IoT Analytics for Retail
Bring together data, analytics and marketing processes to gain an omnichannel view of your customers across all devices.
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.
As the 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.
- White Paper The Internet of Things Opportunities and Applications Across Industries
- E-Book Four Use Cases Show Real-World Impact of IoT
- White Paper IoT in Retail: Engaging the Connected Customer
- Analyst Report Customers Use IoT, Why Can't Retailers? Retail Systems Research Benchmark Report
- Article Analytics at the edge Examples of opportunity in the Internet of Things
IoT Analytics Solutions for Retail
- SAS® Analytics for IoTMake fast, confident decisions – while reducing data transport and storage costs – whether your data is at the edge, in motion or at rest.
- SAS® Customer Intelligence 360Infuse your marketing decisions with unprecedented customer insights, and create relevant, satisfying, valued customer experiences.
- SAS® Data PreparationQuickly prepare data for analytics in a self-service, point-and-click environment with data preparation from SAS.
- SAS® Real-Time Decision ManagerDeliver highly relevant, interactive offers based on automated analytical techniques.
- SAS® Size Optimization: SAS® Size Profiling and SAS® Pack OptimizationImprove profitability by identifying and supplying the right sizes to the right stores at the right time.
- SAS® Supply Chain IntelligenceDeliver quality improvement, customer satisfaction and higher profits with sound supply chain strategies.
- SAS® Visual AnalyticsVisually explore all data, discover new patterns and publish reports to the web and mobile devices.
- SAS® Visual Data Mining and Machine LearningSolve your most complex problems faster with a single, integrated in-memory environment.
- SAS® Visual Text AnalyticsUncover insights hidden in text data with the combined power of natural language processing, machine learning and linguistic rules.