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Agent Coaching & Development

The Key to Customer Service Coaching Is More Data (and Fewer Opinions)

August 15, 2021
0 minute read

A Google search for “customer service coaching” turns up nearly 300 million results that give conflicting, vague, or unhelpful advice. This can be infuriating for customer experience (CX) leaders, especially when you’re under pressure to improve agent performance.

Plug-and-play coaching tactics don’t account for the complexity of agents’ interactions, not to mention they can create friction between CX leaders and agents who feel misunderstood. So, for now, let’s set aside the tips and tricks to focus on the foundation of an effective customer service coaching strategy: data.

Applying individualized data to customer service coaching closes communication gaps between CX managers and agents, enabling more efficient learning. In this article, we’ll explain why data is the key to boosting agent performance, what metrics to track, and how to incorporate data into your coaching sessions.


3 Benefits of Incorporating Data into Customer Service Coaching

Data is like a GPS for customer service coaching—it tells you what direction to take with agents and how close you are to your goals. Here are three key benefits CX teams can glean from data-backed coaching.

1. Data Enables Customized Feedback

Templated training modules can be useful for ramping up new agents or introducing new product features. However, check-the-box training falls short when it comes to helping agents solve unique, complex customer issues.

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.

For example, if an agent’s record shows they consistently take longer than their peers to close tickets, you get a clear signal to coach them up on quick resolutions rather than taking up their time (and yours) with less pressing matters.

2. Coaches and Agents Get Irrefutable Evidence of Performance Levels

Without data, coaches and agents are left to guess when it comes to evaluating performance levels. This is problematic because agent evaluations may be unfair, inaccurate, or inconsistent.

Coaches can bridge this knowledge gap by referencing data such as First Call Resolution (FCR) rate or Average Handle Time (AHT) to evaluate performance. These metrics create a single source of truth and prevent coaching sessions from being tainted by subjective opinions.

For example, if an agent and coach disagree over what it means to “solve customer issues thoroughly,” the coach can use internal benchmarks for FCR as a reference point.

3. Data Makes It Easier to Set Realistic Goals

Benchmarking agent performance isn't the only thing that's difficult without concrete data: Setting reasonable, measurable goals is also a shot in the dark. Agents can feel lost when setting goals if they don’t have a system to gauge their progress over time.

The most efficient way to benchmark performance is with Quality Assurance (QA) scores, which supply a goldmine of data to set goals based on a historical precedent, not arbitrary standards. (We’ll discuss this in-depth in the next section.)

Coaches can even use data to incentivize agent performance. For example, if the agent exceeds the company’s standard for QA scores, they could receive a bonus at the end of the quarter.

2 Types of Data You Need to Level Up Your Customer Service Coaching

CX teams need two main categories of data to optimize coaching sessions: data about how agents adhere to a company's unique quality standards and data that gauges how efficiently and effectively agents resolve customer issues.

1. Quality Assurance (QA) Scores

QA scores are actionable metrics that indicate how agents perform relative to the company’s standard for quality. That can include factors such as friendliness, authenticity, and adherence to approved processes.

For example, if a QA grader gives an agent 78 out of 90 points on their QA scorecard, the coach can examine the scores for each criterion on the scorecard and immediately know which aspects of their customer interactions to address in upcoming coaching sessions to achieve a higher score.

example of a quality assurance scorecard for customer experience teams



QA scores are the most reliable source of intel to coach agents effectively because they provide a granular look into the areas in which they thrive and where they need a boost. In fact, after monday.com increased its volume of quality audits by 48%, it slashed its AHT from 24.1 minutes to 16.9 minutes—an improvement of almost 30%.

2. Agent Performance Metrics

These four foundational metrics quantify efficiency and customers’ perceptions of the agent or the brand in general. While they aren’t as actionable as QA scores, they can still spark productive dialogues between coaches and agents, especially when it comes to the company’s customer service KPIs.

Customer Satisfaction (CSAT) Score

CSAT scores indicate how satisfied customers are after receiving assistance from an agent. CSAT scores are relatively quick to calculate and analyze. The downside is they’re one-dimensional because the score alone can’t tell you which aspects of customer experience contributed to the customer’s survey response.

