According to Future Market Insights, the global AI-driven web scraping market size has reached US$ 306.1 million in 2018. Demand for AI-driven web scraping recorded Y-o-Y growth of 12.7% in 2022, and thus, the global market is expected to reach US$ 638.5 million in 2023. Over the projection period 2023-2033, AI-driven web scraping solutions sales in the region are projected to exhibit 17.8% CAGR and total a market size of US$ 3,295.0 million by 2033-end.
AI-driven web scraping refers to the practice of using artificial intelligence (AI) technologies and algorithms to automate and enhance the process of extracting data from websites, forums, and blogs.
AI-driven web scraping takes web scraping a step further by leveraging AI techniques to improve the efficiency, accuracy, and adaptability of the scraping process. AI-driven web scraping involves the application of various AI technologies and methods to address common challenges and limitations in traditional web scraping. The techniques include natural language processing (NLP), computer vision, and machine learning enable AI models to understand the structure and semantics of web content, ensuring precise extraction of relevant data.
Other Drivers Propelling the Demand for AI-driven Web Scraping include:
Challenges for Companies /Manufacturers in the AI-driven Web Scraping Market:
Opportunities in the AI-driven Web Scraping Industry:
Latest Trends in the AI-driven Web Scraping Market:
| Attributes | Details |
|---|---|
| AI-driven Web Scraping Market Size (2023) | US$ 638.5 million |
| AI-driven Web Scraping Market Projected Size (2033) | US$ 3,295.0 million |
| Value CAGR (2023 to 2033) | 17.8% |
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From 2018 to 2022, the global AI-driven web scraping market experienced a CAGR of 17.8%, reaching a market size of US$ 638.5 million in 2023.
From 2018 to 2022, the global AI-driven web scraping industry witnessed a notable growth due to the businesses increasingly relied on data-driven insights to gain a competitive edge, leading to a surge in the need for web scraping services.
Companies across various sectors realized the potential of AI-driven techniques in extracting valuable information from websites at scale. The demand for AI-driven web scraping experienced substantial growth. Businesses across various industries recognized the value of extracting data from websites using AI technologies to gain insights and make data-driven decisions.
Looking ahead, the global AI-driven web scraping industry is expected to rise at a CAGR of 17.8% from 2023 to 2033. During the forecast period, the market size is expected to reach US$ 3,295.0 million.
The AI-driven Web Scraping industry is expected to continue its growth course from 2023 to 2033, driven by increasing demand for data-driven insights, automation, and efficiency in data extraction processes will drive the adoption of AI-driven web scraping solutions across industries. The market is projected to expand as businesses recognize the value of extracting valuable data from websites for competitive analysis, market research, and decision-making.
Principal Consultant
| Country | The United States |
|---|---|
| Market Size (US$ million) by End of Forecast Period (2033) | US$ 375.6 million |
| CAGR % 2023 to End of Forecast (2033) | 26.5% |
The AI-driven web scraping in the United States is expected to reach a market share of US$ 375.6 million by 2033, expanding at a CAGR of 26.5%. The AI-driven Web Scraping in the United States is likely to witness notable growth due to collaboration with data providers and technology partners can unlock new opportunities for AI-driven web scraping providers. By collaborating with data providers, such as data aggregators or specialized industry, data sources, web scraping providers can expand their data sources and offer more comprehensive datasets to their customers. Moreover, there are detailed factors expected to drive the growth for AI-driven Web Scraping in the country are:
| Country | The United Kingdom |
|---|---|
| Market Size (US$ million) by End of Forecast Period (2033) | US$ 316.3 million |
| CAGR % 2023 to End of Forecast (2033) | 29.4% |
The AI-driven web scraping industry in the United Kingdom is expected to reach a market share of US$ 316.3 million, expanding at a CAGR of 29.4% during the forecast period. The United Kingdom market is projected to experience growth due to companies requiring timely and accurate data to analyze market trends, consumer behavior, and competitor activities. AI-powered web scraping solutions can extract data from various sources, enabling businesses to make informed decisions, identify emerging opportunities, and stay ahead in a competitive market.
| Country | China |
|---|---|
| Market Size (US$ million) by End of Forecast Period (2033) | US$ 359.1 million |
| CAGR % 2023 to End of Forecast (2033) | 28.9% |
The AI-driven web scraping industry in China is anticipated to reach a market share of US$ 359.1 million, moving at a CAGR of 28.9% during the forecast period. The AI-driven web scraping market in China is likely to grow due to its large e-commerce sector, data-intensive industries, and advanced technology adoption.
