Machine Learning in Retail: Essentials and Ten Key Applications
In recent years, between lockdowns, supply chain disruptions and energy crises, retailers must have felt like dinosaurs trying to avoid a rain of asteroids and avoid extinction.
But unlike those giant prehistoric reptiles, the retail industry can rely on a range of technological innovations to better cope with these challenges in hard times.
And one of the most influential tools is undoubtedly artificial intelligence, including its powerful sub-branch machine learning (ML). 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 used to process data and find recurring patterns and anomalies among variables , and learn independently how this relationship affects or determines industry trends, phenomena and business scenarios.
The self-learning and situational understanding potential of machine learning systems can be used in the retail industry to:
- Identify the underlying dynamics driving retail. For example, machine learning-based data analysis systems are widely adopted in marketing to personalize the shopping experience through recommendation engines and targeted advertising based on customer data, and to predict product demand or other market trends to optimize inventory management. , logistics and pricing strategies.
- Advance cognitive technologies related to artificial intelligence, such as computer vision and natural language processing (NLP), which recognize and learn from visual and linguistic patterns, respectively, to imitate human vision and communication. Retailers often use these tools to collect data from textual and visual sources, power 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 practical capabilities of the above machine learning algorithms? Below are the most relevant machine learning use cases in typical retail scenarios.
1. Targeted Advertising
Although primarily used in e-commerce, targeted marketing is a powerful tool for directing 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 their purchase and browsing history, age, gender, interests, region, etc.) and targeting them with fully personalized advertising and promotions .
2. Contextual Shopping
A different, more interactive solution is contextual shopping, which grabs users’ attention and directs them to your e-commerce platform . The marketing tool uses machine learning and computer vision to identify and point out products shown in videos and images on social media, while providing a "shortcut" 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 put products in front of your customers that they might actually need.
To provide tailored recommendations, these systems can either employ a content-based filtering approach, which recommends items with similar characteristics to those purchased in the past, or collaborative filtering, which means recommending items with similar purchasing patterns. , personal characteristics and interests of other customers.
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 demand and supply, 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 provide customers with 24/7 user support (including information on available products and shipping options) while sending reminders, coupons and personalized recommendations to increase sales.
6. Supply Chain Management
Product replenishment and other inventory management operations must not be left to chance. To better match commodity supply and demand, optimize warehouse space utilization, and avoid food spoilage, it’s worth relying on the analytical and predictive capabilities of machine learning algorithms. This means taking into account multiple variables, such as price fluctuations or seasonality-based buying patterns, to predict future sales trends and plan appropriate replenishment plans accordingly.
7. Delivery Optimization
Another aspect of logistics that can be enhanced through machine learning is the delivery of goods. Traffic and weather data collected by a network of IoT sensors and cameras powers a machine learning-driven system that can easily calculate the fastest delivery routes. And, by processing user data, they can recommend appropriate delivery methods to better meet customer needs.
The exemplar of this approach should be the machine learning-based anticipatory shipping technology implemented by Amazon, which allows predicting future deliveries based on customer purchasing patterns and moving products to the warehouse closest to the customer, thus The ability to ship faster and cheaper when customers actually order.
8. Self-driving cars
This implementation of machine learning and computer vision for merchandise delivery is far from perfected and implemented at scale, however, companies like Amazon and Kroger are working on Bet on this technology, we may soon be relying on self-driving cars to speed up the delivery of goods.
9. Video Surveillance
Computer vision systems powered by machine learning can detect 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 is prone to high numbers of false positives. Machine learning systems, on the other hand, can identify more subtle patterns of behavior and alert managers when something suspicious occurs.
10. Fraud Detection
For online retailers and e-commerce platforms, thieves are more likely to steal money from credit cards than goods off the shelves. 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.
Using Machine Learning to Overcome Modern Challenges
Artificial intelligence, machine learning and cognitive technologies are proven operational in increasing profits and optimizing costs, personalizing customer experiences, and improving logistics and inventory management efficiency, and ensuring a safe retail environment.
In fact, Fortune Business Insight’s 2020 report highlights that the global retail AI market is expected to reach $31.18 billion by 2028, with machine learning being a core part of it.
From a retail perspective, this will make machine learning a beacon, allowing it to find the right course and dock in a safe port after more than two years of storms. (Compiled by: iothome)
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