IoT Analytics for Retail

Business Challenges

Retailers are striving to find ways that the IoT can create a direct channel between retailers and consumers, and to use IoT data streams to inform the customer relationship at every step along the customer journey. The differentiation with IoT will come from a retailer’s ability to sense, understand and act on IoT data with analytics. To take advantage of this new promising area, retailers will need to focus on IoT applications that better serve customers and create value.

How SAS Can Help

IoT solutions from SAS bring together data, analytics and marketing processes to help you:

• Sense customer needs and preferences in real time. Use smart devices to gather location-based information. Then integrate that data with previously known information to form contextual, real-time insights that you can act on.
• Gain a deeper understanding of customer patterns. Merge detailed, online customer behavior data with data from offline channels for a complete customer view that reveals what customers truly need so you can respond in real time with relevant, meaningful offers.
• Provide profitable new services to customers. Combine what you’ve learned from previous customer experiences with what you anticipate about future needs – then take action to seize opportunities ahead of the competition.

Why SAS?

As a leader in advanced analytics, SAS has the experience and expertise to deliver cutting-edge IoT capabilities that give you:

• A comprehensive view of your customers and business. Our customer information mapping technology means your data is always available and accessible – no matter where it comes from or where it resides. And you can integrate multiple online and offline data sources, including IoT, customer, merchandise and location information.
• Automated, large-scale forecasting. An unmatched library of retail forecasting models takes into account underlying trends, seasonality, promotions, inventory effects and more. The best-fit model is selected automatically based on each individual time series.
• High-performance predictive analytics. Create memorable, personalized interactions – across channels and in real time – using analytically driven scoring, segmentation and decision making based on historical and current interactions, combined with context.