Utilities Tech Outlook Weekly Brief
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What allows customers to transform their business? What keeps customer stories awake at night? It is the reliable data that enables business owners to decode, take risks and gain customer confidence by analyzing and insights. Digital technologies play an imminent role of business in Artificial Intelligence and its prevalent form for efficient data analysis, Machine Learning is now the most sought-after technology to help companies to diversify and transform. Through machine learning models, the perks of predictive analytics tools are seen by companies in retail, insurance, energy, meteorology, marketing, healthcare, and other industries. Companies of all sizes now find that combining predictive analytics with AI can help them stay ahead of their competing companies.
By associating with the latest trends, retail brands are constantly seeking to remain relevant. According to Business of Fashion, AI-based demand projection approaches can reduce forecast errors by up to 50 percent. This enhancement can mean large savings for the bottom line of a retail brand and positive ROI for inventory-sensitive organizations. Utilities also use predictive analytics to help identify energy use trends. Smart meters supervise activity and report consumption spikes to customers at certain times of the day, aiding them to reduce power consumption. Utilities also help companies predict if they can get a high bill based on a variety of data points and receive notifications to warn customers if they run a big bill that month.
Few Predictive Analytics Companies: Cloverleaf Analytics, MicroStrategy, Asset Performance Networks
Unforeseen downtime can be expensive for industries that rely heavily on equipment such as manufacturing, agriculture, energy or mining. To help detect and prevent failures, companies are using predictive analytics and AI systems. Predictive maintenance systems enabled by AI can monitor and report problems with equipment in real time. IoT sensors connected to critical devices can collect data in real time, identify problems or potential problems as they arise, and notify teams so that they can respond to them immediately. The systems can also establish predictions of future problems, reducing expensive unplanned downtime.
Brands should stay on top of their online presence, and on social media what is being said about them. It is important to monitor social media for customers to receive real-time feedback, especially for retail brands and restaurants. Bad reviews and negative comments, especially for smaller brands, can be detrimental.
With such awareness and in near-real-time tracking of social media comments, businesses can gather immediate feedback and respond quickly to situations.
With companies of all sizes trying to remain ahead of their competitors throughout every industry and foresee market trends, this forward-looking attitude to predictive analytics proves valuable.
Check out: Retail Tech Insights
Check This Out: Top Predictive Analytics Companies
