Market Analysis and Insights:
In 2022, the Global Text Analytics Market was estimated to be worth USD 2.9 billion. The text analytics market industry is predicted to develop at a compound annual growth rate (CAGR) of 17.00% between 2023 and 2032, rising from USD 3.39 billion in 2023 to USD 11.91 billion by 2032.
The increasing need for text analytics from a variety of industries to extract important insights from text that is unstructured is driving the market. Natural language processing (NLP) and machine learning (ML) approaches are used in text analytics to extract insights from unstructured text data. NLP is a branch of computer science which focuses on how computers interact with human language. ML is a sort of artificial intelligence (AI) that enables software applications to grow more accurate at predicting outcomes without explicitly programming them to do so. Text analytics can be applied to a wide range of text data, such as customer feedback, social media posts, product evaluations, email communications, chat logs, news stories, and research papers. Text analytics can be used to gather insights into a wide range of business topics, such as customer sentiment, market trends, product development, risk management, fraud detection, and regulatory compliance. The text analytics market is classified according to type, enterprise size, application, and geography. There are three types of markets in the world: software, hardware, and services. By 2022, the software will generate more than 70% of total market revenue. Customer relationship management (CRM), marketing, sales, risk management, and fraud detection are all examples of business processes. Based on market size, the market is segmented into three segments: major corporations, small and medium-sized enterprises (SMEs), and micro corporations.
Text Analytics Market Scope:
Metrics | Details |
Base Year | 2022 |
Historic Data | 2022-2023 |
Forecast Period | 2022-2032 |
Study Period | 2022-2032 |
Forecast Unit | Value (USD) |
Revenue forecast in 2032 | USD 11.91 billion |
Growth Rate | CAGR of 17.00% during 2022-2032 |
Segment Covered | By Types ,By Applications, By Market Size, By Region. |
Regions Covered | North America, Europe, Asia Pacific, South America Middle East and Africa |
Key Players Profiled | IBM, SAS, Microsoft, Google, OpenText,Clarabridge, Amazon Web Services (AWS), KNIME, RapidMiner, Luminoso Technologies, Infegy, Indium Software, Brandwatch, Lexalytics. |
Market Definition
Text analytics is the process of extracting insights from unstructured text data using Natural Language Processing (NLP) and Machine Learning (ML) techniques. NLP is a discipline of computer science that explores the interface between computers and human language.
ML is a type of artificial intelligence (AI) which allows software programs to become more efficient at anticipating events without being expressly designed to do so. A corporation might employ text analytics to analyse client comments to find common issues and opportunities for improvement. Alternatively, a corporation might employ text analytics to analyse social media posts to assess public attitudes towards their brand. Text analytics is a useful method for gaining significant perspectives from unstructured text data. These insights can help firms make better decisions, boost efficiency, and decrease costs. In the real world, text analytics is applied in places like Text analytics is used by social media businesses to analyse user posts and spot trends. This data can be utilised to better the goods and services they provide, as well as more effectively target advertising.
Text analytics is used by e-commerce businesses to analyse product reviews and uncover common customer complaints. This data can be utilised to enhance the products and services they offer, as well as to gauge client happiness. Text analytics is used by financial services companies to analyse financial records and identify potential dangers. This data can be utilised to improve investment decisions and decrease fraud. Text analytics is used by healthcare firms to analyse medical information and detect patterns and trends. This information can be used to develop new medications while enhancing patient care. Text analytics is a quickly growing field with numerous applications. Text analytics is growing in significance for businesses and organisations who wish to extract relevant insights from their data as the number of unstructured text data grows.
Key Market Segmentation:
Insights on Key Types:
The Software category dominates the text analytics market, taking into consideration over 70% of market revenue in 2022. This is precisely because text analytics software is critical for any firm that wishes to extract ideas from its unstructured text data. Text analytics software packages can be installed either on-site or in the cloud. Text analytics software can be used to analyse a wide range of text data, such as customer feedback, social media posts, product evaluations, email communications, chat logs, news stories, and research papers. Text analytics software can be used to gather insights into a wide range of business sectors, including consumer sentiment, market trends, product development, risk management, fraud detection, and regulatory compliance.