To get over this obstacle, CX teams can review CSAT scores in tandem with QA reviews. This contextualizes why a customer gave a specific CSAT rating: Did the agent use a friendly tone? Did the agent get to the root cause of the problem? Was their issue resolved in a timely manner?

Applying these insights to the coaching sessions should improve CSAT scores.

First Call Resolution (FCR) Rate

If an agent has a low FCR rate, this is a coaching opportunity to help them become more thorough with customers, whether that’s by diagnosing the root causes of problems, preempting customer issues, or taking advantage of knowledge management solutions.

Just as with CSAT, analyzing FCR within the context of QA scores uncovers insights into why certain interactions exceeded FCR expectations or why they fell short.

Average Handle Time (AHT)

Monitoring AHT helps CX leaders see if an agent takes too long to resolve customer issues. Once AHT is tracked, coaches can take the necessary steps to improve it, whether teaching agents how to leverage an internal knowledge base to find solutions quicker or when to reroute tickets to a teammate who’s better prepared to solve the problem.

Keep in mind that AHT can’t (and shouldn’t) be the sole indicator of productivity when the agent’s ultimate goal is delivering quality experiences and solving customer problems thoroughly.

Net Promoter Score (NPS)

Tracking and discussing NPS can help coaches and agents see a correlation between customer interactions and a customer’s willingness to advocate for the brand.

NPS shouldn’t be the focal point of coaching sessions since it’s often influenced by factors outside of agents’ control. That said, if NPS scores indicate that customers consistently don’t want to recommend the brand after interacting with a specific agent, that can be a sign to gear coaching toward soft skills such as empathy and authenticity.

How to Apply Data to Coaching Sessions

Now that we’ve covered what data to collect, let’s take a look at four ways coaches can use it to maximize the effectiveness of coaching sessions.

1. Organize QA Scores, Performance Metrics, and Historical Feedback in One Place

Compiling all of the data you’ve tracked—including QA scores and performance metrics—ensures you’ll have quick access during coaching sessions. This also enables coaches to show contextualized, side-by-side data comparisons, as we mentioned previously with reviewing CSAT and FCR in tandem with QA scores.

Staying organized is tough when you’re juggling multiple spreadsheets and tools—that’s why MaestroQA compiles key data by default in the Coaching tab.

MaestroQA's coaching dashboard

2. Back Up Suggestions with Evidence

Citing objective data instead of lecturing with subjective opinions can boost agents’ confidence in the coaching system since the feedback comes from a single source of truth.

For example: Instead of telling an agent to “work on incorporating approved brand language into greetings,” you could point out specific instances on QA scorecards in which the agent forgot to use the approved brand greeting.

3. Set Measurable Goals

An open-ended goal such as “improve efficiency” is a recipe for frustration since agents can’t set their sights on a specific milestone. Rather, goals must be SMART: specific, measurable, attainable, relevant, timely—and data makes that possible.

“Having a clear, compelling goal mobilizes your focus toward actionable behavior,” says Jeff Boss, author of Navigating Chaos: How to Find Certainty in Uncertain Situations.

In terms of customer service coaching, that could be identifying a weak point in the data (such as a low FCR), then setting a goal to improve it by 10% within a month.

4. Track Agent Progress Over Time

Monitoring and discussing agent progress across key metrics help agents and coaches celebrate milestones and find correlations between coaching sessions and improved performance. If this data isn’t part of the conversation, getting agent buy-in can be tough.

For example, if an agent sees a steady rise in their QA scores month over month, they’ll feel motivated to maintain that standard and let their performance stagnate.


graph showing an agent's performance improving over time in MaestroQA


No More Cookie-Cutter Coaching

The more you dig into the data surrounding agents’ interactions, the more you’ll realize how unique their strengths, weaknesses, wants, and needs are. These nuances are what inform successful coaching sessions—not legacy training manuals or trendy tips.

Want to see how quality data can help your agents reach their potential? Get a free demo of MaestroQA today.

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