標題: You can then prioritize the critical [打印本頁] 作者: shar.minjahan25 時間: 2024-5-15 18:49 標題: You can then prioritize the critical 8 Best Practices Customer Satisfaction Surveys Whether you are new to creating a customer satisfaction survey or regularly employing it in your business, there are some best practices to keep in mind while running the survey campaign, like: 1. Keep the Continuum Scales Consistent The number one practice in our book is to keep the scale consistent every time while creating a customer satisfaction survey. It makes it easy to track and analyze the scores over time. Let’s see it with an example. Suppose you use a 5-point Likert scale for your CSAT surveys. From a total of 100 respondents, 65 choose either 4 or 5 options as their response.
So, your current CSAt score Cambodia Email List becomes 65. Please rate your satisfaction with the recent purchase. Very Dissatisfied Dissatisfied Neutral Satisfied Very Satisfied Now, after six months, another team run a CSAT survey, but this time, they use an inverted scale running from positive to negative sentiments as shown below: Please rate your satisfaction with the recent purchase. very Satisfied Satisfied Neutral Dissatisfied Very Dissatisfied Now you can see that 1 and 2 become favorable responses. You can still get a score of 65 if 65% of the respondents select these options. But it may confuse the interdepartmental teams while analyzing the responses and require more time to categorize the feedback.
In the same way, when using multiple Likert scales questions in the same survey, try to use a consistent scale so respondents don’t get confused. 2. Estimate Your Sample Size to Get Reliable Results Sample size estimation is a critical step while creating a customer satisfaction survey to produce reliable results. The main reason behind this is to flatten the data curve. The more responses you collect, the more are chances that particular feedback conforms to the opinion of the majority of your audience. It helps you separate the genuine feedback from the outliers and reduces the chances of you chasing the wrong feedback data.
