Data analysis is critical for all employees, no matter what department or role you work in. Whether you’re a marketer analyzing the return on investment of your latest campaign or a product manager reviewing usage data, the ability to identify and explore trends and fluctuations in your data is an essential skill for decision-making. Unfortunately, many companies today struggle with data organization and analysis.
The traditional way to measure customer service metrics is to track averages, such as average reply time or average resolution time. But Geckoboard’s Customer Support Experience Report found that some companies measure first response time as a service level agreement (SLA) metric rather than an average. SLA metrics are internal metrics used by customer support teams to set targets for the proportion of customers who will receive a predefined minimum level of service.
Your customer service framework is like the instructions manual for building some flat-pack furniture. Without directions, you’re guessing how your new desk goes together. Likewise, your support team will improvise how best to respond to incoming customer queries if they don’t have a guiding framework – which can lead to long delays and inconsistent customer experiences. A framework is useful for clarifying how, when, and why you provide the type of service you do. It maps out.
Helpful, friendly customer service shouldn’t be seen as “optional” by your business. It has serious implications for whether customers will return to your company and spread the word about your products. According to a Qualtrics study, 95% of consumers who rate a company’s customer experience as “very good” are likely to recommend the company.
Building a support team is like doing a jigsaw where all the pieces keep moving around: You’re trying to match customer needs with company priorities, maintaining a high level of service while onboarding new team members, and predicting how ticket volumes will shape hiring roadmaps. Even worse, if you’re the first (and only) person doing support at your company, you’re still juggling incoming tickets and covering all your support channels.
So you have a Customer Satisfaction Score (CSAT) benchmark. How do you figure out if the benchmark you’re using is realistic? Benchmarking can be a very helpful exercise but, if done wrong, can hinder team performance by setting unrealistic goals or expectations.
According to KPMG, 58% of consumers say being able to shop 24/7 is the number one reason they shop online. Companies have international customer bases who are online round-the-clock, and that means a decent chunk of customers will be disappointed if they can’t receive help during off-hours. The Geckoboard Customer Support Experience Report 2021 found that 50% of customer support teams are only available during business hours Monday to Friday, and only 28% are available 24/7.
Customer support is demanding work: You’re on the front line handling queries, issues, and complaints all day. To motivate and retain support agents, companies need to acknowledge their work. Customer service recognition is when the customer service team’s hard work is seen, understood, and valued by the whole organization.
In 2020, Customer Support teams worked harder than ever before. Not only did most have to adapt to new ways of working but for many the pandemic lead to an increase in support requests. Geckoboard customer Ember described a situation where people were more readily available to contact Support but also expected agents to respond faster than usual. So how is the industry faring in 2021?
Customer Satisfaction Score (CSAT) is one of the most widely used customer service metrics. Companies send short CSAT surveys after a support interaction—but these are tied to your customers’ most recent interaction with your company and can be skewed if they had an issue or poor experience. And because CSAT surveys are kept short to make it easy for customers to respond, they don’t tell you why customers feel the way they do.
