Scaling Support Quality Efficiently
DailyPay works with companies to give their employees access to earned wages before their scheduled payday. With about 1,200 clients across the United States, many employees depend on DailyPay’s service to receive their earnings on demand.
These users typically need assistance with account setup, payment access, and fully understanding all the benefits of enrollment. DailyPay supports them through its own agents as well as outsourced contact centers in Colombia, Egypt, and South Africa. However, as the company expanded, maintaining consistent quality in customer support became challenging. Feedback took a while to reach the agents because there were only one to four evaluations per agent each month.
“This delay meant that agents could be making the same mistakes repeatedly, leading to a cycle where we couldn’t address the errors in time, which ultimately hurt our quality,” says Ryan Moore, VP – Customer Operations at DailyPay.
As the business grew 30–40% annually, ensuring quality while scaling became even more crucial, according to Sigmond Varga, Senior Manager – Shared Services at DailyPay. “Managing QA became increasingly difficult as we approached mid to high hundreds of agents. We couldn’t keep hiring additional QA analysts as we expanded,” he said.
“We have moved from weekly to daily coaching, which has been instrumental in helping new agents find their footing more quickly.”
– Sigmond Varga, Senior Manager – Shared Services, DailyPay
After evaluating multiple solutions, DailyPay implemented the Observe.AI Conversation Intelligence Platform and Auto QA to improve QA efficiency and maintain high service standards across its contact centers.
Driving Strategic Impact
By automating quality evaluations and gaining full insights into customer interactions, the company has eliminated the need to hire additional QA analysts, freeing its existing QA team to focus on more strategic tasks.
Previously, the QA team was limited to grading calls and providing feedback. Today, they analyze data, identify performance trends, and address specific needs at outsourced contact centers.
According to Varga, QA analysts are now seen as coaching resources who help teams improve their performance, rather than punitive individuals out to catch mistakes. “This change has encouraged a more collaborative environment, where frontline supervisors regularly turn to the QA team for guidance on performance issues.”
These role adjustments have contributed to an 8.5% rise in service quality scores and a 22.3% improvement in customer satisfaction (CSAT) scores. Overall, enhanced operational efficiency resulted in more than $2 million in cost savings as DailyPay streamlined its contact center operations and improved resource allocation.
Precise Monitoring for Accurate Performance Insights
DailyPay also uses Observe.AI Moments to monitor interactions and flag certain agent behaviors.
This feature captures key instances in customer interactions, providing critical insights into agent performance and behavior. For example, Moments track instances where DailyPay agents refer users back to their employers—an action generally discouraged. This data helps the company address and reframe discussions about agent performance with clients.
“The Observe.AI Conversation Intelligence Platform, with its comprehensive Moments architecture and search functionality, has become invaluable not only for understanding customer interactions, but also for gaining marketing and product insights,” adds Moore. “We plan to expand our use of the platform across other departments to fully leverage its potential.”
Improved Performance Among New Hires
Automating QA evaluations has notably accelerated the feedback process, enabling agents’ mistakes to be identified and corrected quickly. This shift has had a profound impact on new hires, especially at DailyPay’s outsourced contact centers.
New agents traditionally faced a steep learning curve during their initial weeks, as they struggled with nerves and the real-time demands of customer interactions. In the past, it could take several weeks for them to reach satisfactory performance levels.
By identifying and scoring the calls attributed to poor CSAT scores, DailyPay can provide agents with tailored feedback almost immediately to course-correct. This rapid feedback loop speeds up new agents’ progress to proficiency.
“We have moved from weekly to daily coaching, which has been instrumental in helping new agents find their footing more quickly,” says Varga.
Boosting First Contact Success
Through the Observe.AI platform, DailyPay gained significant insights into the impact of first contact resolution (FCR) on its operations. By filtering and analyzing customer calls, the QA team identified communication gaps and inefficiencies that were preventing agents from resolving issues on the first contact. Addressing these issues led to a 4–5% improvement in FCR for certain teams.
As Moore explains, “FCR was a major issue affecting a substantial percentage of our calls. These findings empowered us to push for necessary changes and better align our internal processes with user needs.”
Similarly, analyses of customer interactions through Observe.AI revealed that agents often used terminology that didn’t match the language customers were using. By revising its customer-facing materials, DailyPay has improved the user experience while reducing the number of inquiries, saving both customers’ time and the company’s resources.
“The Observe.AI Conversation Intelligence Platform, with its comprehensive Moments architecture and search functionality, has become invaluable not only for understanding customer interactions, but also for gaining marketing and product insights.”
– Ryan Moore, VP – Customer Operations, DailyPay
Advancing Efficiency with AI Insights
DailyPay’s partnership with Observe.AI continues to evolve. The company recently implemented Summarization AI to transform its note-taking process. This AI-powered product generates accurate, structured, and comprehensive notes in real time, saving DailyPay agents 40–60 seconds per call. These efficiency benefits not only ease their workload but also enhance overall productivity.
Likewise, the company plans to use Auto QA and Moments to implement predictive customer satisfaction models, to proactively coach agents and improve overall customer interaction quality.
“We’re excited about future features that will help us understand conversation dynamics and improve our training programs through data-driven insights,” adds Varga. “We aim to provide better insights across the organization by extending Observe.AI’s use to other areas, such as card servicing and client success teams.”
Book a demo today or explore the new features of Observe.AI to enhance your operations.
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