Hardware
Specialised hardware platforms can be utilised to speed up text analytics processing. Text analytics hardware devices are frequently powered by High-performance Computing (HPC) technologies. Text analytics platforms for hardware can be utilised to improve text analytics application speed and scalability.
Services
Professional services such as text analytics consultation and implementation, along with managed services such as hosted text analytics systems, are examples of services. Text analytics consulting services can assist firms in identifying their text analytics requirements, as well as selecting and implementing the appropriate text analytics systems. Text analytics implementation services can assist firms with the deployment and integration of text analytics technologies into their existing IT infrastructure. Text analytics managed services allow enterprises to gain access to text analytics solutions without no having to invest in their very own software and hardware infrastructure.
Insights on Key Applications:
CRM will account for more than 30% of market sales in 2022. To get insights into consumer sentiment, churn risk, and creation of goods prospects, text analytics is an effective way to analyse CRM data such as customer comments, social media posts, and email conversations. This data can be utilised to enhance customer service, lower churn, and create new goods and services that fit client expectations.
Marketing
Text analytics may be employed to analyse marketing data including website traffic, social media interaction, and ad campaign performance to acquire insights into customer behaviour and preferences. This data can be utilised to create more effective marketing efforts and to target clients with relevant communications.
Sales
Text analytics may be used to analyse sales data, including sales calls and email exchanges, to acquire insights into sales effectiveness and find areas for development. This data can be utilised to train sales representatives and generate more effective sales techniques.
Risk Management
To identify possible dangers to a firm, text analytics may be used to analyse risk data including financial statements and news stories. This data can be utilised to reduce risks and safeguard the company from financial loss.
Insights on Enterprise Size
Large firms dominate the market, accounting for more than 60% of total revenue in 2022.
Large corporations can afford to invest in text analytics software, hardware, and services. Large organisations also have enough data to justify investing in text analytics. Large organisations can utilise text analytics to improve customer interactions, increase revenue, and cut costs. The following are the reasons why huge enterprises dominate the text analytics market. Large corporations can afford to put money into text analytics software, hardware, and services. Large organisations have enough data to justify investing in text analytics. Text analytics has numerous uses in major corporations. Large organisations are more likely to have in-house skills to set up and handle text analytics technologies.
SMEs
SMEs are increasingly using text analytics to gain a competitive advantage. However, small and medium- sized businesses (SMEs) may lack the means to invest in the same degree of text analytics skills as major corporations. SMEs may also have lower data volumes, making text analytics solutions more challenging to implement. Text analytics tools for SMEs are available from a variety of suppliers. These solutions are often cloud-based and charge on a per-user basis. This enables SMEs to begin using text analytics without making a huge upfront expenditure. As SMEs increasingly recognise the benefits of text analytics, the text analytics market for SMEs is likely to rise substantially in the coming years. However, the large corporate category is projected to continue to dominate the text analytics market.
Insights on Regional Analysis:
North America is the most populous region, accounting for more than 40% of market revenue in 2022. North America is the market leader in text analytics. The reason behind this is Early technological uptake. The region generates a large amount of unstructured text data. The presence of several top text analytics solution vendors North America has the most developed text analytics market. This is attributable to a variety of variables, including early technology adoption, the enormous volume of unstructured text data produced in the region, and the existence of several major text analytics solution vendors. Europe Europe is the world’s second-largest text analytics market. The increased adoption of text analytics by enterprises across many industries, including BFSI, retail, and healthcare, is driving the European market. Asia Pacific Asia Pacific has the fastest-growing text analytics market.
