1st May 2024

Lost in Transmission: The “Telephone Game” of Sample Requests in Market Research

Lost in Transmission: The “Telephone Game” of Sample Requests in Market Research

Improving sample requests for better results.

by Ariane Claire, myCLEARopinion Insights Hub

In the world of market research, the process of requesting market research sample often resembles a game of telephone. Like in the childhood game, where a message gets distorted as it passes from person to person, crucial details are usually omitted along the way. This can lead to confusion, inefficiency, and, ultimately, compromised research outcomes.

When a sample request begins, it may contain specific requirements and a detailed project scope description. However, as it reaches the panel provider, it can become distilled down to vague and incomplete information. Details such as survey length, target audience, and the reasoning behind certain specifications are often omitted, leaving the panel provider with more questions than answers.

How often have you encountered sample requests that started with detailed specifications but ended up looking like this? • Survey length: 15-25 minutes • Target audience: HVAC contractors • Incidence rate: 30%

Why would you whittle it down to that when the initial request was much more detailed, including: • Survey length varying based on brand awareness and product usage questions, targeting HVAC contractors in the U.S. • 30% of respondents work needed to be in commercial buildings • Requirement for experience with VRV/multi-split air conditioning systems

When selecting a sample partner, you want them to provide the best possible service to your client. With a clear understanding of the project's scope, the panel provider will be able to assess feasibility more accurately and provide an accurate quote for the sample. Omitting crucial details like these can lead to a range of issues, which can lead to project delays, increased costs, and compromised data quality.

Moreover, incomplete sample requests can lead to misalignment between the sample provider and the research objectives. Suppose the requester fails to specify critical details, such as the target audience or survey methodology. In that case, the sample provider may select respondents who do not accurately represent the target population, leading to biased or unrepresentative data.

Inaccurate sample quote requests lead to inefficiencies and compromised data quality and contribute to panel burnout, lower incidence rates (IRs), and even fraudulent behavior. When sample requests lack detailed and accurate information, panel providers may be forced to over-utilize their panel, contacting the same respondents multiple times for different projects. This can lead to respondent fatigue and lower response rates, ultimately impacting the quality of the sample. Additionally, without clear specifications, panel providers may struggle to accurately match respondents to surveys, potentially resulting in fraudulent responses from individuals who do not meet the survey criteria but participate anyway.

Improving the quality of sample requests is essential for ensuring the integrity and validity of market research studies. Requesters should provide detailed and accurate information to sample providers, including clear objectives, target audience specifications, survey methodology, and other relevant details. If they don’t have it, they should request it and get the bigger picture of the project before farming out sample requests. By doing so, researchers can minimize the risk of miscommunication, ensure the accuracy of sample selection, and ultimately enhance the overall quality of their data and research.

We must shift our approach from working against each other to working collaboratively. Panel partners offer more than just samples; they can assist in defining your target audience and achieving your research objectives. It's time to stop whispering on the other end of the “telephone line” and start communicating clearly. Transparency and communication are crucial for ensuring data quality.

Q&A Session