While the phrase artificial intelligence often still feels a little futuristic, the reality is that AI has been revolutionizing business processes for over 15 years.
In the form of predictive analytics, AI is transforming the retail landscape by helping businesses anticipate customer needs, optimize operations, and drive profitability.
By leveraging historical data, machine learning algorithms, and statistical modeling, retailers can forecast trends and make data-driven decisions. Here’s a closer look at how predictive analytics is reshaping the retail industry, from its most common applications to exciting new trends.
Starting at the Top, What is Predictive Analytics in Retail?
Predictive analytics involves using data to make informed forecasts about future outcomes. In the retail industry, this translates to helping brands better understand consumer behavior, forecast supply and demand, optimize pricing, and provide personalized experiences.
The retail industry is no stranger to using vast quantities of data to refine business processes, with Dr. Eugene J. Kelley first using consumer data to inform decisions about future store locations in 1958 (source). Predictive analytics simply makes these decisions faster and more accurate.
Top Use Cases of Predictive Analytics in Retail
Demand Forecasting - Retailers use predictive models to forecast product demand across seasons, regions, and customer segments. This ensures optimal inventory levels, minimizes overstocking and prevents stockouts. For example, IKEA's AI-driven forecasting models consider seasonal events, such as festivals and celebrations, to help IKEA forecast demand across different regions.
Personalized Marketing - Personalized marketing has come a long way from the initial discount codes sent to you on your birthday. Predictive analytics helps create tailored marketing campaigns by analyzing customer preferences, purchase history, and browsing behavior. This use case can take many forms, from grouping consumers into segments based on purchase behavior to enabling hyper-local marketing campaigns based on localized trends.
Dynamic Pricing - Algorithms automatically adjust pricing based on the current market rate, ensuring e-commerce brands maintain a competitive price in crowded markets. Working in tandem with demand forecasting technology, some brands have tested using predictive analytics to adjust pricing based on demand levels for specific items, although this use case is often met with pushback from consumers.
Customer Churn Prediction - As many SaaS founders will know, customer churn can make or break a business. By identifying patterns that indicate customer dissatisfaction or disinterest, retailers can take proactive measures to retain customers, such as offering discounts or personalized recommendations. Identifying these patterns often takes the form of outlining 'trigger events', key moments that correlate with customer churn. While trigger events vary depending on the target market and products in question, common triggers include competitor activity and changes in purchasing habits.
Supply Chain Optimization - The field of supply chain optimization is vast, with use cases ranging from using analytics to predict potential disruptions, optimizing delivery routes, and streamlining inventory management. One of the more innovative use cases sees large brands analyzing supplier performance data to predict delays in product production. There’s potential to combine historical supplier data with macro factors, such as extreme weather and transport conditions, to identify where delays may arise.
Emerging Use Cases in the Last Two Years
While the above use cases are the most common, advancements in technology are bringing in a new wave of predictive analytics:
Sustainability Forecasting - Forecasting is now looking further than supply and demand. Retailers are predicting the environmental impact of their operations, helping them make sustainable choices, such as optimizing energy use or sourcing eco-friendly materials. This provides brands with a route to improve their public image, while further driving cost efficiencies.
Hyper-Personalized In-Store Experiences - As an expansion of personalized marketing, larger brands are utilizing AI to improve the personalization of their brick-and-mortar locations. These refinements can be fairly minor, such as tailored offers when consumers visit a specific store, or much larger in scope. By analyzing footfall and seasonal purchasing habits, retail locations can optimize their product placement and store layout on a case-by-case basis.
Social Sentiment Analysis - The world’s thoughts and feelings are now accessible, on-demand, through social media. Retailers leverage predictive models to analyze social media trends and predict product demand, especially for fast-moving consumer goods. Additionally, this technology can be used to gauge perceptions toward specific products or trends. Using social media posts, comments, and reactions, brands can understand how their target market feels towards certain topics and adjust their stance accordingly.
What Could the Future Hold?
The future of predictive analytics in retail looks promising, driven by advancements in AI and big data technologies. Here are some possibilities:
Real-Time Predictive Analytics: Retailers could use real-time data streams to make instantaneous decisions, such as recommending products to customers as they shop online or in-store. In a similar lane to AI shopping assistants, consumers could be provided custom discounts based when ‘completing a look’ or purchasing matching accessories.
AI-Driven Product Development: Predictive analytics could help identify gaps in the market and predict future trends, enabling retailers to develop products that meet emerging demands. We’re closest to this being commonplace when analyzing weather data, helping retailers predict demand for seasonal items.
Predictive analytics is no longer a luxury for retailers but a necessity to thrive in a fast-paced, data-driven world. By adopting and evolving with these technologies, businesses can exceed customer expectations, ensuring long-term growth and success.