Globally, the retailers are striving hard to deliver great customer service, especially by enhancing better personalized in-store experiences. With tremendous growth in the retail sector in the past decade, it has become difficult to build individualized relationships and consistently provide remarkable experience. Advancements in the areas of IoT, cloud services, and cognitive computing have truly transformed the ways in which organizations deal with customers. The development of low-cost sensors, affordable connectivity, storage and processing of vast amounts of structured/unstructured data with the help of big data platform, and mobile applications have helped the retail giants to simplify their operations. Such technological development helped to provide more responsive customer experience, bring better visibility into supply chains operations, and improve customer management.
The vast data collected from the IoT devices is the key to improve customer insights and meet the dynamic customer demands. AoT assists retailers by providing solutions to efficiently manage the immense data generated from IoT devices, which include RFID tags, scanners, and bar code readers. Some more benefits provided by AoT to the retail industry include vendor management, conversion rates, dock and yard management, transaction management, and potential stock management. Due to these opportunities, the overall market share of the retail sector in AoT is growing at a fast pace.

Frequently Asked Questions

  • What are the opportunities in the IoT analytics market?
    The IoT analytics market size is expected to grow from USD 7.2 billion in 2017 to USD 27.8 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 31.0% during the forecast period. Factors such as the tremendous growth of IoT data is expected to fuel the market growth. However, the ownership and control of IoT data is expected to restrain the market growth.
  • What is the competitive landscape in the market?
    The IoT analytics solution and service vendors have implemented various types of organic as well as inorganic growth strategies, such as new product launches, product upgradations, partnerships and agreements, business expansions, and mergers and acquisitions, to strengthen their offerings in the market. The Major players operating in the market include IBM Corporation (US), Microsoft Corporation (US), Oracle Corporation (US), SAP SE (Germany), Cisco Systems, Inc., (US), Dell Technologies, Inc. (US), Google, Inc. (US), Amazon Web Services, Inc. (US), HP Enterprise Co (US), PTC, Inc. (US), Hitachi, Ltd. (Japan), Teradata Corporation (US), Salesforce.com, Inc. (US), Greenwave Systems, Inc. (US), and mnubo, inc. (US).
  • What are the emerging technologies impacting the overall IoT analytics market?
    Predictive analytics transforms data into valuable insights, facilitating enterprises to optimize business processes, enhance process efficiencies, and increase customer satisfaction. Predictive analytics makes use of real-time data as well as historic data to come up with futuristic outcomes before the actual event takes place. The predictive analytics type is expected to have the largest share in the AoT market due to its ability to determine data and predict future outcomes and trends. The predictions that flow from predictive analytics are transformed into a set of recommended actions that will generate business value. This assists procurement professionals to view business scenario and build future operational strategies. Predictive analytics can also be used for demand forecasting using multiple regression models, which helps organizations determine demand and supply forecasts for different product categories. Such forecasts can prove beneficial while calculating the company revenue and budget growth. Companies are also focusing on providing predictive maintenance and asset maintenance using predictive analytics to understand the device repairing time, minimize downtime, and take swift corrective actions. Predictive analytics when incorporated in AoT, assists a manufacturing unit to ensure continuous operations by implementing asset maintenance or helps a government institute to shut down their servers if suspicious web traffic is detected.
  • What are the key use cases existing in the IoT Analytics market?
    The top 10 use cases in IoT Analytics market are: Predictive maintenance, Quality inspection & assurance, Manufacturing process optimization, Supply chain optimization, AI-driven cybersecurity & privacy, Automated physical security, Automated data management, Smart assistants, AI-driven research & development, Autonomous resource exploration.
  • What are the key trends and dynamics existing in the IoT analytics market?
    The IoT Analytics Market is moving towards the cloud based environment. Cloud-based solutions require less physical set-up, incur lower maintenance costs, and provide 24/7 accessibility from anytime, anywhere. Cloud-based solutions prove to be flexible, agile, and cost-effective, as compared to on-premises solutions. Cloud-based AoT helps organizations to reduce cost and IT complexity; it also improves business agility and increases collaborations by shared services. Globally, large enterprise and SMEs are shifting toward cloud-based solutions to streamline their processes easily and minimize upfront costs. Some of the prominent vendors offering cloud-based AoT software and services include IBM Corporation, Oracle Corp., AWS, and Salesforce.com, Inc.
  • Which are the recent developments made in IoT Analytics market?
    In February 2019, IBM announced access to its IBM Watson on any cloud. Also, the vendor can deploy AI software to automate business wherever the data resides. In June 2019, Cisco announced software innovations for simplified management and security of networks. Also, it introduced new artificial intelligence and machine learning capabilities that enable IT teams to function at machine speed and scale through tailored network insights.

