Crowd Analytics Market Expected to Reach USD 5.7 Billion by 2030 | Top Players such as -Geodan, Mira and Spigit
Crowd Analytics Market
There is increase in expenditure on analytics tools & solutions, owing to rise in need for business intelligence (BI) solutions, drives growth of the market.
PORTLAND, PORTLAND, OR, UNITED STATE, April 18, 2023 /EINPresswire.com/ -- Allied Market Research published a new report, titled, " The Crowd Analytics Market Expected to Reach USD 5.7 Billion by 2030 | Top Players such as -Geodan, Mira and Spigit." The report offers an extensive analysis of key growth strategies, drivers, opportunities, key segment, Porter’s Five Forces analysis, and competitive landscape. This study is a helpful source of information for market players, investors, VPs, stakeholders, and new entrants to gain thorough understanding of the industry and determine steps to be taken to gain competitive advantage.
The global crowd analytics market was valued at USD 912.68 million in 2020, and is projected to reach USD 5.7 billion by 2030, growing at a CAGR of 20.4% from 2021 to 2030.
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Rise in demand for efficient crowd distribution planning in smart cities, growth in IoT adoption, and growing adoption of cloud-based analytics solutions are expected to propel the growth of the global crowd analytics market. The software segment held the lion’s share in 2016, contributing nearly three-fourths of the total market. On the other hand, the market across the Asia-Pacific region is expected to register the fastest CAGR of 27.4% during the forecast period.
The crowd analytics market is segmented on the basis of solution, deployment model, application, end user, and region. By solution, the market is differentiated into software and service. Depending on deployment model, it is fragmented into on-premise and cloud. According to application, it is classified into safety & security, crowd flow management, mobility & tracking, and others. As per end-user segment it is categorized into transportation, retail, hospitality & tourism, and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
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On the basis of deployment model, the market is bifurcated into on-premise and cloud. The cloud segment is the most lucrative segment and is projected to register the fastest CAGR of 25.6% during the study period. Moreover, the segment held the largest share in 2016, accounting for more than three-fifths of the total market. On-premise segment acquired the major share of crowd analytics market. The on-premises deployment mode is used by large enterprises, as it involves significant investment to implement and purchase interconnected hardware, servers, and software to manage the system.
Based on application, the market is segmented into safety & security, crowd flow management, mobility & tracking, and others. The mobility & tracking segment held the largest share in 2016, garnering nearly two-fifths of the market. However, the other segment is expected to manifest the fastest CAGR of 27.0% through 2022.
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The global crowd analytics market is analyzed across various regions such as North America, Europe, Asia-Pacific, and LAMEA. The market across North America held the largest share in 2016, contributing more than one-third of the market. However, the market across the Asia-Pacific region is expected to register the fastest CAGR of 27.4% during the forecast period.
The market report includes an extensive analysis of the major market players such as AGT International GmbH, Crowd Dynamics International, Crowd Vision Limited, Geodan Next, Mira, NEC Corporation, Nokia Corporation, Savannah Simulations AG, Spigit, Inc., Walkbase, and others.
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Covid-19 Scenario:
● Crowd analytics industry innovation has aided medical treatment and several organizations.
● Swarm examination arrangements help establishments comply with COVID-19 guidelines by enabling them to screen the live inhabitancy of regions, distinguish human internal heat levels in swarms, identify face covers, perceive faces, and dissect crowds in real-time using a combination of human-made reasoning, big data, and investigation apparatuses.
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