With customers now engaging through various channels like SMS, websites, chats, and social media, the identification of customers’ motives for initiating contact has emerged as a fundamental analytics scenario for nearly all contact-center operations. 

A modern leader handles an unprecedented amount of data. Effective data management becomes even more critical for enhancing the customer experience.  

However, numerous businesses continue to face challenges in capturing and processing voice conversations with customers. These interactions constitute a substantial portion of incoming volume across industries, and future projections indicate their continued significance. Failing to analyze voice conversations hinders the ability to fully leverage digital investments and analytics, impeding significant improvements in customer service. 

Key Challenges 

Traditional organizations encounter three key challenges when striving to comprehend the direct voice of the customer.  

Firstly, they rely on random, manual call-sampling methods that only capture a meager fraction—less than 2 percent—of all interactions, leading to incomplete or unrepresentative raw data sets.  

Secondly, they grapple with outdated processing systems that convert speech to text, but their poor accuracy severely limits the extraction of valuable information.  

Thirdly, these organizations struggle to transform accurately transcribed conversations into meaningful insights, often failing to generate measurable initiatives with a tangible impact on the bottom line. 

Why Speech Data Is Important? 

Speech data provides unique customer insights that are not easily obtainable from other sources. It aids in identifying the root causes of customer dissatisfaction and uncovering opportunities to improve compliance, operational efficiency, and agent performance. 

The outcomes of leveraging speech data include cost savings ranging from 20 to 30 percent, improvements in customer satisfaction scores of 10 percent or more, and bolstering sales performance. Companies that neglect to harness this information run the risk of lagging behind their peers, as speech analysis becomes an essential expectation within contact centers. 

Continuous advancement of digital tools, coupled with natural language processing capabilities and industry expertise, is enabling businesses to enhance quality, efficiency, and customer experience.  

AI Advances Offer Improved Speech Recognition 

The latest approaches in automated speech recognition utilize neural network language models that consider a larger volume of data, resulting in more precise transcriptions. Numerous analytics providers have enhanced their offerings with complementary capabilities, including security features like automatic data masking and password protection. These tools often provide flexibility in deployment options, allowing companies to choose between on-premises or cloud-based solutions based on their infrastructure and data hosting strategy. 

Despite persistent challenges, companies can successfully overcome them by adopting a few key strategies through awareness, careful planning, and judicious intervention. 

1. Addressing unclear or timid use cases

Some companies may not fully comprehend the potential value of speech analytics, leading to a lack of imagination regarding how to utilize the derived data effectively. To overcome this, analytical teams should have clear direction on how to leverage the insights obtained from customer sentiments, such as reducing call volumes, increasing sales, or improving customer satisfaction. 

2. Enhancing contextual recognition

Relying solely on a manual, word-based tagging approach for understanding customer and employee intent can result in poor categorization. For instance, if only “very happy” is captured from a sentence where a customer expresses dissatisfaction, the intent will be miscategorized. It is crucial to develop a comprehensive data dictionary that accurately captures multiple intents present in the same interaction. Additionally, recognizing brand names or products, including their nicknames or abbreviations, adds complexity to the process. 

3. Expanding analytical capabilities

The true value of speech recognition emerges when it is integrated with other data sources. Combining speech data with additional customer or telephony data provides the complete context of a call, which is often essential to understand its meaning. For example, if a customer has made multiple calls about the same issue, it may indicate a systemic problem rather than a particular agent’s ability to resolve the issue. Leveraging customer data allows organizations to identify recurring patterns and root causes of frustration. 

4. Limited scalability

Without buy-in from other business units, progress can falter, especially when voice analytics competes for limited resources among various technology and strategic priorities. To avoid falling short of expectations, it is crucial to involve all relevant stakeholders in the development and implementation of insights derived from speech analytics. By fostering collaboration and engaging all units, organizations can overcome scalability challenges and maximize the value of their speech analytics initiatives. 

Natural Language Processing and Analytics 

Speech analytics capabilities offer a wide range of applications and can be tailored to specific industries, yielding endless use cases. Here are some standard, cross-industry use cases: 

  • Increase data coverage: Instead of sampling, organizations can examine all available unstructured voice data to generate sharper insights. 
  • Monitor KPIs: Personalized data visualization dashboards enable clients to track various conversational moments, including supervisor escalations, compliance violations, customer satisfaction, and average handle time (AHT). 
  • Accelerate time to insights: Automated AI transcription speeds up analysis and provides full call coverage, significantly reducing diagnostics time and facilitating the faster implementation of recommendations. 
  • Uncover hidden inefficiencies: Monitoring contact center KPIs helps identify inefficiencies and uncover root causes while gaining insights from the customer’s perspective. 
  • Personalized training: In addition to tracking operational KPIs, leveraging deep insights from customer calls enables customized coaching sessions for agents and supervisors, leading to improved customer satisfaction. 
  • Improve customer experience: Sentiment analysis allows teams to identify factors driving positive customer engagement, such as empathy statements, as well as indicators of negative experiences, such as supervisor escalations. 
  • Create better interactions: Action-based insights generated by speech analytics enhance the external communication environment, enabling companies to prioritize tech-enabled strategic initiatives for maximum returns on investment. 
  • Uncover automation opportunities: Speech analytics can reveal automation possibilities by identifying extended periods of silence during calls, indicating potential areas for automation. 
  • Improve self-serve options: Speech analytics provides insights into the percentage of unsuccessful self-serve calls, breakdown by category, and agent education about self-serve options. This helps improve problematic self-serve options based on caller feedback. 
  • Increase upselling and cross-selling: Speech analysis identifies agents who excel in upselling and cross-selling, enabling targeted coaching to improve these skills for others. 

Capturing Call Center Data 

Companies that effectively capture, extract, analyze, and utilize call-center voice data gain valuable insights into customer motivations, improve the customer and employee experience, and measure customer satisfaction. This approach surpasses the limitations of infrequent and unreliable customer surveys. The process of capturing call-center data involves five key steps: 

  • Create a list of use cases: Define specific objectives such as targeted coaching, automated quality assurance, understanding customer sentiments, workforce management, fraud reduction, collections optimization, or sales enhancement. 
  • Think systematically: Align use cases with industry and strategic objectives, considering the entire call-center operation rather than focusing solely on individual metrics. 
  • Partner with the right speech-analysis provider: Select a vendor that can provide expertise in sound quality recording, accurate transcriptions, intention understanding from transcriptions, emotion identification, and granular metadata generation for detailed call analysis. 
  • Listen to the front line: Foster collaboration between Quality Assurance (QA), analytics, and business-unit leaders to actively test and leverage speech insights with frontline staff. Break down handle time into smaller segments to facilitate effective agent coaching and analyze call volume in granular detail to gain accurate workload estimates. 
  • Show financial impact: Establish a connection between measurement, initiatives, and financial value. This enables business leaders to prioritize changes that deliver significant bottom-line impact and drives improvements at all levels of the contact center. 

Summing Up 

Despite the availability of advanced listening tools that offer valuable data, extracting actionable insights remains a challenge. It requires organizations to possess the necessary expertise and understanding to interpret the implications of the data and effectively use them to drive measurable initiatives with tangible business impact. To achieve this, organizations should invest in comprehensive analytics capabilities that not only interpret voice-analytics results but also measure the customer experience and swiftly implement necessary changes. By prioritizing these investments, organizations can unlock the true benefits and reap the rewards of their voice-analytics efforts.