Artificial Intelligence in Retail Site Selection

The term Artificial Intelligence or “AI” has been used since the 1960s to describe the programmed intelligence contained in “machines that think.” While many of the early attempts to build AI systems were unsuccessful, today’s combination of fast computers, unlimited memory and statistical methods that complement the logic in AI systems has led to many successes. Today, AI programs can land a passenger jet without help, track weather systems around the world, beat human opponents in chess, recognize speech or handwriting, determine battle strategies, and even perform various types of surgery. At Experian Business Strategies, AI methods are also being used to build simulation systems that help restaurant and retail chains make better decisions about their locations, marketing and operations. What AI Adds to Conventional Statistical and GIS Models Companies greatly increased their use of statistical and GIS models in the 1990s, when personal computers and the data to do modeling (such as demographics) became readily available. These models typically “looked back” at the history of a company’s performance and then attempted to forecast this history using various types of statistical methods. While often useful, these models lacked the ability to see through the statistical “noise” created by the differences in operations, marketing, building style or age, name recognition, and the many other factors that can influence sales. The Prediction approach to this problem was to: Experian Business Strategies uses Artificial Intelligence to overcome the limitations that are inherent in conventional modeling approaches. ■ Study the behavior of customers to create a “logic” or set of AI rules to describe how the world works for a particular concept. ■ Combine AI rules with knowledge from statistical or GIS-based analysis to create predictive models. ■ Integrate these models into computer systems that simulate the “retail world” of a particular concept and can be used for business functions such as market planning, site selection and sales forecasting. The biggest advantage of this approach is that it makes intelligent use of statistics and GIS while AI-based logic keeps the models from being exposed to the statistical noise that is always present in real-world situations. In a conventional approach, the researcher would need to see that “good visibility” correlates with “high sales” in order to include this variable in a predictive model. Leveraging AI, we begin with a rule-based logic that knows visibility is important. But we also know that this importance differs in mall locations, urban locations, suburban locations and so forth. The rules or logic, similar to those of a real estate expert, guide the decision-making based on these nuances... helped by the statistics but not determined by them. Artificial Intelligence Helps Filter the “Noise” How Statistics and AI Combine to Create the Best Models For nearly 30 years, members of the Prediction team developed Retail Performance Models, using information obtained from nearly 3 million consumers about how they use the retail world. Prediction leveraged the following key findings from this research to substantially increase model accuracy and reliability: 1. There is a lot of consistency in how people use the retail world for all concepts. If you divide concepts into three classes – “destination,” “convenience,” and “in-between” – you already know a great deal about the rules of the world.