Customer Service Analytics: How To Make Sense of Your Data

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 & incident management.

Every day, customers reach out to your contact center for help. These interactions create mountains of customer data – information your business can use to drive growth, especially when customers demand personalization and speediness. According to the State of Service, 86% of service agents and 74% of mobile workers says customer expectations are higher than they used to be.

Both of these stats point to one conclusion: Service teams that use customer service analytics as the new enabler of championing customer satisfaction stand to win brand loyalty – and a whole lot more.

Here’s what you need to know to benefit.

Table of contents:

• What is a go-to-market strategy?
• Why is a go-to-market strategy important?
• How to build a go-to-market strategy framework
• Go-to-market strategy example

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.

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.

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.

Though many leaders can get behind the idea of data analysis, putting it into practice can prove daunting. Consider our findings in the State of Data and Analytics report. While 96% of service leaders say that trustworthy data is more important in times of change, only 44% of service leaders consider themselves to be very data-driven, compared to more than half of marketing and sales leaders. A lack of data skills and confidence in data sources appears to be hampering many leaders’ abilities to tap into their data for insights. 

But with Salesforce’s powerful analytics tool, Service Intelligence for Service Cloud, you can make sense of all your data quickly and get the trusted insights you need to make better business decisions.

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.
• 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.

All of these types of analytics can be helpful for your service operations. You might use omni-channel analytics, a form of descriptive analytics, to understand what happened on a recent Tuesday. Then use diagnostic analytics to understand the reasons. Perhaps you’ll see that wait times were too high on voice, because agents don’t have adequate training on that channel. Maybe the average handle time was long, due to one extremely lengthy call. You might then review the call transcript to see if agents need more training on that issue.

Predictive analytics help you forecast the staffing capacity you’ll need during anything from a regular Tuesday to an upcoming holiday season, based on past patterns. You can also use predictive analytics to determine the category or severity of a case as it is logged.

Predictive analytics – in combination with prescriptive analytics – lets your agents make better decisions. For example, you can provide agents with insights — right in the flow of their work — that help them better serve your customers. Analytics helps you optimize service operations.

The power behind many analytics is AI, which crunches the data and offers up useful facts and suggestions in real time. AI algorithms can analyze vast amounts of customer data rapidly, providing valuable information on customer behavior, preferences, and trends.

In order for AI to do its job, your customer data – from purchases to web engagements to customer service cases – must be connected and unified. AI then takes this clean, reliable data to the next level by serving up intelligent predictions and recommendations to help your team improve the service you provide. AI can analyze your customer messages, for instance, grab the details it needs, and create in-depth responses to questions, speeding up response times and improving customer satisfaction.

AI’s ability to handle large amounts of complex data makes it ideal for today’s businesses. According to the State of Data and Analytics Report, in the next 12 months, more than two-thirds (68%) of data professionals are expecting a large increase in data – around 20% across a variety of sources, including first-party data, third-party data, and device data. Meanwhile, companies are expected to churn out about 22% more data on average in the coming year. As data volumes explode, the need for effective management grows, too.

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 satisfaction.
3. Operational efficiency: We found that 69% 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.

While customer service analytics is the broad category, at a more granular level you need to decide which data you want to measure. This is where your KPIs come in. Here are some important ones to consider and how to use them:

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.

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.

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.

Calculate the average time taken to respond to a customer query from the initial contact. Lower ART indicates efficient service.

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.

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.

This is a way to measure customer issues that never became cases – often because customers were able to help themselves through self-service tools, or via automated processes.

Agent case volume is important and straightforward. Tracking it can show when agents are overloaded or conversely, when you’re overstaffed.

CLV estimates the value a customer brings to the business throughout their entire relationship with the company. Knowing this information helps the business with strategic decision-making for customer acquisition and retention, and may help service agents recognize high-value customers who deserve a little extra help.

This is the percentage of customers at the beginning of a specified period who remain customers at the end. Higher customer retention rates demonstrate loyalty. Service agents have the power to help turn poor situations around and contribute to customer retention.

Through the power of AI, you can use natural language processing (NLP) to analyze customer feedback and comments to gauge overall sentiment (positive, negative, neutral) and identify areas for improvement.