The times of long surveys are long gone. People only spend their time answering 10+ something question surveys to get paid for it. If you are planning to launch a survey campaign with more than 5 questions expecting genuine answers then you are in for a challenge.
What are micro-surveys?
For the past 10 years, modern SaaS companies use micro-surveys to improve their products and to assess business opportunities by identifying pain points and demands.
Micro-surveys are very short, 1 or 2 questions surveys that don’t take more than a few seconds to answer. Using micro-surveys not only increases engagement rates but dramatically improves data quality.
These are both mission-critical factors in market research. High survey engagement will reduce your cost per response, enabling more respondents for your budget. Genuine answers will naturally yield better data and more valuable insights for business decision-making.
Rich data from micro-surveys
Amateur market researchers might argue that long-form surveys can collect more answers for more questions which means more data. That is true, however data quality from long-form, incentivized surveys is questionable at best. The average bad response rate from traditional surveys is over 40%. Which means your data is mostly unreliable.
Significantly better data quality
Micro-surveys perform significantly better in terms of bad response rate, below 25% from incentivized respondents, and below 7% from non-incentivized respondents. It is easy to see that micro-surveys, especially non-incentivized generate significantly more representative data. Quality vs quantity should not be a question in a market research context.
Rich data for statistical analysis and segmentation
At Market Sampler we record each response with all user attributes. How does it work? Our survey distribution system logs how that particular respondent was reached, through what targeting criteria, age, demographics, and so on. This information is saved with every response so our system can build rich data tables that are suitable for multi-level segmentation, advanced data analysis, and statistical processing.