Imagine the scenario of your customer support team being inundated with customer complaints. Upon closer inspection, you notice a pattern: Most customers complain about the same issue—your app keeps crashing.
You immediately act and inform the product development team. They discover a bug in the software. The product team then tells you when the problem will be fixed while you update your team, who, in turn, updates your customers.
The problem is fixed within a few hours, and customers are happy. Happy not only because of the short resolution time but the constant updates provided. These updates showed customers their problem mattered and was being attended to by a competent company.
This positive outcome resulted from customer service data being put to work in the right way to fix a product issue.
Read on to learn more about customer support data, including what it is, why it matters, how to collect it, and seven strategies for making the most of it.
What is customer support data?
Customer support is any data that a customer service team collects about their interactions with customers to understand customer experiences, satisfaction levels, and their own performance.
There are two types of customer support data: qualitative and quantitative. Quantitative data is anything you can quantify by assigning a number. Qualitative data is descriptive. Examples include written responses to survey questions.
Why should you collect customer service data?
Here are four reasons why you should collect customer support data:
Assessing performance, identifying issues, and making improvements
Collecting customer support data helps you determine how your team performs on certain metrics and what they can improve. For example, suppose your team takes longer than usual to resolve a customer issue. It may suggest they need more training on delivering customer service or using your support system.
Or, if your team is struggling to meet SLAs (e.g., responding to complaints within 24 hours), it may indicate you need a better system for reminding your team of messages approaching breach so they can prioritize them. One way to manage SLAs is to use a shared inbox to tag messages approaching a breach with something like "SLA warning."
Improving team morale
Tracking data points like improvements in response times also shows your team members how they're improving over time, which can be a huge morale boost.
Making company-wide improvements
The support team does not operate in a silo. Plenty of other departments in your company can benefit from accessing customer services data. Consider the scenario mentioned in the introduction: the product development team was able to fix a product issue thanks to feedback from support.
Getting buy-in from top management
You often need budget approval for making any improvements, like investing in a new customer support system. Strong data points, especially data presented in a digestible format like reports and charts, help present your case to top management.
What customer support data should you collect?
Here are nine top customer service metrics to track:
Average resolution time: the average time it takes to resolve a customer issue from opening to closing a support ticket. Average resolution time is also called average total reply time, mean time to resolution, or time to resolve.
Resolution rates: The number of support tickets assigned to a department or person compared to how many were resolved. Calculate resolution rates using this formula: Solved Requests / Received Requests x 100 = Resolution Rate.
Average first response time: The time it takes for a customer service agent to respond to a complaint from when the complaint is received. Autoresponders do not count as a first response.
Reaction time: Unlike response time, reaction time measures how long before a rep takes any action on a message, whether tagging, assigning, or responding to it.
Ticket volume: Total support tickets received for a certain period. You can measure volume around specific topics to better understand issues. For instance, a higher ticket volume for a particular product can help you identify product issues.
Conversations handled by rep: The number of open cases that an agent is handling. It's essential to have different benchmarks for how many messages junior and senior customer service representatives should handle. Agents with more experience can naturally cope with more.
Replies to resolve: A measure of how many replies it takes to resolve a ticket. It's a good indicator of efficiency. More replies mean more back and forth and may indicate that an agent needs more training to handle customer issues.
Customer satisfaction (CSAT): A measure of how satisfied customers are. Companies will usually send surveys to customers asking specific questions they can rate on a scale (e.g., the quality of customer service on a scale of 1 to 5).
Total breaches: A metric measuring the number of tickets that didn't meet SLAs.
Where do you get customer support data?
Obtaining customer support data doesn't have to be tricky. Simply focus on daily tools like your help desk, email, other support channels, and surveys.
Your help desk. Contact center agents will be able to identify patterns, e.g., why customers contact them. Your help desk can use software like Front to measure key metrics like email volume and resolution time for each agent or your team.
You can view this data at a glance via fully customizable analytics reports and share it with others for improved visibility across your company.
Your email. Gather insights like FAQs and identify common customer issues. Again software can help. Front, for instance, lets you measure email volume around specific topics by assigning specific tags.
