Key application areas of machine learning in retail
Between lockdowns, curfews, supply chain disruptions and energy crunches, retailers must have been feeling panicked in recent years. But, fortunately, the retail industry can rely on a full range of technological innovations to better cope with the challenges of these difficult times.
One of the most impactful tools of these technologies is certainly artificial intelligence, including its powerful subset - machine learning (ML). Below, let’s briefly introduce the nature of this technology and explore the key use cases of machine learning in retail.
The role of machine learning in retail
Machine learning in retail relies on self-improving computer algorithms that are created to process data and discover relationships among variables. Repeating patterns and anomalies among relationships, and autonomously learn how these relationships influence or determine industry trends, phenomena, and business scenarios.
The self-learning and situational understanding potential of machine learning systems can be used in retail to:
- Identify the underlying dynamics driving retail. For example, data analysis systems based on machine learning are widely used in the marketing field to personalize the shopping experience through recommendation engines and targeted advertising based on customer data. They can also predict product demand or other market trends to optimize inventory management, logistics and pricing. Strategy.
- Promote artificial intelligence-related cognitive technologies, such as computer vision and natural language processing (NLP), which recognize and learn from visual and language patterns, respectively, to imitate human vision and communication. Retailers often use these tools to collect data from textual and visual sources, support interactive solutions such as chatbots and contextual shopping, or for video surveillance.
10 Machine Learning Use Cases Redefining Retail
How can retailers benefit from the power of the above machine learning algorithms? Here are some of the most relevant in typical retail scenarios machine learning use cases.
1. Targeted Advertising
Although primarily used in e-commerce, targeted marketing represents a powerful tool that can Direct potential customers to online platforms and traditional stores. This involves segmenting users based on a range of behavioral, psychographic, demographic and geographical parameters (such as purchase and browsing history, age, gender, interests, region, etc.) and targeting them with fully personalized ads and promotions.
2. Situational Shopping
A different, more interactive solution can capture the user’s attention and keep them Boot into your own e-commerce platform, which is contextual shopping. This marketing tool uses machine learning and computer vision to identify and point out items shown in videos and images on social media, while providing "shortcuts" to relevant product pages in online stores.
3. Recommendation engine
Once users log into an online platform, they may get lost in the massive amount of products. Recommendation engines are powerful tools designed to direct customers to products they may actually need.
To provide tailored recommendations, these systems can employ content-based filtering methods, which recommend items with similar characteristics to those purchased in the past, or collaborative filtering, which means The author recommends items ordered by other customers with similar purchasing patterns, personal characteristics, and interests.
4. Dynamic Pricing
Product recommendations and advertising aren’t the only things that change dynamically thanks to machine learning. Today, most online stores and e-commerce platforms constantly adjust prices based on factors such as fluctuations in product supply and demand, competitors' promotion and pricing strategies, broader sales trends, and more.
5. Chatbots
Chatbots and virtual assistants are highly interactive tools powered by machine learning and NLP that can serve customers Provide 24/7 user support, including information on available products and shipping options, while sending reminders, coupons, and personalized recommendations to drive sales.
6. Supply Chain Management
Product replenishment and other inventory management operations should never be left to chance. In order to better match product supply and demand, optimize warehouse space utilization, and avoid food spoilage, it is worth relying on the analytical and predictive capabilities of machine learning algorithms. This means taking into account multiple variables, such as price fluctuations or buying patterns based on seasonality, predicting future sales trends, and therefore planning an appropriate replenishment schedule.
7. Delivery Optimization
Another aspect of logistics that can be enhanced through machine learning is product delivery. Systems powered by machine learning can easily calculate the fastest delivery routes, driven by traffic and weather data collected through a network of IoT sensors and cameras. Instead, by processing user data, suitable delivery methods may be recommended to better meet the customer's needs.
8. Self-driving cars
This embodiment of machine learning and computer vision for product delivery is far from perfected and large-scale. Implementation at scale. However, companies like Amazon and Kroger are investing in the technology, believing they will soon be able to rely on autonomous vehicles to speed up product distribution.
9. Video Surveillance
Machine learning-driven computer vision systems can drive vehicles and spot thieves. The main difference between these tools and traditional video surveillance solutions is that the latter are based on a rather inaccurate rules-based approach to identifying intruders, which has a high number of false positives. Machine learning systems, on the other hand, can identify more subtle patterns of behavior and alert management when something suspicious is happening.
10. Fraud Detection
For online retailers and e-commerce platforms, thieves are more likely to steal from credit cards than from shelves steal. Because machine learning algorithms are designed to identify recurring patterns, they can also pinpoint any deviations from the norm, including unusual trading frequencies or inconsistencies in account data, and flag them as suspicious for further inspection.
Overcoming Modern Challenges with Machine Learning
Artificial intelligence, machine learning and cognitive technologies have been proven to increase profits and optimize costs, personalize customer experiences, improve logistics and operational efficiencies in inventory management, as well as ensuring a safe retail environment.
