Business decisions that impact the customer experience shouldn’t be based on guesswork, low-response surveys, or a quality assurance process that only analyzes 1-2% of your customer service interactions. Conversation intelligence software allows you to review and extract insights from 100% of your customer conversations to optimize your contact center’s performance, improve key business metrics, and ultimately elevate your customer experience. See how Tethr can turn your conversation data into actionable insights:
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Conversation intelligence refers to the process of analyzing and interpreting customer interactions across multiple channels, such as phone calls, emails, chat, social media, and text messages, to gain insights into customer behavior, preferences, and sentiment. It is a technology-driven approach that uses sophisticated software and algorithms to automatically transcribe, categorize, and analyze customer interactions, to provide actionable insights that can be used to improve customer experience, agent performance, and overall business performance.
This component converts spoken words into text that can be analyzed and understood.
This component uses machine learning algorithms to understand and interpret the meaning of the text transcript.
Conversion analytics use the data generated from speech-to-text and NLP to provide insights and metrics that can be used to evaluate customer interactions, agent performance, and overall business performance.
Conversation intelligence provides a holistic view of customer interactions across multiple channels, which can be used to identify patterns and trends, understand customer needs and preferences, and improve the overall customer experience. It helps companies reduce contact center and operational costs, improve customer retention, and drive revenue.
Conversation intelligence platforms like Tethr give companies a competitive advantage by providing valuable insights into customer interactions, agent performance, and overall business performance. Here are a few of the ways businesses benefit from Tethr’s conversation intelligence platform:
Tethr can analyze customer interactions across multiple channels to identify common issues or pain points that customers have with the company's products or services. This can help to address these issues and improve the customer experience, creating a competitive advantage by providing better customer service than competitors.
Contact centers use Tethr to pinpoint leading indicators of churn, allowing them to proactively address churn risks, strengthen loyalty, and increase the lifetime value of their customers.
Tethr helps operational leaders look at big-picture trends, such as agent behaviors or other factors that impact average handle time and first-call resolution. Ops leaders can use conversation insights to inform initiatives to improve these key metrics and reduce contact center costs.
Managers use Tethr to evaluate the performance of individual agents and identify areas where additional training or coaching may be needed. This helps agents provide consistently high-quality service.
Most contact centers only review 1-2% of their customer conversations due to staffing constraints and the time-consuming nature of the QA process. Tethr allows businesses to upload their QA criteria and automatically review every conversation, saving their QA managers time and enabling them to provide better feedback to agents.
Tethr can surface insights into customer needs and preferences so companies can improve their self-service options, such as chatbots or virtual assistants. Tethr’s AI can analyze customer queries to help data science teams train chatbots on how to best respond, as well as detect what chatbot responses frustrate customers. This can create a competitive advantage by providing faster, more effective self-service options than competitors.
Tethr can generate a predicted CSAT score for every customer interaction based on the words used in the conversation. This can help companies better understand the drivers of satisfaction and dissatisfaction so they can deliver better overall experiences.
Companies can use Tethr to analyze customer interactions and identify the top-performing sales offers, helping them grow their revenue while delivering the products or services that best meet their customers' needs.
Conversation intelligence can show operations leaders where they have the biggest opportunities to reduce contact center costs and remove friction points for customers.
Conversation intelligence software can identify and highlight leading indicators of churn, helping customer service teams save more customers and increase loyalty.
Manual call monitoring is extremely labor-intensive and time-consuming, and most companies can only audit a small percentage of interactions. Conversation intelligence enables customer service teams to automatically review 100% of customer interactions based on specified criteria.
Customer service leaders can use conversation intelligence software to better understand how customers feel about their experience based on what they are saying. Tethr even generates a predictive CSAT score, eliminating the need for post-call surveys.
Contact center managers can see how every agent is performing on key metrics across every customer conversation, allowing them to tailor their coaching to each team member.
Conversation intelligence enables CX leaders to capture valuable voice of the customer data from service interactions, helping them uncover common customer challenges and needs.
Conversation intelligence helps CX leaders find and address the factors that are increasing the length of calls, driving repeat contacts or channel switching, or causing customers to churn.
CX leaders can use conversation intelligence software to measure customer sentiment in each interaction–and identify the controllable factors that are impacting it.
From CSAT to sentiment, conversation intelligence allows CX leaders to measure key performance indicators without needing to rely on low-response surveys.
By analyzing customer interactions, sales teams can identify and prioritize process improvements, such as updating sales scripts based on the most successful offers.
Conversation intelligence can uncover trends in customer needs, preferences, and pain points, enabling sales teams to create more targeted and effective sales pitches.
Sales teams can use conversation intelligence to identify customers who are more likely to make a purchase or have a higher lifetime value, allowing them to prioritize their sales efforts.
Conversation intelligence can identify potential compliance issues and ensure that sales reps are adhering to company policies and procedures.