IoT Analytics in Retail and Consumer Goods

Comparing 27 vendors in IoT Analytics Software across 237 criteria.
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IBM offers IBM Watson and IBM Bluemix solutions under its IoT Analytics Software. Organizations can make use of Watson IoT, which uses the Bluemix platform to analyze IoT data. Bluemix platform takes data from sensors and sensor-enabled devices and performs analytics on it. The platform makes use of Complex Event Processing (CEP) to analyze real-time streaming data for generating and determining patterns and create insights using business indexes and data streams. Watson AoT processes structured and unstructured data obtained from flat files, in-database, data warehouse, Hadoop, and spreadsheets (IBM SPSS Statistics, Excel files, CSV files, and SAS files). IBM also offers readymade IoT solutions enabling organizations implement it quickly.
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Microsoft offers IoT analytics software through its Azure IoT Suite and Stream Analytics, which are used in various business areas, including banking, financial services, retail, education, healthcare, and marketing sectors. Any device, regardless of the IoT device type or OS can be connected to the Microsoft Azure IoT suite and the cloud, where they can be used to communicate between employees and customers. Microsoft also provides Business Intelligence (BI) tools to gain deeper insights into what employees and customers need, by analyzing the IoT data.
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SAP offers IoT analytics software through the SAP HANA Cloud platform. SAP HANA is being widely accepted by various industry verticals as it can be integrated not only with the social media module in custom Customer Relationship Management (CRM) but also with R and Hadoop to store, pre-process, analyze, and compute data in real-time analysis. The platform helps analyze data from machines, streaming events, and business operations and allows frontline workers to make use of real-time analytics to maintain productivity.
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Oracle provides an IoT platform to develop new IoT services quickly, using Oracle portfolio of products. This enables organizations to handle growing volumes of data, streamline application development and deployment, automate integration of data, and protect the data across the entire IoT value chain. Oracle provides a variety of IoT analytics solutions, including Oracle IoT Cloud Platform as a Service (PaaS) and Oracle IoT applications with built-in analytics for Key Performance Indicators (KPIs), anomaly detection, comparative analytics, and predictive analytics. Oracle also provides an IoT cloud service which is a secure and scalable platform to quickly build and deploy IoT applications, and capture and analyze IoT data.

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Dell Technologies, Inc. is one of the prominent players in IoT analytics software marketplace. The American multinational specializes in digital transformation, IT transformation, security transformation, and workforce transformation. Dell’s enterprise solution includes servers, networking, storage, mobility, and desktop products. Dell provides IoT analytics infrastructure that encompasses Dell IoT platform and similar software, such as edge analytics and Dell Statistica predictive analytics software.
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Hitachi offers Lumada as its core IoT platform. This open source platform enables technologies from Hitachi IoT partner system to integrate and provide numerous solutions for a variety of industrial applications. The company offers various services in the areas of cloud, big data analytics, infrastructure, data protection, and application services. Moreover, it offers software solutions such as Hitachi Content Intelligence, Hitachi Streaming Data Platform, Pentaho Data Integration and Analytics, which can be integrated into Lumada IoT platform.
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HPE provides Vertica Analytics Platform and Universal IoT Platform as its AoT offerings. The platforms offer data visualization for representation of data in pictorial or graphical format and offer in-memory analytics for faster data processing. The platforms are powered with data engines which process thousands of rows, utilizing the full system, for achieving fast query processing. They support a variety of data sources, such as Excel spreadsheets, flat files, and Hadoop.
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The company offers an array of software solutions, such as Computer-Aided Design (VCAD) modelling, Service Lifecycle Management (SLM), and Product Lifecycle Management (PLM). The company also offers IoT solutions to enable customers to connect smart things, manage and analyze data, and develop applications. PTC offers IoT analytics solutions under its ThingWorx solution, a technology platform which enables customers to create and deploy applications and solutions. The solution also includes cloud-based tools which facilitate users to connect products and devices to the cloud, process, transform, store, and organize product and sensor data. The on-premises and on cloud deployment models provide the customers with an option to choose the delivery model. This has strengthened PTC’s customer base.
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Google provides its AoT solution through Google Cloud IoT Core. Google’s global clientele base is spread across various industries, such as automotive, banking, consumer goods, education, energy, engineering, entertainment, environmental, financial services, food, beverage, government, healthcare, insurance, manufacturing, media, retail, technology, telecommunications, transportation, and travel & hospitality. Google Cloud IoT Core offers analytics solutions to enterprises. The company is striving to expand its portfolio with new technological developments. To accomplish this, Google has invested a significant share (15% during the last 3 years) of its revenue on R&D activities.
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Teradata has specialized itself as a leading provider of software solutions and services. Its major offerings include Teradata Database Software, Teradata Workload-Specific Platforms, Teradata Aster Analytics Discovery Platform, Teradata QueryGrid, Teradata Marketing Applications. Apart from software solutions, the company offers Teradata Technology and Implementation services, Teradata Cloud Services, Teradata Managed Services, and customer support and training services.
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The Cumulocity IoT from Software AG is an IoT platform which connects any device from any of the cloud platforms and can be integrated with any of the applications. The platform is very quick to start, in just ten minutes and in very simple steps. The platform offers the benefit of not requiring coding and analysis for business users and analysis can be carried out without coding or programming skills. Users require the same APIs, data and analytics models everywhere, in the cloud and on-premises.

