January 12, 2024
Decoding the Power of Words: A Detailed Overview of Contact Center Speech Analytics
Key Features to Consider When Choosing a Speech Analytics Tool
Factors such as poor audio quality and noise must be considered in the solution. A good solution will also be Natural Language Processing (NLP) to answer questions, classify texts, and resolve information search problems.
1. Designed for contact centers
Avoid inflexible "black box" solutions lacking adaptability or updates. Identifying business terms is crucial for speech analysis, enhancing transcription accuracy. Inaccurate transcriptions yield useless or potentially harmful insights that impact compliance adherence when applied across numerous calls.
2. Customized recognition of business terms
Your solution should analyze customer interaction’s tone, pitch, and other vocal cues. Businesses may evaluate client sentiment, comprehend emotional reactions, and modify customer care tactics with this capability.
3. Detecting emotions
Your solution must stay adaptable, incorporating regular transcription improvements and an easy feedback loop for sustained high accuracy.
4. Quality enhancement
Consider these four key components when implementing a speech analytics solution:
Natural Language Generation (NLG) is essential here. As a subset of NLP, NLG enables AI to generate real-time notes and suggestions for agents during conversations, summarize calls, and create detailed post-call summaries, alerts, and training contributions.
Find Your Partner
The development of speech analytics applications presents numerous challenges that demand robust software engineering and machine learning expertise. At byVoice, our AI engineers have practical experience gained on multiple projects and research focused on voice applications.
Do you have a project idea involving speech recognition or related technologies? Please contact us for a detailed discussion of your ideas.
We have reached the most crucial part of this article. The logical conclusion from all that has been said above is that speech analytics is an indispensable tool. If you are a software provider for telecommunications companies, this tool should be among your offerings.
Tool Development vs. Out-of-the-Box Solutions: What to Choose
But what way will be the best fit for your business — to develop your own speech analytics tool or to integrate/implement a ready-made one? Let's weigh the benefits and drawbacks of each option.
Developing a speech analytics tool from scratch
Cons
Pros
More time and financial resources. Initial investment may be higher compared to implementing a ready-made solution.
A team with expertise in speech analytics and software development is a must.
Ongoing maintenance and updates are solely your organization’s responsibility.
Bug fixes and feature updates may take longer to implement.
Complete control over the features, functionality, and design. It means you can tailor the tool to your specific requirements.
Easier integration with existing systems and workflows.
Flexibility to add or modify features as your business grows.
Cons
Development from scratch typically requires more time and financial resources. Additionally, initial investment may be higher compared to implementing a ready-made solution.
Development project requires a specialized team with speech analytics and software development expertise.
Ongoing maintenance and updates are solely the responsibility of your organization.
Bug fixes and feature updates may take longer to implement.
Implementing a ready-made tool for speech analytics
Cons
Pros
May lack specific features or customization options.
May require adjustments to existing workflows to accommodate the tool.
Integration with other in-house systems may be less seamless.
You will have to find a new product if the provider stops developing and supporting the current one.
Rapid implementation, as the tool is already developed and tested.
Ready-made solutions have lower initial costs compared to developing from scratch.
Access to new features and improvements without the need for in-house development.
The vendor usually provides regular updates and support.
Often comes with a wide range of features and functionalities developed based on industry best practice
Cons
May lack specific features or customization options needed for unique business processes.
May require adjustments to existing workflows to accommodate the tool.
Integration with other in-house systems may be less seamless.
If the vendor discontinues the product, it could lead to a need for a replacement.
Attempting to develop a speech recognition system from scratch would be a mistake. Large companies invest years in this technology, and catching up with them is challenging. Additionally, maintaining a speech recognition system at the required level of quality demands constant investment.
byVoice Comments
Similarly, a mistake would be to develop text analysis components from scratch. By the time the project is completed, everything can be outdated. All because of rapid changes in the AI field.
The optimal strategy is integrating and tuning ready-made AI components into your own RA (Recognition and Analysis) system. A good architect anticipates the possibility of replacing one component with another, allowing you to choose AI component providers and provide your users with options. For example, solutions from different speech recognition solution providers can vary significantly in quality depending on language and domain.
