Mesure Your Customer Service Value with Customer Effort Score
Customer Effort Score (CES)
CES – Customer Effort Score
CES gives you insight into the information how effortless is it to work with you. Onboarding customers, nurturing them, resolving their issues, all come into the territory of customer care and you’ll want to know what your customers feel about your processes.
 CES is used for: | • measuring how easy is it for your customers to work with you • how overwhelming are your processes for your customers • how easy is it to resolve an issue your customers might have with your service |
What makes a CES the industry standard is a question-and-scale combination – “The company made it easy for me to fix my issue?” and the answers scale in the following order:
This approach tests the client’s objectiveness by asking them if it was easy to get the questions answered and issues resolved. By making clients read the options, there’s no reflexive rate. If it was, the rate would represent customer service satisfaction – since they are interacting with customer service at the very moment. Instead of numbers that can be misinterpreted, you give them statements that can not be tied to the customer satisfaction scale. This way, we are ensuring accuracy.
How to Calculate CES
Unlike NPS, CES has simpler math behind its metric system. It relies on calculating percentages for each rate/grade the customer service agent/rep received. To calculate the score, we simply get the percentage for each of the unique grades.
If you keep your user’s contact details during this evaluation and give them the option to leave comments, you can define a strategy for improving this score. Here are some steps:
- Examine the responses.
- Map the core reason for your customers to feel unsatisfied.
- Find overlapping areas and you’ll find your areas to improve.
You’ll find a list of reasons for low grades which you should address together with the team.
Interested in learning more ways in which you can utilize CES to your advantage? Check out our blog pages:
