Table of Contents
Table Of Contents
Ecommerce machine learning is getting progressively vital for online stores. As online consumers demand increasingly personalized and immediate services. Hence, as it is easy to switch to the best store for (me) shopping, each online merchant must do everything necessary to outperform their competitors.
In this context, many large organizations have already understood this (Amazon in the lead). In addition, in view of solutions that take advantage of the Cloud, smaller businesses can also have access to machine learning.
But, how can eCommerce benefit from machine learning?
In this article we shall review together with the following:
- What is machine learning (ML)? How does it work?
- The importance of machine learning in eCommerce
- Precise product recommendation
- Improved research
- Chatbots for answering questions
- Better fraud detection
- Decision support
What Is Machine Learning (ML)?
Any entity is said to “learn” as long as its performance improves with experience; that is when the ability was not present in their genotype or birth traits.
Machine learning is the subfield of computer science and a branch of artificial intelligence, whose goal is to develop algorithms that allow computers/robots to self-learn through collecting data, as well as trial and error techniques.
Machine learning is not new, the first algorithms were created in the late 1950s. Though, its precise definition is still confused for many people. It is a modern science for discovering patterns and making predictions from data based on statistics, data mining, pattern recognition, and predictive analytics.
How Does Machine Learning Work?
Essentially, machine learning works according to these two approaches:
- Supervised learning (or discriminatory analysis) which is based on predetermined information.
- Unsupervised learning (clustering) which leaves the machine free to cross and group information the way it wishes.
Today, all professions are using it. We are thinking of Google services such as their famous search engine or the company’s autonomous car.
The Importance of Machine Learning in eCommerce
At Amazon, eCommerce machine learning has been used to its full potential for a long time. Consequently, due to this technology, the site will recommend to its visitors products that they might like.
Furthermore, the eCommerce giant also utilizes it to rank and display consumer reviews based on their relevance. Not to mention Amazon Echo, the smart speaker that responds by voice to its user.
Examples of Applying eCommerce Machine Learning
1. Predictive Analytics
Predicting outcomes, usually future outcomes, according to historical data. Predictive analytics allows marketing and sales agents to predict the customer’s lifetime value, to identify customers that are more likely to be loyal, to predict whether a prospective customer resembles an appreciable value or not, and how much the company should spend (time and resources) on each specific potential customer.
It is also allowing us to predict how much a specific customer or group of customers will be worth throughout their whole customer lifetime. Predictive analytics is so significant for online stores, because it’s not difficult to implement, and it has been proven most of the time.
Also, the positive thing is that the company will not need a huge amount of data to apply predictive analytics. 700-1000 customers, and monitoring the right historical data, will be sufficient to return some results.
2. Visual Recognition
Visual Recognition is the most mature technology in the fields of application of ML, it is a tremendous opportunity for eCommerce. This is actually the feature that is making most of the new wave, and it is attracting most of the attention to products, such as autonomous vehicles, facial recognition, and robot vision… etc.
Actually, few companies have started to implement this for sales and marketing. Although we are highly optimistic about visual recognition for eCommerce, we think you should not choose it as your first step.
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3. Precise Product Recommendations
Product recommendations is one of the key cornerstones of eCommerce machine learning, it is based on everything I know about you, or the data I have about you.
Currently, product recommendations are made only by category (or according to predefined criteria). Owing to this predictive technology, incremental sales are becoming more habitual, because they perfectly meet the desires and needs of Internet users.
4. Improved Research
In case you have an eCommerce site with a lot of references, your internal research could be rather limited. Today, the technologies offered by the most renowned Content Management Systems (CMSs) rely only on semantic correlations to find the products sought by Internet users.
Thanks to eCommerce machine learning, it is now possible to go a step further by refining the results according to other criteria that you have configured (reviews, conversion rates, products with high margins, etc.) The algorithm thus shall be able to guess precisely what consumers are looking for, without being limited to what a user types in the provided field.
5. Finding Potential Customers
As stated by a newly conducted survey, the sales departments lose contact with nearly 1/3 of the prospects. This means that potential pre-qualified buyers interested in your product or service are left behind.
In addition, many businesses are overloaded with customer data that they use little, if at all. Still, it is a treasure that should be exploited to enhance the sales cycle.
