Become a stickler for the details in your research data

There are times when you want someone to paint with broad, messy, rainbow strokes. This kind of unbridled creativity has a critical role to play in market research. Thinking outside the box, curiosity and passion are all essential qualities. But equally, we need a stickler with a check-list — someone with an eye for detail, who knows the rules and enforces them.

When it comes to data, we always want a strict protocol to ensure data quality. Because let's face it, it doesn't matter how great your analysis is—or how fancy the visualizations you share are—if the core underlying data isn't reliable and accurately reflect the market. At Infotools, we implement protocol and data quality for many of our clients and believe this step should be a "non-negotiable" for successful market research outputs. For clients who participate in things like global tracking programs across multiple markets, this approach is increasingly crucial for consistency.

Over the last three decades, I've learned many lessons about how changes to survey methodology can impact data quality and the ability to uncover market trends. These lessons ring especially true when working with clients who own multiple brands, operate across borders, and have many stakeholders along the research continuum. For example, Infotools works with The Coca-Cola Company, which owns more than 500 brands in more than 200 countries. Here are some tips based on our work with Coca-Cola and other clients:

1. Involve data expertise in the early stages of complex research design implementation

Clever survey design is even more difficult and complicated when you're aiming for maximum participant engagement by ensuring ease of use and minimal time spent. Survey design is a vital piece, but data processing is just as necessary. Failing to account for complex scripts in the final processes can mean that the collected data doesn't translate into the final data output correctly. The impact of this can be substantial.

To illustrate this, imagine building a pyramid representing brand equity (where the goal is to identify which brands have the strongest affinity with consumers – i.e., are the favorite/preferred in their category). For a brand with multiple sub-brands—let's say Grape Co. also owns Red Grape Co., Green Grape Co., and Vitamin Co.—it's easy to miss a step in your survey coding. Let me explain: If Question One is, "What is your favorite fruit brand?" and the survey participant answers, "Grape," your coding will list 'Grape Co.' as their favorite brand. However, what if the follow-up question is, "What type of Grape were you thinking of?" and they answer, "Vitamin Grape"? Have you coded the survey data to make sure 'Vitamin Grape Co.' is now listed as their favorite brand? It sounds like a small detail, but when you look at the big picture, failing to make this distinction could result in the conclusion that 'Grape Co.' is the favorite brand, when actually 'Vitamin Grape Co.' could be more accurate.

2. Minimize brand list revisions

Managing the demands of survey-data users can be a big job for many clients. If a company has multiple brands in the market, stakeholders can often disagree about the importance of one brand or another. Long brand lists impact the quality of the data collected and the experience for the survey participant, who may feel overwhelmed if asked about too many different brands at once.

For example, if a company like Coca-Cola is conducting global research, they have a high number of brands that they want to explore. From its flagship cola to Disani water, Powerade, Minute Maid, and so many more, it's easy to see how lists can get overwhelming. Add this to naming challenges across borders—think Diet Coke and Coca-Cola Light—and it becomes clear that parameters need to be set up ahead of time.

We can start to overcome these kinds of challenges by following these recommendations:

• Create and follow strict criteria of both when a brand can be removed from a study OR is considered important enough to be included in a study.
• Follow strict quotas on the maximum number of brands that can be included in any particular survey.
• Minimize the number of times the survey can be changed to increase the chance of identifying trend breaks. Where possible, calendarize brand list reviews to a couple of times a year to match up with periods when information is required for brand planning.

3. Be wary of the impact of logo design

Logo design and sizing changes can majorly impact survey data collection. Sometimes, even well-known brands plummet in claimed usage following what was considered a simple change to the logo.

For example, when one of Infotools' client's brands dropped drastically in the charts, we took a look at the logo within the survey. On review, the brand font size was so small, it was almost unreadable. It's no wonder the survey participants did not recognize it!

The impact of a logo change to a very well known brand, revert to old logo, and trend returns immediately back to previous levels. Sales remain stable in the market.