This article originally appeared on Nisum.com and was re-published with permission from the author
The retail industry is experiencing exponential growth, generating vast amounts of data. To handle this data flux efficiently, artificial intelligence (AI) and machine learning (ML) applications are proving invaluable. By harnessing AI and ML capabilities, retailers can drive rapid growth, streamline internal processes, and outpace their competition. In this article, we explore the five common applications of machine learning in retail and how they can revolutionize the industry.
Retailers are embracing the power of machine learning and AI technology to enhance customer experiences through personalized interactions, utilizing valuable customer data to drive business decisions that yield tangible results. By automating pattern identification and predictions in vast datasets, machine learning optimizes system performance. Its extensive applications in the retail industry range from managing inventory, orders, transportation, and delivery to ensuring the availability of goods and services. Discover below some of the key applications and techniques employed in machine learning for retail.
There are many applications and advantages of machine learning in the retail sector. Some of these applications include:
- Customer experience personalization
- Fraud detection
- Inventory management improvement and supply chain optimization
- Customer retention
- Markdown selection
Customer Experience Personalization
Machine learning can personalize the customer experience by analyzing user’s past purchases, browsing history, and consumer behavior. This technology can provide product recommendations of similar products of interest to the customer, creating a customized product promotion.
A chatbot is another machine learning application that can enhance the customer experience. This virtual assistant can be programmed to adapt to customer behavior and improve the way businesses service customers by helping consumers find information, solve problems, and make purchases, resulting in improved customer service and a seamless consumer experience.
Fraud Detection
Pattern recognition can detect and prevent fraud by reviewing historical sales and analyzing customers’ spending patterns. These anomaly detection techniques can help to identify fraudulent transactions and prevent them from happening.
Inventory Management Improvement and Supply Chain Optimization
Machine learning can improve inventory management by analyzing sales data and market trends to create a demand forecast and predict consumer demand. This can help identify products selling well and ensure the correct inventory is in stock.
Predictive analytics can improve the supply chain by predicting future demand for products. This helps ensure that products are available in stores when customers need them.
Customer Retention
Machine learning uses data mining which can improve customer retention by analyzing customer behavior. By analyzing customers’ online behaviors, businesses can tailor their digital marketing efforts to suit their customers’ needs and identify customers who are at risk of leaving for a competitor.
Markdown Selection
Machine learning can help with freshness and markdown detection and identify an item’s freshness based on its price, purchase date, and other factors. Using this information, retailers can determine how to create effective pricing strategies and display products in a way that will maximize sales.
ML Usage in the Retail Industry
In the ever-evolving retail landscape, machine learning is revolutionizing how businesses operate. Retail giants like Amazon and Walmart are at the forefront of this transformation, utilizing machine learning to gain invaluable insights into customer behavior and preferences. By harnessing the power of AI-driven algorithms, businesses can tailor their products, services, and marketing efforts to deliver exceptional customer experiences.
Machine learning can answer questions such as:
- Which products are commonly purchased together?
- What do customers with similar demographics tend to prefer?
- After a customer selects an item in their shopping basket, what links does the customer navigate to next?
Machine learning technology has become the industry standard for retail giants like Amazon and Walmart, empowering them to stay ahead of the competition. By uncovering customer patterns, enhancing engagement, tailoring marketing strategies, and streamlining operations, businesses of all sizes can harness the potential of machine learning to thrive in the dynamic retail landscape. Embrace this transformative technology and unlock the path to retail success.
How Nisum Can Help
Machine learning is a key technology to address the voluminous data processing business challenges in retail and wholesale. Today, businesses need real-time data processing for agile marketing decisions and machine learning. We’re applying cutting-edge artificial intelligence and machine learning to solve real-world business challenges to improve business outcomes significantly. We can provide the insights your company needs to achieve a competitive advantage. Using machine learning, our Forecasting and Continuous Estimation Accelerator can extract real-time and actionable data to increase accuracy and decision-making. These insights can be used with our personalized recommendation system to create a highly customized, immersive shopping experience for your customers, increasing sales, engagement, retention, and customer lifetime value (CLV). Contact us today to get started.
Disclosure: This article mentions a client of an Espacio portfolio company.
