Our Surveys 101 series is fundamentally about the science of survey research as it has developed over the past century.
Its primary focus is getting you up to speed on the latest that scholars and practitioners have to say about survey methodology best practices in questionnaire design, data analysis, and more recently, data visualizing.
Whether you’re interested in how many friends will show up for a dinner party, what your customers think about your latest product offering, or how people will vote in the next election, surveys are often the best—and often only reliable way—to discover what people think and want.
People also conduct surveys out of a desire for “social comparison,” which drives us to learn about others, and surveys are the best way to get this information. After all, context is king.
There are at least four main reasons that people conduct surveys. We say “at least,” because with more than 17 million customers, the SurveyMonkey platform is making ever more exciting uses of surveys possible.
In the news, surveys are used to elevate stories beyond the few people who may get quoted in an article. Stories have far more power if we know they reflect something that’s happening to a significant proportion of people versus just a handful.
Similarly, in customer service surveys, one should know whether an angry customer is expressing a unique or common complaint. Likewise, one happy customer does not mean a startup is on its way to a huge IPO. Surveys can help gauge the representativeness of individual views and experiences.
When done well, surveys provide hard numbers on people’s opinions and behaviors that can be used to make important decisions. Just as aspiring politicians are certainly more apt to win election if they understand what voters really want, the manager of a little league team is more likely to succeed if he can quickly identify problems in a training program by surveying coaches and parents.
Surveys are regularly used to make individual decisions—like whether to run a particular advertising campaign or create a new service—but they get even more powerful when repeated over time.
One common phrase among survey researchers is “trend is your friend.” After all, repeatedly asking the same question at different points in time gives a clear vantage point on how things are changing.
The U.S. Census—a survey itself (albeit a massive one)—is particularly powerful at cataloging the major demographic changes in the country, like what is the racial makeup of the United States. A company’s NPS score may not mean a whole lot on its own, but a major dip in its score in the second quarter would rightly send its executives scrambling for an explanation, and a fix.
“Big data” is the rage these days. But there are also big limits. The term largely refers to implicit data, or data that are derived from observing and analyzing your and others’ behavior, online and elsewhere.
There is an ever-increasing amount of these data (yes, “data” are plural) but there are flaws. Think about the Amazon recommendation engine. It can’t tell whether Elaine, a grandmother, added the latest Madden NFL video game to her cart for herself, or her grandson’s upcoming birthday—and it pollutes her recommendations with things like the FIFA or NBA video game.
To uncover why Elaine added the Madden game to her shopping cart, explicit data is needed to compliment what Amazon’s algorithms reveal. Explicit data is just that: information that’s fully revealed or expressed without vagueness or ambiguity.
Explicit data are insights taken directly from the individual, generally using survey methodology. They are inherently more reliable when it comes to understanding the motivations behind actions. If Amazon collected some explicit data by asking a simple question, “Are you buying this product as a gift?” they could avoid providing unhelpful recommendations for their customers.
The importance of surveys is perhaps best framed by a book that’s not about surveys. In his classic book “Exit, Voice, and Loyalty,” Princeton economist Albert Hirschman examined the main ways people react when facing a poorly performing organization: They can either “exit” and take their business elsewhere, or “voice” their concerns and try to change things from within. How loyal people are to a cause or a company affects whether they choose to vote with their feet and exit or voice their opinions and speak up.
Hirschman points out that in general people and businesses rely on exit to identify an issue — for example, do we have fewer customers than last month? But by the time you see this, it may be too late. Exit is a lagging indicator.
Organizations of all stripes succeed when encouraging voice over exit. Engaging your customers to voice their concerns helps with engagement, and lowers the likelihood that they’ll pick up and spend their time or money elsewhere.
In other words, voice is your canary in a coal mine.
So let’s get surveying to gather hard numbers, benchmarks, uncover the why, and give voice to our respondents.
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