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HOW DOES ARTIFICIAL INTELLIGENCE BENEFIT E-COMMERCE?
Have you ever heard of artificial intelligence ( AI )?
If for some it still evokes only science fiction and robotics, it still fits in all aspects of our daily lives. From automatic cash registers to advanced security controls in airports: nowadays, artificial intelligence is almost everywhere. And, little by little, she begins to interfere in e-commerce.
Moreover, many companies are already taking advantage of the latest advances in AI and machine learning ( ML ) to provide a better shopping experience for their customers. As it improves, artificial intelligence could therefore change forever, and will most likely change the landscape of e-commerce in the years to come.
By definition, artificial intelligence is the ability of a machine to perform “smart” tasks, such as learning and decision-making, as a human being would.
Machine learning is a current AI application based on the idea that we should be able to give machines access to data and let them learn on their own.
Applied to e-commerce and marketing, machine learning corresponds to the various methods of data analysis in which computers find information without being told exactly where to look for this information. ML algorithms, when exposed to massive amounts of data, can extract models and use them to generate ideas or predictions about future conditions.
Although still relatively new, artificial intelligence has already had a huge impact, in a short period of time, on industries such as finance or healthcare. And the benefits of AI are now starting to spread in e-commerce.
It is important to note that artificial intelligence by itself is not a product, but a powerful tool for creating better products that meet the needs of customers. Yes, even if it may seem paradoxical for a machine, the greatest strength of artificial intelligence is that it can help e-commerce to create a more humane customer experience by personalizing it!
Indeed, an online sales activity generates monumental volumes of data from dozens of channels. There is even too much data for a human being to know where to look for or even what he is looking for — the perfect conditions for machine learning.
As a result, many e-merchants are already trying to differentiate themselves by using forms of AI to better understand their customers, generate new leads and provide an improved customer experience.
Personalization in e-commerce is not new. Many businesses and e-merchants currently use a filtering system to provide customers with product recommendations. These filters usually base their results on bestseller data, consultation history, and other general aggregation parameters.
At best, the most successful referral systems can remember what your client likes.
But, you will agree, all this remains a bit impersonal. “People who bought this product also bought this product” is not the best way to personalize an offer.
This is where AI comes in.
While the word “artificial” connotes some dehumanization, artificial intelligence instead allows merchants to set up a more personalized customer experience by providing recommendations to subscribers according to their preferences.
How ? With the ability of AI to more effectively analyze than a human being from large data sets. This means that the technology can quickly analyze different aspects of the navigation behavior. Whenever a user examines a product, posts a message or even a tweet about it, the information can be used.
Artificial intelligence technology is also able to learn the interests, passions and triggers that make a consumer more likely to make a purchase.
In other words, millions of transactions and communications can be analyzed each day to target offers to a single customer.
By exposing machine learning algorithms to truly massive amounts of data, marketers can build automated analytic models that are not limited by the ability of humans to suggest why some people buy particular products.
Such AI-based applications can uncover better ways to model user behavior. Finally, technology facilitates:
- the sales process, identifying who is most likely to buy a product (based on history of past purchases, demographics, etc.)
- Customizing the sales cycle, allowing you to engage the right prospects with the right message at the right time
Example of using the AI for personalized recommendations : Starbucks recently launched “My Starbucks Barista”, which uses AI to allow customers to place orders by voice or email. The algorithm relies on a variety of inputs, including account information, customer preferences, purchase history, third-party data, and contextual information. The coffee giant can provide more personalized messages and recommendations to its customers.
According to a recent study, at least a third of prospects are not followed by the sales team. Which means that potential pre-qualified buyers interested in your product or service end up in oblivion.
In addition, many companies are overloaded with customer data that they do not exploit, or at all. It is a gold mine that can be used to improve the sales cycle.
In retail, for example, artificial intelligence is used with face recognition to capture a customer’s behavior in a store. Basically, if a consumer lingers for a while in front of a product — a coffee maker for example — this information will be stored for use on his next visit.
As AI improves and develops, you’ll even be able to start seeing special offers on your computer screen based on your in-store wait time or even your reaction to a product! Microsoft offers for example “Mall kiosk”, which recommends products through facial or voice recognition of reactions.
Now, thanks to virtual assistants, online businesses can leverage the AI to appropriately select and recommend useful and desired products from a buyer, avoiding the need for the buyer to do all the research work in the database. the catalog.