This is due to several causes, including increased technological adoption, an increase in the volume of unstructured text data created in the region, and an increase in the number of firms in the region seeking a competitive advantage through text analytics. Latin America the Middle East and Africa Text analytics markets in Latin America the Middle East and Africa are smaller, but they are likely to grow substantially in the future years. This expansion is being driven by increased technological adoption, an increase in the volume of unstructured text data processed in these regions, and an increase in the number of businesses in these regions looking to gain an edge over the competition
through text analytics.
Company Profiles:
These companies offer text analytics software, hardware, and services. They also serve other industries, including BFSI, retail, healthcare, and manufacturing. The Global Text Analytics Market’s key players include IBM, SAS, Microsoft, Google, OpenText, Clarabridge, Amazon Web Services (AWS), KNIME, RapidMiner, Luminoso Technologies, Infegy, Indium Software, Brandwatch, Lexalytics.
COVID-19 Impact and Market Status
The Global Text Analytics Market has benefited from the COVID-19 epidemic. As a result of the epidemic, organisations and individuals have become more and more dependent on digital interaction and communication technology, resulting in an increase in the amount of unstructured text data generated. The pandemic has additionally raised the availability of text analytics solutions from enterprises of all sizes, especially those that seek insights from unstructured text data to enhance decision-making and operations. The pandemic of COVID-19 has also spurred the uptake of cloud-based text analytics technologies. Cloud-based text analytics systems are more scalable and adaptable than on-premises solutions, and they also are accessible from any location with an internet connection. This has increased the appeal of cloud-based text analytics tools to enterprises of all sizes, particularly during the pandemic, when many staff have been working remotely.
The Global Text Analytics Market is predicted to expand substantially in the coming years, owing to the increasing number of unstructured text data generated, a growing need for text analytics solutions from organisations of all kinds, and the expanding usage of cloud-based text analytics solutions. The COVID-19 epidemic has emphasised the relevance of text analytics in assisting businesses and organisations in making educated decisions and responding effectively to crisis circumstances. The Global Text Analytics Market is likely to expand substantially in the future years as businesses and organisations increasingly recognise the intrinsic worth of extracting insights from unstructured text data.
Latest Trends and Innovation:
? 2023: OpenAI launches ChatGPT, a powerful language model that may create text, translate languages, write various forms of interesting material, and answer your queries in an educational manner. ChatGPT is likely to have a big impact on the text analytics market since it may be utilised to generate new and unique text analytics solutions.
? Microsoft will spend $10 billion on OpenAI, the inventor of ChatGPT, in 2023. This investment is expected to accelerate the formation of up-to-date text analytics technology and applications.
? 2023: Google AI launches Bard, a huge language model comparable to ChatGPT. Bard is still in the works, but it has the ability to transform the fields of text analytics.
? 2022: Amazon Web Services (AWS) introduces Textract, a service that extracts text and data from documents automatically. Textract can extract text from many different types of documents, notably PDFs, invoices, and receipts.
? LaMDA, a Google AI factual language model trained on a vast dataset of text and code, will be released in 2022. LaMDA is currently in its early stages of development, but that can be leveraged to create novel text analytics applications.
? 2021: Microsoft releases Azure Cognitive Services for Text Analytics, a collection of services that may be used to execute text analytics activities including sentiment analysis, entity extraction, and topic modelling.
? 2021: IBM introduces Watson Discovery, a text analytics service that can be used to gather insights from a wide range of data from various sources, such as text, documents, and photos.
Significant Growth Factors:
The volume of unstructured text data is quickly expanding as businesses and individuals progressively rely on digital communication and collaboration tools. This unstructured text data includes important information that can be extracted with text analytics technologies. Businesses of all sizes are more and more looking for analytics from their unstructured text data to better their operations and decision-making processes. This is fueling the requirement for text analytics solutions. Cloud-based text analytics systems are more scalable and versatile than on-site solutions, and they can be used from any place with a connection to the Internet. As a result, cloud- based text analytics tools are becoming more appealing to enterprises of all kinds. NLP and machine learning (ML) technologies are critical for the creation of successful text analytics solutions. Text analytics systems can now extract more accurate and valuable insight from unstructured text data thanks to improvements in NLP and ML technology. Text analytics solutions that are new and unique are always being created.
