A 3-pronged approach to creating (and scaling) legendary customer service experiences

1. Move beyond one-dimensional customer service metrics and embrace in-depth CX analytics

When it comes to ensuring great customer experiences at scale, tracking the right data is critical.

Most CX teams rely on a combination of standard productivity (AHT and FCR) and satisfaction (CSAT and NPS) metrics to measure agent performance and inform CX strategies. Unfortunately, these metrics give incomplete, and often unreliable, information that doesn’t take you any closer to your goal of providing better customer experiences en masse. They fail to provide any actionable items that allow you to iterate on or improve CX because they don’t help identify and address the root causes of friction in your customer experience.

“CSAT can tell you how a customer feels in a particular moment, but that feeling could be the result of anything relating to your business.” Vasu Prathipati, CEO and co-founder of MaestroQA

If an agent handled a situation perfectly but the customer was upset with your billing policy, they might give a low score. Not only does the low score reflect poorly on the agent—even though the billing policy is out of their control—but the CSAT score itself doesn’t provide any direction on how to improve your support operations or the quality of the interaction. So, companies are left with a massive blind spot between what traditional metrics tell CX leadership and what customers are actually experiencing.

The solution? If companies want to ensure they’re providing a superior customer experience across the board, they need to move beyond these one-dimensional metrics and start focusing on more in-depth conversation analytics that provide a clear root cause analysis. Only then can you start to understand the true impact of your agents’ performance and your company’s processes and policies on the customer experience.

2. Unlock data-driven QA insights

You probably use quality assurance (QA) to evaluate the performance of your customer service teams. You listen in on recorded calls to learn if the protocol was followed and to find out if customers were satisfied with your services. And depending on what you find, you may need to coach an agent after a call or take disciplinary action.

The process is mostly about fixing mistakes—which is an inherent part of assuring quality for your customers—and providing individual-based feedback. There is generally little to no reporting or analysis from this process to point to when discussing trends or action items with your wider CX team. This is fine in terms of helping guide managers on utilising QA to coach, but as your team grows and you look to scale, trying to provide best-in-class support in this reactive and strictly individualised manner quickly becomes ineffective and unsustainable.

The good news? If you already use a QA solution, you’re sitting on a goldmine of data that can be used to learn about your agent experiences, create a continuous feedback loop and identify opportunities for team growth so you can nip potential issues in the bud. Digging into this data and maintaining a structured QA grading process allows you to benchmark good support practices—the ideal tone agents should adopt, the fastest resolution times they can achieve without sacrificing quality, and the tools they should use to be more productive—so you can achieve consistency in service, no matter how large your support team grows.

3. Implement a customer service coaching program

In efforts to scale, companies frequently turn to templated training and plug-and-play coaching modules to ramp up new agents or introduce new products and services. However, these methods generally fall short when it comes to helping agents solve unique customer issues because they don’t (and can’t) account for the complexity of agents’ interactions and nuances of real-world human interactions.

Every agent excels and struggles in different aspects of their work. Accordingly, extracting tangible insights based on their individual performance data lets you know what to focus on during coaching sessions. Applying this individualised data to customer service coaching closes communication gaps between CX managers and agents, enabling more efficient learning.

But how can you do this at scale? A data-driven customer service coaching program that looks something like this:

• Collect QA data to find opportunities for improvement

  
  You can’t improve what you don’t measure. Accordingly, CX managers need to know exactly what agents need help with to maximise the effectiveness of coaching meetings. The most effective (and efficient) way to do this is by collecting quality data from customer-agent interactions.
• Establish a coaching cadence to ensure timely feedback

  
  Coaching needs to be conducted systematically to hold agents and coaches accountable. There isn’t a set-in-stone rule for how often agents need coaching—it can range from once a week to once a year—but every agent can benefit from it. Routine coaching sessions can refine skills and refresh motivation, while also serving as a touchpoint to discuss any work-related issues the agent might be facing.
• Set up a system to track agent progress over time

  
  CX managers should always have a pulse on the progress agents make to know whether coaching sessions drive results (or need reevaluation). Tracking progress over time also lets CX teams celebrate key milestones that agents reach. You can even set up incentives—bonuses, prizes—to reach KPIs by a certain date.