For example, integrating artificial intelligence into your CRM will allow you to customize your solutions and create an effective sales message. Indeed, if your AI system allows learning natural language and voice input, like Siri or Alexa, your CRM will respond to customer requests, solve their problems and even identify new sales opportunities.
Even better ? Some IA managed CRM systems can be multitasking to handle all these functions and more.
In this case, artificial intelligence helps users dive deeper into e-commerce product catalogs to find the perfect item that otherwise might not be discovered.
There are also several virtual assistant technologies online. These robots use large sets of data, collected in real time, to “learn” the buying habits, interests and personal tastes of users.
Example of an online virtual assistant : You may have heard of “Mona”, the virtual sales assistant developed by former employees of Amazon. It helps simplify mobile shopping and offers customers the best deals to suit their preferences. The longer the user spends time interacting with the Mona robot, the better he will know it.
Virtual Assistant Example : The North Face brand harnesses the power of virtual assistants to better understand their customers while providing tailor-made recommendations. With the help of IBM’s intelligence solution called Watson, the company allows buyers to discover their ideal jacket. For this, several questions are asked to customers, such as: “where and when will you use your jacket? “. IBM’s software then scans hundreds of products to find the best matches based on correlated responses to other data, such as weather conditions. To get an idea, you can test the tool here .
At least 30% of online shoppers use the search function of an e-commerce. However, it is often a tedious task for the consumer who is forced to choose and then refine a keyword accurately describing the product he is looking for.
The scenario often happens as follows: a consumer enters “smartphone with the best camera” in the search bar. While a human interlocutor would immediately understand the request, or ask questions to get more details about the client’s needs, the numerical results provided are often beside the plate. In short, in the majority of cases, the research does not lead to the expected result.
This is due to lack of user context, rigid and irrelevant filters, and problems with keyword understanding. In fact, the algorithms of these e-commerce search engines have neither the practical intelligence, nor the ability to understand a query with the nuances of the language.
The key is to use the power of machine learning to improve the results for consumers who use research. The ML can also generate a search ranking, which allows the site to sort the search results by relevance, instead of matching a keyword.
In doing so, e-commerce platforms will be able to turn a massive number of failed search experiences into successful conversions.
To replace textual searches, a solution is also beginning to be implemented: visual search — a technology that uses artificial intelligence to analyze a photo submitted by a customer, then find the desired product or products that match that image .
Visual search allows customers to take a picture of a product they like and then download it. The IA software is then able to evaluate this specific product, its brand, its shape, its style, its fabric, its color, etc., then to propose suggestions of similar products likely to interest the customer.
Finally, in addition to using images to search for products they want to buy, consumers will be able to use voice search — the ability to search for objects using speech. Voice search uses AI to understand what is being said and to improve the recognition of voices and sentences.
Voice research has been popularized with voice assistants like Alexa and Siri, forcing e-merchants to re-optimize their web pages, including FAQs, to respond to voice-based searches.
Example of using the AI for search results : A company that uses machine learning to provide better search results is eBay. With millions of items listed, the auction site harnesses the power of AI and data to predict and display the most relevant search results.
Example of use of visual research : One of the innovative companies in terms of visual research is Neiman Marcus. With its application “ Snap. Find. Shop. The fashion and beauty brand allows users to take pictures of real-world objects and then find them in the catalog.
If your business deals with customers daily and you encounter recurring issues or questions, creating a chatbot is a good way to provide customers with information faster and more efficiently than a customer service representative.
For simplicity, chatbots are automated programs that can “converse” with people to answer questions and perform specific task queries. They have been around for a while now, but have made considerable progress in their ability to adapt to the customer through the machine learning process.
Specifically, chatbots can help you reduce customer service costs and engage consumers more effectively, 24 hours a day, 7 days a week.
They also provide a good opportunity to customize consumer recommendations based on the conversation history and can actively take on some of the important responsibilities of running an online business, such as automating ordering processes.
At the moment, bots have pre-recorded responses and do not detect the use of sarcasm or humor. But, in the near future, a chatbot will be able to analyze new parameters and opt for a more sympathetic and precise answer.
Without a doubt, artificial intelligence has already begun to have an impact on e-commerce, for which it is developing the ecommerce in an intelligent way so that customers are no longer offered solutions that are neither appropriate nor appropriate. And, day after day, the AI becomes more and more sophisticated. Over the next few years, the application of machine learning and AI to e-commerce will become a differentiating factor that is increasingly important in terms of performance. E-merchants who do not take advantage of the benefits may be caught off guard by early adopters who are reshaping the e-commerce market and the expectations of buyers.
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