In retail stores, for example, ML is used with facial recognition to capture the behavior of a buyer in a store. Essentially, when a customer is hanging around for a period of time before a product – e.g. a microwave oven – this is information will be stored to be used on their next visit.
As ML improves and expands, you may even start to see special offers on your computer screen based on your wait time in-store or even your reaction to a product. Microsoft, for example, offers a “Mall kiosk”, which recommends products using facial or voice recognition of reactions.
6. Virtual Assistant and Efficient Sales Process
Now, with virtual assistants, online businesses can leverage ML to appropriately select and recommend products that are useful and desired by a buyer, eliminating the need for the buyer to do all the catalog research work.
For instance, incorporating ML into your Customer Relation Management (CRM) will allow you to individualize your solutions and deliver an accurate sales message. Indeed, if your ML system deals with natural language learning and voice input, such as Alexa or Siri, your CRM will respond to customer requests, solve their problems and even identify new sales opportunities.
Example of an online virtual assistant:
You may have heard of “Mona“, the virtual business assistant developed by former Amazon employees. It helps simplify mobile shopping and provides customers with the best deals tailored to their preferences.
7. Chatbots for Answering Questions
An internet store never closes. An Internet user can therefore make a purchase in the middle of the night, with no one to contact in case of need. Thus, based on this observation, chatbots that use machine learning are becoming an interesting technology for online merchants.
By allowing this artificial intelligence to answer all the basic questions, you will spare yourself precious time to tackle other urgent issues (or save on your customer service). Especially since they are able to learn from interactions and, therefore, improve their knowledge as they go.
It is an important part of eCommerce machine learning because it requires the bot to process natural language to understand what humans are talking about when they are chatting with a bot. In practical terms, chatbots can help you reduce customer service costs and engage with consumers better, 24/7.
8. Machine Learning to Enhance the Omnichannel
From intelligent chatbots to personalized cross-selling, there are many tangible applications of machine learning. Many brands are already integrating it into their omnichannel strategy.
For example, the retail giant Walmart has invested in technology capable of analyzing the mood of customers in points of sale, and deducing different purchasing paths.
The point of sale’s information systems integrate these forecasts and help to implement various actions, including adding reinforcement at the cash register, if the majority of customers are dissatisfied, and the average time between two collections is too long, or notifying the members of the sales to make direct contact with the buyer to hinder a possible non-purchase.
As you can see, the point of sale clearly exemplifies the impacts of Machine Learning on practices as well as its potential.
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9. Personalized Email Marketing
Why are we seeing the same messages to everyone in our email marketing campaigns when each one of the recipients has very individualized needs and desires?
With eCommerce machine learning, we can treat it as a production problem to individualize the email according to each person’s mannerism. It’s not enough to simply segment groups, or lists, we have to send emails completely on an individual basis.
10. Better Fraud Detection
Usually, a small eCommerce site does not have to experience large-scale computer fraud. Thus, this type of investment turns out to be unprofitable.
Alternatively, for organizations that are prone to online credit card fraud, a system to secure transactions will be a huge advantage, both for the firm and for the customers. In fact, many banks are already using eCommerce machine learning to protect transactions and detect over 90% of fraud.
11. Decision Support
Ecommerce machine learning is also an excellent decision-making aid for an online store owner. By bringing together a lot of internal and external data, you can, among other things:
- Define the right price in real-time for each product.
- Forecast demand.
- Anticipate stock problems.
- Deploy more precise remarketing campaigns.
The fact is: the more technologies advance, the more online sales sites need to have the tools to handle their customers’ demands, accurately and instantly. In this, solutions using machine learning are becoming essential.
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In a Nutshell
It is possible to state that it is difficult to imagine a successful store to operate without a good sales staff, also it is as difficult for an eCommerce site to work without an effective machine learning technology.
In our opinion, machine learning is faster and more powerful, than explicit programming. It allows us to automatically adapt, assimilate all of the input data, react to that, and consequently, deliver a very personalized user experience.
As for the type of ML to install, there are no predefined solutions, a solution suitable for one merchant site will not necessarily be suitable for another. It all depends on the type of customers and the products sold.
By integrating machine learning into your marketing strategies, you will be able to make better decisions to increase your conversions. The points of contact with users will be personalized, and may thus enhance their experience and satisfaction.