AI-driven web scraping is widely used in China for e-commerce, price monitoring, product data aggregation, and sentiment analysis. E-commerce companies heavily rely on web scraping to gather product information, monitor competitor prices, track consumer behavior, and optimize their offerings.
| Country | Germany |
|---|---|
| Market Size (US$ million) by End of Forecast Period (2033) | US$ 329.5 million |
| CAGR % 2023 to End of Forecast (2033) | 28.1% |
The AI-driven web scraping market in Germany is estimated to reach a market share of US$ 329.5 million by 2033, thriving at a CAGR of 28.1%. Germany is known for its innovation and technological advancements. Exploring emerging technologies like computer vision, natural language processing, and edge computing can give companies a competitive edge. Leveraging these technologies to extract data from diverse sources, such as images, videos, and social media, can expand the scope and applicability of AI-driven web scraping solutions.
| Country | India |
|---|---|
| Market Size (US$ million) by End of Forecast Period (2033) | US$ 266.9 million |
| CAGR % 2023 to End of Forecast (2033) | 27.1% |
The AI-driven web scraping industry in India is expected to reach a market share of US$ 266.9 million, expanding at a CAGR of 27.1% during the forecast period. The market in India is estimated to witness notable growth due to a significant digital transformation with entrants of AI technology across various industries. This transformation opens up opportunities for AI-driven web scraping to extract valuable data from websites, enabling businesses to gain insights, optimize operations, and make data-driven decisions. Companies in India are leveraging web scraping to monitor competitors, track market growth, gather customer feedback, and enhance their products and services.
The dynamic web scraping segment is expected to dominate the AI-driven web scraping market with a CAGR of 19.4% from 2023 to 2033. This segment captures a significant market share in 2023 due to its prominent application of extracting real-time data updates.
The segment of dynamic web scraping in the AI-driven web scraping market is expected to experience rapid growth because many websites today use dynamic content. Which means the content on the web pages changes dynamically based on user interactions, real-time data updates, or other factors. Dynamic web scraping is necessary to extract data from such websites and capture the updated information in real-time. Examples include e-commerce sites with real-time product availability, social media platforms with live feeds, or news websites with constantly updated articles.
The static web scraping segment is expected to dominate the AI-driven web scraping market with a market share of 29.8% over the forecast period. This segment captures a significant market share in 2023 due to its simple and time efficient data extraction techniques.
Static web scraping driving factor in the AI-driven web scraping market is automating the web scraping process, static web scraping saves time and effort compared to manual data collection. It enables businesses to retrieve large amounts of data from multiple web pages quickly and accurately. Automation also reduces human errors and ensures consistent data extraction across different sources.
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The AI-driven web scraping industry is highly competitive, with numerous players vying for market share. In such a scenario, key players must adopt effective strategies to stay ahead of the competition.
Key Strategies Adopted by the Players
Companies in the AI-driven web scraping market focus on continuous product innovation to enhance their offerings and meet the evolving needs of customers. Companies are investing in advanced web scraping technologies to improve data extraction accuracy, speed, and scalability. These innovations include the use of machine learning algorithms, natural language processing, computer vision, and other AI techniques to extract and analyze data more efficiently and accurately.
Companies in the AI-driven web scraping market form strategic partnerships and collaborations to leverage complementary strengths and expand their market reach. Companies may collaborate with data providers, software vendors, or other technology companies to integrate their web scraping solutions with existing platforms or data sources. These partnerships enable companies to offer comprehensive solutions to customers and access a wider user base.
To capitalize on the growing demand for AI-driven web scraping solutions, companies are expanding into emerging markets. They identify regions with significant potential for market growth and establish a local presence through partnerships, acquisitions, or setting up subsidiaries. By entering emerging markets, companies can tap into new customer segments and gain a competitive advantage over their rivals.
Mergers and acquisitions play a crucial role in the AI-driven web scraping market, allowing companies to consolidate their market position, acquire new capabilities, and gain access to a broader customer base. Companies may acquire smaller competitors to eliminate competition or acquire complementary technologies to enhance their product offerings. Mergers and acquisitions also enable companies to enter new geographic markets or expand their existing market presence.
Key Players in the AI-driven Web Scraping Industry
Key Developments in the AI-driven Web Scraping Market:
The net worth of the market is expected to be US$ 3,295.0 million by 2033.
