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.
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