By Nathaniel Luce
In the world of customer service, “first-contact resolution” (when a customer’s issue is fully resolved on the first try) is paramount. Studies show that higher rates of first-contact resolution lead to reductions in operations costs, higher customer satisfaction, and higher customer retention rates, among other benefits. Companies strive to increase first-contact resolutions by lowering rates of “retrial” (when a customer’s issue requires multiple resolution attempts).
For many companies, lowering retrial rates can be a struggle, and the problem often boils down to resources. Existing research suggests that 2 factors – pickup speed (how quickly representatives address the request) and service quality – can reduce the volume of retrials, but improving speed and quality may require budget-busting levels of investment in personnel and training.
Study Background
A new study by Kejia Hu, Assistant Professor of Operations Management at Vanderbilt’s Owen Graduate School of Management, analyzed customers’ online and offline preferences from a bank’s customer contact center’s records to build a set of frameworks that companies can use to reduce retrial rates across service channels without unpalatable levels of investment. “The research illustrates how a data-driven approach enables firms to make informed decisions and unlock valuable insights,” Hu says.
“Understanding Customer Retrials in Call Centers: Preferences for Service Quality and Service Speed”, which appears in the upcoming edition of Manufacturing & Service Operations Management, was co-authored by Gad Allon from the University of Pennsylvania’s Wharton School, and Achal Bassamboo from Northwestern University’s Kellogg School of Management.
Solving the Problem with Customer Service
The study finds that customers do indeed value high service quality and quick pickup speed, but that sensitivity to the 2 factors depends on the type of customer. Results show that private customers are more sensitive to quality and less sensitive to speed than business customers.
With differences like these in mind, Hu and her co-authors developed 2 design systems that account for the preferences of private and business customers.
The first involves no expansion of service team size, only different allocations along customer groups. The second involves adjusting the ratio of high-quality to ordinary-quality support center agents based on the number of customers of each type. Both models resulted in significant gains in “customer surplus” (a measure of value that accounts for the rewards of customer services and the cost of waiting) for both customer types.
These design systems aren’t relegated exclusively to traditional call centers, where support services are provided exclusively over the telephone. They can be applied to identify preferences across different types of service channels.
“For example,” Hu says, “the solution team on social media like Twitter and Facebook, the SMS interaction channel, and the rising trend of live chat channel on the website are alternative contact channels that may trigger retrials. Using our framework of analysis, firms can tailor the best channel to serve distinct preferences in customer segments.”
What does this mean for customer relationship management?
The authors note in the paper that their research “has already received strong interest from a globally renowned hotel chain to improve its customer relationship management across call centers, live chat, social media, and other digital and virtual service channels.”
Regardless of industry, the study highlights the need for managers to prioritize customer preferences in order to improve customer services within budget. “It is crucial for service providers to understand customer behavior across channels,” the paper states, “so that they can effectively plan their investment for multichannel customer relationship management.”