Let’s take a look at what’s new this time. We prepared something easy-to-read and super-helpful-to-use. We want to tell you about the possibilities of personalization in e-commerce using the algorithms of machine learning.
keep in mind for the rest of our life, that modern consumer is very sophisticated and what is more – super busy. Thus, showing content or sending offers that are not relevant to him is a real failure. Just imagine, a customer goes to an online store and selects a vacuum cleaner. He has around $100 as a budget for this purchase and a great desire to stick to the allotted limit. What will be his disappointment, if all the information left about himself, will lead just to the offer with “discounts for the Karcher appliances” received? The online store did not understand what the potential consumer wants or did not find it necessary to understand.
This approach can cause a client loss for this online store. In order, to prevent this from happening, it is important to make the potential consumer feel special. In offline shops, it is achieved by the consultant. He listens to wishes of the client and offers suitable goods or services. Within the e-commerce segment, personalization is a key to solving the problem of individual approach. So, read below to find out about what is it, and how it affects the loyalty of customers.
Personalization is a selection of content, products, method, and channel for a communication with a specific person. Marketers personalize mailings, sites, and landings, as well as advertising campaigns. All the information presented in such campaigns tells about what the company offers in a unique way to every segment of the audience. Segments are recognized by similar characteristics, such as:
The data about users can be of any type. It takes a lot of time and effort to analyze such amount of information manually, as well as it will be pretty cost-inefficient. Thus, machine learning comes as a solution.
David needs a vacuum cleaner for his new apartment, his budget is $100 for it. He looked at several options under $100, however, he liked Phillips device the most for $115. The cost of vacuum cleaner goes beyond David’s budget for now. So all he does — leaves his e-mail address to get an email, if some special deal appears. Next — he closes the page. Within a few hours, David receives an offer with the Phillips vacuum cleaner he looked at.
The machine analyzed the behavior of David on the site: determined that he was looking for vacuum cleaners up to $100, but looked for a long time at Phillips device, which costs $115.
There were Bob, Sally, and Natalie together with David in the target group. They have a budget for a vacuum cleaner, too, no more than $100, and they also liked that perfect Phillips product as well, which does not fit into this budget. All of them live in Minneapolis-St. Paul, MN, their average monthly income is just above $4, 500. They all are married.
Dynamic pricing is an important way of using machine learning in e-commerce. This is a change in the price of goods or services, depending on their value for the client and his ability to pay for them. Machine learning is able to take into account demand, supply and determine the elastic limits at a price for each product or service.
In the case of David, machine algorithms have determined that the Philips vacuum cleaner is more valuable than all other vacuum сleaners on the site. But it does not fit into the budget of a potential customer. Therefore, the price of the goods was changed, giving David an ability to purchase a vacuum cleaner. The final cost of the appliance at the same time remained within the permissible limits for the seller.
Because algorithms self-learn all the time, they easily adapt to changing market or consumer behavior designs. Remarkably, the situation works not only towards the buyer. It may happen that the machine will show the product at a slightly higher price than it actually is, after studying the user’s behavior in the online store.
David changed job and within the next year his salary triples. He decided to make a surprise for the anniversary with his wife and buy a robot vacuum cleaner this time. Since last time he liked the online store, he went there once again. As soon as David entered the site, the algorithms of machine learning identified him. And when he filtered out to display only robot vacuum cleaners, they determined the parameters of the search and increased the cost per model by 3%.
According to a study by Criteo and IDC, 66% of marketers believe that machine learning technologies can create a high-quality, relevant content that is relevant to the needs of the audience.
Machine algorithms are able to show each visitor that version of the site that will draw his attention entirely and will more likely lead to a targeted action.
Analyzing the behavior of David on the site, the algorithms of machine learning showed on the main page an offer with Phillips vacuum cleaner at a reduced price.
Here’s also another example of an interesting car repair case. The goal was to increase the number of requests convertible into sales. Source of leads — contextual advertising, which led to the landing page. The potential customer, putting in the Google search line “replacement of the wheel on Land Cruiser”, proceeds on the first link from the issuance and gets on the landing page. In order to make the client stay Machine learning was showing the content that meets the request of a potential customer on the landing page. The first message seen by the customer was “replacement of the wheel on the Land Cruiser of any year of release.” The potential buyer, after seeing the relevant content, quickly moved along to the target action.
By collecting visitors’ data on the site, the machine groups them into narrow target groups. It automatically creates lists for mailing with a specific offer and content. Messages are sent the time when the user looks at the mail accurately. The machine also analyzes the activity of the potential buyer and chooses the time preferred for sending the letter. Thus, the open rate of letters increases. If the visitor, in addition to the e-mail address, indicated additional means of communication, the algorithm will select the channel most suitable at the moment. These can be SMS, push-notifications in the browser, messages in instant messengers or emails.
Algorithms of machine learning can be taught to identify visitors who are more likely to buy than others. To do this, marketers need to develop their own assessment of the “perspectives” of contact. The algorithm can analyze language features that increase the involvement and increase the number of clicks. After that, it will make a list of keywords that marketers use when compiling ads for advertising campaigns.
David is a potential buyer of an online store with house appliances, but he needs to be pushed. We need to show him the advantages of Phillips vacuum cleaner. The algorithm calculated that all the vacuums that David watched had a wet cleaning function. This means that David is looking for a vacuum with such characteristics, and it is important to him. The machine knows about what is important to David so it shows a banner on the main page with the text about technical advantages and reducing the cost to the vacuum to David’s allowable budget.
Machine learning can predict when and why a potential customer will contact the company. This helps to personalize the communication with the user and plan the costs for the technical support. For example, you can learn about a person’s taste preferences, create a pattern of his consumer behavior and determine the average check. You can even go further and find out how much the fan of Georgian cuisine is willing to spend, and how much is French or Italian would spend.
Before the potential customer understands what he wants, the machine offered it. For example, machine tracks how often the user cleans the memory. If he does this regularly, then the company will come up with a connection to the cloud storage. No more magic — just algorithms!