3 Near-Future Solutions of AI E-Commerce
Foto: Daniel Surmacz COO RTB House
The e-commerce industry produces vast amounts of data, which has had companies, merchants and distributors scrambling for Big Data solutions to make their data more valuable to their business. Despite so many new mainstream intelligence solutions, Big Data can still be a blessing or a curse, depending on how you use it. The AI revolution aims to make it easier than ever to get the most of your e-commerce data, with intelligent, deep learning technologies.
Marketers have already jumped on some major AI innovations for powering their data-driven, e-commerce activities. These technologies have the ability to make customers journeys ultra-personalized and help optimize online processes to be much more efficient than before, making some e-commerce activities fully automated. This all adds up to making online purchases easier, more efficient, more engaging, and more adjusted to personal needs – at every stage of the decision-making process.
#1 Product Searching – Algorithms That Recognize Images
Have you ever seen something that you wanted to buy, but had no idea where to find it? Whether it’s online in a video, or a stranger in real life – there’s not much you could do to search for the product.
Image recognition has been around for a long time, but artificial intelligence is finally making it mainstream and acutely powerful. AI-based image recognition is making it possible to snap a photo with your smartphone and get the information about the product, e-shop, and price, offering the exact match with the one in the photo.
The first new step in a customer journey can begin with a photo in the way we search, buy or browse products.
Google, Microsoft, Facebook or Yahoo are leaders in the field of making systems that perceive objects better than humans. Google’s Cloud Vision API makes it possible for developers to identify objects in an image, recognize words or text, and even guess what emotion a person in the picture might be experiencing. Microsoft Cognitive Services, in turn, lets you build on top of a collection of visual image recognition APIs, including emotion, celebrity and face detection. Clarifai’s vision APIs help companies organize their content, filter out unsafe user-generated images or videos and make purchasing recommendations based on viewed or taken photos. Yahoo’s open source neural network solution, in turn, can detect images not suitable or safe for work (NSFW), including prohibited or adult pictures, could significantly change the way e-shop runs its activities.
AI image recognition can be extremely useful for the e-commerce industry. Marketplaces, aggregator websites (like price comparison engines) or e-shops which need to moderate millions of pictures can do so automatically. It also opens new avenues for customer experiences, when smartphones and social media are so popular and people produce a huge amount of images and videos related to brands. Deep understanding of that content is an incredibly valuable step towards true personalization.
#2 Buying Decision – Technology That Makes Ultra-Precise Recommendations
Let’s return back to the topic of customer journey. Say you saw something you wanted to buy in a photo, checked the shop’s website and almost made a purchase. Something interrupted you, or you had doubts, insufficient funds, etc. It happens a lot to every shopper.
Personalized advertising banners create an impulse to make a final decision, whether by reminding you of the product, or showing similar products.
Of course, this is a well-known tactic to modern marketers already. However, an exciting prospect in the near future of AI is deep learning – an innovative branch of AI that solves problems by imitating the work of the human brain – which has the potential to take typical retargeting campaigns to new heights.
Deep learning algorithms are used to craft features that recognize the attitude, intention and the overall state of every user visiting a website and based on that knowledge prepare highly-targeted products recommendations. This precision can make advertising activities up to 50% more efficient than with the typical machine learning approach.
The real power of deep learning from the e-commerce point of view is that AI can use a massive amount of data and learn and act like humans – without specific instructions or rules. There is no guessing potential sales peaks or scenarios to how people react. E-shops would leave full decisiveness to algorithms which learn from practice and intuit from experience how to play optimally, but much faster than any human could ever do.
#3 Delivery – Algorithms That Predict Your Decision-Making
Let’s try to imagine that for ex. iTunes could wisely filters your tags to what you’d most likely be interested in based on your existing library. And further than that, with artificial intelligence, would be able to decide which to go ahead and add to your library, even making a purchase on your behalf.
This is similar to what Amazon plans to do with so-called “anticipatory shipping”. The distribution system and network ultra-precisely defines customer purchase patterns and predicts brand, price-range and product that will be bought. After this prediction, items could be sent to nearby distribution centres before an order is even placed – meaning the package is already at the shipment hub or on a truck before you know it. It works even better with everyday products, like tea. Just imagine – if algorithms can anticipate supply and demand, you’ll never run out of your favourite tea, and it merchants will benefit from expedited sales.
If applied properly, this idea could take analytics and logistics to the next level, allowing companies to react quickly (and automatically) based on people’s needs, expanding its base of loyal customers.
Imagining the E-Commerce of Tomorrow
E-commerce is data-driven by nature, and marketers, merchants and distributors have already seen the tip of the AI iceberg with personal assistants, chatbots, automated merchandising and retargeter systems. But combining new deep learning AI with E-commerce has not truly gone mainstream yet, at least not to the extent of utilizing neural networks on a typical basis.
By combining artificial intelligence with massive data, the future of e-commerce will see a smarter, more self-intelligent shopping ecosystem that can make good decisions on its own, something that we might only have imagined a decade ago, but that is now definitively possible today.