3 Predictive Analytics Models for Retailers
Two dramatic shifts in just a few years — COVID’s acceleration of ecommerce and the subsequent surge in demand for brick-and-mortar stores — have proved how critical it is for businesses to make accurate predictions and timely business decisions. By doing so, they are able to respond to market changes more quickly.
However, this is just one side of the coin. There is also the perpetual challenge around balancing operations and costs around managing inventory, raw materials, pricing and more. This, coupled with a desire to drive efficiency, reduce costs, and meet ever-evolving customer demands, has driven businesses and brands to invest heavily in software and technology that facilitates predictive analytics and decision-making.
But how do brands understand the predictive analytics models available to them and determine what they need to improve their business?
Types of predictive analytics models
There are several types of predictive analytics models available for virtually any industry. Within retail, however, three types are most commonly used – Clustering Models, Propensity Models, and Collaborative Filtering.
- Clustering models
The clustering model, as the name suggests, is designed for customer segmentation. Using this model, an algorithm analyzes various aspects of a customer and then clusters them based on your requirements. The clusters themselves are divided into three subcategories — behavioral clustering, product clustering, and brand-based clustering. Using data derived entirely from the browsing and shopping histories of each customer, these clusters are segmented based on more variables than a human could possibly analyze manually.
- Propensity models
When people think of predictive analytics, they probably think of propensity models. This is the model you turn to in order to predict customers’ future behaviors. This includes determining future purchases, how long you are probably able to retain them as customers, how likely they are to accept an offer, how much they are likely to spend, and the probability of them reading emails you send as well as how likely they are to unsubscribe. Using this model, you can even tell whether a customer is ready to buy, so you can reach out at the right time. You can also use this form of analytics to identify high-value customers whom you are likely to lose and take action to retain them, reducing risk factors associated with business operations.
- Collaborative filters
Collaborative filters are a predictive analytics technology that provides customers with recommendations. While found more commonly on e-commerce websites, they are increasingly making their way into brick-and-mortar stores. While these filters are primarily used for cross-selling and up-selling products, they are also commonly used to provide next-sell recommendations.
Benefits of incorporating predictive analytics in decision making
In addition to enhancing business intelligence, predictive analytics and decision-making can offer your brand and business numerous advantages over your competitors. The following are some of the most prominent benefits:
- Forecast product demand
Few things are more effective than forecasting when it comes to running your retail business smoothly and keeping costs low. It is also the most obvious benefit of integrating predictive analytics into your business operations. These systems anticipate local demand based on price, advertising, sales, promotions, and economic factors. By doing this, you can determine how many units of your product, at the SKU level, should be stocked at each location. This will not only help keep inventory numbers in check and reduce safety stock but also improve consumer engagement and satisfaction.
- Smoother supply chain management
Demand forecasting is only one component of an effective supply chain management strategy. By integrating predictive analytics and decision-making into your supply chain management system, you can manage all aspects of the system more effectively, including planning, forecasting, sourcing, stock, logistics, staffing, fulfillment, delivery, and returns.
- Predict trends and anticipate sudden changes
Maintaining a profitable business requires informed decisions about what products to carry and when, as well as investing assets and cash flow appropriately. Predictive analytics can also be helpful here. Analyzing historical sales data and setting trigger points can help predict when certain product categories are likely to grow. Furthermore, it can anticipate and address sudden event-driven fluctuations, giving you the time you need to protect your business.
- Enhance marketing efforts
Besides anticipating trends, predictive analytics can also assist in planning promotions, pricing, end caps and more. Using consumer behavior and demographic changes, it can determine if the current mix of products will entice consumers to make a purchase under the current economic circumstances. It can also be used to implement dynamic pricing, applying discounts and price increases as needed to potentially improve margins.
- Improved personalization
Whether you run a brick-and-mortar store or an e-commerce store, predictive analytics can play a critical role in helping provide a seamless and personalized shopping experience to your customers. The system analyzes a user’s previous behavior, purchase history, and browsing history in real-time. Using this information, the system can align the store’s offerings with customer expectations as well as generate recommendations, offers, invitations to in-store events and more.
- Fraud management
It’s not uncommon for e-commerce businesses to receive fraudulent online payments. By analyzing customer buying patterns, payment methods, business history and more, predictive analytics can identify potential fraud and other similar criminal behavior. It can also help detect threats and vulnerabilities in your system, prevent any recurring risk factors, reduce credit card payment failures, and make your customers’ overall online experience more secure, which can help improve conversions and sales.
The predictive difference
In retail, predictive analytics-driven decision making is becoming increasingly critical. Not only can it help you meet changing demands and deliver operational efficiencies, it can also help address operational difficulties with ease.
However, incorporating these systems into day-to-day operations and campaigns is not an easy task, and businesses should begin with just a few predictive analytics models and integrate them into their marketing strategies and operations over time. Implementing this approach is much more effective than adding multiple models all at once and ensures that your data is actionable.
Susan Jeffers is CEO of XY Retail.