Ensuring the acquisition of conversation records in an optimal format will be essential. The recognition system will necessitate dual-channel conversation recordings in lossless formats such as WAV or FLAC. You can access voice communication by using the following protocols:
SIP
RTP
MGCP
Megaco
However, the integration approach frees you up to concentrate on creating your products' user interfaces and helping your customers integrate RA into their business procedures.
Let’s See What Your Business May Lose Without Speech Analytics
A B2B partnership is typically a win-win combination. It can easily become a lose-lose scenario if you cannot give your customers the speech analytics solution they require. Speech analytics allows you and your customers to generate additional revenue.
Opportunities for upselling and cross-selling
As mentioned earlier, telecom companies recognize the value of a tool like speech analytics and are actively integrating it into their IT infrastructure. The providers may offer speech analytics as part of their solutions. Otherwise, they risk losing their competitive edge and businesses' interest.
Customer retention
The telecom industry is dynamic and requires constant technological advancements. Offering speech analytics tools is a commitment to adapting to evolving customer needs.
Adaptability to industry trends
Picture a call center with thousands of operators handling hundreds of calls daily. Managing such call volumes requires advanced technology and a proficient analytical team. However, manual processing is limited to approximately 1-3% of calls, even with a skilled team.
When calls are processed manually, the true extent of problematic areas remains undisclosed.
Speech analytics is valuable; many telecom companies that have incorporated it into their daily operations have long understood this. Companies who do not provide this tool risk to lose these significant benefits:
How Your Customers Can Benefit from Speech Analytics: TOP-6 the Most Popular Use Cases
Use case
Benefit
Customer experience improvement
Analyzing customer calls and identifying common pain points and concerns.
Proactively resolves client complaints and raises the standard of service and client satisfaction levels.
Quality assurance
Evaluating agent performance by analyzing call interactions.
Provides insights into agent communication skills, script adherence, and overall performance, facilitating targeted training and coaching for improvement.
Sales optimization
Identifying successful strategies, customer objections, and areas for improvement.
Helps sales teams reconsider their approach, tailor pitches to customer needs, and enhance conversion rat.
Operational efficiency
Identifying bottlenecks and inefficiencies in call center processes.
Streamlines workflows, optimizes resource allocation, and enhances overall operational efficiency.
Market research
Gaining insights into market trends and competitor analysis.
Helps organizations stay informed about market dynamics, customer preferences, and competitor strategies.
Employee training and development
Identifying training needs and areas for agents’ improvement.
Facilitates targeted training programs and continuously improves agent skills and performance.
So, what is real-time speech analytics? In simple terms, speech analytics converts phone calls into text and then works with the text rather than the audio. Text fragments are easier to segment, sort by keywords, and identify objections, threats, or other expressions.
Further analysis allows businesses to promptly identify and resolve problematic issues, enhancing the efficiency of operator work. Recently, the quality of call transcription is not just acceptable but very high: with good recording quality and the absence of extraneous noises, accuracy reaches 99%.
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This is a big deal when it comes, for example, to the sales department, where employees make at least 100 calls daily with an average duration of 5 minutes, that's already 500 minutes. Manually processing such volumes is time-consuming and expensive.
Transcribing calls into text and further analyzing textual information comes in handy. The obtained text is much easier to process than most audio files.
Speech and text analytics are branches of natural language processing (NLP) that involve the analysis of spoken or written language, respectively, to extract meaningful insights and information.
Speech and text analytics: let's delve into the basics
Let’s consider how they differ:
Speech analytics
Text analytics
Data source
Spoken language, typically in real time, is recorded from phone conversations or other audio sources.
Various sources, such as emails, social media posts, articles, etc.
Channels optimized for
Two-sided exchanges such as service, support, and sales calls.
One-side feedback such as surveys, customer reviews, social media, etc.
Complexity
More complex. It entails processing various accents, dialects, background noise, and other acoustical information-related factors.
Can be more straightforward since text is usually more structured and less susceptible to external factors such as intonation and pronunciation.
