When something gains in popularity, research in the field increases and problems are solved. That is exactly what is happening with online surveys, today. Its growth has been important in the last few years, and it forced specialists to look into how to ameliorate its speed, while making it simple for respondents to answer, keeping in mind that this should lead to better insights. The solution they found is a mix of machine learning technology and crowdsourcing.
1) Online Surveys: Providing Valuable Insights
Marketing has changed drastically since the arrival of the internet in our lives. Today, a great part of the money spent in that department is spent online, either through paid advertisement, SEO campaigns, or other opportunities. Online surveys are part of the new marketing strategies for businesses, as it helps them to gather valuable insight into the consumers’ mind. It can inform them on their needs, of their views on particular subjects, products, and services and it can even lead them towards discovering a new product by inserting it into the survey.
Although it is already easy to create and distribute an online survey, as you will discover when you find this website, organizations are now looking into speeding the process, so that respondents don’t stop before completing them. The most important elements for the companies creating these surveys, is the quality of the information that they will receive in the end. Therefore, that is another aspect they are looking to improve.
2) Moving Away from Matrix Tables
A study showed that by asking questions in a matrix format to make it more space-efficient (which is the most popular way today), it skewed answers towards mid-range. It indicated that users would often select the same response for all rows inside one matrix. When the complexity and the length of the questions grew, the time it took the respondent to answer, also increased.
These are but a few of the problems specialists found with the use of matrix tables inside online surveys. They finally discovered a solution to this issue through a combination of machine learning technology and crowdsourcing concepts. By mixing the two, they were able to reduce the amount of time a respondent needs to complete an online survey, without using the matrix tables. It resulted in responses closer to the true thoughts of the consumer.
There is no doubt that these technologies are delivering a higher level of accuracy in online surveys. People who have tested them, found that the experience was much more pleasant, which should increase the number of individuals willing to take the time to fill them. It will also provide better Intel, to the companies paying for them.
