This article is provided by Reshift Media, a Canadian-based digital marketing and development organization specializing in retail businesses.

When you think of machine learning, what comes to mind? Some may think of  facial recognition scanners or self-driving cars, and while these are valid examples, machine learning can actually be much more of an accessible tool that can be used in our everyday environment, sometimes without us realizing it. From a numbers standpoint, the global machine learning market reached $8 billion in 2021 and is expected to grow to $117 billion by 2027, which involves a variety of different industries, including retail. 

Machine learning in retail is taking off in more ways than one. As more business owners are beginning to implement digital solutions within their day-to-day operations, machine learning is not only becoming more normalized, but it is helping assist in some of the more difficult aspects of retail, which benefits business owners and customers alike.

What is the difference between artificial intelligence and machine learning?

If you are interested in machine learning, then you have likely also familiarized yourself with artificial intelligence (AI). The two can be easily mistaken as the same thing, and while they can go hand-in-hand, there are some key differences.

Machine learning is considered a subset of AI, meaning anything that is labelled machine learning is also AI. However, not everything that is considered AI is machine learning. AI centres itself around solving problems; any type of machine that uses AI has the “intelligence” programmed into it that is capable to do so. The problems AI can solve would typically be completed by a human (aka using natural intelligence), so in some ways, it replaces or cuts down the steps that can be easily automated or computer-controlled.

Machine learning, on the other hand, is a type of artificial intelligence that can learn by itself through the data that users feed into it. The more data it receives, the more effective and accurate it becomes. While machine learning can still solve problems or perform specific tasks, it does so by automatically “learning” from the data it receives, which has not been initially programmed into the machine. Machine learning can then make predictions or rely back on the data it has previously received, and it exceeds the abilities that a human is capable of doing, such as quickly processing large amounts of data.

AI is implemented to mimic human cognition as a way to perform tasks and solve problems, while machine learning does the same but instead, takes the data it receives and becomes “smarter” on its own, thus developing intelligence of its own through the use of algorithms.

Types of machine learning algorithms

Machine learning algorithms typically fall into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each type of algorithm consumes data differently, thus creating different outcomes, depending on what you want to use it for. Without getting too technical in our explanation, here is a basic summary of each:

• Supervised learning is used when developers already know what they want to teach a machine. This is typically always known in advance since there is a large amount of training data that needs to be inputted into the model so that it can adjust until it yields the desired output. Over time, the algorithm learns from this and can make more accurate predictions. For example, machine learning can analyze prior behaviour patterns, such as from a customer, and then predict their future behaviour.
• Unsupervised learning examines a set of unlabelled data* to uncover patterns, and it helps form the data into groups. It can also differentiate similarities and differences within the information it receives (also known as clustering), which can be useful for a variety of purposes in retail, such as customer segmentation. Unsupervised learning doesn’t require a large amount of data, so it can be used at a much more efficient rate.
• Semi-supervised learning takes methods from both supervised and unsupervised learning. It takes data that is only partly labelled, while a large number of data is unlabelled. From there, the algorithm learns and makes predictions. By labeling some of the data, it can help the algorithm group the rest of the data.
• Reinforcement learning is feedback-based since it interacts with the environment. When the algorithm performs an action, the results are classified as either good or bad; when it performs a good action, it receives positive feedback and vice versa. Over time, the algorithm learns from the actions and feedback it receives.

* Data labelling takes raw data (such as images or text files) and adds tags or labels that help identify or represent what it is. These informative tags are then inputted into a machine learning algorithm so that it can learn from the labelled data and identify it later on when it comes across unlabelled data. When data is unlabelled, the machine learning program has to take the steps to identify the data based on its characteristics.

While labelled data helps machine learning algorithms by already providing all of the properties of the data, unsupervised machine learning can take unlabelled data and create extremely accurate results, so both scenarios (labelled and unlabelled data) can still be ideal.

Now that we have a better understanding of what machine learning is and the types of algorithms it can use to process data, let’s look at how it can be implemented in the retail industry.

Customer Segmentation

Customer segmentation in retail is a great way to separate customers into groups based on their behaviour, interests, and other more specific characteristics like gender, age, location, shopping patterns, etc. One of the main goals is to identify the customer base that has the highest growth potential or is most profitable, and the results from customer segmentation can help retailers strategize more effectively, such as with more personalized marketing campaigns.

Machine learning can help retailers achieve this much more easily by utilizing unsupervised machine learning algorithms. Remember, unsupervised means unlabelled data, so the algorithm takes the dataset and identifies the different characteristics in order to make clusters, and then these clusters become groups, which makes it easier for retailers to examine and create segmented groups out of.

Product Matching

Product matching uses machine learning to match identical products from a variety of sources based on their similarities. Traditionally, retailers use information like product SKUs, titles, and other data points to create comparisons between the products. However, this can be extremely time-consuming, especially when you want to product match at a large scale against multiple competitors.

From a retail perspective, product matching is an important element, especially when a large majority of consumers are researching the products they want to purchase and comparing prices. Product matching can help ensure there is consistent pricing across retailers, and it can help a retailer remain competitive in their industry.

At the most basic level, machine learning can check the data between two product images to determine if there are similarities, even down to a minuscule pixel level. Using deep learning algorithms (which is a subset of machine learning), image similarity can determine similarities between products no matter the differences in the images (such as quality or design).

Demand Forecasting

Demand forecasting is made up of several components, but in essence, the goal is for retailers to predict the future demand of their products. Combining historical demand patterns, internal decisions, and external factors, demand forecasting can help retailers:

• improve their relationship with their suppliers so that retailers have a better idea of the number of products they need to order
• develop better customer relationships because demand forecasting allows retailers to meet their customers’ product needs (i.e., retailers can order products at certain times so that they will be in stock when customers usually begin searching for them)
• optimize their supply chain so that in-demand products can be in-stock

Demand forecasting involves a considerable amount of data to factor in, and that’s where machine learning can save the day. Since machine learning thrives off of data, especially the more it receives, it does a very good job at identifying patterns and cause-and-effect relationships, which is ideal for retailers engaging in demand forecasting since that can help predict trends.

Because of the wide range of factors that influence demand, such as seasonality, trends, price changes, weather, and local events, machine learning can sift through all of that data and produce accurate forecasts for retailers.

Inventory Prediction

Similar to demand forecasting, machine learning algorithms can examine purchase data in real-time, which can help retailers better understand their inventory levels and order accordingly. For instance, if one product receives a lot of traction, retailers can make better predictions as to the amount they need to reorder. These algorithms can provide suggestions to the individual who makes purchases, and it can take in a variety of factors that may go unnoticed.

Recommendations Engines

More often than not, e-commerce retailers have recommendation engines on their websites to recommend products to consumers, with the goal of boosting sales. There is an immense amount of data that goes into predicting users’ interests and making accurate recommendations, but machine learning can help filter through it all. In simple terms, machine learning algorithms identify the patterns in the consumer behaviour data that is collected, and then it can spur the most relevant items to the customer. This makes the entire experience more personalized for the customer, and it allows them the opportunity to find products they may have not searched for before, thus increasing the likelihood of purchasing.

Machine learning is making such great advancements in the retail world, and implementing its time-saving capabilities can be a great way to better meet the needs of customers and improve overall efficiency for retailers across industries.

About Reshift Media

Reshift Media is a long-time partner of the Retail Council of Canada. The company is a Toronto-based digital marketing and development organization that provides leading-edge social media, search and website/mobile development services to retailers around the world. Please visit www.reshiftmedia.com to learn more.