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AGT offers IoT analytics as its predictive analytics offering based on the cloud deployment option. The platform allows an entire new generation of vertical IoT applications that will drive enhanced business processes, resulting in improved value and new revenue streams. The platform also resolves key issues in IoT analytics, data management and visualization, employing solutions that have been tailor made for each application. Also, the platform integrates easily with existing sensors, network infrastructure and end-user applications.
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Splunk Enterprise ingests data from applications, sensors, devices, and so on. Post setting up the data sources, Splunk Enterprise indexes the data stream and resolves it into a series of individual events and metrics that the users can view and search. Splunk IAI is an application that the users can use to track asset health, notify on metric anomalies, design custom metrics, conduct root cause analysis for issues across data sources, and measure entire equipment effectiveness in real time. Splunk IoT Platform Data also helps in reducing downtime of critical assets.
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Vitira's platforms offer enhanced customer experience by deliverng a resilient and predictable infrastructure for service delivery that reduces churn and cost of managing increasingly complex environments. The platforms easily manage streaming or batch data from disparate devices and help users in deploying ML and augmented intelligence using VIAs drag and drop libraries. Also, they automate processes and workflows like anomaly detection, incident lifecycle automation, dynamic change management, dynamic failure prediction.
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Striim’s patented software collaborates real-time data collection, stream processing, pipeline monitoring, and real-time delivery with validation in a single product. It continuously collects a wide variety of high-volume, high-velocity data from organization's databases , log files, messaging systems and IoT devices in real time. The platform also integrates rapidly with existing data management and ML solutions to operationalize earlier defined models via stream processing. The platform is robust, sclaable, secure and reliable.
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Greenwave Systems, Inc. is an IoT and machine-to-machine company delivering comprehensive software and services to network architects and service providers to address the interoperability, flexibility, security, and scalability in a single unified IoT solution. The company specializes in offering IoT solutions, managed services, enterprise solutions, network services, home energy management, connected LED lighting solutions, and others.
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Information Builders' WebFOCUS possesses an interactive user interface, which provides visually intuitive and highly efficient workflows. It also incorporates UI/UX best practices throughout. The solution enables users and administrators to rapidly create dashboards that combine content from multiple data sources and formats. The solution also provides users with full-featured report writing and casting capabilities which facilitate self-service, enterprise, financial, or managed reporting. The company's API management technology allows users to reuse existing application and infrastructure investments for web-oriented and event-driven architecture.
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Mnubo is specialized in offering IoT analytics for service providers, enterprises, and device manufacturers. mnubo’s solution empowers manufacturers to improve product usage, enhance customer engagement, and lower customer churns with insights generated from data collected from IoT devices. The company’s SmartObject allows exploration and analysis of sensors data and events to gather insightful information about connected devices.
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RapidMiner has been seamlessly integrated and enhanced for developing ML models. It helps organizations in designing models with the help of a visual workflow designer or automated modeling. The platform collaborates multiple datasets together and creates new columns using a simple expression editor. It automatically creates new features from data to enhance model accuracy. The platform also allows organizations to execute data prep and ETL inside databases to keep their data optimized for advanced analytics.
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