Your other support channels. Don't be afraid to cast your net wider to other support channels like text messages, phone, and social media to gather valuable customer data (e.g., FAQs).
Surveys. Send surveys to customers after every agent interaction to gauge satisfaction levels. Two common survey examples include net promoter score (NPS) and customer satisfaction (CSAT) surveys.
NPS surveys involve sending one survey question to ask respondents how likely they are to recommend a company. They often have to select a number on a scale of 1 to 10.
CSAT surveys often consist of multiple questions. Respondents answer them by selecting a number on a scale—usually 1 to 5 (1 is very poor and 5 is excellent). Questions may include:
How would you rate the service?
How helpful was the agent?
How do you get the most of your customer support data?
Collecting data is pointless if you don't use it to make improvements. Here are seven effective ways for getting the most out of your customer support data:
Monitor the right metrics
There are so many metrics, making it challenging to know what to track. As a guideline, focus on metrics that have the most significant impact on your customer service.
These include the earlier metrics, like resolution time, response time, messages handled per rep, and customer satisfaction scores.
For a deep dive into these metrics and others, read Support metrics to track: The secret to extraordinary customer service.
Use the right tools to track your support data in charts and graphs
You need to use the right tools to track your metrics effectively. You can use software like Front to measure metrics like resolution time and response time. You can then view this data at a glance in charts and graphs. Having data in a presentable, visual format makes it easier to spot trends.
Use the customer data to decide who to hire, when to hire, whether training is needed, and how to optimize staff shifts. Notice higher ticket volumes on certain days or times? Maybe you need more staff on shift.
See your average resolution times gradually increasing? Perhaps you need to train staff to more efficiently deal with requests or tweak your support workflows to route messages to the right people automatically.
Organize your data centrally
With customer data coming from various sources like email, your help desk, and social media, things can quickly get disorganized. To make the most of the data, organize it in one place so your team can easily reference it.
Automate your data management with Zapier to route customer feedback from your data sources into a database or spreadsheet. Front, which can act as an in emails, live chat, social media, and more. Organize all messages by topic like customer sentiment.
Tie support data to company-wide goals
Top management and departments outside support generally won't care about support data if they can't see how it affects them directly. This makes it difficult for you to push for change or get buy-in from top management.
To get management to care, tie customer support data to company-wide goals. For instance, instead of getting approval for new support software by presenting written feedback from employees on the complexities of using the software, show them how it affects the company's bottom line.
You could present the written feedback alongside higher average resolution times and a declining CSAT score and show them how many customers (and the corresponding revenue) the company is losing.
Measure a metric over time
A snapshot of a metric at a point in time doesn’t tell you much. How does it change over time? How does customer satisfaction differ? Is there improvement?
To that end, make sure you measure your metrics and gather data over time so you can make these comparisons and get an accurate picture of changes. Then, make the needed changes to see improvement.
Focus on multiple metrics to tell a story
A single metric can be limiting. Multiple metrics help you present a complete story. A declining average resolution time suggests your team resolves customer issues efficiently and they are performing well.
But by comparing it against another metric like CSAT, you can identify how happy customers are and even at what point the CSAT score begins to increase (or decrease). For example, you may notice a drastic increase in customer satisfaction levels when reducing average resolution times to below two hours.
You can also focus on combining qualitative and quantitative data to add more color. For instance, a CSAT score tells you how satisfied a customer is based on a number. But if you let the customer provide written feedback (a qualitative data point), you get more context. You understand what your support team is doing well to achieve that number.
Share data analytics with other departments
As already mentioned, customer support data is valuable to other departments. Product development can use customer feedback to make product improvements. The sales team can use data for sales collateral, e.g., to understand customer objections which they can address in presentations. They can even use positive reviews to showcase the benefits of a specific product.
Just make sure departments understand the value of that data (tie the data to company goals) and receive the information in a digestible format (charts and graphs).
Use tools Like Zapier and Front to organize and share that data with those departments.
Customer support data is gold—make the most of it
Customer support data is crucial for assessing team performance, making improvements, boosting team morale, and getting buy-in from top management.
The important thing is using the right strategies, tools, and processes to put it to work. Do that, and you'll be able to truly take advantage of it to drive your business forward.
Written by Nick Darlington