For many businesses, conducting surveys has become as common an occurrence as fine-tuning digital marketing campaigns. Survey data collection is seen as an essential tool for many teams—for good reason. Survey research can be a great way to learn what customers like, identify issues that need to addressed, uncover industry or market trends, and collect information to help drive the business forward.
But it also has the potential to become surprisingly destructive when used incorrectly or inaccurately.
For your survey research to work, it has to be optimized to collect statistically valid data. Think of it this way: When you send a survey out to 2,000 customers, you might get as many as 600 answers depending on the tactics you use to maximize your response rate. If you go to analyze the data and discover that half of those responses are invalid, you’re left with just 300 to examine.
Will a sampling size of 300 respondents accurately represent the needs of 2,000 or more customers? Or, if you work with all 600 responses instead of weeding out the invalid ones, how does that impact the decisions you make?
It’s an easy enough mistake to make. Many teams go through this process numerous times without ever realizing they’re working with less-than-reliable data. So the next time you go to build a survey—be it to create an industry report, collect client testimonials and stats, or get a sense of where employees and clients stand on satisfaction—keep these common errors in mind.
1. Starting With the Wrong Research Sample
Surveys aren’t meant to study your entire target population, but rather a small sampling that’s representative of the larger group. Problems can arise when your chosen segment fails to represent the overall population in every way.
For example, if 25% of your users operate in Europe and Asia, you can’t say that a survey sent only to U.S. citizens represents your customer base. Likewise, if your responses come primarily from female consumers but you sell products used mostly by men, the results could lead you to erroneously target your messaging to the wrong audience.
2. Overlooking Biases in Responses
Response bias can occur when one segment of a population responds to a survey, while others do not. Or it might happen if recipients feel compelled to answer questions a certain way, rather than truthfully or candidly. In either instance, the result is inaccurate or misleading data.
For instance, consider the company that sends a survey to its entire workforce with the goal of understanding where employee engagement stands. If responses come primarily from managers, any initiatives that human resources decides to launch in response to the survey aren’t likely to reflect the actual needs of staff. Or, if employees feel pressured to provide answers they think leadership wants to hear, the organization will end up with biased feedback that fails to provide the intel it truly needs.
3. Making Errors in Measurement and Analysis
This can happen when a survey fails to collect enough data points to reach meaningful conclusions. If you send a survey to 2,000 recipients and just 100 respond, the likelihood of getting an accurate read on reality is extremely low. There’s very little chance that the 5% who took your survey are representative of the 95% who did not.
Or it may be that the survey should’ve leveraged tools such as conditional logic, which allows researchers to gather more data by showing additional questions based on answers to previous questions.
In both instances, researchers might make assumptions or take unreasonable leaps when tying the data together.
Once you begin to watch for these survey data collection errors, it will become exponentially easier to gather reliable intel with survey research. Start getting into the habit of watching for these survey data collection errors now, and you’ll be able to move forward with new confidence in the data you collect from here on out.