Customer service analytics makes sense of the data from all your customer interactions and turns them into insights you can use to improve your customer service operations.

1. Customer Service Analytics:
How to make sense of all your data
DAUDA
EMMANUEL
emmanuel.dauda@trailblazercgl.com

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As a service manager, imagine spending 10 minutes in the
morning reviewing your most important key performance
indicators (KPIs) — and in that short time quickly seeing which
types of cases have the highest handle times. Or, as a service
agent, suddenly having the superpower to know when a
customer is likely to churn — and what you can do to prevent it.
These are real-world applications of today’s customer service
analytics.
INTRODUCTION

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What is customer service
analytics?
Customer service analytics means assessing the
data created by service interactions to uncover
actionable insights. This data comes from multiple
sources – including phone conversations, emails,
chats, social media, and customer surveys – and
can be categorized into quantitative and qualitative.

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Quantitative customer service data includes the
measurable facts of a customer service interaction –
like how long the customer had to wait, which agent
responded, how long the service interaction took, and
which channel the customer used. Qualitative service
data includes information like customer sentiment,
complaints about product flaws, feedback on branding,
or even customer insights on your competitive
weaknesses and strengths.

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Customer service analytics distills all of this data into
useful nuggets of information, which is especially
helpful as your business grows and takes on a greater
volume of service interactions. Analytics may reveal
customer preferences, potential product improvements,
or ways to increase operational efficiency.

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First, let’s get an understanding of the different types of
customer service analytics you may want to use. Some
categories are:
•Descriptive Analytics: This involves analyzing historical
data to understand past customer interactions and
patterns, providing insights into what has happened.
•Diagnostic Analytics: This category focuses on
identifying the reasons behind specific customer service
outcomes, helping your business understand why certain
events occurred.
What types of customer service analytics are there?

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•Predictive Analytics: This uses AI, data, statistical
algorithms, and machine learning to identify the likelihood
of future outcomes based on historical data, enabling
proactive measures.
•Prescriptive Analytics: With AI, this type of analytics
suggests actions to optimize outcomes based on the
insights from predictive analytics, guiding decision-making
in real time.

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Customer service analytics offer many advantages to businesses
that want to retain their customers and also save on costs. These
include:
1.Improved customer experience. Our research shows that 94%
of customers say a good service experience makes them more
likely to make another purchase. Analytics help you understand
customer expectations and experiences, so your business can then
tailor services to meet those expectations effectively.
2.Identifying pain points: By analyzing customer feedback and
complaints, your business can identify recurring issues and
address them proactively, leading to improved customer
What are the benefits of tracking customer service
analytics?

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3. Operational efficiency: We found that 78% of service
agents say it’s difficult to balance speed and quality. Analyzing
customer service data allows your company to optimize their
processes, allocate resources efficiently, and enhance overall
operational effectiveness.
4. Product and service improvement: Insights from analytics can
guide product and service development, ensuring they align with
customer needs and preferences. After all, 73% of
customers expect you to understand their needs and expectations.
5. Customer retention and loyalty: Understanding customer
behavior and preferences allows your business to design strategies
that foster customer loyalty and improve retention rates.

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1. Customer Satisfaction (CSAT)
To find CSAT, conduct post-interaction surveys and ask your
customers to rate their satisfaction on a scale (e.g., 1-5). The
average score indicates overall satisfaction.
2. Net Promoter Score (NPS)
After a customer interaction, ask, “How likely are you to
recommend our company to a friend or colleague?” Have
customers answer on a scale of 0-10. Categorize respondents into
Promoters (9-10), Passives (7-8), and Detractors (0-6). Calculate
the NPS by subtracting the percentage of Detractors from the
percentage of Promoters.
What to measure for customer service
analytics

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3. Customer Effort Score (CES)
After an interaction, ask your customers, “How easy was it for you
to get the help you wanted today?” The scale is usually 1-7, with
higher scores indicating a seamless experience. Fun fact: our
research found that CES was one of the fastest growing service
KPIs between 2020 and 2022. It’s that important.
4. Average Response Time (ART)
Calculate the average time taken to respond to a customer query
from the initial contact. Lower ART indicates efficient service.

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5. Average Handle Time (AHT)
This metric is the average duration taken to handle a customer
interaction comprehensively. AHT is a bit tricky, because you don’t
want agents rushing off calls or chats before the problem is solved.
The goal is to optimize processes to reduce AHT without
compromising quality.
6. First contact resolution
Also known as first call resolution or first touch resolution, first
contact resolution is the percentage of customer queries or issues
resolved successfully on the first interaction.

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emmanuel.dauda@trailblazercgl.com

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THANK YOU
emmanuel.dauda@trailblazercgl.com