Technology has changed the landscape of market research in many ways. In primary data collection methods, the world has moved away from paper to online surveys, and the data collected amounts to Big Data. With the development of automated programmatic sampling platforms, such as the PureSpectrum Marketplace, sophisticated sampling techniques are now just a click away. According to Gartner’s 2020 CMO spending study, 32% of CMOs put Market Research and Marketing Analytics as two of their top 3 priorities. 76% of marketing leaders say they use data and analytics to drive key decisions. Investment into data collection and analytics is not optional to organizations anymore.
Unfortunately, garbage in, garbage out is the status quo. As the adoption of online data collection increases, it opens up quite a few challenges. Market leaders’ dependency on data for decision making makes it imperative that it is reliable. For one, online data collection processes are most prone to fraudulent and unpredictable behavior. A number of rule-based quality checks should be applied at various stages of the survey taking process to detect and flag unacceptable behavior.
How do we maintain the quality of the data collected if the bias is introduced even before the respondent started taking the survey? What if a respondent registers a different gender or age just to qualify for better incentives? For every bias we find, there are many more that need to be addressed. This is where Machine Learning technologies come in.
With Machine Learning, computer systems use existing data, analyze it to detect the patterns, and use these patterns to predict new cases. These algorithms have the capability to analyze huge amounts of data while taking into account the most intricate details and learning on their own, all with minimal human intervention. Deep learning further builds on this and replicates the functionality of a human brain into what’s called an artificial neural network system. Here, various skills can be layered one on another to make better decisions in many different scenarios.
Machine Learning/Deep Learning (ML/DL) technologies are game changers for ensuring data quality in online primary data collection.
Machine Learning/Deep Learning (ML/DL) technologies are game changers for ensuring data quality in online primary data collection. Based on historical data, respondents can be profiled based on various aspects such as their demography (age, gender, education, etc), their survey preferences (low incentive short surveys, high incentive longer surveys, etc), and their behavior on the survey platform (time taken to answer the interview, drop or complete, etc). These profiles are modeled and categorized into various risk categories. When new respondents enter the platform, these models use demographic prescreening data and a similarity-based comparative analysis to categorize them based on probability of risk. Machine learning algorithms like these are similar to the FBI’s behavioral unit who track and study convicted fraudsters/criminals. They then build profiles based on the data and use them to catch criminals by matching the profiles.
Quality checks embedded into the platform are a great way to detect and flag fraudulent behavior during the session as the respondent takes a survey. ML/DL technologies go one step further and help minimize bias inherently introduced into the survey even before the session begins. Building on the basic human psychology of behavioral patterns at a highly aggregated level and combining it with individual behavioral level patterns to ensure inherent data quality could be the next step in changing the way market researchers look at primary data collection.
At PureSpectrum, we are harnessing this new technology to further our dedication to data quality. We have combined our resources to now offer PureScore, an advanced Machine Learning driven scoring system designed to measure independent respondent quality on a scale of 0-10. PureScore was built to combat modern research challenges and keep up with the ever-evolving data quality landscape. The model works by finding patterns in respondent behavior and then creating an ideal respondent with a perfect PureScore. The further respondent behavior deviates from the ideal, the lower their PureScore. Respondents with a PureScore of 5 and under are blocked from taking surveys.
Want to learn more about PureScore? Check out our webinar recording here, or contact us at email@example.com. We would love to talk more about how PureScore can revolutionize the quality of data our clients rely on for critical decisions.