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Speech Analytics Market Prediction: The Speech Analytics market size is expected to reach US$5.34 billion by 2028 from US$2.55 billion in 2023, with a CAGR of 15.91%. The rapidly growing Speech Analytics Market has been on a fast track, facilitated by AI and ML advancements. With innovations like this, organizations can intelligently interrogate voice data, improving overall customer engagement and operational efficiency.
The need for more advanced speech analytics solutions in order to help with improved customer experience, compliance and performance is on an uptick as businesses look to better understand their customers and boost operational efficiency.
Continuing our exploration of this industry, in this article, we take a look at some of the crucial trends unfolding in the speech analytics market, the driving factors behind these trends, and predictions on the future of Speech Analytics market.
These voice analytic solutions help in analyzing the conversations that help in understanding customer needs and preferences. Businesses use these insights to find room for improvement and work to better customer experience. As companies begin to appreciate how critical it is to hang onto customers, the need for speech analytics solutions increases.
The rise of the e-commerce economy has inevitably overtaken some other customer interactions through its digital channels, including voice. These e-commerce platforms and contact centers require suitable tools to analyze these conversations with a view to ensuring high levels of customer service. These platforms carry out the real-time evaluation of conversations made possible by speech analytics solutions.
The incorporation of AI and ML technologies in speech analytics platforms increases the efficiency of these solutions, makes them more accurate. AI and ML algorithms enable to analyze copious amounts of voice data promptly and accurately; catching onto patterns and trends (that would not have been identified by human analysts)
Regulatory compliance is very important in industries like BFSI and Healthcare. These industries are highly regulated and privacy, transparency, and data security compliance are mandated. Companies in these industries can benefit from speech analytics to monitor conversations for compliance, alert to potential risks, and adherence to regulatory requirements.
With real-time speech analytics, organizations can analyze conversations in the moment, gathering instant feedback and addressing customers' issues instantly. In a customer service scenario, this is a particularly useful capability as the ability to intervene at the right time can have a big effect on customer satisfaction.
Digitalization and cloud-based solutions have modified the prevailing scenario in many markets, of which the speech analytics market is a part. Cloud native speech analytics solutions have multiple benefits; they are easier to deploy, more scalable, and less costly.
The limited use cases of real-time speech analytics hinder the market growth. Primarily used for call centers and customer service applications, their application is so specific to their capability use that it limits the broader industry adoption and scaling, which has led to almost no widespread acceptance of these solutions.
Generating transcription and analyzing transcribed voice data is expensive, resource-intensive, and requires a specific set of skills. Advanced software, hardware, and expertise are needed for effective analysis, which many agencies do not have at their disposal. These impediments directly increase the effort and time for speech analytics technologies to be more widely adopted and become more effective.
Many sophisticated speech analytics solutions cost too much and require resources to implement and maintain. The sizeable up front payment and the continued upkeep require significant financial and technical resources. Small businesses, in particular, might find these requirements to be overly- burdensome and are thus restricted from adopting and maintaining the technologies.
Adoption of advanced speech analytics is hindered by a lack of in-house machine learning expertise. Specialised knowledge is needed in developing and operationalizing such tools i.e. algorithms for data processing, linguistic analysis etc. Unfortunately most call centers do not possess these skills leaving these speech analytics technologies to be used little or not at all.
The only challenge with transcribing voice data is ensuring the quality and accuracy, something really difficult to obtain in noisy or complex settings. Imprecise transcriptions bring inauthentic analyses along with superficial understanding.
Taking on these challenges requires substantial investment in next-generation technologies, some of which can be costly, and many of which not only require specialized skill sets to master, but also place considerable demands on resources, constraining implementation.
Voice transcription is difficult to interpret and analyze and usually quite spotty in most organizations. This is where a bit of an elegant understanding of data science and linguistics is needed to pick up on linguistic nuances and context. This dearth of knowledge hinders the ability to utilize or deploy any existing speech analytics applications to their full potential, which only serves another barrier to widespread adoption and efficacy.
Verint Unveils Real-Time Speech Analytics Solution - Verint Systems has improved its Real-Time Speech Analytics offering with a blend of phonetic and full transcription, along with advanced language understanding. They help organisations to proactively roadmap interactions and provide agents with contextual help using complex rules engines.