These solutions are tailored to the specific needs of businesses in a variety of industries. Aside from the previously mentioned considerations, the COVID-19 epidemic has propelled the growth of the Global Text Analytics Market. As a result of the epidemic, organisations and individuals have become increasingly reliant on digitised communication and collaboration technology, and the amount of unstructured text data created has increased. The pandemic has also increased the requirement for text analytics solutions from companies of all sizes, as they seek insights from unstructured text data to improve decision-making and operations.
Restraining Factors:
The Global Text Analytics Market is likely to expand rapidly in the next years, but several obstacles may limit its expansion. Among these elements are the Purchase and implementation of text analytics systems can be costly, in particular for small as well as medium-sized organisations. Many businesses are unaware of the advantages of text analytics or how it might help them enhance their operations and decision-making processes Because of a scarcity of trained text analytics personnel, firms may find it challenging to properly use and set up text analytics systems. When businesses use text analytics tools, they tend to worry about the privacy and security of their data. Text analytics is a complex topic, and businesses may find it challenging to select the best text analytics solution for their purposes, as well as to deploy and use it successfully. Despite these impediments, the Global Text Analytics Market is expected to grow rapidly in the next years.
The value of text analytics is becoming evident to enterprises of all sizes, and the price of text analytics systems is decreasing. In addition, an increasing number of trained text analytics specialists are entering the field. Businesses who wish to keep ahead of the competition must be aware of the Global Text Analytics Market’s difficulties and opportunities in text analytics. Businesses must carefully assess their needs before selecting a text analytics solution, and they must have a plan in place for efficiently adopting and utilising the solution.
Frequently Asked Questions (FAQ) :
The sheer volume of the data has grown exponentially over the past five years and new technologies have been developed to turn this plethora of data into insights. Text analytics includes analysis and extraction processes which take advantage of techniques such as statistics, computational linguistics, and other computer science disciplines. The output of the text analytics process can be combined with structured data (demographic and sales data) and analyzed using applications such as business intelligence, predictive and automated techniques. Also, the text can be extracted and transformed and then analyzed to derive relationships, trends and other information. Social media analytics is the major driving force behind the rise of the text analytics market. Numerous companies have emerged who provide social media monitoring platforms and have moved from simple search-based techniques to text analytics to analyze the sentiment of customers.
However, converting a world full of data into a data-driven world is a task that many companies have found difficult to pull off in practice. The biggest progress of text analytics is seen in the retail domain globally. For domains such as healthcare, manufacturing, and others, the progress is relatively slow. Companies from these domains have responded to the competitive pressure by making big technology investments but have failed to make necessary organizational changes to make most of them. Many companies struggle to switch from legacy data systems to more flexible architecture to store and combine big data.
The text analytics market is highly fragmented with many different players offering the text analytics services. These players include research organizations offering complete research to pure players, open source software, social media analytics companies or university spin-off. Text analytics market players have adopted various strategies to expand their global presence and increase their market shares. The key market players such as Medallia, IBM, Clarabridge, OpenText and others have adopted growth strategies through partnerships, agreements, collaborations and business expansions as eminent strategies to increase their customer base and enter into new market spaces.
Text analytics solution providers are stepping up to provide tools with increased sophistication, accessibility and language capability. For instance, Machine Learning is one example including algorithmic approaches from statistical regression methods have rapidly advanced to the forefront of analytics. This progress is only achieved due to the steady progress in the computational power by the help of better processors combined with massive investment in cloud computing technologies. Also, digitizing customer interactions provides a treasure of information for sales, marketing, and product development, while internal organizational digitization creates data that can be used to improve productivity and optimize operations.