With so much 3rd party data available, how do you know how well that data represents your intended consumer target? Lotame and Survata partnered to link 1st and 3rd party data and boosted accuracy rates for identifying auto intenders on a publisher’s website by nearly 1/3rd.
Member Only AccessMeasuring the impact of in-market investments can be like finding the proverbial needle in a haystack. Combining the granularity of panel-data and the heft of Big Data can make finding that needle a lot easier. Oracle Data Cloud shows how they bring the two data sources together.
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The ARF has announced two new efforts to tackle data quality questions: Data Labeling Initiative - with the ANA’s Data Marketing & Analytics division, the IAB Tech Lab, and the Coalition for Innovative Media Measurement (CIMM). Data Validation Initiative - independently conducting research to determine whether surveys can be used to measure the accuracy of those targets.
Academic advertising researchers argue that multiple measures are needed for accuracy. Practitioners argue that a single measure of attitudes is sufficient. Who’s right? This article suggests it is better to have one valid measurement item that fully captures the semantic meaning of the construct rather than having multiple bad ones, no matter how internally consistent the measurement scale may be.
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The continuing rise in non-response rates has led to growing concern about the representativeness of surveys. Andrew Mercer, Senior Research Methodologist at the Pew Research Center, provides an overview of the issue along with best practices in modeling representativeness, for example, think through your modeling assumptions before collecting data.
History is no barrier to learning. Consumer surveys were going mobile even back in 2014, and drop out rates were climbing as a result. See how one FocusVision client implemented seven best practices to redesign their surveys, and as a result, reduced drop outs by 40%.
Member Only AccessNon-probability samples have become a de facto norm in on-line survey and marketing research. But are there opportunities to improve the demographic screening tools to reduce non-probability error and come closer to Random Probability samples? This JAR article shows that additional demographic screening did not reduce bias or improve data accuracy when measured against benchmarks from large-scale Random Probability studies.
Member Only AccessMobile closed-end survey designs are improving, but the dreaded open-end text box remains a challenge. Can smartphone technology make things better? Although in voice-to-text and video offer potential, results showed that there’s more work to do to make either the new gold standard.
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