As speech analytics technology continues to evolve, the future is looking promising. Artificial intelligence (AI) and Natural Language Processing (NLP) are becoming increasingly important in the fields of customer experience and data-driven decision-making, fueling the adoption of speech analytics across multiple industries and use cases. As the potential of speech analytics becomes clearer to enterprises, their investments and adoption increase, fueling further evolution of an already powerful technology.
Today we investigate our top 5 predictions of what the future might hold for speech analytics.
The development of more advanced capabilities in the fields of AI – and in particular, NLP will enable higher accuracy combined with a more sophisticated analysis of speech data. This will lead to more valuable insights for users and enable them to make more effective data-driven decisions.
Real-time analysis is a potential capability we could see hitting the scene thanks to greater processing power becoming available. As computing power increases, real-time speech analytics will be able to process greater quantities of speech data and make increasingly accurate predictions in live time. This will allow for improvements for example regarding critical emergency response use cases where immediate action will be taken based on the real-time conversations. We predict we’ll be able to recognise emotional states more accurately in live time too, meaning call agents will be able to provide immediate support and interventions in mental health crises. Overall, we expect real-time analytics to increase efficiency across the board also when it comes to basic call re-routing, transcriptions, and reducing the workload on human operators.
As globalization continues, the capacity to handle speech in multiple different languages will become more important for enterprises and their customers. Today the main languages of the world are relatively well-served, but we expect to see similar growth soon for other languages. Customers are becoming more demanding and competition on the market is tough. As enterprises search for new ways to differentiate and serve their customers more appropriately, speech analytics will have to be available in local languages around the world. By analyzing speech in multiple languages, speech analytics tools will improve accuracy and provide more reliable insights on how different demographics interact with enterprises, allowing the enterprises to make strategic decisions that work on a more targeted level. It will allow a greater diversity of contexts and reach a wider audience, serving more people.
In turn we predict this will lead to more culturally appropriate deployment of speech analytics tools. Speech analytics tools that can analyze speech in multiple languages will be able to provide insights into cultural nuances and variations in communication. This will help to bridge the gaps in communication that we still see today. Even better would be if we could imagine the tools would be able to provide insights into global cultural trends and patterns of communication. This would enable enterprises to make better-informed decisions about their operations and their global strategic directions overall.
Accuracy is already a crucial aspect of speech analytics, and we expect to see significant improvements on this, which in turn will make the tools more reliable and provide knock-on benefits.
Insights from speech analytics tools can be expected to become more reliable and cohesive, providing information about more aspects of the speech data. We expect to see insights about the content of the speech, the meaning, and the context of the speech data. This will prove particularly important for us to be able to advance in fields like mental health, where accurate interpretation of the spoken language is critical. However, we expect it also to provide more valuable insights to other fields too such as customer service. As multiple new languages integrate to different speech analytics tools, we’ll see a wealth of improved insights to new markets and cultures, new customer bases and ecosystems.
We expect efficiency to skyrocket thanks to such tools. The market is already buzzing as employees and customers start to understand more about conversational AI in particular and promises of efficiency have been a key driver of the hype. We predict the technology to deliver on its promises X-fold, even in ways we can’t yet imagine. The workload on the shoulders of human agents will be greatly alleviated, call handling times reduced drastically, and heavy strategic decision-making processes will lighten. Organizational processes will improve thanks to decisions based on data that consider multiple aspects of the business functions and that understand more deeply the needs of the customers and the employees.
Indeed, as the data provides deeper understanding to the customers’ needs, the overall customer experience will improve too. Already we see enterprises using speech analytics to find new ways to enhance the customer experience, and we expect to see this grow continuously over the next few years at least. The improvements in speech analytics will provide more accurate, and importantly, more relevant insights into interactions between the enterprises and the customers, which will allow enterprises to make efficient and targeted improvements to every step of the customer journey. We foresee that more personalised services will start to crop up, not only in terms of the languages served but also in terms of the processing of calls, tone of response, and other such improvements.
Generally speaking, “emotional intelligence” refers to a person’s capacity to understand, manage and express emotions effectively. In the context of conversational AI, emotional intelligence can play a significant role in enhancing communication, as the person will have the feeling they’re communicating with someone who understands them and their emotions.
Emotional intelligence will most likely play an important role in the future of speech analytics by improving the accuracy, personalization, empathy, and overall effectiveness across a host of applications, industries and use cases. We expect speech analytics to involve analysis of emotions during customer interactions as standard, which in turn should lead to higher perceived empathy and an improved customer-centric approach to customer service.
As accuracy in emotion detection takes off, the technologies could be extended to new industries such as health care, especially with a focus on the currently underserved mental health sector. We expect to see emotional intelligence used for training speech analytics algorithms to detect emotions with higher precision, leading to more reliable insights and more targeted action, for example, regarding mental health interventions. Services will be more personalized – by analyzing cues such as tone, pitch, and volume, speech analytics tools will provide more personalized services to improve customer and patient experience and business outcomes.
As we see evolution in more nuanced communications, emotional intelligence will grow over areas such as understanding cultural differences. This will enable enterprises to serve a wider range of customer demographics more accurately and with a vastly improved customer experience. We also expect more accurate recognition of sarcasm and irony. This will lead to more effective communication with less instances of misunderstanding, especially in sensitive situations.
