The AI in Social Media Market Size was valued at USD 1.82 Bn in 2023 and is predicted to reach USD 10.42 Bn by 2031 at a 25.1% CAGR during the forecast period for 2024-2031.
Social media platforms widely use AI to improve user experience, produce content, target advertisements, and offer better services. Additionally, trend analysis, fraud detection, image and video identification, and personalized content recommendations are common uses of AI in social media usage. These are but a few instances of AI being used in social media. AI is being used in social media in ever-changing ways, and new apps are being created to give users even better experiences. The market is expanding due to the increasing use of AI in social media applications for efficient advertising and the expanding acceptance of AI in social media as a real-time source of audience targeting data.
Furthermore, the market for artificial intelligence in social media is expanding due to the explosion of data produced by these platforms. However, worries over privacy and data security are impeding this market's expansion. On the other hand, throughout the projection period, it is projected that the growing accessibility of machine-learning applications for social media will present several prospects for market growth.
The AI in social media market is segmented based on technology, application, service, organization size and end-use industry. The market is segmented based on technology as machine learning, deep learning, and natural language processing (NLP). The market is segmented by application into customer experience management, sales and marketing, image recognition, predictive risk assessment, and other applications. Based on service, the industry is bifurcated into managed service and professional service. Based on the organization size, the global AI in social media market is divided into small & medium enterprises and large enterprises. Based on the end-use industry, the market is segmented into retail, e-commerce, banking, financial services and insurance (BFSI), media and advertising, education and other end-user industries.
The machine learning and deep learning segment is expected to hold a major share of the global AI in social media market. Market expansion is driven by social media platforms' and companies' increasing embrace of ML and DL technology. Businesses invest capital into these technologies to improve their products and remain competitive. Additionally, more user retention and platform utilization result from ML and DL's capacity to customize and engage, which can boost income from both advertising and subscription models. Additionally, social media platforms can handle massive data volumes, scale their operations effectively, and provide continuous performance using ML and DL solutions, all promoting long-term revenue development.
Retail segment is projected to grow at a rapid rate in the global AI in social media market. Retail industry development is anticipated to be driven by the increasing use of social media for online buying and other e-commerce activities. As a means of enhancing their interactions with customers, social media platforms are growing in popularity among retail enterprises. Retail firms may make their promotions more effective, rank higher than their rivals, and anticipate marketing trends with the use of artificial intelligence in social media. Data-driven in-store experiences, personalized product recommendations, and picture recognition searches through social media posts to uncover hidden patterns are all made feasible by artificial intelligence (AI). Given that businesses rely on consumers spending more money and that they face fierce competition from e-commerce platforms, retailers such as Target, Walmart, and others must leverage artificial intelligence (AI) to increase sales and customer loyalty.
The North America AI in social media market is expected to register the highest market share in terms of revenue in the near future. The market growth in the region is attributed to the rising investment by the tech companies. For instance, the Canadian start-up Cohere Inc. is in talks to receive a $200 million investment from Alphabet Inc. subsidiary Google. A long-term agreement was reached between the artificial intelligence startup and Google to supply processing power for the software models' training. In addition, Asia Pacific is projected to grow at a rapid rate in the global AI in social media market due to rapid digital transformation and increasing government initiatives and investments.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 1.82 Bn |
| Revenue Forecast In 2031 | USD 10.42 Bn |
| Growth Rate CAGR | CAGR of 25.1% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Technology, By Application, By Service, By Organization Size, By End-User Industry and By Region |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| Competitive Landscape | Google LLC, Microsoft Corporation, Meta, Amazon Web Services, Inc., IBM Corporation, Adobe Systems Incorporated, Salesforce.com Inc., Baidu Inc., Snap Inc., Clarabridge Inc., HootSuite Media Inc., Meltwater News US Inc., Crimson Hexagon Inc., and Sprout Social Inc. |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
AI in Social Media Market- By Technology
AI in Social Media Market- By Application
AI in Social Media Market- By Service
AI in Social Media Market- By Organization Size
AI in Social Media Market- By End-Use Industry
AI in Social Media Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.