The market is calculated to expand at a CAGR of 17.8% through 2033.
Extraction of information from unstructured data like charts, images, or non-textual elements.
Emerging opportunity to extend their footprint in media, travel and e-commerce, etc., is making the market attractive.
China is expected to expand at a robust CAGR of 28.9% through 2033.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand Side Trends
1.3. Supply Side Trends
1.4. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Key Market Trends
3.1. Key Trends Impacting the Market
3.2. Product Innovation / Development Trends
4. Pricing Analysis
4.1. Pricing Analysis, By Subscription
4.1.1. Subscription Pricing Model
4.1.2. Perpetual Licensing
4.2. Average Pricing Analysis Benchmark
5. Global Market Demand (Value in US$ Million) Analysis 2018 to 2022 and forecast, 2023 to 2033
5.1. Historical Market Value (US$ Million) Analysis, 2018 to 2022
5.2. Current and Future Market Value (US$ Million) Projections, 2023 to 2033
5.2.1. Y-o-Y Growth Trend Analysis
5.2.2. Absolute $ Opportunity Analysis
6. Market Background
6.1. Macro-Economic Factors
6.2. Forecast Factors - Relevance & Impact
6.3. Value Chain
6.4. COVID-19 Crisis – Impact Assessment
6.4.1. Current Statistics
6.4.2. Short-Mid-Long Term Outlook
6.4.3. Likely Rebound
6.5. Market Dynamics
6.5.1. Drivers
6.5.2. Restraints
6.5.3. Opportunities
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Scraping Type
7.1. Introduction / Key Findings
7.2. Historical Market Size (US$ Million) Analysis By Scraping Type, 2018 to 2022
7.3. Current and Future Market Size (US$ Million) Analysis and Forecast By Scraping Type, 2023 - 2033
7.3.1. Static Web Scraping
7.3.2. Dynamic Web Scraping
7.3.3. API Scraping
7.3.4. Image and Text Recognition
7.4. Market Attractiveness Analysis By Scraping Type
8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Subscription Model
8.1. Introduction / Key Findings
8.2. Historical Market Size (US$ Million) Analysis By Subscription Model, 2018 to 2022
8.3. Current and Future Market Size (US$ Million) Analysis and Forecast By Subscription Model, 2023 - 2033
8.3.1. Paid Services
8.3.2. Free Services
8.4. Market Attractiveness Analysis By Subscription Model
9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Industry
9.1. Introduction / Key Findings
9.2. Historical Market Size (US$ Million) Analysis By Industry, 2018 – 2022
9.3. Current and Future Market Size (US$ Million) Analysis and Forecast By Industry, 2023 – 2033
9.3.1. IT & Telecommunication
9.3.2. BFSI
9.3.3. E-commerce
9.3.4. Healthcare
9.3.5. Retail & Consumer Goods
9.3.6. Energy & Utilities
9.3.7. Others
9.4. Market Attractiveness Analysis By Industry
10. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
10.1. Introduction / Key Findings
10.2. Historical Market Size (US$ Million) Analysis By Region, 2018 to 2022
10.3. Current and Future Market Size (US$ Million) Analysis and Forecast By Region, 2023 - 2033
10.3.1. North America
10.3.2. Latin America
10.3.3. Europe
10.3.4. East Asia
10.3.5. South Asia Pacific
10.3.6. Middle East and Africa
10.4. Market Attractiveness Analysis By Region
11. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033
11.1. Introduction
11.2. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
11.3. Current and Future Market Size (US$ Million) Forecast By Market Taxonomy, 2023 - 2033
11.3.1. By Scraping Type
11.3.2. By Subscription Model
11.3.3. By Industry
11.3.4. By Country
11.3.4.1. U.S.
11.3.4.2. Canada
11.4. Market Attractiveness Analysis
11.4.1. By Scraping Type
11.4.2. By Subscription Model
11.4.3. By Industry
11.4.4. By Country
11.5. Market Trends
11.5.1. Key Market Participants - Intensity Mapping
12. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033
12.1. Introduction
12.2. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
