Retail, as an industry, is known to generate impressive amounts of data that was, until recently, not used to the full. There are market insights and consumer sentiment data, there’s data on inventory, logistics of retail, merchandising – the industry is sitting on a treasure that only Machine Learning has recently helped truly appropriate, and the use cases of ML in retail are piling up even now. In this article, we explore the most promising applications of AI-driven technology in this realm, from inventory management to CX.
Understanding Machine Learning in Retail
At its core, Machine Learning is about training algorithms to learn from data (as opposed to mechanically processing spreadsheets) and improve their performance over time without explicit programming. In this sense, an ML algorithm is like a trained employee who never gets sore eyes from detecting patterns in numbers. Up until recently, this was something to dream of unless you were a behemoth global corporation. However, since the late 2010s, advancements in cloud computing, big data analytics, and AI have made ML tools more accessible and affordable for businesses of all sizes. At the same time, retail giants like Amazon and Walmart have paved the way for the integration of machine learning into retail operations, using it for demand forecasting, inventory management, and personalized marketing.
The proliferation of SaaS platforms and open-source frameworks has somewhat democratized access to ML, enabling businesses across the industry to harness its power for improved decision-making and operational efficiency.
ML has, by now, evolved to address complex challenges across various domains. It is used in customer behavior analysis, inventory management, pricing strategies, and supply chain optimization. Retailers leverage ML models to forecast trends, predict customer preferences, and personalize shopping experiences, driving efficiency and boosting revenues. For instance, by analyzing data from millions of transactions and customer interactions, machine learning enables businesses to make informed decisions that enhance operational performance.
Here are some of the most important general areas of ML use in retail – covering very diverse aspects.
Enhancing Customer Experiences with Personalization
The first massive field of data in retail is, of course, customer data and preferences. This is used to finally nail that ever-elusive goal, personalization. Its importance is hard to overestimate; it’s known that about 80% of customers are more likely to purchase from brands that offer personalized experiences. Over the past several years, personalization has improved noticeably, in large part thanks to ML.
Machine Learning can analyze things like browsing history, past purchases, and demographic information to not just fuel marketing campaigns but let the customer define their own pathway with the brand with recommendation engines.
For example, Amazon’s recommendation engine, powered by machine learning, is responsible for 35% of the company’s sales. By analyzing millions of customer interactions, Amazon suggests products that align with individual preferences, encouraging repeat purchases and increasing average order value. Similarly, Netflix employs ML to recommend movies and TV shows, enhancing user satisfaction and loyalty. Overall, ML-driven personalization has been shown to account for a 10-30% increase in sales.
Inventory Management and Demand Forecasting
Today’s retail is more than ever about balancing the fleeting nature of momentaneous impulses with the hard reality of what’s in stock and how many cubic meters are to be paid for. Inventory management has gained a lot in recent years thanks to AI and ML, helping prevent overstocking and stockouts. The key is its capacity to analyze historical sales data, seasonality, and market trends, and then automate restocks or alert the managers. This sort of predictive analytics helps reduce understocking situations by up to 20-50%.
For instance, Walmart employs machine learning to forecast product demand and optimize inventory levels across its stores. By leveraging ML models, the company has reduced stockouts by 16% and improved inventory turnover rates.
Dynamic Pricing Strategies
The whole point of dynamic pricing is that it needs to happen in real time to work, so having someone study documents to have their strategic suggestions approved in several days is not a good option – ML, on the other hand, can be trained to perform this task within certain set confines.
The data that goes in includes things like demand, competitor pricing, and customer behavioral insights.An example is Uber’s surge pricing model, which uses machine learning to adjust ride fares based on demand and supply conditions. Similarly, e-commerce giants like Amazon and eBay utilize dynamic pricing to optimize product prices and boost profitability, achieving a 2-5% increase in margins.
Fraud Detection and Prevention
While digital tech expands the possibilities for commerce and consumerism, it also does the same thing for fraud. Machine Learning has become quite good at fraud detection by analyzing transaction patterns and pointing out anomalous activity; the best part is that unlike a simple if… then… system, ML models learn over time, adapting to newer fraud tactics, while detecting suspicious activities 40-70% faster.
For example, PayPal employs machine learning to detect fraudulent transactions. By analyzing millions of transactions daily, the company’s ML algorithms have reduced fraud losses to less than 0.32% of revenue.
Supply Chain Optimization
ML is also used successfully enough on the side of the supply chain – predicting delays, identifying bottlenecks, suggesting alternative vendors, and so on. This not only helps reduce logistics costs by 10-15%, but allows to improve forecasting accuracy by ⅓-½ in many cases.
For instance, Zara uses machine learning for its own supply chain management. By analyzing sales data and customer feedback, the company adjusts production schedules and inventory allocations in real time, minimizing waste and improving responsiveness.
