August 10, 2017, 6:27 PM EDT
Machine Learning for Retailers
Driving Results with Artificial Intelligence ToolsDOWNLOAD
Table of Contents
Driving Results with Artificial Intelligence Tools
Ever wonder how Netflix gives you recommendations for the next movie or how your smartphone knows that you will be driving to work Monday morning? Those are both examples of machine learning.
Machine learning is a subset of artificial intelligence. It creates computer-based algorithms designed to learn from data—and adapt to new data—without being explicitly programmed. In the past, using traditional methods such as regression analysis, an analyst had to define the objective and look for correlations between that objective and a defined set of data inputs. If new data came in, the analyst needed to rerun the analysis, create new correlations and develop a new algorithm. Machine learning basically alleviates the need for manual intervention with computing programs that automatically take new data inputs and “learn” from them.
How can a company choose the best offer to present to customers?
In the retail environment, machine learning starts with SKU level data—the individual items purchased in each customer transaction. Data scientists can store this information on a massively parallel processing database, with multiple processors carrying out coordinated computations simultaneously. Using NBO models, they can more efficiently develop a list of probabilities that a consumer will shop in one of several predefined categories, depending on the retailer. For example, a model built for a mass merchandise retailer can predict whether a customer’s next purchase will likely involve automotive goods, baby care items or housewares.
Inputs are provided by information, such as what the consumer buys next and/or how they rate the offer presented. These inputs allow the model to continually modify probabilities. This continuous process integrates new information, closing the learning loop so the customer can effectively “tell” the company what he or she wants based on past behaviors and present preferences.
How can a retailer determine the efficiency of each of its stores?
Comparing the efficiency of various locations can be challenging at face value. One location could be newer—with upgraded amenities and square footage—but located in a less trafficked location. In the past, it was more difficult to determine if this location should overperform or underperform a smaller, older location with fewer amenities but more foot traffic. By using machine learning, thousands of characteristics can be entered in numerous combinations to determine which store is performing above or below expectations.