With the fast-paced demands of customer service, contact centers are facing a common challenge: harnessing the wealth of insights and data captured in every customer interaction to drive performance and improve the overall customer experience.
It's a puzzle that many have tried to solve, but few have truly cracked.
Imagine being able to predict customer satisfaction, prevent unnecessary callbacks, and identify at-risk customers before they even think about switching services. Does it sound like science fiction? Well, buckle up, because we're about to show how it's becoming a reality.
From unveiling the true power of quality forms and measurement processes to diving deep into the reasons behind customer calls, we are talking about data points of customer interactions.
We'll explore how Large Language Models (LLMs) are eliminating bias and standardizing evaluations, how we can leverage our strengths to address common mistakes, and even how to predict and prevent customer escalations.
This isn't just about improving metrics – it's about turning every interaction into an opportunity for growth, learning, and customer delight.
Quality Form and The Process of Measurement: The True Power of LLM, Standardization, and No Bias
Today, customer experience has become a key differentiator for organizations across industries. At the heart of delivering exceptional customer service lies an often-overlooked tool: the quality form.
These forms, when designed and implemented effectively, significantly impact crucial business outcomes such as customer satisfaction, compliance, and agent efficiency. Let's dig deep into what we mean by quality forms and explore how they can be leveraged to drive success in an organization.
During our research into quality forms across various organizations, we stumbled upon a startling revelation: there's an enormous disparity in how these forms get structured and utilized. Some companies employ a cluster of 51 parameters in their quality forms, while others keep it lean with just 9. This variation begs the question: what's the optimal number of parameters for a quality form?
The answer, as with most things in business, is that it depends.
However, what's clear is that many organizations lack a clear vision for how their quality forms should drive a structured call flow. This absence of strategy can lead to missed opportunities to improve customer interactions and agent performance.
Another concerning finding was the lack of regression or multivariate analysis linking quality scores to business outcomes. Without this crucial connection, companies are flying blind, unable to quantify the impact of their quality initiatives on their bottom line.
Speaking of quality forms, there's an ongoing debate between binary scoring (Yes/No) and graded scoring (No, Partial, More, Full). Our research uncovered an interesting trend: wherever partial scoring was used, there was a higher incidence of calibration issues and bias.
This finding suggests that binary scoring might be the way to go for organizations looking to minimize subjectivity in their quality assessments. After all, the clearer and more objective the scoring system is, the easier it is for agents to understand expectations and for managers to provide consistent feedback.
On a more positive note, we uncovered some excellent examples of companies using quality forms to drive brand awareness. These forward-thinking organizations have integrated their brand values and key messaging into their quality forms, ensuring that every customer interaction reinforces their brand identity.
For instance, a luxury hotel chain incorporated parameters in its quality form that assessed how well agents conveyed the brand's commitment to personalized service and attention to detail. This approach not only improved customer satisfaction but also strengthened brand recognition and loyalty.
Now, let's talk about the elephant in the room: artificial intelligence. Our team recently leveraged ChatGPT 3.5 Turbo in evaluating quality forms. The results were nothing short of revolutionary.
We followed a simple iterative process to configure and train the engine, and what amazed us was its power to comprehend interactions and provide contextual feedback. The AI's insights were so detailed that we found ourselves in the unusual position of needing to "dumb down" the engine to conform to the limitations of our existing quality process.
Here's a beautiful example that illustrates the AI's capabilities:
In a call, a customer had made a booking in the wrong parking lot at an airport and called to cancel the booking. The agent successfully canceled the booking and thanked the customer for their service. However, the AI engine marked the agent down on "Provided complete and accurate solution," noting that the agent should have offered to make a booking in the correct lot.
This level of insight goes beyond traditional quality assessment, highlighting opportunities for agents to go above and beyond in serving customers.
Key Learnings from Our AI Experience
As we look to the future, it's clear that quality forms will continue to play a crucial role in driving business outcomes. Here are some key considerations for organizations looking to optimize their quality assessment process:
In conclusion, quality forms are far more than just a box-ticking exercise. When designed thoughtfully and implemented effectively, they can be powerful drivers of customer satisfaction, compliance, and agent efficiency.
By leveraging the latest technologies and aligning the quality assessment process with business objectives, we transform quality forms from a routine task into a strategic asset that propels organizations toward success in the customer experience industry.
Remember, in the world of customer experience, quality isn't just about meeting standards – it's about exceeding expectations, one interaction at a time. Our key learning was that as we continue to embrace the process we learn something new with every iteration. This is just the first article in a series of many. In the next one, we explore how do we train the engine to capture “Why Customers are Calling”….. so stay tuned!