Despite the rise in ecommerce, 70 percent of consumers still prefer to shop in-store as much as they did last year. But these consumers expect shopping experiences that reflect their digital habits. In fact, retailers missed out on $150 billion revenue in 2016 by failing to provide shoppers with personalized services.
Leveraging the Data from IoT in Retail Transformation
By 2020, the IoT network will consist of more than 30 billion connected devices, according to IDC. But today, nearly 90 percent of the data generated by the IoT isn't actually put to use.
The retail store is, however, poised for a comeback with cognitive IoT, which uses data from the connected stores to reveal hidden insights, helping retailers make real-time, contextually-driven decisions, and paving the way for differentiating customer experiences. Cognitive computing has become critical to IoT for the following reasons:-
- Scale, granularity, & velocity of data generation
- Computing’s movement into the physical world to create human-aware devices
- Integration of multiple data sources and types
Thanks to IoT, real-time insights from connected stores, combined with cognitive computing leveraging rich data such as weather and transaction histories, can help retailers transform the in-store experience and streamline operations.
This increasingly affordable connectivity is now having an impact on the retail industry. The new shopping experience, created through cognitive solutions, will differentiate retailers from their competition, help them gain market share, and enable them to capture customer attention using hyper-personalization delivered by the connected stores.
Building Intelligent and Connected Stores with Cognitive IoT
In a connected store, the IoT and cognitive solutions capture data from day-to-day retail operations across all channels to enable new levels of interaction with the store employees and customers. The most common examples include store-monitoring to predict customer preferences, proactive inventory control, and personalized customer experience. Retailers looking to leverage CIOT will innovate in four major areas:-
Delivering a Smarter Shopper Experience
- Delivering contextual and personalized activations, offers, and promotions across channels
- Providing shopper or associate-led guided shopping experiences
- Understanding in-store behavior to generate new shopper insights
Driving Smarter Store Operations
- Optimizing store layouts and managing queues more efficiently
- Monitoring store assets and enabling preventive maintenance
- Improving energy management and sustainability
- Enhancing store security and loss prevention
Building Smarter Merchandising and Supply Networks
- Optimizing inventory management for omnichannel operations
- Tracking product quality and safety across the supply chain
- Managing logistics more efficiently and effectively
Discovering New Channels and Revenue Streams
- Allowing kitchen pantries and refrigerators to automatically restock groceries and washing machine buttons to order more laundry detergent
- Suggesting recipes based on ingredients available in home and generating automatic shopping lists
- Automatic remote monitoring of temperature and lighting using connected home comfort devices
Potential Applications of Cognitive IoT in Retail Transformation
Rather than being explicitly programmed, cognitive systems learn from interactions with humans and their experiences with their environment. Cognitive systems can make sense of the 80 percent of the world’s data that computer scientists call “unstructured data” – using tools like ML, NLP, and text, video and image analytics. That will enable some fundamentally new characteristics and applications of the IoT to emerge. They are:-
Store Wellness: Physical asset performance, e.g. refrigeration, data can be captured from devices to identify proactive actions needed by store staff, or via automated responses delivered through asset management systems processes, for reduced operating costs, and improved health and safety compliance.
Customer Engagement: Customer service levels can be raised by utilizing data from beacons and sensors to analyze customer demographics, sentiment, mobile application interaction with cognitive shopping assistant, personalized point of sale, and by heat mapping against transaction log data to understand product demand by time of day. Using heat maps of shopper density and location, retailers can offer timely services and reallocate staff to meet demand in real time.
Cognitive Building: Real estate and facilities management solutions could enable advanced management of sustainability strategies, supplier contracts management, next best maintenance action, Building Information Modeling (BIM), and space optimization and utilization planning that can help differentiate the brand.
Next-Gen Supply Chain: Insights could be improved, operational efficiency delivered, and working capital can be optimized through agile operations. NFC and RFID tags can improve availability and product security by capturing data including GPS location, temperature, pressure and other information, helping retailers track inventory across the supply chain and restock shelves to meet demand. Insights could be enhanced with near real-time inventory predictions by incorporating analytics from weather and social media platforms. GPS location and weather data can also provide information about road and environmental conditions that enables better route planning and ensures driver safety.
Emerging Technologies: This includes exploration of the potential use of drones, blockchain, process automation, robotics and wearables for interconnected, intelligent and insightful adoption methods for new ways to be efficient.
Smart Home: Retailers can offer new convenience driven by demand for rapid auto-replenishment in perishable and non-perishable goods through reactive and cognitive self-learning shopping list and home delivery services. Retailers will also be able to integrate their customer loyalty.
Weather Insights: Weather influences what people wear, and how they feel, eat, and buy. In a connected store, the retailer can combine local weather data with cognitive computing. Seasonal offerings can be designed based on weather insights, where customer engagement can be mapped to personalized requirements to maximize results and customer satisfaction. Weather can also be used to predict behavior, enabling optimized revenue forecasting and supply chain management as part of cognitive operations.
Perishability Tracking: Temperature sensors can trigger alerts if perishable products reach an unsafe temperature, thereby preventing spoilage and shrinkage.
Energy Management: With data from occupancy sensors, smart HVAC, and lighting can automatically adjust to variations in need between peak and lean hours.
Predictive Maintenance: By analyzing equipment performance indicators such as temperature, vibrations or power consumption, retailers can predict when equipment will fail before it actually does. Shopper Insights: Insights from in-store behavior, purchase history and social media activities can help retailers to provide personalized offers and predict future trends.
With so much emphasis on the collection of structured as well as unstructured data through IoT, mobile and omnichannel loyalty schemes, customers are expecting retailers to cater to their personal needs. By leveraging Cognitive IoT, retailers can learn behaviors and shopping trends, and personalize offerings that provide specific recommendations linked to availability, health requirements, budget, and convenience.
However, the retail industry is at the beginning of the IoT journey. And the important thing for retailers to realize at this point is that CIoT isn’t just about intelligence and connectivity — it’s actually about real business outcomes, such as creating new customer experiences, revenue streams, and business models.
The true transformation will come when the retail CDOs will look beyond the four walls of a store and build an end-to-end connected CIoT strategy with their network of customers, suppliers, assets, and ecosystem.