Overall, we can expect emotional intelligence to lead to higher levels of empathy when it comes to decision-making. When an enterprise has access to more information regarding the emotions of the customer, it can make better decisions to ensure the emotions remain positive throughout the entire customer journey with them – in turn, leading to better business outcomes.
As a wider recognition of data privacy and security takes hold, speech analytics providers will be expected to invest more heavily in ensuring regulatory compliance and protecting sensitive customer information. This will involve a deeper focus on privacy and security regulations as well as more careful considerations of the ethics involved in speech analytics.
Informed consent will continue to be considered a bare minimal legal necessity, meaning that consent will be required from those whose speech is analyzed. They should be informed clearly on the purpose and scope of the analysis, and they must retain the right to opt-out of the analysis at any time. To protect an individual’s privacy further, speech analytics tools can anonymize the data being analyzed. This means that any personally identifiable information (PII) such as names, addresses, phone numbers, and dates of birth will be automatically removed from the data before any analysis.
Data security will also become stricter, and tools will be designed to ensure that all data analyzed is secure. The data needs to be protected from any unauthorized access internally or externally and must not be used or disclosed in any unauthorized way. Such protocol can involve procedures such as encryption of the data to ensure it is secure in compliance with laws such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA) in the United States. Furthermore, limiting the data retention time can help to minimize the risk of data breaches or unauthorized access. The data should be retained only for as long as is necessary to achieve the intended purpose, then should be disposed of properly – and we don’t foresee or advise any changes to this practice.
On top of the legal considerations, ethical considerations will remain a hot topic. As mentioned, data security is already a major concern for speech analytics, and as the technology becomes more sophisticated, and data collection becomes increasingly widespread, the risk of privacy violation also increases. Enterprises should ensure compliance to avoid not only the legal issues but also any ethical implications relating to breaches. Transparency along with the aforementioned informed consent is advisable. Enterprises should be transparent about their processes to all individuals concerned and should protect against any misuse of the data they process. There is a risk that speech analytics tools could be misused for unethical purposes such as surveillance or manipulation. Enterprises should become increasingly held accountable and should ensure they minimize any risk of unethical misuse of their data.
We also predict that enterprises will be held increasingly accountable for bias and discrimination in the algorithms they use. Like any machine learning algorithm, speech analytics tools can be biased and lead to discrimination. They could reinforce existing biases if they’re not designed and tested with care and expertise. We have seen some famous examples recently of racism and sexism in different fields of AI, such as racism in healthcare and sexism in employment. As speech analytics tools become more widespread, it will become increasingly critical that they’re designed and tested to be fair and unbiased. Using diverse data training sets can help to mitigate biased outcomes and enhance reliability – vigilance is key.
We predict that speech analytics will be integrated with other technologies such as chatbots, virtual assistants, predictive analytics and more, and that this will lead to a near seamless customer experience, clearer and more personalized communication, and smoother automation. As communication is improved, so will be decision-making. The customer and the enterprise both will enjoy the benefits across a growing range of industries. While speech analytics has already been widely adopted in industries such as contact centers, we predict growth in other industries such as finance, retail, and hospitality to gain insights to customer behavior and improve business outcomes. Here are the main industries where we expect to see developments.
Speech analytics will be used to analyze customer interactions and provide insights to their preferences, pain points and overall satisfaction levels, which can be leveraged to improve customer service and drive customer loyalty.
Analysis of patient-doctor interactions will provide insights to medication adherence and treatment efficacy, which will be used for improving patient outcomes and to reduce the costs of healthcare. Emotion recognition will be implemented to health industries to alleviate the mental health crisis in a cost-efficient way. Tools could be used to analyze speech patterns to identify signs of depression or anxiety, or other mental health concerns, and this information could be used to allocate early intervention and treatment for people at risk.
Student-teacher interactions can be analyzed to provide insights into student engagement, their understanding of topics, and their educational progress. The insights can be used to improve learning outcomes and teacher performance, and even to personalize educational programs.
Speech analytics tools will be used to analyze financial transactions and customer interactions with financial institutions, providing insights into security around fraud prevention, risk management, as well as customer experience topics such as satisfaction. The insights are expected to reduce fraud and enhance product understanding for enterprises providing financial services.
Analyzing customer interactions with marketing campaigns will provide key insights into customer preferences and behaviors, to improve on marketing campaign performance and drive sales. Analytics tools could be used for analyzing social media posts and customer interactions to reveal insights to customer sentiment and behavior, improving social media marketing and customer engagement strategies.
When it comes to analyzing legal proceedings, technology could provide insights into witness credibility, jury sentiment, and case outcomes. Such information could improve legal proceedings and allow for fairer outcomes. Real-time analysis could be used for allocating immediate law enforcement and security measures, reducing crime, and providing assistance more quickly.
So, speech analytics has the potential to drive results across many industries, providing insights into customer behavior, patient health, student progress, financial transactions, marketing campaigns and legal proceedings. As speech analytics technology continues to advance, it’s likely that new applications will emerge to support the specific use cases too. We can expect to see new and innovative applications emerging over a variety of industries with the potential to improve customer experience, patient outcomes, employee development and more.
Are you looking for ways to enhance your speech analytics? Contact us to find out about our StageZero Speech Analytics Suite.