In fact, Fortune Business Insights’ 2020 report highlights that the global retail artificial intelligence market is expected to reach $31.18 billion by 2028, with machine learning at its core part.
From a retail perspective, this will allow machine learning to be the beacon that can find the right course and dock in a safe port after more than two years of storms.
The above is the detailed content of Key application areas of machine learning in retail. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1386
52
15 recommended open source free image annotation tools
Mar 28, 2024 pm 01:21 PM
Image annotation is the process of associating labels or descriptive information with images to give deeper meaning and explanation to the image content. This process is critical to machine learning, which helps train vision models to more accurately identify individual elements in images. By adding annotations to images, the computer can understand the semantics and context behind the images, thereby improving the ability to understand and analyze the image content. Image annotation has a wide range of applications, covering many fields, such as computer vision, natural language processing, and graph vision models. It has a wide range of applications, such as assisting vehicles in identifying obstacles on the road, and helping in the detection and diagnosis of diseases through medical image recognition. . This article mainly recommends some better open source and free image annotation tools. 1.Makesens
This article will take you to understand SHAP: model explanation for machine learning
Jun 01, 2024 am 10:58 AM
In the fields of machine learning and data science, model interpretability has always been a focus of researchers and practitioners. With the widespread application of complex models such as deep learning and ensemble methods, understanding the model's decision-making process has become particularly important. Explainable AI|XAI helps build trust and confidence in machine learning models by increasing the transparency of the model. Improving model transparency can be achieved through methods such as the widespread use of multiple complex models, as well as the decision-making processes used to explain the models. These methods include feature importance analysis, model prediction interval estimation, local interpretability algorithms, etc. Feature importance analysis can explain the decision-making process of a model by evaluating the degree of influence of the model on the input features. Model prediction interval estimate
Identify overfitting and underfitting through learning curves
Apr 29, 2024 pm 06:50 PM
This article will introduce how to effectively identify overfitting and underfitting in machine learning models through learning curves. Underfitting and overfitting 1. Overfitting If a model is overtrained on the data so that it learns noise from it, then the model is said to be overfitting. An overfitted model learns every example so perfectly that it will misclassify an unseen/new example. For an overfitted model, we will get a perfect/near-perfect training set score and a terrible validation set/test score. Slightly modified: "Cause of overfitting: Use a complex model to solve a simple problem and extract noise from the data. Because a small data set as a training set may not represent the correct representation of all data." 2. Underfitting Heru
Transparent! An in-depth analysis of the principles of major machine learning models!
Apr 12, 2024 pm 05:55 PM
In layman’s terms, a machine learning model is a mathematical function that maps input data to a predicted output. More specifically, a machine learning model is a mathematical function that adjusts model parameters by learning from training data to minimize the error between the predicted output and the true label. There are many models in machine learning, such as logistic regression models, decision tree models, support vector machine models, etc. Each model has its applicable data types and problem types. At the same time, there are many commonalities between different models, or there is a hidden path for model evolution. Taking the connectionist perceptron as an example, by increasing the number of hidden layers of the perceptron, we can transform it into a deep neural network. If a kernel function is added to the perceptron, it can be converted into an SVM. this one
The evolution of artificial intelligence in space exploration and human settlement engineering
Apr 29, 2024 pm 03:25 PM
In the 1950s, artificial intelligence (AI) was born. That's when researchers discovered that machines could perform human-like tasks, such as thinking. Later, in the 1960s, the U.S. Department of Defense funded artificial intelligence and established laboratories for further development. Researchers are finding applications for artificial intelligence in many areas, such as space exploration and survival in extreme environments. Space exploration is the study of the universe, which covers the entire universe beyond the earth. Space is classified as an extreme environment because its conditions are different from those on Earth. To survive in space, many factors must be considered and precautions must be taken. Scientists and researchers believe that exploring space and understanding the current state of everything can help understand how the universe works and prepare for potential environmental crises
Implementing Machine Learning Algorithms in C++: Common Challenges and Solutions
Jun 03, 2024 pm 01:25 PM
Common challenges faced by machine learning algorithms in C++ include memory management, multi-threading, performance optimization, and maintainability. Solutions include using smart pointers, modern threading libraries, SIMD instructions and third-party libraries, as well as following coding style guidelines and using automation tools. Practical cases show how to use the Eigen library to implement linear regression algorithms, effectively manage memory and use high-performance matrix operations.
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
Explainable AI: Explaining complex AI/ML models
Jun 03, 2024 pm 10:08 PM
Translator | Reviewed by Li Rui | Chonglou Artificial intelligence (AI) and machine learning (ML) models are becoming increasingly complex today, and the output produced by these models is a black box – unable to be explained to stakeholders. Explainable AI (XAI) aims to solve this problem by enabling stakeholders to understand how these models work, ensuring they understand how these models actually make decisions, and ensuring transparency in AI systems, Trust and accountability to address this issue. This article explores various explainable artificial intelligence (XAI) techniques to illustrate their underlying principles. Several reasons why explainable AI is crucial Trust and transparency: For AI systems to be widely accepted and trusted, users need to understand how decisions are made