Conversation intelligence can be used to evaluate the performance of individual sales reps, and identify areas where they need additional training or coaching.
Conversation intelligence can be used to forecast sales pipeline, identify potential issues and opportunities, and optimize sales strategies and tactics.
Sales team can analyze customer conversations to identify opportunities for upselling or cross-selling and find the best offers, rebuttals, and language that leads to closed sales.
Product teams can gather valuable feedback about customers’ experiences with their products based on insights from service interactions.
Conversation intelligence software can track product issue mentions and send automatic notifications to product teams when they occur, ensuring issues are documented and resolved.
Conversation intelligence enables product teams to see which product questions or requests customers are bringing up most frequently so they can prioritize enhancements or new product development.
Yes. Conversation intelligence software automatically transcribes phone conversations and analyzes the text. Because conversation intelligence software works with both text and audio data, you can use it to analyze customer conversations across channels, including voice, live chat, email, social media, and review sites.
Conversation intelligence can be used in a marketing strategy in a number of ways, such as:
1
By analyzing customer interactions, conversation intelligence can be used to identify customer demographics, interests, and behaviors, which can help to identify target markets for marketing campaigns.
2
By analyzing customer interactions, conversation intelligence can be used to extract information about customer preferences and demographics. This can be used to personalize marketing messages and campaigns, making them more effective. For example, if a customer mentions during a conversation that they are interested in a new product in the future, you could automatically add that customer to a future marketing campaign about that product.
3
Conversation intelligence can be used to analyze customer interactions and segment them based on their behavior, preferences and demographics. This can help to tailor marketing strategies to specific segments of customers.
4
Conversation intelligence can be used to identify common issues or pain points that customers have with the company's products or services. This can help to address these issues in marketing campaigns and improve the customer experience.
5
Conversation intelligence can be used to analyze customer interactions before and after a marketing campaign, to determine its effectiveness and identify areas for improvement.
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Conversation intelligence can be used to analyze customer interactions to understand their sentiment and emotions, which can help companies identify customers who are at risk of churning, and take steps to prevent it.
7
Conversation intelligence can be used to forecast marketing campaigns performance, identify potential issues and opportunities, and optimize marketing strategies and tactics.
Yes, conversation intelligence software can differentiate between various conversation styles. This is accomplished through the use of natural language processing (NLP) and machine learning algorithms, which are used to understand and interpret the meaning of customer interactions.
These algorithms can be trained on a variety of conversation styles, such as formal, informal, technical, or industry-specific language. Additionally, the software can be set up to recognize different accents, dialects, and languages, which allows it to understand and interpret a wide range of customer interactions.
Conversation intelligence software can also be configured to recognize different conversation contexts, such as sales, customer service, technical support, and so on. This allows it to understand the intent behind customer interactions, and provide relevant insights and metrics accordingly.
Furthermore, the software can be trained to recognize different levels of customer emotions, sentiment, and tone, which helps to understand customer's feelings, mood and opinion during the conversation, this information can be used to improve customer experience and agent performance.
Overall, conversation intelligence software can differentiate between various conversation styles, by using natural language processing, machine learning algorithms, and context recognition. This allows it to understand and interpret a wide range of customer interactions and provide relevant insights and metrics accordingly.
Artificial Intelligence (AI) can analyze speech using a combination of techniques, including:
This technique converts spoken words into text that can be analyzed and understood by the AI. This is typically done using automatic speech recognition (ASR) technology, which uses machine learning algorithms to transcribe spoken words into text.
Once the speech has been transcribed into text, NLP techniques are used to understand and interpret the meaning of the text. This can be done using a variety of techniques, such as parsing, semantic analysis, and sentiment analysis.
Machine learning algorithms are used to train the AI to recognize and understand specific patterns, words, and phrases in the speech. This can include recognizing keywords, identifying sentiment, and understanding the intent behind the speech.
The AI can be configured to recognize different conversation contexts, such as sales, customer service, technical support, and so on. This allows it to understand the intent behind customer interactions, and provide relevant insights and metrics accordingly.
AI can be trained to recognize different levels of customer emotions, sentiment, and tone, which helps to understand customer's feelings, mood and opinion during the conversation, this information can be used to improve customer experience and agent performance.
Yes, conversation intelligence tools can find nuanced feedback by using a combination of speech-to-text, natural language processing, machine learning and other advanced technologies.
Conversation intelligence tools can transcribe, categorize and analyze customer interactions, providing a holistic view of customer interactions across multiple channels. This allows the tool to understand customer needs, pain points, sentiment, emotions and the context of the conversation.
It can also provide detailed information about agent performance, such as the use of language, tone, and the effectiveness of the interaction.
Additionally, conversation intelligence tools can also provide feedback on customer interactions in the form of metrics, such as the average call handle time, first call resolution, customer satisfaction, and so on.
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