Introduction:
In today’s competitive business landscape, delivering exceptional customer service is paramount for retaining customers and gaining a competitive edge. Call centers play a crucial role in this domain, serving as the frontline for addressing customer inquiries, concerns, and support requests. To streamline operations and enhance customer satisfaction, many organizations are turning to cloud-based solutions. This case study explores how a leading telecommunications provider utilized Google Cloud to improve customer service at its call centers.
Background:
Telecommunications industry offers a range of services including internet, television, and mobile phone subscriptions. With a large customer base spread across diverse geographic regions, maintaining high standards of customer service is essential for the company’s success.
Challenges Faced:
Before implementing Google Cloud solutions, Organizations faced several challenges in call center operations:
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- High Call Volumes: The company experienced spikes in call volumes during peak hours, leading to longer wait times and customer dissatisfaction.
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- Inefficient Call Routing: Call routing mechanisms were not optimized, resulting in customers being transferred multiple times before reaching the appropriate agent.
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- Limited Scalability: Legacy infrastructure lacked the scalability needed to accommodate fluctuating call volumes and adapt to changing business requirements.
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- Data Silos: Customer data was stored in disparate systems, making it challenging for agents to access relevant information quickly, leading to delays in issue resolution.
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- Lack of Analytics: The absence of robust analytics tools hindered the company’s ability to gain actionable insights into call center performance and customer behavior.
Solution:
To address these challenges, leverage Google Cloud solutions, including Google Cloud Contact Center AI (CCAI) and Google Cloud Platform (GCP). The key components of the solution included:
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- Google Cloud Contact Center AI: By integrating CCAI into its call center operations, implement advanced features such as virtual agents, sentiment analysis, and real-time language translation. Virtual agents handled routine inquiries, freeing up human agents to focus on more complex issues. Sentiment analysis helped gauge customer satisfaction levels during interactions, allowing agents to tailor their responses accordingly. Real-time language translation facilitated seamless communication with customers speaking different languages.
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- Google Cloud Platform: Migrate call center infrastructure to GCP, taking advantage of its scalability, reliability, and security features. GCP’s flexible architecture allowed the company to dynamically allocate resources based on call volume fluctuations, ensuring optimal performance during peak hours. Additionally, GCP’s data analytics tools enable to gain deep insights into call center metrics, customer behavior patterns, and agent performance.
Results:
The implementation of Google Cloud solutions yielded significant improvements in call center operations:
- Reduced Wait Times: Virtual agents and optimized call routing mechanisms led to a reduction in wait times, enhancing the overall customer experience.
- Improved Efficiency: By automating routine tasks and providing agents with real-time insights, the company increased operational efficiency and agent productivity.
- Enhanced Scalability: GCP’s scalable infrastructure allows it to handle spikes in call volumes without compromising performance, ensuring seamless customer service delivery.
- Unified Data Platform: Integration with Google Cloud facilitated the consolidation of customer data, enabling agents to access comprehensive customer profiles and resolve issues more effectively.
- Actionable Insights: Leveraging GCP’s analytics capabilities, gain actionable insights into call center performance metrics, enabling data-driven decision-making and continuous improvement initiatives.
Using Google Cloud to Improve Customer Service at Call Centers
Google Cloud Results
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- Significantly speeds up the time to review the quality of sales calls, helping to highlight any opportunities to improve the quality of conversations and compliance with customer needs.
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- Helps to improve the overall customer call center experience and service by quickly feeding back opportunities to improve quality at an agent and center level.
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- Reduces the time needed to run machine learning models from one week to one hour.
HSBC: Using Google Cloud to Improve Customer Service at Call Centers
HSBC is a digital bank, with over 90% of its retail transactions globally and in Hong Kong being conducted through digital platforms.
However, while digital banking is increasingly popular, call centers remain an important service and business channel. The Hong Kong Contact Centre for HSBC, which celebrated its 30th anniversary in 2020, serves over 1.5 million customers through over 20 million annual interactions.
Enter HSBC’s Intelligence Hub
The Intelligence Hub is a team of data scientists, engineers, and architects dedicated to migrating data and analytics workflows onto Google Cloud and looking for ways to leverage the data using AI and machine learning. This team works closely with different departments and business units to design and build end-to-end data and AI solutions.
The HSBC Intelligence Hub is a team of data scientists, engineers, and architects dedicated to migrating HSBC data and analytics workflows onto Google Cloud and looking for ways to leverage the data using AI and machine learning. This team works closely with different departments and business units within HSBC to design and build end-to-end data and AI solutions.
“People in the team come from a broad range of disciplines and backgrounds, from mathematics, business, computer science, behavioral economics, and engineering. You don’t necessarily have to be an expert in statistics. The most important thing is for our people to stay curious and have a drive to learn new things,” says Richard Bates, Global Head of HSBC’s Intelligence Hub.
“Google is one of our strategic partners,” continues Richard. “We’re investing in machine learning and data capabilities with them because their cloud solution is approved to handle personal information securely and reliably.”
“As we continue to build a customer-centric culture, data empowers us to conduct a more scientific and quantifiable assessment of our services,” adds Yusuf Demiral, Head of Data and Analytics, Asia Pacific at HSBC. “We see AI as an opportunity to better understand customers’ needs, hear their voices, and alleviate the risks for customers and our bank.”
An Automatic Quality Management System
Within the Intelligence Hub is the Data Science Squad, led by Mary Chu, Senior Analytic Architect, who took on the Contact Centre Language processing challenge.
“We turned to AutoML Natural Language and Speech-to-Text to train machine learning models to classify, extract, and detect customer sentiment,” says Mary. “Using Google Cloud computing resources and BigQuery as a data analytics warehouse, we applied Speech-to-Text powered by Google’s AI technologies to the data, which accurately converted spoken combinations of Cantonese and English.”
The result was an Automatic Quality Management system (AQM), the first in-house voice-processing solution powered by AI that helps to identify areas of improvement in customer conversations, a key milestone for HSBC’s Hong Kong Call Centre.
Better Agent Service
The HSBC Intelligence Hub Data Science Squad completed this development in just three months, and, working closely with 10+ HSBC teams, launched the solution in September 2020.
“With Google Cloud, we could run a machine learning model in an hour, rather than a full week as would have been required in an on-premises environment, which considerably shortened our rollout time,” says Mary.
HSBC now processes through this solution every Cantonese-English call into the contact center that involves presentation of information on terms and conditions.
The solution helps the institution quickly identify sales agents with room for coaching and improvement. The monitoring team automatically receives alerts to such calls, which enables them to follow up with agents in a timely manner. It also allows HSBC to capture more insights to sharpen the way the bank serves its customers. “These insights also become valuable resources for us when we design our future customer journeys or work on staff training and reinforcement. Theoretically, it will involve 1,200 man-hours to monitor 100% of our sales calls. It is now unnecessary to spend this amount of time for monitoring,” says Richard.
Optimizing ATM Cash Stocks
Over the last six months, the HSBC Intelligence Hub has used its Data Science Workbench to develop capabilities in addition to AQMs. For example, HSBC has launched an internal tool, known as iCash, that predicts the stock of cash in ATMs and enables optimization to ensure they never run out of cash. iCash also provides strategic solutions for cash forecasting and planning for further development and rollout.
Conclusion:
By harnessing the power of Google Cloud, successfully transform call center operations, delivering superior customer service while optimizing operational efficiency. The implementation of Google Cloud Contact Center AI and Google Cloud Platform empowered the company to address key challenges, improve scalability, and gain valuable insights into customer interactions. As a result, a company solidifies its position as a customer-centric organization committed to delivering exceptional service experiences.
