The commercial building market is estimated to be one of the top five applications for Big Data. But the diversity of building types, wide range of sizes and complexities, single or multiple sites and ownerships ensures that one solution will not satisfy all needs. It is therefore imperative that before investing billions of dollars in Big Data solutions for the Building Internet of Things (BIoT) those buildings that can benefit most from it are identified and thoroughly assessed.
This article taken from our report Big Data for Smart Buildings Market Prospects 2015 to 2020 explains why some building types will benefit more than others.
There are 2 major opportunities in buildings at stake for Big Data. The first is to optimise the performance of the buildings environment and automate it without the need for human intervention. Retail sector buildings are very energy intensive and have much to gain through implementing Big Data with a long term savings potential of over 35% on energy use.
The second is to use the data and intelligence from the building services sensors to improve the performance of the business enterprise taking place in the building. A good example here is using the video surveillance cameras to analyse that is happening in the retail space and in addition to reducing shrinkage it has the potential to improve customer engagement, stock management, business competitiveness.
Convergence with the business enterprise even without Big Data is proving to be a vital factor in winning Building Automation contracts and with it has the potential to deliver financial benefits that could be much higher than those realised from improving the buildings environmental performance. Whilst most opportunities related to potential energy saving and improved performance of buildings can apply to all kinds of building types, the 5 key markets that offer the additional market opportunities for big data in smart buildings are Retail, Healthcare, Education, Hospitality and Real Estate.
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Below we home in on the Retail market. Specific Big Data market opportunities in this vertical include:
The retail market can be a huge consumer of both HVAC and lighting energy, driving up the cost of operations. Retail managers may wish to leverage BIoT innovations to reduce their lighting and HVAC costs without impacting their customers’ comfort. Some retailers have aggressively pursued demand-controlled ventilation, lighting and controls upgrades, and advanced efficiency compressors for HVAC and refrigeration to reduce operating costs. But the cutting edge of smart building technology for retailers often focuses more on improving the consumer experience than on energy efficiency.
Smart Product Management
Optimization of inventory across channels and fine-tuning of local assortment planning by drawing on insights from social media, market reports, internal sales data and customer buying patterns. Control of rotation of products in shelves and warehouses to automate restocking processes. Stock-out prevention through improved connectivity with intelligent supply chain processing of data. Also the use of RFID tags to track product location, as well as monitor the storage conditions of products along the supply chain.
Near Field Communications (NFC) payments enabled by the use of smart sensors, Bluetooth and RFID to speed the payment process and reduce the need for staffing of payment points. Payment processing can also be relocated to a more convenient location for consumers in public transport, gyms, theme parks, etc.
Price Elasticity Management
Transactional data from loyalty programs and credit cards across thousands of stores can be used to understand where individual customers tend to shop. That data can be layered onto an analysis of current store locations and pricing zones. All this information can be combined, analyzed, and mapped to show geographic clusters of price awareness based on observed shopping behaviour rather than artificially set boundaries. Predicting optimal pricing and maintaining a price leadership position by analyzing price and demand elasticity.
For example, new dynamic pricing models - linking electronic pricing display with analytics, sourcing data from sensors that monitor customer flow, combined with car parking sensors and CCTV systems integrated with Video Surveillance analytics to predict the flow of incoming customers. Retailers can effectively adjust prices to encourage the sale of perishable goods before their sell-by date, thereby reducing wastage.
Improved Operational Efficiency
Systems to increase operational efficiency based on heat sensors monitoring the people flow within supermarkets and thereby predicatively assigning additional staff to man the tills has already proved successful in UK supermarkets.
Improved Customer Engagement
Providing customers with product data at the point of sale using “personal shopping assistant” apps, providing custom data based on specific customer preferences, such as the presence of allergic ingredients, the calorific content, recipe ideas or expiry dates of products. Improved gathering of customer preference data from video surveillance, customer loyalty card schemes, social media, Internet or mobile feedback, can also be used to predict customer behavior or trends over time.
Several big data software and platform providers are providing pre-built analytical services that focus on specific vertical processes and analytical requirements. Some of these Companies have formed strategic alliances with BAS manufacturers to ensure that connectivity issues and existing software application packages are incorporated where appropriate.