12.3. Current and Future Market Size (US$ Million) Forecast By Market Taxonomy, 2023 - 2033
12.3.1. By Scraping Type
12.3.2. By Subscription Model
12.3.3. By Industry
12.3.4. By Country
12.3.4.1. Brazil
12.3.4.2. Mexico
12.3.4.3. Rest of Latin America
12.4. Market Attractiveness Analysis
12.4.1. By Scraping Type
12.4.2. By Subscription Model
12.4.3. By Industry
12.4.4. By Country
13. Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033
13.1. Introduction
13.2. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
13.3. Current and Future Market Size (US$ Million) Forecast By Market Taxonomy, 2023 - 2033
13.3.1. By Scraping Type
13.3.2. By Subscription Model
13.3.3. By Industry
13.3.4. By Country
13.3.4.1. Germany
13.3.4.2. Italy
13.3.4.3. France
13.3.4.4. U.K.
13.3.4.5. Spain
13.3.4.6. BENELUX
13.3.4.7. Russia
13.3.4.8. Rest of Europe
13.4. Market Attractiveness Analysis
13.4.1. By Scraping Type
13.4.2. By Subscription Model
13.4.3. By Industry
13.4.4. By Country
14. South Asia & Pacific Market Analysis 2018 to 2022 and Forecast 2023 to 2033
14.1. Introduction
14.2. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
14.3. Current and Future Market Size (US$ Million) Forecast By Market Taxonomy, 2023 - 2033
14.3.1. By Scraping Type
14.3.2. By Subscription Model
14.3.3. By Industry
14.3.4. By Country
14.3.4.1. India
14.3.4.2. Indonesia
14.3.4.3. Malaysia
14.3.4.4. Singapore
14.3.4.5. Australia & New Zealand
14.3.4.6. Rest of South Asia and Pacific
14.4. Market Attractiveness Analysis
14.4.1. By Scraping Type
14.4.2. By Subscription Model
14.4.3. By Industry
14.4.4. By Country
15. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033
15.1. Introduction
15.2. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
15.3. Current and Future Market Size (US$ Million) Forecast By Market Taxonomy, 2023 - 2033
15.3.1. By Scraping Type
15.3.2. By Subscription Model
15.3.3. By Industry
15.3.4. By Country
15.3.4.1. China
15.3.4.2. Japan
15.3.4.3. South Korea
15.4. Market Attractiveness Analysis
15.4.1. By Scraping Type
15.4.2. By Subscription Model
15.4.3. By Industry
15.4.4. By Country
16. Middle East and Africa Market Analysis 2018 to 2022 and Forecast 2023 to 2033
16.1. Introduction
16.2. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
16.3. Current and Future Market Size (US$ Million) Forecast By Market Taxonomy, 2023 - 2033
16.3.1. By Scraping Type
16.3.2. By Subscription Model
16.3.3. By Industry
16.3.4. By Country
16.3.4.1. GCC Countries
16.3.4.2. Turkey
16.3.4.3. South Africa
16.3.4.4. Rest of Middle East and Africa
16.4. Market Attractiveness Analysis
16.4.1. By Scraping Type
16.4.2. By Subscription Model
16.4.3. By Industry
16.4.4. By Country
17. Key Countries Analysis- Market
17.1. U.S. Market Analysis
17.1.1. By Scraping Type
17.1.2. By Subscription Model
17.1.3. By Industry
17.2. Canada Market Analysis
17.2.1. By Scraping Type
17.2.2. By Subscription Model
17.2.3. By Industry
17.3. Mexico Market Analysis
17.3.1. By Scraping Type
17.3.2. By Subscription Model
17.3.3. By Industry
17.4. Brazil Market Analysis
17.4.1. By Scraping Type
17.4.2. By Subscription Model
17.4.3. By Industry
17.5. Germany Market Analysis
17.5.1. By Scraping Type
17.5.2. By Subscription Model
17.5.3. By Industry
17.6. Italy Market Analysis
17.6.1. By Scraping Type
17.6.2. By Subscription Model
17.6.3. By Industry
17.7. France Market Analysis
17.7.1. By Scraping Type
17.7.2. By Subscription Model
17.7.3. By Industry
17.8. U.K. Market Analysis
17.8.1. By Scraping Type
17.8.2. By Subscription Model
17.8.3. By Industry
17.9. Spain Market Analysis
17.9.1. By Scraping Type
17.9.2. By Subscription Model
17.9.3. By Industry
17.10. BENELUX Market Analysis
17.10.1. By Scraping Type
17.10.2. By Subscription Model
17.10.3. By Industry
17.11. Russia Market Analysis
