Retailers are facing increasing profit margin pressure, new competitors, and supply chain challenges with tariffs, creating an uncertain landscape. Predictive retail can help businesses navigate these challenges by enabling them to make more informed decisions regarding inventory, pricing, and marketing.  

Recent improvements in artificial intelligence (AI) and machine learning have strengthened predictive retail, creating more accurate and faster insights. With these advancements, retailers are better prepared to face today’s challenges.  

AI Applications in Predictive Retail for Shopper Behavior 

There are a few ways that AI is being leveraged for predictive retail to help businesses better predict shopper behavior:  

Churn Prediction 

Churn prediction is the process of identifying which customers are likely to stop using a product or service, allowing businesses to intervene and prevent customer churn. This is important for retaining customers because keeping existing customers is more cost-effective than acquiring new ones.  

AI can help with churn prediction by analyzing factors like customer demographics, transactions, pricing, economic factors, and customer behavior. Natural language processing (NLP) can also be used to analyze customer interactions and reviews to predict which customers are most likely to churn.  

These AI insights can be used to design strategies to retain customers, such as loyalty offers and re-engagement campaigns. This allows for more targeted marketing and prevents companies from having to make unnecessary discounts for customers who aren’t at high risk of churn.  

Basket Analysis 

Basket analysis is a technique that collects and examines customer purchase data to uncover associations between products. This analysis shows which items are frequently bought together and can boost sales as retailers suggest frequently bought items to consumers.  

Advancements in underlying technologies have enabled AI to improve basket analysis, allowing retailers to analyze and process large datasets faster by finding patterns that manual processes may have missed.   

The insights gathered from AI can be used to offer personalized promotions and discounts, increasing customer satisfaction. Additionally, the personalized suggestions from AI not only encourage more purchases but also create a specialized shopping experience that can increase customer satisfaction. By utilizing AI, retailers can leverage a data-driven approach to drive long-term brand loyalty.  

Demand Forecasting 

Demand forecasting analyzes patterns in shopping and sales  to predict customer sales demand, allowing retailers to make sure inventory matches the demand. Demand forecasting can be critical for maintaining proper inventory levels while avoiding waste and out-of-stocks.  

AI for demand forecasting can incorporate multiple factors, such as location, weather, and historical data, to make more accurate predictions. It can offer real-time and short-term forecasts that are continuously updated.  

By increasing forecasting accuracy, retailers will experience less excess inventory waste and fewer out-of-stocks. More demand visibility also enables faster adjustments to production and delivery schedules, allowing retailers to more effectively place orders with suppliers and better align their orders to customer demand. 

Simulations 

AI can also be utilized across various areas to create simulations. These simulations generate realistic scenarios that enable organizations to test potential outcomes and inform their decision-making processes.  

With growing economic uncertainty and tariffs, AI simulations can help optimize supply chains. One example of this is digital twins —  virtual 3D replicas that mimic a real-world object or system.   

Digital twins offer a variety of uses for improving a retailer’s supply chain. For example, retailers can create a digital twin of their warehouse to test new technology, like a new fulfillment center robot, and assess the potential impact on productivity and efficiency before buying the costly product. Retailers can also create a digital twin that encompasses an entire supply chain to run and test different scenarios to help identify risks and increase optimization.  

These simulations can help organizations prevent potential problems and ensure smoother operations of the supply chain, in turn ensuring the company meets inventory needs.  

Key Considerations 

AI offers many benefits for enhancing the work of predictive retail. However, there are some things to consider when utilizing AI.  

Data Quality & Model Maintenance 

When using AI, data quality is extremely important for efficient and accurate results. Higher-quality data produces higher-quality AI insights. AI also comes with the potential for bias or hallucinations. AI models need to be properly trained to avoid providing false information. These models can also drift and — even when trained correctly initially — over time, can start to provide false information. AI models need to be actively trained and maintained to continue to provide useful insights.  

AI Regulations & Data Privacy 

Companies also need to consider AI regulations before implementing the technology. Explainable AI refers to the emerging legislation that is aimed at increasing transparency and accountability surrounding AI usage. Explainable AI legislation will require companies utilizing predictive AI to explain the reasoning behind a prediction. The EU AI Act, for example, is just one of many new pieces of legislation being put in place to regulate AI usage. As organizations prepare to implement predictive AI, regulatory compliance is an important factor for them to consider.  

Data privacy is also an area of concern with the use of AI. Creating more accurate insights with AI requires the collection and storage of massive amounts of data. Retailers using these tools need to adhere to privacy regulations to secure information and reduce the risk of data breaches.  

Turning Insights into Action 

AI can provide many useful insights, but changing operational processes to take advantage of these outcomes can be tricky. For example, while AI can predict churn, it’s up to the marketing team to decide the best way to respond and retain the customer. Even if the action is automated, a human must make the strategic decision on what action to take. 

As companies prepare to implement AI, they need to ensure they have the right team in place to make the most of the insights it provides. Ensuring AI literacy is a crucial step towards successful AI adoption. Companies need to consider restructuring processes or training to guarantee that they are leveraging AI to its full capabilities.  

Looking Forward 

AI continues to change the retail landscape. Predictive retail is just one area where AI can be leveraged to anticipate customer needs and optimize inventory. Companies must invest early in improving data quality and privacy to lay the foundation for AI adoption. Our team at Clarkston can help your organization find and implement the right AI tools to reach your goals. Click here to learn more.  

Contributions from Natalie Pollock