Machine Learning and Automation – Maximize Your Efficiency

We break down machine learning and how implementing it can increase your business’s efficiency.

min read

Arthur Samuel, an IBM employee, is credited with first using the term, "machine learning," back in 1962. Today, it's a rapidly developing field — but some companies still find it a little intimidating to hand off crucial parts of their business processes to machines.

Still, those who try it often see significant results. You'll be rewarded by freeing up your (human) employees to perform tasks that require a human touch while your machines handle the more repetitive work.

Let's dive into what machine learning and automation are and how they can improve the way your company does business.

The Machine Learning System

Machine learning is a term that refers to one branch of artificial intelligence (AI) within the computer science industry. They aren't something to be confused though: your computers won't start talking back to you or asking you to call them HAL. Instead, machine learning algorithms do what it sounds like they do: allow machines to learn in much the same way that humans do; with a learning process.

You may have heard the terms deep learning, neural networks, or artificial neural networks (ANN); these are also sub-fields of AI.

As websites continue to collect more data from customers, machine learning functions will become more accurate at predicting future customer behavior. As with any kind of scientific analysis, the larger the sample size, aka the more data you have, the closer your results will be to the truth.

Without the technical jargon, you should know that machine learning uses statistics and a learning algorithm to predict behavior. Of course, for any machine learning process (like the human learning process), there should be constant feedback from actual users. For example, Amazon recommends products based on what a user has been searching for or has purchased in the past.

These recommendations stem from statistical analyses of what other people who have made the same purchases went on to buy. Continuing the example, if a customer purchases a rug and a lamp, Amazon might conclude that they are currently decorating a room. That prediction is based on the commonalities between the purchased items (interior décor), the user's search history (interior décor), and what other people have bought after purchasing a rug and a lamp (more interior décor.)

How Machine Learning Works

Machine learning works in three basic steps: hypothesis, evaluation, and remodeling.

Hypothesis

The hypothesis comes from pre-existing data. Using our previous example, a customer has bought a rug and a lamp which indicates to Amazon that they are looking for interior decorations. This prompts Amazon's algorithm to make product recommendations for décor.

Evaluation

The evaluation part of the machine learning model comes as Amazon continues to collect new inputs from the customer. During this time, Amazon might monitor whether the customer clicks on any of their recommendations or what they search for on their next visit. At this point, Amazon might notice that the customer is searching for completely unrelated items, like video game controllers, and hasn't clicked on any of the recommendations.

Remodeling

From there, Amazon moves into the remodeling step. Clearly, the rug and lamp purchases were not part of this customer's typical buying pattern. They might have been gifts for someone else, or the buyer may be redecorating an entire room, just replacing two aspects. For whatever reason, the customer is clearly not planning on buying any more interior décor any time soon.

During this step, the algorithm learns that the customer is not planning on purchasing more rugs, lamps, or home décor. So, it changes the newly recommended items based on your recent search history. This cycle will repeat every time that user logs in and browses or purchases from Amazon, ensuring that the recommendations are always up to date.

Different Types of Machine Learning

Amazon is far from the only company that leverages machine learning to enhance its customers' experiences. Because there are so many applications for machine learning in businesses, it's important to understand the three primary types of machine learning and how they work in a business environment.

Unsupervised

The title refers to the fact that humans have no interaction with this type of machine learning; everything happens through algorithms and pattern-detecting. Unsupervised machine learning relies on the algorithm's ability to find patterns and analyze datasets without needing someone to monitor them. Unsupervised learning is the ideal type of machine learning for tedious, time-consuming workflows or large amounts of data that needs exploratory data analysis, or that are simple to define so that your employees can free up their time to do other, more productive tasks.

Some examples of how unsupervised machine learning can benefit your company are:

  • Image recognition
  • Segmentation of customers
  • Customer recommendations based on previous searches/purchases
  • Autocomplete in specific forms

Semi-Supervised

Semi-supervised machine learning starts out needing some human intervention but, like human children, eventually learns enough to strike out independently and competently perform tasks without ongoing supervision. In the beginning, the algorithms will start with small sets of data that are labeled by a human to show the machine where and how to make the connections between pieces of information.

After the machine learns the methods the supervisor used, people test the program on larger datasets and ones that haven't been labeled to make sure the program is following the correct procedure. This type of machine learning works well for more complicated analyses.

Supervised

Supervised machine learning is best for complicated algorithms that are meant to predict outcomes or classify data that has already been labeled. In layman's terms, that means you still need someone to label your data as it comes in so that you're feeding pre-labeled data into your program. Your program can skip the labeling step from that labeled data (and you know it's correct) to draw inferences about the data.

Methods of supervised learning include random forest, neural networks, linear regression, logistic regression, and support vector machine learning.

One familiar example of this type of machine learning is your email's spam folder. Emails are automatically labeled through the sender's address, the subject line, and the content inside. Your spam folder makes judgment calls based on the emails you choose to open about whether incoming emails are spam or not. You can also mark certain emails as spam to help the spam folder make more accurate predictions in the future.

Examples of Machine Learning

Machine learning is what helps Netflix recommend shows to you, enables self-driving cars to steer, powers the Google search engine, and Yext Answers. Another use of machine learning you may be familiar with is in fraud detection, natural language processing, and speech recognition. Data scientists are constantly finding new ways to incorporate machine learning into our daily lives.

And there are numerous ways for machine learning to positively affect your business outside of simply making recommendations for customers based on their search histories. We list some of the significant benefits below.

Analytics Software

Analytics software helps your business draw insights from customer interactions with your products, services, and brand so that you can make data-driven decisions for the future.

Depending on the type of software, you might receive information about which features customers use the most, common areas for complaints, how well your website ranks on search engines for keywords and phrases, and what your customers see when they search your company.

One of the most popular metrics is conversion rate, aka how many people visit your website and eventually make a purchase. Good analytics software will allow you to track multiple metrics and offer ways to generate reports for specific timeframes so that you can compare your progress between quarters.

Chat Bots

Customer service representatives get a small break when you bring chatbots online. They can help customers online by directing them to helpful pages like tutorials and FAQs, process orders or refunds, route questions to the representatives best equipped to answer them, and answer simple questions.

Depending on what types of questions people ask often, you can program your chatbot to be an interactive FAQ. When people use an online chat, they don't have time to wait on hold for a free customer support person. With a chatbot, they never have to feel frustrated or confused.

The Human Element

It should still be noted that no matter what types of machine learning you implement, technology will never serve as a replacement for people. Companies are made of the people who work there and no amount of statistical analyses can yet take the place of human innovation and creativity.

All of the best businesses are using technologies as tools to augment humans, not as replacements.

In the customer service industry, many people will be able to solve their simple issues with a chat, but there will always be people with more complicated concerns who need to speak to a human about finding a resolution.

In Conclusion

Machine learning might require complicated types of statistical analyses and data science, but you don't have to be a math genius to see how it can benefit your company. Machine learning and automation increase your overall speed and efficiency, allowing your employees to focus on areas in your business that require human ideas and innovation.

Sources:

What is Machine Learning? | IBM.com

Machine Learning For Ecommerce: How Does it Work? | Big Commerce

Machine Learning Improves E-Commerce Site Search Read More | Eventige.com

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