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Key KPIs to measure
With all the efforts that go into customer support, all of it must equate to something substantial, measurable, and quantifiable to account for how the team is performing both individually and together. However, there isn’t a “one size fits all” approach to measuring KPIs. What your company needs and the way you measure success are different from the rest; priorities, dynamics, functionalities - everything can vary based on what you value as an organization.
But, as a general rule of thumb, there are certain KPIs and benchmarks based on industry standards that can help you understand how well are you satisfying your customers and compare it to global standards. This can help you understand where you stand and what changes are necessary to improve these metrics.
Tom Blossom talks about the right success metrics to track and the importance of customizing the ticketing system being used so that across the organization different teams can benefit from information such as how many questions were asked regarding a feature or tickets pertaining to a specific problem that can be prioritized.
Part 1: KPIs for Customer Support Teams
Service Level Agreement (SLA)
SLA stands for Service Level Agreement, and it's essentially a contract between a service provider and a customer. It outlines the level of service the customer should expect, detailing metrics like response times, resolution times, and the quality of service.
Why is SLA Important in Customer Support?
Sets Clear Expectations
SLAs establish a clear understanding between the support team and the customer, setting the stage for a harmonious relationship.
With defined metrics and timelines, SLAs hold the support team accountable for delivering a certain standard of service.
SLAs serve as a yardstick for performance, helping you identify areas that need improvement.
Meeting or exceeding SLAs can significantly boost customer satisfaction and loyalty, which is gold in any business.
Knowing your SLAs helps in better resource allocation. You can prioritize tasks based on the severity and the SLA attached to them.
In extreme cases, SLAs can act as a legal safeguard for both parties, clearly outlining what is expected and what will be delivered.
1. Average Ticket Count
Average Ticket Count measures the average number of tickets that your company or team receives in a given time period. This metric can be measured daily, weekly, monthly, etc., whatever seems comfortable to you.
Being aware of the Average Ticket Count helps you in resource allocation and identifies potential issues in the product that may be causing frequent queries.
How can AI help?
Essentially, AI can reduce the volume of support tickets raised by offering efficient self-service options and automating routine tasks, reducing the overall cost-per-ticket. A lower cost-per-ticket indicates a more efficient support system, allowing for resource optimization and cost savings.
2. First Response Time (FRT)
The First Response Time (FRT) is the measure of how fast a support agent responds to a query after it’s been posted by the customer. FRT is an indication of how well an agent can handle support queries or tickets which eventually amounts to better customer satisfaction.
Example - If a query was raised at 5 PM and the allotted support agent responded to it at 5:30 PM, the FRT is 30 minutes.
There’s also Average Response Time (ART) which is the average of time taken to respond to support queries by the entire team. This determines the efficiency of your team as a whole while FRT focuses on individual response time.
ART = Total time taken to respond to all tickets / total number of tickets
Example - If the total time taken to respond to all tickets is 200 hours and the total number of tickets is 200, then the ART is 1 hour for your entire team.
How can AI help?
AI-based solutions can be used to not just respond promptly but also monitor how quickly the responses are all together which highlights your team’s performance overall. AI tools can assist support agents by providing instant access to information and suggesting responses, reducing the time taken to handle each query. This means quicker resolutions, leading to higher customer satisfaction and more efficient operations. Implement AI-powered chatbots to handle routine queries autonomously, and use AI analytics to identify areas for further time optimization.
3. First Contact Resolution (FCR)
First Contact resolution (FCR) is when a support agent resolves a customer query during the first interaction itself. This is a very vital KPI for support agents because this accounts for the fastest resolution time, and does not require follow-ups or the need to transfer tickets between teams or agents.
FCP = Issues resolved on the first contact / total number of issues
How can AI help?
A well-trained AI bot can leverage multiple layers or sources of information to give accurate answers. The better you train the bot, the better it can serve as the first line of defense for L0 or L1 support. With an intelligent bot in place, such support queries can be resolved in the first contact itself, helping tremendously improve FCP.
4. Average Resolution Time
Average Resolution Time is the time taken for your team to resolve a query completely. This includes all back-and-forths, escalations, follow-ups, and any other concerns that come up while resolving the query.
Avg. Res. Time = Total time taken to resolve all queries / Total number of tickets/queries
Example - If the total time taken to resolve queries in a given month is 1000 hours and the total number of tickets is 100, then the Average Resolution Time is 10 hours.
How can AI help?
By automating ticket routing, deploying chatbots for basic queries, utilizing predictive analytics for proactive support, assisting agents with a knowledge base, conducting sentiment analysis, and automating follow-ups, AI can dramatically cut down the time it takes to resolve customer issues. For a cutting-edge platform like Threado AI, integrating these AI functionalities can supercharge your support team, making them the superheroes of customer satisfaction.
