How Online Businesses Use Machine Learning

Online business relies on machine learning for success, which makes this a smart investment for you.

How Online Businesses Use Machine Learning

With increased use of artificial intelligence in the world, industry and technology experts have stated that machine learning is the next big thing in the economy. According to a recent report by PwC, the future of business is artificially intelligent and the current state of technology is no barrier to its development and growth.

It is estimated that more than half of the world’s economy will be automated by 2020.

And with the trend of technology savvy clientele on the rise, more and more companies have been finding it of interest to implement machine learning techniques. Here is how a few businesses, both in the retail and service industries, use machine learning.

How to Analyze Customer Behavior

There are some automated algorithms which can be used to analyze consumer behavior for marketing purposes. More often than not, machine learning systems are used to make personalized recommendations to customers in an effort to ensure that their product sales improve. Other uses of machine learning are to improve the speed and efficiency of customer service and to provide a more efficient resource management approach in areas where it is needed most.

Retail Chains Can Use Machine Learning to Track Sale Pricing

Many retail chains have implemented a plethora of tactics in an effort to create enhanced consumer experiences and efficiency for their customers. However, they often overlook the impact that machine learning has on their pricing of their products. Some of the things that can be done using machine learning systems to assist retail chains include:

Analyzing discounts and promotions: These systems can pick up when each brand has increased their product prices in an effort to move the product off the shelves. Using technology to track promotions based on value and time, the system can catch when a product is out of stock and place an order for another one to replace the last one that has run out.

Analyzing inventory: While many discount brands rely solely on warehouse sales and promotions to drive sales, they rarely analyze inventory levels. This can result in making the wrong pricing decisions about the products.

Recommendation Engine and Personalized Messages

Unlike promotions and discounts, recommendations can be a little more difficult to track. Many of the similar products that are similar to ones a customer has purchased before have a stock or distribution date. Finding out when these products have been sold out and the “new” stock is available can be a bit overwhelming. The recommendation engine can help with this by sending personalized messages to customers.

Highly Facilitated Supervised Machine Learning Engines

Machine learning systems can be highly facilitated by highly facilitated supervised machines. The goal here is to ensure that the programs’ tasks are quantified. The system is then presented with as many scenarios as possible and must assess the probability of success for each of those scenarios. Thus, higher level automated algorithms and requirements help to optimize the machine learning system to achieve goals. These systems can also create artificial intelligence that adapts to and applies processes in the order in which those processes can be optimized.

Continuous Learning, Trained Lab Humans, and Automated Feedback

It takes a long time to train machine learning systems so they are constantly learning and assessing their own performance. As a result, there is no surefire way to ensure that the machine learning system will be effective until and unless it is evaluated.

However, there are a couple of ways to get experts to evaluate a system’s work. One involves using human subject matter experts in the form of cognitive testing. Cognitive testing evaluates the mental states of data-informed system and determines whether the data is adequate to train the system and guide its learning.

Another method involves making the system an online test. Instead of submitting the system to an education institution, you can use the system through a self-learning environment. The idea is to provide a feedback score to the system by providing the product in the form of a questionnaire. Once the system receives the answers to the questions, the system can then determine whether the data is sufficient to train its learning algorithm.

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