In-Store Use Cases of Machine Learning
While e-commerce has embraced machine learning extensively, brick-and-mortar stores are also leveraging ML to enhance operations and customer experiences in transformative ways. These applications are reshaping how physical stores operate and engage with their customers:
Foot Traffic Analysis
Over the past decade, advancements in ML technology have revolutionized foot traffic analysis, providing retailers with actionable insights that were once impossible to gather. By utilizing ML-powered cameras and sensors, stores can monitor customer movements in real time, identifying high-traffic areas and underutilized spaces. This data allows businesses to strategically position high-demand products, optimize aisle layouts, and improve overall store navigation. For example, major retailers like Target have employed ML-based systems to redesign store layouts, leading to measurable increases in sales and customer satisfaction. Additionally, these systems can adapt dynamically, analyzing seasonal patterns or promotional events to suggest temporary adjustments, ensuring that stores remain responsive to changing shopper behaviors. This leads to increased sales and better customer experiences.
Checkout Automation
Over the last decade, checkout automation has evolved significantly, with machine learning playing a central role in transforming the traditional shopping experience. Amazon Go stores are a prime example, leveraging advanced machine learning algorithms and computer vision technology to enable a completely cashier-less shopping model. Customers simply scan their phones upon entry, pick up items, and walk out, with purchases automatically tracked and billed to their accounts. Beyond Amazon Go, other retailers have begun experimenting with similar systems, integrating mobile apps and ML to streamline checkout processes. For instance, companies like Walmart and Kroger are piloting smart cart technologies that use sensors and machine learning to detect and tally items as they are added, offering customers a frictionless and efficient alternative to traditional checkouts. Customers can pick up items and leave the store, with purchases automatically billed to their accounts.
Employee Scheduling
Additionally, employee scheduling has benefited significantly from machine learning. Algorithms analyze historical store traffic data and seasonal trends to predict peak and off-peak hours accurately. This ensures that stores are adequately staffed during busy times while minimizing labor costs when traffic is low. Such precision reduces employee burnout and enhances customer satisfaction by providing consistent service levels.
Challenges and Ethical Considerations of ML
Despite its benefits, machine learning in retail poses challenges, including:
- Data Privacy: Retailers must ensure compliance with regulations like GDPR and CCPA while collecting and processing customer data.
- Bias in Algorithms: ML models can inadvertently perpetuate biases present in training data, leading to unfair outcomes.
- Integration Costs: Implementing ML solutions requires significant investment in technology and talent, which may be prohibitive for small businesses.
Retailers must address these challenges to harness the full potential of machine learning responsibly and effectively.
How to Implement Machine Learning in Retail
While off-the-shelf ML solutions offer a starting point, custom development provides tailored insights and functionalities. Custom ML solutions can:
- Align with specific business goals and operational workflows.
- Integrate seamlessly with existing systems and data sources.
- Offer competitive advantages through proprietary algorithms and models.
For example, a mid-sized fashion retailer might develop a custom ML model to predict trends based on social media data, enabling the company to launch new collections ahead of competitors.
While it may seem complex, breaking the process down into manageable steps can help retailers successfully integrate this technology.
- Identify Key Business Problems. The first step in implementing ML is identifying the areas in your retail operations where machine learning can make the most impact.
- Data Collection and Preparation. Machine learning relies on data, so it’s crucial to collect high-quality, relevant data from multiple sources. For retail, this could include transaction history, customer demographics, website interactions, and social media feedback. Once collected, this data needs to be cleaned and preprocessed to ensure it’s accurate and structured properly for machine learning models.
- Selecting the appropriate machine learning algorithms is critical. There may be supervised learning vs. unsupervised learning approaches, the former being used for tasks like demand forecasting or sales prediction, the latter, for clustering products, customers, or pattern identification. Another variant is what’s called reinforcement learning, which can be applied for dynamic pricing, making the algorithm learn from real-time feedback.
- Once the data is prepared and algorithms chosen, the next step is to train the model. This involves feeding the data into the algorithm and allowing it to learn patterns. After training, the model must be evaluated using validation techniques to ensure it performs well in real-world scenarios. Retailers may need to experiment with multiple models and fine-tune parameters for optimal results.
- After achieving satisfactory model performance, it’s time to deploy the model into the retail environment. This could mean integrating it with point-of-sale systems, websites, or inventory management tools. It’s important to monitor the model’s performance regularly to ensure it adapts to changing consumer behaviors or market conditions.
- Iterate and Improve. Machine learning models are not static; they need continuous refinement. As new data comes in, the model should be retrained to improve accuracy and adaptability. Retailers should establish a feedback loop to ensure the model evolves with changing trends and customer preferences.
Conclusion
As the retail landscape continues to evolve, businesses that invest in machine learning and address its challenges are now better positioned to meet customer expectations, reduce costs, and drive sustainable growth than 10 years ago. By leveraging statistics, real-world examples, and tailored solutions, retailers can fully embrace the potential of machine learning and secure their position in the future of commerce. Lionwood has experience in implementing and training ML models that deal with both the supply chain and the demand prediction side of things, and is ready to offer its expertise for developing your company’s projects.
