Customer Care, streamlined with AI.

Advanced analytics tools combine artificial intelligence, existing data, and call center predictive analytics to help improve your call center’s performance.

Using a combination of the data we extract from your customer engagement channels and machine learning, we allow you to easily spot problems in your contact centers, accurately measure customer satisfaction and agent behaviors, and identify customer trends.

You can use these actionable insights to drive business outcomes, such as improving customer loyalty.

A good customer service agent can provide an excellent customer experience while reducing the cost of services. Tethr taps into the power of machine learning and artificial intelligence to measure how effective call center agents handle customer feedback and customer relationships.

We score each agent and team based on the agent’s impact and on the call difficulty, and provide succinct, actionable information that tells you what your call center team is doing right – and what they need to fix.

Our call center data analytics software analyzes data from contact centers such as average handle time and other key performance indicators, but also works on improving customer satisfaction by measuring and identifying other agent behaviors such as uncertainty, confusion, and negative language.

We know your customers don’t always speak directly to call center agents. We have many customers who use Tethr to analyze the call center operations, but we don’t stop there.

Our suite of contact center analytics solutions can also analyze conversations you have via web conference, chatbots, live chat, and case management. We also offer integrations with customer relationship management platforms and several cloud service providers.

The voice of customer (VOC) refers to the customer feedback about a particular product, service, or experience. It is important for companies to listen to the voice of the customer to understand their overall customer experience and customer journey and identify what's needed to improve their offerings. Companies often collect voice of customer data from customer surveys, but increasingly, companies rely on artificial intelligence tools to capture actionable insights about customer sentiment from customer support team conversations and sales calls.

Voice analysis software is a technology that analyzes conversations using AI and machine learning to determine customer and agent engagement, customer sentiment, and effort level. It is often used in call centers and customer service settings to improve communication between customer service agents and customers.

Customer service metrics are performance indicators that measure how well a company is serving its customers. Examples of these metrics include average handle time, response time, resolution time, customer effort score, customer retention rate, and customer satisfaction scores.

Conversation intelligence software, also called call intelligence software or conversation analysis software, is a platform that analyzes customer interactions, such as phone calls or chat messages, to extract valuable insights and improve communication strategies. This software can help companies improve customer service, cut costs, reduce churn, and increase revenue. Companies use conversation intelligence software to gain insight into customer expectations, customer behaviors, sentiment analysis. They can also gain contextual information about key metrics for customer service department performance and individual agents' performance.

A call center agent performance scorecard is a tool that measures the performance of call center agents based on various metrics such as call volume, call duration, customer satisfaction, contact resolution rates, and more. Scorecards help managers identify areas for improvement and track progress over time.