Using AI to understand relationships between employee engagement and customer loyalty

One of the major players in the cutthroat telecommunications industry had long been focused on optimizing its customers’ and employees’ experiences in order to remain competitive. Its Business Analytics team plays a key role by compiling and analyzing feedback from customers and employees and recommending steps to improve performance across the company. The company operates on a global scale, dealing with millions of customers and employees, making it essential to understand the intricate relationships between employee engagement and customer satisfaction. The company’s commitment to leveraging advanced technologies to enhance its operations is evident in its decision to employ AI and NLP for deeper insights.

The Business Analytics team recently undertook a new initiative: to find connections between employee and customer satisfaction. To be specific, are there drivers of employee satisfaction that have an impact on customer satisfaction as well? The Business Analytics team realized that identifying such connections, if indeed they exist, would enable them to prioritize initiatives that would be the most beneficial to both employees and customers.\n\nUntil this point, the team had relied upon rigorous statistical methodologies to analyze predominantly quantitative data. However, the team would need to focus on unstructured data, such as open-ended survey responses, in order to fully understand the key drivers of customer and employee satisfaction. Traditional statistical methodologies, while valuable, are not as effective or rapid at analyzing unstructured feedback.\n\nThe team was also determined to eliminate as much bias as possible from the analytical process. However, traditional approaches to processing text-based data rely extensively on keyword lists and ontologies created by a human analyst. The Business Analytics team was concerned that an analyst might inadvertently skew the results by cherrypicking keywords or being subject to confirmation bias.

After vetting a number of different analytics companies, the telecommunications giant selected Luminoso to help them find connections between their employee and customer data. The Business Analytics team asked Luminoso to help them answer the following questions: Is there a link between employee engagement and customer satisfaction? Which factors influence employee engagement that also impact customer satisfaction?\n\nUsing Luminoso, the Business Analytics team analyzed over 1.325 million anonymized pieces of customer and employee feedback collected from five distinct data sources. By using cutting-edge methodologies in artificial intelligence and natural language processing, Luminoso was able to quickly map out relationships between customers and employees. Within a few minutes, Luminoso’s software identified the most relevant topics in the data – which were not necessarily the most prevalent or most-discussed. The software then visualized those topics in a concept cloud, which grouped the topics into clusters based on how the topics related to each other.\n\nIndividual topics could be clicked on and explored to get more information, such as which other topics were most related to the topic of interest; what emotions employees and customers associated with that topic; and to see verbatims that best represented what people were saying about that topic. The team could also easily filter their view of the data based on metadata like data source (employee vs. customer), NPS category (promoter, passive, detractor), geographic region, and others.

• Several links were found between employee and customer engagement.
• Employees desired more tools, resources, and training, and these comments were reflected in customer comments as well.
• Having the data processed and displayed in this way made it much faster and easier for the Business Analytics team to uncover new insights and spot relationships between their employee and customer feedback.
• Luminoso’s software quickly uncovered a relationship between employee comments about training programs and customer feedback about inconsistent information from employees in retail stores versus call centers.
• The telecommunications company was able to prioritize providing more tools, resources, and training to its frontline employees, leading to a decrease in the number of employees and customers talking about this issue.

• Analyzed over 1.325 million anonymized pieces of feedback.
• Data collected from five distinct data sources.
• Data collected over the course of one year.