Data quality – we here in the Bunker are fanatics about it. Without data quality control, the results of a 1,000-complete telephone survey becomes nothing more than noise. Worse still, it is noise that might serve as the basis for a costly decision for people who trust the erroneous findings.
There are a number of measures that research analysts can undertake to help ensure quality in telephone surveys. It starts with careful survey script writing. Rooting confusing and poorly worded questions out of a survey before it is administered will go a long way toward avoiding mistakes by callers and misinterpretation by respondents. It goes without saying that thorough training of callers is key. Beyond that, much of it comes down to scrupulously monitoring data that comes in and working with the call center supervisor to address issues as they arise. The ability to do this easily is a key advantage to having a call center that is at the same site as the other research staff.
Of course even with the best scripts, the best training program and the most conscientious daily monitoring of data, the biggest factors in data quality for telephone surveys are the callers themselves and the expectations that are placed on them. Callers need to be clear communicators, obviously. But they also need to be engaging on the phone, and trustworthy. Engaging not only because positive energy will persuade respondents to participate in the survey, but also because it will fight fatigue on the part of the respondent. Engaged respondents are less likely to drop out early and more likely to provide good, open-ended verbatims. Trustworthiness is also important. Callers need to be objective – they shouldn’t be tempted to coach respondents to answer a certain way. Close supervision and monitoring can mitigate those issues, but the best solution is to eliminate the underlying cause.
Callers may be tempted to fudge data or cut corners when too much emphasis is placed on hitting quotas. It’s easy for them and their supervisors to lose sight of the fact that the amount of data collected is irrelevant if it’s bad. The key is to create a culture of quality over quantity in the call center. Impress upon the callers that the data they collect will be scrutinized, analyzed and be used by people to make very important decisions. Train them to point out issues with the administration of the survey if and when they arise. Above all else, show them that the organization cares about quality rather than just quantitative metrics of their performance. Addressing that issue at the point of data collection will lead to better analysis and insights down the road.
Interested in inbound call center services in Syracuse, NY? Contact Lauren Krell at LaurenK@RMSresults.com or by calling 315-635-9802.
I agree with your main argument here, quality should trump quantity with call center fieldwork. However, I do think you have to look at data collection on a project by project basis and even a caller by caller basis. If you have 14 callers obtaining adequate completes at the rate of 3/hour and 1 caller obtaining top quality completes at the rate of 1/hour, is that worth the trade off? It’s a difficult call sometimes.
What I am saying is quality should trump quantity – within reason. If the survey lends to getting more in-depth responses with a focus on obtaining quality feedback from respondents, maybe the team could look at other methodologies – IDIs, focus groups, etc.
There are varying degrees of quality. If it’s a case of the better surveys being better by virtue of more in-depth feedback, then in the scenario you present, the tradeoff would not be worth it. But if they are better because the less “productive” caller is administering the survey correctly and the others are rushing through to the point of collecting data of questionable integrity, then garbage data is still garbage no matter how efficiently you’re gathering it.
The latter is an extreme case, but it can and does happen in call centers where proper training doesn’t happen, where callers are treated as a commodity, and where there isn’t enough monitoring. I think more than anything that was the point of the post: If you ONLY pay attention to productivity numbers, it invites data quality issues.
[…] quality recommendations. In previous blog posts we’ve talked about things such as pre-testing, call-center quality vs. quantity, and things to look for when proofing your market research report. But to get straight the point, […]
[…] centers, whether the focus is market research, telemarketing, or even customer service, to value quantity over quality. There will be some level of pre-employment screening and training, but ultimately there seems to be […]