Artificial intelligence has gotten a lot of credit for transforming the way the car and airline industries, operate. However, it gets less credit for the things it can do for retail and e-commerce businesses. If you're in either of these spaces and aren't currently incorporating AI, or its counterpart, machine learning (ML) into your company's strategies, you risk being left behind because AI is the way forward for retail.
This post uncovers the ways that retail and e-commerce providers can use AI, and machine learning specifically, to transform their businesses.
When we talk about machine learning, we're specifically addressing the ways that computers are able to learn from data and make predictions based on those learnings. When it comes to using machine learning for business, the goal around this is to use data to obtain actionable insights. At the end of the day, this is primarily what ML is used for. You collect data, observe it, and then run models to try to get insights--it’s a mixture of programming, statistics, and science.
Before you can make a prediction, you have to train and run a model. The steps can be boiled down to the following:
The prediction stage is the ultimate goal, but getting there can be complex if one tries to do it on their own without the help of machine learning automation technologies. With the right machine learning automation tool, however, it's quite easy to go from having data to getting predictions from it.
So, how exactly are companies using machine learning, particularly in retail & e-commerce?
Companies can use machine learning to tag millions of products and display them to the right users at the right time. Some companies such as classified app provider, lafofo, go one step further than basic product categorization by enabling sellers to upload pictures of the goods they want to market. Once the seller uploads those goods, the site--through the use of machine learning--recognizes the items in the pictures, places them in the right category, and prices them automatically. Outside of the initial product upload, all of these processes occur through automation.
Virtual Fitting Rooms
A Chinese retailer, named Pulsion, created a virtual fitting room where customers create virtual avatars with their physical characteristics and then are able to ‘try’ different clothes on in a short amount of time. Using a database with body type information on millions of people, Pulsion was able to create a 3D model with about 20 parameters. Other ventures like this in Europe and North America also use scanners to identify hundreds of thousands of data points in the customer's body which they use to provide an accurate virtual model of a person’s body type. They use this model to give their users the ability to choose a body type most similar to theirs so that they can try on merchandise without having to be in a physical store.
Personalized pricing occurs when a company determines a price based on variables related to not just what they perceive a product is worth, but also what they believe a particular customer will pay based on their customer profile or past behavior. Retail giant Amazon is one of the most recognized companies when it comes to dynamic pricing. Place an item in your cart, leave it alone, come back to it later, and notice that the price has either gone up or down? This happens because Amazon has determined by your online behavior that you're more likely to buy that item at the specific price they show you. Another customer, based on their behavior on specific sites, is likely to receive an entirely different price.
Other equally interesting machine learning applications in the retail space include:
As you can see, there are varied use cases for machine learning in retail and e-commerce. The possibilities are vast and the field is in the initial stage of exploration and is still trying to figure out how to apply the best techniques out there to produce the best results. Of course, everything isn't all roses. I'd like to include a word of caution here.
First, machine learning works with models and no model is 100% accurate. There is no magic. As Jeff Leek put it, the keyword in Data Science is not data: it is science! Inevitably, this means that you make progress in data science through trials, tinkering, and a lot of experimentation.
Secondly, we can’t ignore the potential negative impact of some of those use cases. Dynamic pricing provides a good example of the potential downsides to making predictions that affect the buyer experience. Say a customer purchases an item from a retailer's website and then notices that the price of that product was cheaper when they visited that retailer's site through another browser. This customer is likely to question whether they obtained a fair price for the item they purchased. This can ultimately lead the customer to feel frustration toward the retailer.
Regardless of these risks, AI and machine learning seem to be here to stay. We can be sure that more and more businesses will make use of AI and machine learning and profit from it. The future will definitely bring more examples and some companies, like MindsDB, are making an effort to speed up this process. Democratizing machine learning is our mission and we enable this by making it possible for anyone to build and use machine learning models and obtain the insights they need to propel their businesses forward. MindsDB supports all of the retail and e-commerce applications illustrated in this post. To learn more, read about our industry-specific use cases.
Amie helps lead community and sales efforts at MindsDB. She is a sales and marketing lead who has has spent much of her career working in sales and marketing capacities at both startups and mid-market companies. She grew up in Providence, RI, lives in Austin, Texas, and graduated with honors from Brown University.