AI Improves Your Contact Center Performance

By Mingren Xiang

Embrace the trend of using data-driven analytics to reveal business insight within your Call Center recordings.

Call Centers sit on the frontline of customer experience where they provide sales, support, and customer service functions. They’re often customers’ first — and sometimes only — human interaction with a company. When effective Call Center interactions build a company’s brand image, it can delight people to the point that they recommend the company/brand to friends, generating incremental sales.

There is a Huge Amount of Data Stored in Your Contact Center

Forrester Research estimates that a call center with 3,000 agents and an average of only 50 calls per agent per day, has the opportunity to make 1.05 million personal connections each week — totaling 54.6 million every year. With an average call duration of 5 minutes this yields 4.6 million hours of valuable voice of the customer recordings yearly.

Unlock the Value of Your Data – Speech is Your Untapped Gold Mine

The value of AI growth correlates directly to volume of available data to train systems. This is especially true for many artificial intelligence use cases that are supporting to customer experience and contact center operations. Therefore, organizations gain tremendous value by applying AI to more of their interactions and data sources, including metadata and dark data from customer engagements. Larger data sets contribute toward making categorization for speech analytics more accurate, adding predictive confidence to manage outcomes and improve customer experience.

Macrosoft’s CallMiner Services

Download Macrosoft’s CallMiner Implementation Services brochure to learn more how we can help you achieve your Customer Satisfaction goals

Use Case Examples

In our experience, call center audio recordings are the most valuable untapped source of customer data. This data has been historically overlooked by only listening to a small percentage of calls manually. Now AI makes it efficiently reasonable for organizations to mine insights from all conversations. For example, AI has been applied to analyze past calls, compare them with customer history records and create models that accurately predicts a customer’s risk of attrition. That predictive analysis is used to guide interactions with customers in real time – during the engagement – to reduce the risk of customer attrition and improve agent performance.

AI is used to create highly accurate categorizations of customers that are likely to call back or otherwise reengage the company based on initial contact output. These categorizations are based on the words used during the call, sentiment, metadata and more. By anticipating reengagement, companies proactively reach out to customers with an appropriate message or content, such as sending more information, a special offer or troubleshooting tips.

Artificial Intelligence Holds the Future of Your Contact Center

Artificial Intelligence represents the next step in customer contact evolution. It gives new value to old previously untapped data and helps organizations continuously make customer experience better by learning what works and applying the results. Today AI is creating differentiation for the early adopters and giving them a competitive advantage by helping them know their customers and deliver positive experiences. Soon, having artificial intelligence embedded into customer contact processes will be table stakes, and enterprises that can’t mine their data and quickly predict customer and agent behavior will be at a competitive disadvantage. It is no over exaggeration that this is a game changer for better customer experience, contact center operations, agent performance and bottom line for your organization.

CallMiner – A Leader in The Forrester New WaveCallMiner is a pioneer in developing Artificial Intelligence for contact center operations and a leader in helping organizations apply it to improve customer experience. CallMiner has embedded AI and machine learning throughout its engagement analytics platform Eureka, including through easy-to-use tools like TopicMiner® to automatically discover topics on calls and other channels to identify trends, and Eureka Analyze, which automatically scores and categorizes calls and provides data visualization for easy analysis. The platform analyzes 500,000 customer interactions per hour. CallMiner has years of analytics experience, which has helped us build what we believe is the largest data set of interactions. The scale of our data set makes AI and the machine learning it powers more accurate and valuable.

Macrosoft’s CallMiner Services

Download Macrosoft’s CallMiner Implementation Services brochure to learn more how we can help you achieve your Customer Satisfaction goals

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By Mingren Xiang | February 24th, 2020 | CallMiner

About the Author

Mingren Xiang

Mingren Xiang

Mingren is a Data Science professional at Macrosoft. He is Macrosoft's technical lead in voice and conversational analytics using the CallMiner suite of utilities. The practice includes both partnering with CallMiner to deliver speech analytic solutions and developing customized NLP applications. Mingren has a Master of Science from the University of Wisconsin-Milwaukee.

Aside from leading the speech analytics practice at Macrosoft. Mingren's research work focus on Deep Learning applications for medical image processing. He presented the Master thesis on training a CNN (Convolutional Neural Network ) based Encoder-Decoder model to reconstruct CT scans using only one X-ray image. Such a task remains to be one of the hardest challenges in the computer graphic community

Coming from a strong computer science background, Mingren is also sufficient in multiple programming language such as Java, Python, C/C++, various JavaScript libraries and SQL scripts. His specialty in software development is to utilize API to create functional backend services using web development framework like Java Spring and Django in Python.

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