Because they can learn from experience, recognize patterns and predict trends, they can tell what tactics people responded to in a marketing campaign, and what should be scrapped and re-thought.
Artificial intelligence technologies will continue to offer customers better services, products, and personalized experiences. They will also maximize a company’s marketing efforts while minimizing the need to spend money on ineffective ad campaigns.
The more data you have, the harder it is to check for inconsistencies. One way to handle this problem is automatic anomaly detection.
A popular e-commerce application of this approach is fraud detection. Retailers frequently have to deal with customers that use stolen credit cards to make excessive orders, or customers that retract payments via their credit card company once products have already been delivered.
Also, anomaly detection can be used to ensure a high level of data quality for product information. Large databases in the e-commerce sector often contain errors like incomplete product titles, missing images, or products sorted in the wrong categories. Detecting these cases quickly and efficiently can therefore save companies a lot of time, money, and effort.