With the evolution of technology, consumer behavior also continues to evolve. Naturally, to stay ahead of the competitive curve the retailers need to make more rigorous use of the customer data. As the data volume is increasing at a rapid pace Big Data analytics are being utilized by retailers to use the most relevant customer insights. But over time, even gathering large volumes of multifaceted user data for analytics didn’t prove to be much useful.
This is where new technologies like Artificial Intelligence (AI) and Machine Learning (ML) cane with bigger promises. Both AI and ML by focusing on learning key aspects of customer behavior and individual preferences can better predict purchases and interactions that earlier analytics could not do. Machine Learning particularly came as the most promising technology for retailers across the niches and sizes. No wonder, companies opting for CMS like Magento for e-commerce development, readily opt for AI and Machine Learning plug-ins just like regular analytics.
What is Machine Learning?
Don’t confuse Machine Learning technology with Artificial Intelligence (AI) technology. While AI is more about the ability of the computer to take better and faster decisions imitating human logic, Machine Learning is more about the ability of computers to learn about users and customers by analyzing their interactions and behavior.
Machine learning refers to the way a computer learns the human logic, behavioral patterns and preferences from their interactions with the computer and various computing software applications. Machine Learning technology helps a computing machine to update itself continuously by learning about the users through interactions, computing behavior, and individual choices.
Machine Learning quickly became popular as a technology for hardware improvements for handling volumes of complex data and for running complicated algorithms. As Machine Learning is increasingly being used in sophisticated algorithms for understanding customers, it has become tremendously popular among the retailers for targeted marketing and also for delivering customer-centric shopping experience.
If you want to get an easy and widely popular example of Machine Learning, take the instance of Google search engine. It continuously adjusts to your search behavior and accordingly delivers you search results that are more likely to have traction. Just as it happens with any machine learning algorithms, the more the search engine learns about the particular user, better user-specific search results it can deliver.
Machine Learning in Retail
While there is a lot of talk about the use of Machine Learning in retail, we are still clueless about how it actually works. Let’s see the role of Machine Learning in product price optimization for the retailer.
Gathering Data for Training the Machine: By gathering data corresponding to the choice of products and their respective price range, the pricing model is pre-trained.
Using an Algorithm: Now the retailer also needs to use an algorithm for analyzing the features of the products mentioned in the training data and come with the precise prediction about the right price of the product.
Training the Model for Pricing Optimisation: Now the Pricing optimization model of the algorithm checks the predictions about the right price for the customer against the real product prices.
Changing the Prediction Mechanism: The retail algorithm equipped with the Machine Learning technology continues to change and adjust the prediction mechanism over time.
Pricing Optimisation for the Model: As soon as the pre-training is completed, predictions on a variety of selling prices measured against product features and quality attributes come to surface.
Feedback Loop: Whenever a product is sold the price of the product in that respective sale is considered as a fresh input in the feedback loop for training the pricing model to come with more accurate prices.
New Data Inputs: To utilize the pricing optimization model to the advantage of product marketing on a continuous basis, new product data is always incorporated for the model to refine the price predictions further.
Key Benefits of Machine Learning in Retail
Machine Learning has opened a new vista of marketing and business process optimization in the retail sector. To understand the principal advantages of Machine Learning for retail, let us have a look at the various contexts this technology is used for retail.
- To offer retail customer truly personalized product recommendations.
- Offering a better price to boost sales by real-time and dynamic adjustment of prices.
- Making better inventory planning and ensuring better maintenance with right predictions.
- Offering faster and more efficient delivery based upon past customer data and customer behavior.
- Better prediction of sales and customer service based upon earlier customer behavior data.
- Perfecting app user experience and optimizing website content based upon in-app and on-web customer behavior and interactions.
- Better segmentation of customers on the basis of previous customer behavior.
Learning from Top Retail Brands about Implementation of Machine Learning
This US retail giant headquartered in Bentonville, Arkansas needs no introduction. Walmart makes use of machine learning technology to map better delivery routes, offer faster checkout and make better recommendations and product matches based on individual web browsing and purchase history. Machine Learning is also used by Walmart to create and show specific advertisements to the target users.
Amazon is already a household name as the undisputed retail leader in the world. Amazon having access to the largest volume of retail customer data applies machine learning to get precise insights from that data for various purposes. For example, it applies Machine Learning on customer data to make an accurate forecast for many products, detect fraudulent activities and offer customer-specific product recommendations.
Target is another renowned “one-stop shop” selling almost everything we need in daily lives ranging from garments to groceries. Target started utilizing Machine Learning to analyze its customer data to identify specific customer conditions and to make suggestions accordingly. For example, the Machine Learning program started to identify women with maximum chances of being pregnant. After identifying such condition it is easier for the program to detect key buying behavior and preferences. Based upon such insights the retail shop could make suggestions or provide relevant merchandise for the targeted users.
4.Alibaba: Making Big Data Accessible for Smaller Retailers
Alibaba, the Chinese giant in B2B retail e-commerce actually serves millions of small retailers. After utilizing big data analytics for some years, the retail giant started using Machine Learning to analyze in-shop customer behavior and find the most popular price points for the products purchased by its retailer customers.
5.The North Face
Headquartered in Alameda, California, North Face is a world-famous adventure gear and outdoor wear brand that recently tied up with the IBM Watson to take advantage of the Machine Learning technology to help customers to the product they need. The natural language processing (NLP) capability of IBM Watson and the interactive interface gave the brand’s presence a human touch while making use of the cutting-edge Machine Learning technology.
- AMERICAN EAGLE OUTFITTERS
Based in Pittsburgh, Pennsylvania, American Eagle is a well-known garment brand that recently made a partnership with Slyce, a promising image recognition startup brand using Machine Learning and image search technology to browse and find products in a store. Slyce offers a visual search engine through its mobile app that allows their customers to search for specific garment through images taken by their handheld device camera.
From the above-mentioned examples, it is quite clear that an increasing number of business brands are now relying on Machine Learning technology for pushing business conversion, growth and customer engagement. In the time to come, we can see more innovative ways to use Machine Learning for specific business contexts.