As an e-commerce business owner or marketer, you can arm your clients with something that will take the role of a machine gun with a full clip. What are we talking about? About the notorious, though still somewhat mysterious, Artificial Intelligence.
Just hit the spot!
Let's start from the beginning. Every online order consists of 4 key components:
Each of these elements can be processed through Artificial Intelligence to make the purchasing process a unique experience. This sounds impressive, but what does it really mean?
You must understand that AI is neither a magic wand, nor a universal cure for every problem. It is a complex area of technology and research in the field of Computer Science, which also draws on neuroscience, psychology and even modern philosophy. Therefore, the practical use of tools generally referred to as "Artificial Intelligence" can cover a lot of different fields.
The customer knows best (but AI knows even better)
The human aspect is the most complex element of every shopping process, which makes it the most difficult to influence. We do not have mind-reading technology (yet!) and it is very difficult to argue with individual preferences and tastes.
Consumer characteristics consist of innumerable quantities of parameters that are worth considering when building relationships with customers. Analyzing their behavioral and declarative data (age, sex, place of residence, etc.) as well as purchase decisions (volume of purchases and type of products purchased), you can provide them with personalized offers that will be able to meet their needs and, even better, spark desires in them.
If you use AI algorithms, Machine Learning and other areas of knowledge in the field of Big Data analytics, you have the chance to gain trust, affinity and even loyalty for your brand. The data can be collected from multiple touchpoints at every stage of the customer journey, no matter if it’s a mobile application, website or email campaign.
The AI engine constantly monitors all channels and device activity to create a comprehensive view of the customer and create tailor-made offers.
Artificial Intelligence, but real possibilities
Today's e-commerce search engines can index an enormous number of product names or descriptions to facilitate product searches, but they do not always provide the most relevant results. Instead of wasting your customers’ time on unimportant or unmatched content, you can improve its accuracy and increase the flexibility of search suggestions. How? Natural Language Processing will do all the work for you.
NLP is a tool that can help you make semantic search engines more precise. The traditional search process is slightly flawed in terms of context matching. In contrast to searches focused on keywords, the use of Machine Learning allows you to recognize the relationship between individual phrases, as well as catching typos and synonyms.
Example: Anna is a retro-style lover. This is reflected in her passion for old music, the style of interior design, but above all in her fashion choices. That's why if she types in the search engine the phrase "retro look" then the online store will be able to generate personalized results for her, based on the purchase history and individual parameters, such as size or style.
The algorithms know, even before you think
All customers leave traces of their preferences and possible subsequent moves on the Internet. From what they say on social media, to how and when they browse the web, all these activities create digital footprints that contain huge amounts of data.
AI allows us to condense these information packages and predict what actions the person will perform in the future. What's more, you can determine what events your future clients will perform.
Example: Netflix is a company that has successfully dealt with customizing its offer based on data analysis. Initially, a significant part of the site's guests appeared there with the thought "I want to watch a specific movie".
It is obvious that not every film ever made was available in their database, so an excellent way to keep the size of the database and the budget in check was to adjust to viewer preferences and change the philosophy around the way you use Netflix. It began to be associated with a way of spending free time, not watching a particular movie.
No more hit-or-miss recommendations
One of the most popular solutions in the use of Artificial Intelligence in personalization are product recommendations. While a customer browses particular products on your site, AI analyzes them visually. Then it chooses such articles from the product database, which are as close as possible to the original in terms of appearance.
Size, color, shape, fabric and even brand—AI takes into account all visual parameters of products, in order dto offer the customer alternative or complementary goods, matching the contents of the basket or the history of displayed articles.
Example: Adam visited an online store because he wanted to buy a laptop. After viewing several models on the site, he decided to place the selected item in the cart.
It turned out that the assortment of the store has a computer of a different brand, with the same parameters, but at a better price. The purchase recommendation for this model was displayed to Adam.
What's more, Adam could also read the proposals available to him in the "See also" section. He could complete his order with a printer, case, and headphones. When ordering additional accessories, he could expect an individual discount.
Can’t touch this? Not yet, but getting there!
The biggest advantage of physical stores over e-commerce
is the possiblity to experience the product tangibly, look at it from every angle or try it on. Artificial intelligence has taken on the challegne of recreating these experiences without making customers leave their homes.