5. Customer Satisfaction Score (CSAT)
Customer Satisfaction Score is perhaps one of the most important metrics to track in customer support. This determines the overall satisfaction of customers from the support that has been provided to them.
CSAT is a qualitative metric i.e. it cannot be measured in numbers. You will need to conduct surveys to ask how their experience was regarding their concern. Typically, this survey should go out right after the customer’s ticker/query has been resolved.
One of the persisting problems with CSAT scores is the lack of response rates. But there are ways to improve how you position these surveys such as to improve the likeliness of responses from the customers. Support teams can make changes to improve response rates such as by simplifying the survey and timing it correctly. For example - keep only thumbs up (👍) and thumbs down (👎) as reaction options so customers don’t have to spend much time on it and position it after a ticket/query has been resolved.
6. Net Promoter Score (NPS)
A Net Promoter Score (NPS) is also a measure of the customer’s satisfaction with your support or a measure of their experience. An NPS focuses on asking customers how likely are they to recommend your service to others.
It’s a qualitative metric that asks the customer to rate how likely are they to recommend your customer service to others on a scale of 1-10.
You can identify “Promoters” who are highly likely to recommend your service and “Detractors” who are dissatisfied with your service and are not likely to recommend it. Your aim should be to increase the number of Promoters as much as possible.
Customer Effort Score (CES)
The Customer Effort Score (CES) is the measure of the effort customers need to put in in order to get their queries resolved. A higher CES means higher effort to get queries resolved and this can result in bad customer experiences. Lower CES indicates a smoother customer experience, fostering loyalty and satisfaction. CES is a qualitative metric that can be achieved through surveys to understand if customers are having a hard time getting their concerns resolved. In case of higher CES, focus on self-service options, improve the infrastructure, tools, and multi-channel support, and include AI-based chatbots so customers don’t have to go through hurdles in order to get queries resolved.
Part 2: KPIs for Customer Success Teams
1. Churn Rate
The churn rate quantifies the percentage of customers who have left your service during a specific time frame. A high churn rate can be a red flag, affecting both customer retention and revenue.
Churn rate = (Total number of customers lost / Total customers) * 100
A higher churn rate can be worked on by analyzing exit surveys and customer behavior to identify churn triggers. Implement retention strategies like loyalty programs or personalized outreach and assign accounts that have a higher indication of churn to more skilled or experienced CSMs in your team.
2. Monthly Recurring Revenue (MRR)
MRR is the predictable revenue generated from subscriptions or recurring customer payments every month. MRR is a vital metric for business planning and assessing financial health. This is what most businesses base their future plans on and it serves as the foundation for scalability.
A healthy practice for CSMs is to maintain better customer relations and constantly monitor the MRR coming from their accounts. Focus on retention strategies, upselling, and cross-selling to increase MRR and decrease churn.
3. Onboarding Speed
Onboarding speed is the time it takes for a new customer to become an active user. The shorter or smaller the onboarding speed, the lesser are chances of frustration, bad customer experiences, and churn.
Onboarding speed can be measured by the steps used and the time spent by customers to become daily or even weekly active users.
Streamline the onboarding process by reducing steps and in turn reducing friction. Focus on providing instant value or reduce the TTV (Time to value) which measures how quickly customers realize the value of the product. The faster this happens, the faster they’re likely to become active users.
4. DAUs and MAUs
DAU (Daily Active Users) measures the number of unique users who engage with your app or platform within a single day. MAU (Monthly Active Users) tracks the number of unique users who engage with your app or platform over a month.
DAU = Count of unique and returning users who use your product every day
MAU = Count of unique and returning users who use your product every day
DAU/MAU Ratio = (DAU / MAU) * 100
DAU gives you a snapshot of your product's daily engagement level. A high DAU indicates strong user engagement. DAUs can be improved by enhancing user experience, offering daily incentives, or introducing new features to keep users coming back every day.
MAU provides a broader view of user engagement over time. It helps you understand the long-term value and stickiness of your product. Focus on customer retention strategies like personalized content, loyalty programs, and regular updates to keep users engaged throughout the month.
The DAU/MAU ratio indicates strong user engagement and product stickiness. Enhance features that drive daily usage and focus on customer retention to improve this ratio.
5. Customer Health Score
This is a composite metric that gauges the overall health of a customer account. A customer’s
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How to Measure
Use a mix of metrics like product usage, customer feedback, and support ticket trends.
A low health score can be a churn predictor, while a high score may indicate upsell opportunities.
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How to Improve
Regularly review account health and proactively address any issues or concerns.