AI for Retail
Stay ahead of the competition by automating and augmenting retail processes.
Mobile devices, conversational commerce, social networking and other technologies have shifted the behavior of the connected customer – and retailers need to shift accordingly. Customers can quickly research products and compare prices through multiple channels, and you must be ready to respond with relevant offers, competitive prices and the right merchandise. That means moving beyond spreadsheets, using data from every conceivable source to understand your customer's relationship with your brand so you can influence it in real time. And then there are the challenges facing the supply side of your business. In an effort to support unified commerce, supply chain networks are becoming increasingly complex and high-speed, making them almost impossible to track or optimize.
How AI Can Help
Advances in AI have made it possible to automate complex tasks through constant learning, enabling retailers to improve operations – and customer experiences – by breaking down departmental silos and applying omnichannel analytics to every step of the customer journey. Understanding where customers are in their journeys – and optimizing each interaction – drives longevity, loyalty and growth by turning data into action. In turn, you collect more data and learn more, so you can:
- Replace search with conversational commerce. Natural language processing and cognitive computing have given rise to conversational computer interfaces and chatbots that enable customers to shop anywhere, anytime – giving you the opportunity to better understand customers and unify commerce.
- Tailor retail efforts to specific customers. Uncover precise consumer needs by tapping into where and at what price customers shop across all channels and devices. Rule-based systems can't handle the sheer number of products, customers and touch points in real time. But when driven by machine learning, your pricing, assortment and marketing are always on target down to the micromarket level.
- Anticipate your customer’s next move. For every customer, AI uses thousands of pieces of text and digital data to develop a next-best action and drive recommendation engines. And you can use this insight to predict future buying behaviors, shape demand and seize opportunities to maximize margins.
- Optimize inventory and fulfillment for online or in-store shoppers. Planning and adjusting the movement of merchandise can be overwhelming. AI techniques can learn and correct supply chain issues while reducing the need for human intervention. And by adding in machine-to-machine IoT analytics and RFID data streams, you can achieve real-time inventory transparency.
- Control fraud and shrinkage. As the volume of digital transactions with customers and vendors continues to rise, analytic anomaly detection is the only way to keep an eye on what's happening. Deep learning algorithms can uncover and adapt to new fraud vectors, enabling you to address money laundering, vendor procurement fraud, cashier fraud and returns abuse.
- Power the store of the future today with analytics at the edge. Your physical stores have untapped potential to connect with customers. Wi-Fi foot-traffic sensors, video cameras, electronic shelf labels, and warehouse and in-aisle robotics can transform how you use every square foot. Deep learning algorithms, computer vision technology and real-time decisioning enable you to bake the art and science of retail into the customer experience.
As the proven leader in advanced analytics, only SAS bridges both merchandising and marketing data and processes across the entire retail enterprise. With AI capabilities embedded in our software – from the powerful SAS® Platform to our merchandising and customer intelligence solutions – deliver innovative omnichannel analytic capabilities that allow you to better manage inventory and drive profitability. That's why 921 retailers worldwide, including 66 percent of retail companies on the Fortune 500, rely on SAS to stay competitive.
- E-Book How Can Retailers Satisfy Today's Customers? By using analytics to directly connect product demand to customer experience
- White Paper Size Optimization Made Easy With Machine Learning and Analytics
- White Paper Real-Time Analytics: The Key to Unlocking Customer Insights & Driving the Customer Experience A Harvard Business Review Analytic Services Report
- White Paper Data Management for Artificial Intelligence
- Article How to improve your AI marketing skills
AI Solutions for Retail
- SAS® Anti-Money LaunderingTake a risk-based approach to monitoring transactions for money laundering and terrorist financing activities.
- SAS® Customer Intelligence 360Take marketing management to new heights to drive profitable revenue growth.
- SAS® Data PreparationQuickly prepare data for analytics in a self-service, point-and-click environment with data preparation from SAS.
- SAS® Marketing AutomationGet more campaigns out the door in an automated, trackable and highly repeatable fashion.
- SAS® Intelligent DecisioningEnable analytically driven real-time interactions, and automate operational business decisions at scale.
- SAS® Revenue Optimization SuiteOptimize life cycle pricing strategies and corporate profitability with a comprehensive view of consumer demand.
- 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 AnalyticsExplore de manera visual todos los datos, descubra nuevos patrones y publique reportes en la Web y en dispositivos móviles.
- SAS® Visual Data Mining and Machine LearningSolve your most complex problems faster with a single, integrated in-memory environment.
- SAS® Visual InvestigatorAddress a wide variety of intelligence analysis and investigation management needs with speed and precision.
- SAS® Visual Text AnalyticsUncover insights hidden in text data with the combined power of natural language processing, machine learning and linguistic rules.