Applications
Often used in audio transcription, analysis of emotional tone to identify customer sentiment and key topics and assess agent performance.
Applied for social media monitoring, document processing, automated responses to questions, and various other applications.
Speech analytics is a component of conversation intelligence, focusing on the call recording and transcription aspects that convert interactions into business outcomes.
Within the broader scope of conversation intelligence, various elements are encompassed, covering end-to-end operations, such as:
Is speech analytics the same as conversation intelligence?
Verify that the customer call handling procedure satisfies service standards. Tools for interaction analysis and rule compliance are part of automated quality assurance.
Quality monitoring and automated QA
This covers structured techniques for agent coaching and training that will increase agents' output, sharpen their abilities, and address any problems.
Coaching and training workflows
Performance analytics tracks, analyzes, and understands essential indicators (like response times and customer satisfaction scores). These metrics show how efficiently agents are operating overall.
Performance analytics
Real-time AI provides immediate analysis and decision-making during live interactions. This tool offers quick insights, allowing for rapid adjustments and responses.
Real-time AI
Generative AI can generate natural language recommendations, responses, and other data. It can help agents save their time and effort.
Generative AI
How Does Contact Center Speech Analytics Work?
How is this implemented in practice?
Call processing using speech analytics tools can be divided into four main phases:
This is the initial phase where raw call data is recorded, collected, and prepared for analysis. It includes the following activities:
Data collection: Speech analytics tools capture audio recordings of phone conversations between customers and agents.
Data transcription: The recorded speech is transcribed into text, converting spoken words into a more easily analyzed format.
Data cleaning: The transcribed text undergoes cleaning to remove irrelevant information, such as background noise or non-speech sounds.
Data structuring: The cleaned and transcribed data is organized into a structured format. This makes it suitable for further analysis.
Sentiment analysis: Speech analytics tools analyze the tone and sentiment of customer and agent interactions to determine the overall mood of the conversation.
2. Data processing
1. Setup
Setting up the parameters, starting the analytics process, deciding on the scope of the analysis, and determining the best time and method for conducting it are all included in the setup step.
You review the processed data for patterns, trends, and relevant information. Here, data goes through the following stages:
Speaker identification: The tool identifies speakers (customer, agent) to attribute specific statements or sentiments to individuals.
Keyword extraction: Relevant keywords and phrases are extracted to identify common themes or issues discussed during the calls.
Categorization: Calls are categorized based on predefined criteria (product issues, customer complaints, positive feedback, etc.).
3. Data analysis
This phase involves deriving meaningful insights from the analyzed data.
Pattern recognition: Speech analytics tools identify recurring patterns or trends in customer-agent interactions, helping understand common issues or concerns.
Performance metrics: Insights into agent performance, customer satisfaction levels, and areas for improvement are generated.
Root cause analysis: The tools analyze issue context and content. It is required to pinpoint the underlying causes of typical difficulties.
Report generation: Reports and dashboards containing an overview of call trends, customer sentiment, and operational performance are created from the insights.
4. Insight generation
How byVoice implemented this: based on a real project
byVoice collaborates with a leading provider of communication services for telecom companies. They include Cloud Contact Center and Cloud PBX, with a built-in speech analytics tool. For this client, we have integrated solutions according to the following scenario:
1. A Cloud PBX records conversations into files.
2. We transmit this data to a speech recognition system (an external solution).
3. Then, the whole conversation is tagged based on user-defined rules.
4. The text and tags are stored for tag-based searches and tag usage statistics.
Currently, many companies track only about 5% of calls, leading to siloed and inaccessible data. Speech analytics tools can transcribe 100% of conversations using AI. This technology provides detailed reports on interactions along with accurately identifying speech in audio recordings and translating it into text.
In addition to this purpose, speech analytics helps telecom companies with the following:
But if you represent a telecom company, don't rush to launch a project to develop such a tool for your business telecommunication system immediately. In this article, we will answer two questions:
Diversifying their income
Strengthening their position in the market
Increasing the value of their services
Attracting and retaining customers
1. What is under the hood of this technology?
2. Why does developing speech analytics tools from scratch mean throwing money away?
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