Changing the outcome of calls and upgrading customer service are increasingly dependent on real-time speech analytics as per a recent white paper from DMG Consulting. These advancements underscore the growing importance and robust market growth of speech analytics technologies in improving organizational performance and customer satisfaction.
As confirmation of this shift, Bharti Airtel recently announced its partnership with NVIDIA to embed AI into Airtel contact center operations using NVIDIA AI software. Airtel can use automated speech recognition technology to audit thousands of calls, identify performance trends among agents and cut down on costs significantly.
These partnerships demonstrate that AI/ML is not only a breakthrough technology in telecom, they also reflect how AI/ML has the potential to transform not just service providers but also can revolutionize their networks, to meet the goal of delivering 10 billion connections on a 100% renewable energy grid to the global market by 2025 (The Science Based Targets Initiative). This partnership represents the increasing relevance of AI-powered solutions in the current business landscape.
Yactraq has teamed with Versadial to seamlessly blend sophisticated speech analytics into Versadial call recording services -- all without the need for additional hardware or software. The plugin introduced in this partnership further allows Versadial customers to label and index every recorded call, delivering meaningful insight for customer service and agent performance coaching.
Yactraq does this to keep sensitive call data secure, enabling Versadial to offer a complete and efficient call recording, with the advanced and thorough call analysis provided by Yactraq's speech analytics engine, delivering significant value in the combination of a great recording solution with the powerhouse speech analytics technologies.
Aural Analytics and Koneksa announce integration of Aural Analytics' Speech Vitals technology into the Koneksa digital biomarker development platform. This partnership is set against a backdrop of a rising recognition of how the acceleration of disease is underpinning a whole new approach to care in all areas, but particularly in conditions like neurodegeneration.
Through their collaboration, they intend to create state-of-the-art digital biomarkers by combining both expertise in an effort to enable the collection of speech measures in clinical trials and push forward research capabilities across pharma pipelines. This collaboration reaffirms that speech analytics has a crucial role to play in clinical as well as research settings.
Symphony has partnered with Google Cloud to enhance its voice analytics capabilities in the financial services sector. By leveraging Google Cloud’s transcription and generative AI, Symphony aims to improve its Cloud9 voice product with advanced speech-to-text and natural language processing features.
This collaboration will enable real-time voice analytics, improving customer service, trade reconstruction, and post-trade processing for over 1,000 financial institutions. Such partnerships highlight the growing importance of AI-powered voice analytics in enhancing operational efficiency and compliance in specialized industries.
In conclusion, the speech analytics market is poised for substantial growth, fueled by continuous technological innovations and increasing adoption across various sectors. Real-time analytics, AI-driven insights, and comprehensive integration capabilities are setting new standards for how businesses leverage voice data.
Strategic partnerships, like those between Symphony and Google Cloud or Aural Analytics and Koneksa, are pushing the boundaries of what speech analytics can achieve. As the market expands, organizations that embrace these cutting-edge solutions will be better equipped to enhance customer experiences, streamline operations, and maintain a competitive edge in an increasingly data-driven world.
Speech analytics is a technology that transcribes 100% of voice calls using AI and derives deep insights, trends, and metrics from each call.
It offers numerous benefits, including improved agent performance, enhanced customer interactions, and a better customer experience, ultimately driving profitability and reducing risk for organizations.
Speech Analytics Managers in the US earn an average annual salary of US$82,800.
The average cost of speech analytics software is between US$40,000 and US$70,000, with the cost of hardware and licensing fees varying greatly depending on the size of the business and the number of phone lines.
Speech analytics has numerous applications, including improving customer experience by understanding customer needs and preferences, optimizing sales by identifying successful sales tactics, enhancing agent performance through targeted training, ensuring compliance with regulations, and generating valuable business insights from customer conversations.
Speech analytics models include real-time speech analytics, which provides immediate insights during a call, and post-call speech analytics, which analyzes calls after they have finished.
Real-time analytics is useful for guiding agents through customer queries, while post-call analytics is useful for detailed analysis and identifying common trends and issues.