17.11.1. By Scraping Type
17.11.2. By Subscription Model
17.11.3. By Industry
17.12. Rest of Europe Market Analysis
17.12.1. By Scraping Type
17.12.2. By Subscription Model
17.12.3. By Industry
17.13. China Market Analysis
17.13.1. By Scraping Type
17.13.2. By Subscription Model
17.13.3. By Industry
17.14. Japan Market Analysis
17.14.1. By Scraping Type
17.14.2. By Subscription Model
17.14.3. By Industry
17.15. South Korea Market Analysis
17.15.1. By Scraping Type
17.15.2. By Subscription Model
17.15.3. By Industry
17.16. India Market Analysis
17.16.1. By Scraping Type
17.16.2. By Subscription Model
17.16.3. By Industry
17.17. Malaysia Market Analysis
17.17.1. By Scraping Type
17.17.2. By Subscription Model
17.17.3. By Industry
17.18. Indonesia Market Analysis
17.18.1. By Scraping Type
17.18.2. By Subscription Model
17.18.3. By Industry
17.19. Singapore Market Analysis
17.19.1. By Scraping Type
17.19.2. By Subscription Model
17.19.3. By Industry
17.20. Australia and New Zealand Market Analysis
17.20.1. By Scraping Type
17.20.2. By Subscription Model
17.20.3. By Industry
17.21. GCC Countries Market Analysis
17.21.1. By Scraping Type
17.21.2. By Subscription Model
17.21.3. By Industry
17.22. Turkey Market Analysis
17.22.1. By Scraping Type
17.22.2. By Subscription Model
17.22.3. By Industry
17.23. South Africa Market Analysis
17.23.1. By Scraping Type
17.23.2. By Subscription Model
17.23.3. By Industry
17.24. Rest of Middle East and Africa Market Analysis
17.24.1. By Scraping Type
17.24.2. By Subscription Model
17.24.3. By Industry
18. Market Structure Analysis
18.1. Market Analysis by Tier of Companies
18.2. Market Share Analysis of Top Players
18.3. Market Presence Analysis
19. Competition Analysis
19.1. Competition Dashboard
19.2. Competition Benchmarking
19.3. Competition Deep Dive
19.3.1. Import.io
19.3.1.1. Business Overview
19.3.1.2. Scraping Type Portfolio
19.3.1.3. Profitability by Market Segments (Business Segments/Region)
19.3.1.4. Key Strategy & Developments
19.3.2. Diffbot
19.3.2.1. Business Overview
19.3.2.2. Scraping Type Portfolio
19.3.2.3. Profitability by Market Segments (Business Segments/Region)
19.3.2.4. Key Strategy & Developments
19.3.3. Zyte
19.3.3.1. Business Overview
19.3.3.2. Scraping Type Portfolio
19.3.3.3. Profitability by Market Segments (Business Segments/Region)
19.3.3.4. Key Strategy & Developments
19.3.4. Mozenda
19.3.4.1. Business Overview
19.3.4.2. Scraping Type Portfolio
19.3.4.3. Profitability by Market Segments (Business Segments/Region)
19.3.4.4. Key Strategy & Developments
19.3.5. Octoparse
19.3.5.1. Business Overview
19.3.5.2. Scraping Type Portfolio
19.3.5.3. Profitability by Market Segments (Business Segments/Region)
19.3.5.4. Key Strategy & Developments
19.3.6. ScrapeStorm
19.3.6.1. Business Overview
19.3.6.2. Scraping Type Portfolio
19.3.6.3. Profitability by Market Segments (Business Segments/Region)
19.3.6.4. Key Strategy & Developments
19.3.7. Kadoa
19.3.7.1. Business Overview
19.3.7.2. Scraping Type Portfolio
19.3.7.3. Profitability by Market Segments (Business Segments/Region)
19.3.7.4. Key Strategy & Developments
19.3.8. Nimbleway
19.3.8.1. Business Overview
19.3.8.2. Scraping Type Portfolio
19.3.8.3. Profitability by Market Segments (Business Segments/Region)
19.3.8.4. Key Strategy & Developments
19.3.9. Browse.ai
19.3.9.1. Business Overview
19.3.9.2. Scraping Type Portfolio
19.3.9.3. Profitability by Market Segments (Business Segments/Region)
19.3.9.4. Key Strategy & Developments
19.3.10. Apiscrapy
19.3.10.1. Business Overview
19.3.10.2. Scraping Type Portfolio
19.3.10.3. Profitability by Market Segments (Business Segments/Region)
19.3.10.4. Key Strategy & Developments
20. Assumptions and Acronyms Used
21. Research Methodology
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AI-driven Web Scraping Market
