Why machine learning and Data Science are important in B2B e-commerce. Study of buying behavior, relevant output, personalization, selection of analogs, and related products.
Machine learning perfectly copes with tasks that were previously only humanly possible. Based on the uploaded data, the machine itself forms algorithms capable of learning. The more data there is, the more accurate the algorithm will be.
Personalized product recommendations on Amazon, facial recognition on Facebook - it's all about machine learning. For example, Alibaba's artificial intelligence tracks customer behavior and tailors virtual product displays to each customer.
When working with data, machine learning algorithms are good at finding patterns and deviations from them. This is a very useful option that companies use to analyze and optimize business processes and make money from it.
In addition, any company collects data. How to store them, process them, and draw objective conclusions is Data Science. Data science allows you to look at data from a new perspective. It's a combination of statistics, mathematics, programming, business intelligence, and even psychology - anything that can help you analyze large amounts of information and discover useful patterns.
Often, companies apply data science to improve marketing campaigns, and business processes, or to forecast demand.
The main value of machine learning is the tremendous time savings in calculations. And Data Science helps to find serious gaps in business that can negatively affect a company.
Today, machine learning is trendy and important. But you need to understand the essence. Simply making models on data that one does not understand will not work. In addition, a model that worked well in the past may fail badly in the future. Therefore, you should realize that machine learning is only a tool, not a panacea.
It is better to use professional tools and ready-made solutions in the form of an AI ML platform for various tasks of your business.
Most B2B companies use web analytics tools to track page views, visitors, and bounce rates. But today, that's not enough.
Companies need to be able to respond quickly and accurately to changes in customer behavior by building systems that will detect changes in customer behavior in near real time. An obscure Google Analytics report doesn't provide complete information about how customers interact with a website.
Machine learning helps the search engine analyze vast amounts of data about B2B platform visitors, learn the behavior patterns of professional buyers, and then accurately predict what customers will want to purchase.
Also, B2B companies that use machine learning on their e-commerce platforms can continuously improve performance, customize product selection for each customer, and offer a favorable price according to the customer's budget.
Companies need to be able to respond quickly and accurately to changes in customer behavior. Here are some examples of how B2B companies can utilize analytics and machine learning in their business.
Customer segmentation using machine learning will help companies categorize potential customers into a specific group, and predict their behavior to determine exactly what products, information, and promotions to show customers while they are on the website.
Personalization is closely related to segmenting customers based on their behavior. Machine learning can narrow down segments and tailor more unique content to each B2B customer. As a result, the user will be able to feel special without violating their privacy.
How to position products given the recent history of customer activity - machine learning can help with this too. A customer's prior purchase history is also used to determine product recommendations. This is important because B2B buyers want to see relevant search results.
For example, JJ Food Service, a US-based food delivery company, uses machine learning in their B2B e-commerce. In 6 years of operation, their B2B portal takes 60% of orders online.
Mushtaque Ahmed, CEO of JJ Food Service, noticed that after moving to the portal, the company lost the ability to fully communicate with the customer. No one was telling them anything new, no one was offering special products - the company was missing these opportunities.
As a result, JJ Food Service implemented a machine learning engine that uses analysis of customers' past purchases and pre-fills customers' baskets with items that might be important to them. When customers log in, about 80% of their baskets are already filled. As a result, about 5% of the products recommended in this way go into orders. And most of these products are new products that customers didn't know about yet.