Historically, contact centers have been seen as a necessary overhead where improvement efforts focused mainly on efficiency gains. They’ve focused solely on operational data (O-data) like average reply time and resolution rate. While these customer service metrics are necessary to measure, they offer little context for how customers have actually experienced the service. Since 52 percent of U.S. customers have switched providers in the last year because of poor experiences, it’s essential to also measure experience data (X-data), which gauges your relationship with your customers.
Moving towards a service-leadership approach and adding experience data as part of your overall CX strategy, is the key to positioning your contact center as a key pillar of your customers’ experience.
Experience Metrics (X-DataTM)
X-data is the human factor data — the beliefs, the emotions, and the sentiments. It’s the human feedback that points to the gaps between what you think is happening and what’s really happening. Using experience data allows you to build a balanced scorecard that helps to focus effort and resource in the most effective ways to achieve business outcomes.
Customer Satisfaction (CSAT)
CSAT is short for Customer Satisfaction which a is a commonly used key performance indicator to tracks how satisfied customers are with your organisation’s products and/or services. You should measure customer satisfaction after each interaction with a customer service agent. These ratings can be measured over time to analyse how certain agents or teams are performing.
How to Measure it: Number of satisfied customers (4 and 5) / Number of survey responses) x 100 = % of satisfied customers
Note: Only responses of 4 (satisfied) and 5 (very satisfied) are included in the calculation, as it has been shown that using the two highest values on feedback surveys is the most accurate predictor of customer retention.
Customer Effort Score (CES)
CES is a single-item metric that measures how much effort a customer has to exert to get an issue resolved, a request fulfilled, a product purchased/returned or a question answered. The idea is that the customer will be more loyal to brands that are easier to do business with. By focusing on reducing customer effort, you’ll create a better experience for your customer.
How to measure it: The easiest way to to get an average score. For instance, if you ask, “On a scale of 1-7, how much effort was involved to get your question answered?”, you could take the average number.
Net Promoter Score (NPS)
NPS stands for Net Promoter Score which is a metric used in customer experience programs. It’s often held up as the gold standard customer experience metric. NPS scores are measured with a single question survey and reported with a number from 0-100, a higher score is desirable. Customers are bucketed into promoters (score of 9 or 10), passive (score of 8 or 9), and detractors (score of 0-6). This will allow you to gain a bigger picture of loyalty.
How to measure it: Percentage of Detractors – percentage of Promoters = NPS. For example, if 10% of respondents are Detractors, 20% are Passives and 70% are Promoters, your NPS score would be 70-10 = 60.
Social Media Monitoring
Many customers voice both their frustrations and praises on social media, yet few brands embrace it and respond back. This creates a frustrating, one-way experience for the consumer. By tracking social media metrics, you not only know when to respond to a customer, but understand the types of questions that are being asked, so you can put better systems in place to address those issues.
Top metrics to track include:
- brand mentions over time
- negative comments
- technical or account questions
- the number of questions that could be answered through other support material
How to measure it: These metrics should be measured and analysed month over month to understand trends.
Customer churn, also known as customer attrition, in its most basic form, is when a customer chooses to stop using your products or services. This is trickier to measure because there’s no one predictor of churn. You must look at both operational insights (e.g. declining repeat purchases, reduced purchase amounts) as well as experience insights along the customer journey is foundational to predicting churn. For example, a customer who has declined in recent visits and gives a Net Promoter Score of 7 after their latest shopping experience, could have an increased probability of churning.
How to measure it: Use Qualtrics Predict IQ to identify customers and accounts likely to churn.
Operational Metrics (O-Data)
O-data is tangible records of tangible activities and is really helpful because it tells you about win rates, response times, and employee efficiency.
First Response Time
According to research by Forrester, 77 percent of consumers say valuing their time is the most important thing a company can do to provide a great customer experience. When a customer reaches out with a question or concern, they want a fast reply from an actual human. While it’s beneficial to send an automated reply that someone will be in touch, it doesn’t count as a reply until one of your agents has personally responded. Although your response time should be faster than the industry benchmarks, you can use these as a starting point.
- Email or online form- 24 hours or less
- Social media- 60 minutes
- Phone- 3 minutes
- Live chat and messaging- Instant
How to calculate it: Time of first response – time of customer request = (#minutes/hours/days) first response rate
Overall Resolution Rate
When your customer has a question or complaint, your goal is to close the loop and resolve the issue. If you don’t respond or can’t provide adequate support, the customer may be reluctant to do business with you again. Rising resolution rates can indicate the effectiveness of your customer support team.
How to calculate it: Total number of tickets / the number of tickets solved = overall resolution rate
First Contact Resolution Rate
Customers don’t like to be bounced around from agent to agent and want their issues resolved on the first point of contact. First contact resolution rate measures how many cases require only one contact from the customer. A high first contact resolution rate will likely correlate with CES because the customer will produce less effort if they only have to contact your organisation once.
How to calculate it: Number of incidents resolved on the first contact / total number of incidents = first contact resolution rate
In addition, you can also measure the average number of replies it takes for a customer to get his issue resolved and the amount of time it takes from when the customer submits the ticket to resolution.
Customer Ticket Request Volume
While it’s good that customers are interacting with your company and you have an accessible ticketing system, getting too many requests can indicate an issue. If you have an overwhelming amount of tickets, you either need to hire more customer support agents or look into a potential problem with your UI/UX. You could also try updating your support articles so customers can get help on their own.
How to calculate it: Compare the amount of support tickets month over month or week over week. Pay particular attention if the number of tickets spikes after a new product or feature release.
Average Ticket Handling Time
Handling time measures the amount of time an agent actually spends working on a single case. The shorter the time, the more efficient your team is. It’s important not to focus on this solely because agents may rush through customer tickets instead of focusing on great customer service.
How to calculate it: Your goal is a shorter handling time so your agents can be more effective.
If you’re ready to start surveying your customers with X and O-data, check out our next article What Questions Should I ask on a Customer Service Survey?