Our main goal in research is to better understand the audiences that impact our business decisions. And to do this, it's important to reach a representative group of engaged people.
There is no denying that marketing research is evolving. New technologies, the abundance of data available and consumer dependence of smartphones being the biggest contributors. But how does this change the way we sample via online research? And source permission-based, quality respondents?
Today’s consumers already have a voice online; they publish opinions, build their own sites, post videos and share content. Technology is enabling an increase in mobile activity – allowing people to connect everywhere and at any time. Because of this, it stands to reason that that the way we’re interacting with respondents is shifting. Market researchers are now facing the challenge of being on the consumer’s terms and competing for time with them.
We’ve all been there. You spend endless hours perfecting a survey and imagining your ideal audience fallout. Might as well set some quotas to ensure that desired outcome, right? But now, as you take a step back, those quotas are starting to look a bit unrealistic and a little overwhelming. Take a deep breath, because I’m here to help provide solutions for some common pitfalls when it comes to setting quotas.
I recently had the opportunity to lecture at a class of students in the Masters of Market Research program at the University of Texas Arlington. Despite working for years in an industry where I live and breathe sampling every day, I looked back at my old textbook to see what it said about sampling. I noticed a scribbled note I had taken years ago: “No one thinks about sampling, until it goes wrong!!”
There is a lot going on in the world of market research. I have many clients who depend on us as their partner in fieldwork; we act as their hands and feet when ensuring that their fieldwork is executed with a desired quality standard, in budget and on time. In this rapidly changing environment we need to excel in all deliverables, and we depend on our people, technology and (sampling) sources to do so. The best package for clients is the preferred partner to work with in the market that has the ability to hit all the marks well. This means that with our client’s high frequency of projects, we need to excel on a daily basis. We are only as good as the delivery of our last (few) job(s).
The recent compulsory online census in Australia stirred a considerable amount of controversy, and for seemingly good reason. Ahead of census day, concern was raised by many Australian residents at being told they must share information, but when it was then virtually impossible to complete on the night (the handful of those who managed to submit it notwithstanding), many were up in arms.
Sampling often seems to be an afterthought with clients as many simply state they want a ‘nationally representative sample.’ The question is what does the client mean by a nationally representative sample? One client might think it means representation on age and gender only, while another might expect it to include controls on additional variables like region, income, education, etc.
A dozen years ago a debate raged in the marketing research community over the switch from probability sampling methods such as telephone RDD to nonprobability sampling methods as are typical with online access panels. In the interim years, most clients moved to online samples but there are still some that cling to probability methods. However, we now see the quality of probability samples being questioned because of low response rates for RDD. In an interesting twist, the very same techniques that nonprobability samples use to weight and model data now often need to be done on probability samples to account for nonresponse bias.
Research has consistently shown that all panels are not the same. Recruitment sources and management practices vary, and this can cause differences among panels. Beyond panels, there are other sources of online survey respondents, such as river, dynamic, and social media sources – and these can produce data that is different from each other, as well as different from panels. Given the wide variety of sample sources, and their benefits and drawbacks in cost and quality, researchers often struggle with the question, “How can I blend in other sources without impacting my data?”