It is a fact that fashion photoshoots with human models are extremely time-consuming and costly, especially if you want to present the offered product in a unique way. But generative adversarial networks (GANs) can supply an alternative.
GAN is a two-part neural network consisting of a generator that produces fake samples, as well as a discriminator that tries to distinguish the generated, false samples from the test suite from the actual data set of three-dimensional shapes. It is one of the most modern methods for creating 3D models and editing shapes and textures, as well as shifting the point of view.
At the beginning, a studio photoshoot is carried out. However, it doesn’t involve models, make-up artists or hairdressers. The main characters are a green screen and a green mannequin.
In post-production, both of these elements will be replaced by a model's figure, which can have any skin and hair color, posture and facial expression. Pictures can be modified many times, according to current demands, without the need for a new photoshoot.
What's more, the latest artificial intelligence algorithms can see how an object looks from any angle, regardless of whether or not a given perspective was presented to them. Thanks to this, they can create high-quality digital 3D models.
Example: Google has created an AI engine that uses the Generative Query Network. After delivering a few snapshots presenting a given object to the system, the network is able to generate the appearance of the object as a whole, also taking into account the view from those perspectives that were not presented in the snapshots.
According to test results, such an AI system can recognize the shape, size and color of objects by itself, and then combine all of its discoveries into an accurate 3D model. This can be a great solution for many industries, including e-commerce.
Take a shot with a bot
One of the more interesting AI solutions in relation to clients are chatbots. These are specific computer programs that are supposed to simulate a conversation with a real person. Chatbots can automate order processes and provide an effective and cheap way to provide customer service.
When designing chatbots, the first step is to analyze how a group of test users reacts to repetitive questions. Then, on the basis of the acquired knowledge, dialogues are created and a database of potential questions and answers is programmed, which is supported without the participation of employees.
Chatbots not only address everyone by name, but also know how to conduct a dialogue and shape the message to be as personalized as possible. They never ask you to wait for an answer—you can interrupt the conversation at any time and come back to it at a more appropriate time without losing the conversation history.
Often, when consumers browse the offer of online stores, they are simultaneously logged in to social platforms, such as Facebook. With this in mind, there is a great opportunity to use the messenger function to confirm orders or provide instant online help, including using chatbots.
Example: The beauty company L'Oreal launched a bot called Beauty Gifter in 2017. It is a virtual shopping assistant that gives advice to people who would like to give someone a a L'Oreal product as a present. The unique thing about Beauty Gifter is the ability to adapt to individual characteristics and preferences of the person receiving a gift. Based on the Messenger chat with the recipient, the bot suggests a list of gifts to the donor, taking into account the stated budget and the age of the recipient.
Shut up and take my money - dynamic pricing and personalized discount
The word "SALE" greets us on every corner. You see something that was, say, $100 and now it’s just $60—what a bargain! But there’s something else you don’t know, that a week ago the same thing was $50 before being raised when it was put on “sale”.
If you want to really surprise the guests of your store and get them to come back, then take advantage of the individual rebate options and a personalized, dynamic price layer. How? Machine learning and data analytics are excellent tools for understanding user behavior, as well as defining the parameters assigned to it, like purchase history, location, etc.
Based on these features, the algorithms determine the individual price rate. This is a popular option for certain products and services, such as airfares or hotels. Until now, these differences were dictated by the laws of market logic—if the algorithms detected a demand leap, the system raised the fee.
However, new developments in the field of artificial intelligence radically change the relationship between retailers and customers.
Example: Uber comprehensively uses its data resources in real time. Users are presented with different prices in different parts of the city and at different times of the day. When determining the rate, elements such as the number of available drivers, traffic volume, customer location and even the financial status of the area from which they want to set out are taken into account.
Make your clients the sharpshooters
There is a lot of buzz and promises in the history of artificial intelligence. Significant progress has been made in recent years and this technology is finding more and more practical applications, including in e-commerce.
No matter how good your marketing strategies and reach are, there is always room for improvement in the effectiveness of your actions. Although the term "artificial" can mean something negative or dehumanized, AI allows companies to provide a more personalized experience to their clients.
It is a technology that enables e-commerce retailers to analyze millions of interactions every day, and ultimately to target offers to a single customer, an experience that every marketer dreams about.