Machine learning is becoming a common phrase, but understanding what it means is an entirely different matter. In this blog post we will explain what machine learning is, how it works, typical examples, and how machine learning is used by retailers to increase the personalization experience for their customers.
Machine learning is an application of artificial intelligence that makes predictions based on assumptions acquired by surveying large amounts of labeled data. Artificial intelligence refers to systems that appear to think. It encompasses a wide variety of technologies, including robotics, natural language processing, and speech recognition. Machine learning is one of the most well-known applications of artificial intelligence and it is common in complex or dynamic domains. It is the only one currently being applied for retail purposes.
Stage One: Obtain and Clean Data
The first stage of a machine learning program is to obtain and clean data. This is the data that the machine will use to make assumptions about future data, so it is of utmost importance that the data is clear and correctly labeled. The large volume of data required makes this a time consuming and important step.
The example on the right shows a sample of cleaned data. Note that all numbers are facing the same direction, are the same size, and correctly labeled.
Stage Two: Storage and Big Data
As the clean data is acquired, it must then be stored. Machine learning relies on big data and thus storing the data is another large step in the process. There is a significant infrastructure investment required in order to make big data storage and management possible. Choosing a storage system involves choosing a product that can perform general computations over very large volumes of data in a highly scalable and parallel way.
Stage Three: Model Learning
The key step in the machine learning process is model learning, also sometimes known as training. This step is concerned with the creation of the machine’s knowledge about the relationships between inputs and outputs in its environment. In this step, the program begins analyzing the data, trying to find patterns and insights that can be turned into actionable predictions. The program steps through the data multiple times, each time becoming more finely tuned. The more times the system surveys the data, the more accurate the model becomes. More importantly, the larger the base of data fed into the model, the stronger the assumptions can be made.
Stage Four: Model Creation
After training, the model is created. A model refers to the collection of assumptions about how inputs relate to outputs. It is the artificial intelligence that will determine the response to queries. The only way to know if the model is accurate is to test it.
Machine Learning Application
Machine learning has become an invaluable tool to retailers to predict customer behavior and tailor recommendations. It can be leveraged at several levels of customer specificity. To learn more, click here.
Examples of machine learning can be found in most every home. Siri and Alexa are two of the most prominent examples. Classified as virtual agents, they employ machine learning for the purpose of understanding human speech and intention. They use data from the web and the requests of their users to understand different accents and ways of making the same request. Through data modeling they have learned to interpret speech and determine the action or information that the user is requesting.
Personalization engines use the same machine learning techniques to power different outcomes. The data they analyze is not human speech but the actions of customers. Every single action that a customer takes – or doesn’t take – is fed into the model for the recommendation engine. The events that are commonly recorded include search terms, time spent on a specific page, which recommendations have been shown to that user, were clicked on or not, were added to cart, and what was eventually purchased. This data is not limited to digital interactions either. With a fully integrated customer experience, the model will include purchases made at brick-and-mortar stores as well.
Buy It Again is an exemplary feature of machine learning. The machine can analyze how often a specific purchase is made by most people (for example razors or paper towels) and offer recommendations only at the time intervals prescribed by the data analyzed. This also keys into gift shopping versus personal shopping. It is one of the greatest difficulties of customers to buy a gift for someone else and be plagued for weeks with ads for that particular item. It can also be leveraged to stop showing an item once one has been bought. Once you buy a lawnmower, you really don’t need another for quite some time. Machine learning solves for these common failures.
Models trained from finite data always include a certain measure of uncertainty. To test this uncertainty, the machine allows for A/B testing as a way to raise its level of certainty. During A/B testing, hypotheses pertaining to similar contexts or segments can be tested against each other to prove which is more effective.
In conclusion, though machine learning is often thrown around as a high-level concept and mysterious technology, the steps it goes through are understandable by any layperson and an understanding of the process can lead merchandizers to make better marketing decisions.