How To Avoid Telephone Interviewer Bias In CX Surveys?

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Author: Frank Buckler, Ph.D.
Published on: October 19, 2021 * 5 min read

It is becoming difficult to reach the target audience using telephone interviewing. Although the systems can accommodate open-ended responses, capturing them requires interviewers to have accurate skills.

CATI stands for Computer Assisted Telephone Interviewing. Just as computers have replaced the clipboard and questionnaire in face-to-face fieldwork, CATI has replaced traditional telephone interviews. It is best to structure interviews carried out in large numbers where all possible answers have been worked out.

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We will discuss the reasons and the impact of the CATI bias. First, we understand the two types of bias that are:

  • A real person – It is helpful to have a real person because people talk more with the real person. You can get more feedback, but it will be moderated and will be less extreme and explicit.

  • Transcription – The second type of bias is the transcription bias because:

  • What your customer is saying is not one-to-one transcribed. This is how 99% of the telephone interviews work today.

  • In most call centers, the interview is manually categorizing the answer in real-time. So, it is bad as they need to work under time pressure that significantly reduces failures. To categorize in real-time, you can not have more than ten categories, and with ten categories, you can do nothing specific. So, there is no mechanism to ensure that every interview takes place in the same way.

  • If the interview is manually transcribed, it will be a simplified representation of the true feedback.

  • It is nearly impossible to keep the coding among many interviewers consistent, even with good training. Let’s say if 20% of people don’t like your service, you need to know what it means. Is it waiting time? Is it friendliness? Is it the way they are approached? Is it too many cold emails? Whatever it is, you need to know what bothers them to act so you can get better. 

  • Result – The phone interviews are largely biased and simplified. That’s why when we take this kind of feedback into seeking analytics, we see entirely different results because it is simplified. If you use predictive analytics, you will get accurate results. But in this case, the predictive power is lower. You can think of a telephone interview channel as a good channel because what else could be better than having a person speaking with your customer. It’s good for your customer and good for you because you can learn what they are saying exactly. In short, you can manually listen to an active listener.

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How To Avoid The Biases?

You can use the following techniques to avoid the CATI interview bias. 

  • Use the power of CATI interviews, which means you don’t need to apply automated active listening approaches as it is not always optimal. It’s much better to have a human, and you can affirm and clarify what the customer means by saying:

“I get you.” 

Or if you don’t comprehend anything, you can ask:

“What do you mean?”

So, these phrases of active listening have low influence in terms of what the customers will say but still probe them to talk more.

  • You need to standardize your active listening approach using prompts to avoid bias. It is better to ask questions so that the customer can elaborate, like what do you mean? Etc. So, there are some standard questions you can use and which are pretty easy to train. 

It is a MUST to replace manual transcription with:

  • The AI-powered automated transcription is available as API-based cloud services. You can transcribe many different languages your customers are saying, and it is not expensive to program.

The AI-powered categorization is available as API-based cloud services. You can categorize in real-time what your customers are saying. The same algorithm we use to categorize the feedback is real-time capable. So, you need to do this little automation to improve your feedback quality.

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In a Nutshell

So far, we discussed that telephone interviews are significant because the quality can be very rich. There’s nothing better than having a human speaking with your customers. Using open-ends in this form is very nice, but the bias of the interviews is quite huge. So, prefer combining manual active listening with machine-made transcription and coding.

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