Artificial Intelligence (AI) is revolutionizing the retail industry by transforming the way businesses interact with customers, how they optimize their operations and make decisions. Retailers are using AI-powered tools and applications to personalize product recommendations, manage inventory levels, provide customer service, enable virtual shopping experiences, and detect fraudulent activities.
With the proliferation of data and the increasing availability of computing power, AI is now more accessible than ever, and its impact on the retail industry is expected to continue growing.
Here explore 5 Computer Vision Applications in Retail in 2023 and show how businesses can harness its potential.
- Personalized Product Recommendations
- Inventory Management
- Visual Search and Augmented Reality
- Fraud Prevention and Detection
1. Personalized Product Recommendations
By analyzing vast amounts of customer data – such as purchase history, browsing behavior, and demographic background – algorithms can identify patterns and preferences that allow retailers to create highly personalized recommendations for customers. This level of personalization can help retailers to increase customer engagement, loyalty, and sales.
Amazon’s recommendation engine uses machine learning algorithms to suggest products based on purchase history and browsing behavior. As a result Amazon has been able to increase sales by up to 35%.
Other retailers, such as Stitch Fix, are leveraging AI to provide better personalized recommendations. The company uses machine learning algorithms to analyze customer data such as past purchases, style preferences, and feedback on previous shipments. This creates a unique style profile for each customer. The algorithms then use this data to suggest clothing and accessory options that fit each customer’s style and budget, increasing customer satisfaction and retention.
In 2020, the company reported a 10% increase in revenue per active client, indicating that customers are spending more on each shipment. A retention rate of over 80%, signals the majority of its customers continue to use the service after their initial purchase.
2. Inventory Management
Retailers can now get real-time data on inventory levels, predicting demand and optimizing supply chain operations. By leveraging machine learning algorithms and other AI technologies, retailers can analyze vast amounts of data from multiple sources, including sales data, weather patterns, social media trends, and customer behavior. This data enables retailers to make more accurate predictions about demand, optimize inventory levels, and reduce waste. Check out how Sama helped Walmart.
Kroger, one of the largest grocery chains in the US, is using AI to improve its forecasting and replenishment system. The company uses machine learning algorithms to analyze sales data, weather patterns, and other variables to predict demand for products and optimize inventory levels. The system is also able to adjust inventory levels in real-time based on changes in customer demand or supply chain disruptions. By leveraging AI, Kroger has been able to reduce waste, improve product availability, and increase efficiency. The company says its AI-powered forecasting and replenishment system has helped to reduce out-of-stock incidents by 10% and decrease overstocks by 16%.
3. Visual Search and Augmented Reality
Visual search technology allows customers to search for products using images rather than keywords. AI-powered algorithms analyze the images and identify the products, making it easier for customers to find what they are looking for. Augmented reality applications allow customers to visualize products in their own space before making a purchase.
AI algorithms can help to improve the accuracy and realism of these applications, providing customers with a more engaging and immersive shopping experience which can lead to increased sales and customer loyalty.
ASOS, a fashion retailer, uses AI-powered visual search technology to allow customers to take a photo or upload an image of a clothing item they like and receive suggestions for similar items available on their website. The app also features an augmented reality function that allows customers to visualize how a garment will look on them. The app’s AR feature uses AI algorithms to detect the customer’s body measurements and suggest the appropriate size for the garment.
ASOS says this has resulted in increased engagement and sales. In 2018, the company reported that the visual search feature had a 85% click-through rate, while the AR feature had a 20% higher conversion rate than the regular product page.
Retailers are using chatbots to handle routine customer inquiries, such as checking order status or product availability, which frees up customer service agents to handle more complex issues. By automating these tasks, retailers are able to reduce their customer service costs.
Additionally, chatbots can provide 24/7 customer support, which allows retailers to offer round-the-clock service without having to hire additional staff. H&M reported that its chatbot was able to resolve 70% of customer inquiries without the need for human intervention, which helped to reduce its customer service costs.
5. Fraud Prevention and Detection
Retailers face the growing threat of fraudulent activities, such as identity theft, credit card fraud, and account takeover. AI-powered fraud detection systems are able to analyze vast amounts of data in real-time to identify and flag suspicious transactions. These systems use machine learning algorithms to detect patterns and anomalies in transaction data and can quickly adapt to new forms of fraud.
PayPal uses AI-powered fraud detection systems to monitor its transactions. The system analyzes over 200 million transactions per day and is able to detect and prevent fraud with a high level of accuracy. By leveraging AI technology, retailers can protect themselves and their customers, which can improve trust and loyalty.
Other retailers, like Walmart and Best Buy, are using computer vision technology to detect fraudulent returns. AI-powered systems analyze video footage of customers making returns to identify potential cases of fraud. The systems can detect patterns and anomalies such as returning a large number of high-value items without a receipt, or returning items that are different from what was purchased. The AI-powered systems flag these returns for further investigation and prevent them being processed. By using AI, retailers are able to reduce losses due to return fraud and improve the overall profitability.
The importance of high quality data
High quality data annotations play a crucial role in leveraging AI effectively in retail and e-commerce applications. To create accurate personalized or inventory management recommendations, product data must be accurately labeled with product attributes – such as color, style, and size – to ensure that the algorithm can correctly match customer preferences with available products. Algorithms also need access to sales data and external factors such as holidays or seasonal trends that could impact demand.
Inaccurate or missing data annotations can also lead to biased AI algorithms that fail to accurately identify patterns. This is especially important in fraud detection applications as it can result in false positives, false negatives or biased results. Additionally, if a fraud detection system is trained on data that does not accurately represent the range of fraudulent activities that may occur, it may not be able to detect new or emerging types of fraud.