4 ways to ensure data quality in market research today

Data quality is the most important piece of good market research. After all, if you can’t ensure the quality of your data, how can you confidently use it to make business decisions? 

There are many factors that can come into play when it comes to data quality; like the validity of verbatim respondent answers or whether or not there may tend to be a cultural response bias when conducting research in specific regions, etc. 

And in today’s world, if the customer or stakeholder is likely to be the first person looking at the data that's been collected, you want to make sure it's high quality and gives them the answers they need. 

Here’s four things to consider that will help to ensure data quality, straight from our Head of Research Expertise.

Nowadays, businesses tend to conduct a lot of their market research at high volumes. And from these tests, if you’re told that whatever results you receive will be 95% accurate, that sounds fantastic. 

But in reality, 5% of inaccuracy in your results is actually a significant number that can skew your data and leave room for a noteworthy margin of error. 

Think about it: If you’re testing across 500,000 respondents, 5% is 25,000 respondents. That’s a large number of consumers that could completely disagree with your messaging.  

Because of this, multiple rounds of testing are an important tactic to invest in when conducting research at high volumes. By running multiple rounds of tests, you will be able to get a better overall picture of how consumers are truly responding to your stimuli, and make sure you aren’t missing a major issue within your messaging.

Meta analysis, or connected learnings, allows you to compare data across multiple studies to find trends in your data. 

For example, as a large brand, you may want to see how your last few Super Bowl ads have performed and if there were certain patterns you can derive from what you’ve tagged (i.e. whether a celebrity was in the ad or specific call-to-action) to find a winning combination for the next one — saving you millions while also giving you a better understanding of your consumers.  

Of course, tagging and reviewing trends in your data is a hefty time investment, but it will allow you to make better decisions based on many data points over time (quite a worthwhile investment in the long run).  

For more on this (and how to bring on professional help) check out our blog on how to unlock the power of your data with database reviews.  

Another factor to consider to ensure your data quality is how you’re phrasing survey questions or even the order in which you ask the questions. 

Think about it: If a question is leaning towards an agreement statement, the respondent can easily interpret the answer you’re hoping to get, which could heavily influence how they choose to respond — leaving you with an answer that may not be genuine. 

Alternatively, another factor some have considered important are the numeric scales included (i.e. ranking 1 to 5 or 1 to 10) within survey questions. Interestingly enough, we found that this does not seem to have any radical effect on respondents. At the end of the day, the more important element to focus on within the structure of your survey is how the questions are formatted to ensure its accuracy.

On a final note, if you’re passionate about the nuts and bolts of methodology, make the time to experiment.

Run tests on tests, run comparisons on word choice or the order of your survey questions, cross-check responses by regions, the list goes on! 

By experimenting, you’ll gain a greater understanding of the data you’re collecting and how to structure surveys in a way that leaves less room for error — ultimately giving you more confidence in the quality of your data.  

There are many elements to consider when it comes to ensuring data quality. These are just some of the top of mind factors geared to help better inform your decisions and how you look at your own data. 

For more on this, check out our webinar below.