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市場調査レポート
商品コード
1690038
予測分析市場の規模、シェア、成長分析:コンポーネント別、展開モード別、組織規模別、業種別、地域別 - 産業予測 2025~2032年Predictive Analytics Market Size, Share, and Growth Analysis, By Component (Solutions, Services), By Deployment Mode (Cloud, On-Premises), By Organization Size, By Vertical, By Region - Industry Forecast 2025-2032 |
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予測分析市場の規模、シェア、成長分析:コンポーネント別、展開モード別、組織規模別、業種別、地域別 - 産業予測 2025~2032年 |
出版日: 2025年03月21日
発行: SkyQuest
ページ情報: 英文 157 Pages
納期: 3~5営業日
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予測分析市場規模は2023年に163億米ドルとなり、予測期間(2025-2032年)のCAGRは24.2%で、2024年の202億4,000万米ドルから2032年には1,146億3,000万米ドルに成長する見通しです。
世界の予測分析市場は、予測ツールの採用拡大、ビッグデータインフラへの投資、IoTとAI技術の統合に後押しされ、大きな成長を遂げています。しかし、熟練したIT専門家の不足や高い導入コストなどの課題が、市場拡大の妨げになる可能性があります。eコマース分野では、顧客知覚調査やターゲットを絞ったオンライン広告やソーシャルメディア広告によって、顧客体験の向上が売上を伸ばしています。さらに、膨大なデータの増加に伴い、予測分析の応用範囲は金融や人事にも広がると予想されます。様々な分野の企業が、営業、マーケティング、オペレーション、サプライチェーンマネジメントにおいて予測分析を戦略的に活用することで、より高い効率性と収益性を実現しています。
Predictive Analytics Market size was valued at USD 16.3 billion in 2023 and is poised to grow from USD 20.24 billion in 2024 to USD 114.63 billion by 2032, growing at a CAGR of 24.2% during the forecast period (2025-2032).
The global predictive analytics market is experiencing significant growth, propelled by the increasing adoption of predictive tools, investments in big data infrastructure, and the integration of IoT and AI technologies. However, challenges such as a shortage of skilled IT professionals and high implementation costs may hinder market expansion. In the eCommerce sector, improved customer experiences are enhancing sales, driven by customer perception research and targeted online and social media advertising. Moreover, the application of predictive analytics is expected to extend into finance and human resources, with the rise of massive data. Businesses across various sectors are benefiting from the strategic use of predictive analytics in sales, marketing, operations, and supply chain management, fostering greater efficiency and profitability.
Top-down and bottom-up approaches were used to estimate and validate the size of the Predictive Analytics market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Predictive Analytics Market Segments Analysis
Global Predictive Analytics Market is segmented by Component, Deployment Mode, Organization Size, Vertical and region. Based on Component, the market is segmented into Solutions and Services. Based on Deployment Mode, the market is segmented into Cloud and On-Premises. Based on Organization Size, the market is segmented into Large Enterprises and SmallAndMedium-Sized Enterprises. Based on Vertical, the market is segmented into BFSI, RetailAndEcommerce, Telecom And IT, TransportationAndLogistics, MediaAndEntertainment, Manufacturing, HealthcareAndLife Sciences, TravelAndHospitality, EnergyAndUtilities, GovernmentAndDefense and Other Verticals. Based on region, the market is segmented into North America, Europe, Asia Pacific, LatinAmericaand Middle East & Africa.
Driver of the Predictive Analytics Market
The accelerating volume of data generated by companies across various sectors is significantly boosting the demand for predictive analytics. As organizations harness big data to forecast trends, enhance operational efficiency, and make data-driven decisions, the predictive analytics market experiences substantial growth. This trend highlights the critical role that advanced data analysis plays in helping businesses stay competitive and agile in an ever-evolving landscape. By leveraging predictive analytics, companies can gain valuable insights that inform strategic decisions, ultimately leading to improved outcomes and a stronger market position. Thus, the increasing reliance on data-driven approaches is a key driver of this market's expansion.
Restraints in the Predictive Analytics Market
One notable restraint in the predictive analytics market is the considerable initial investment necessary for implementing such solutions. The financial burden associated with acquiring the required technology, integrating systems, and facilitating employee training can pose challenges, particularly for small and medium-sized enterprises (SMEs). These costs may deter potential adopters from leveraging predictive analytics, as they may find it difficult to justify the upfront expenditure. Consequently, the high barrier to entry can limit the growth and widespread adoption of predictive analytics tools among businesses that could benefit from enhanced data-driven decision-making but lack the financial resources to invest in them.
Market Trends of the Predictive Analytics Market
The predictive analytics market is witnessing a significant trend towards the integration of predictive analytics with traditional business intelligence (BI) systems, driven by the demand for a comprehensive and actionable approach to data analysis. This convergence allows organizations to leverage historical data insights alongside advanced predictive modeling, enabling proactive decision-making in dynamic environments. Enhanced accessibility through user-friendly interfaces and sophisticated data visualization tools empowers data analysts and business users alike to interpret integrated insights effectively. As predictive models evolve through continuous learning, organizations benefit from increased accuracy and value, ultimately leading to improved productivity, higher ROI, and a competitive advantage in today's data-centric landscape.