5 min read
How is data quality measured in a survey?
The design of advanced controls to ensure data quality has become more important as insights platforms adoption grows. Automation has simplified market research and reduced the time and cost needed to create surveys. For the results to be reliable and useful to make decisions with confidence, it is very important to build in as much market research expertise in the platform as possible to guide experts and non-experts through the process.
There are three elements that have a major impact on improving data quality in a survey:
Market Research proficiency
Research proficiency is the combination of knowledge of the research techniques and the ability to translate that understanding into technology and innovation. This combination is paramount to successfully managing online research platforms.
An advanced Customer Success team will provide advice and guidance to clients at any stage of the process. The market research expertise is equally or even more valuable when developing features in the platform that builds in this expertise in the internal processes. With Zinklar’s insights platform, anyone, whether or not they are experts in market research, can conduct advanced high-quality surveys.
Engaged respondents
Another factor that determines the level of data quality is the degree of engagement of the respondents. It is important to make participation as easy as possible by designing dynamic and short studies (the average duration of Zinklar studies is 9 minutes).
From the outset, Zinklar has opted for a mobile research approach for surveys, as it has several advantages:
- Higher representativeness, as mobile phones have higher penetration than computers.
- Speed and real-time results
- Richer user experience
- 3 times higher conversion than traditional methodologies
- Higher engagement of respondents
- Higher quality insights
Respondent management and fraud detection
The source and characteristics of the sample also directly impact good data quality. Not all respondent sources are the same, and it is essential to have the expertise to detect and avoid bad practices such as fraud.
At Zinklar, we work with the world’s leading sample providers, continually auditing their strict quality programs and compliance with industry standards. We also apply advanced AI solutions to detect and remove any respondent presenting suspicious or inconsistent behavior. These respondents are removed before being added to the survey results.
The consequences of not evaluating data quality through ongoing control systems can create an undesired impact on business results.
Zinklar has the highest and more strict quality controls in place to ensure the quality of your survey – find out more here!
