Community Panels — A Practical Guide to Improving Data Quality

When it comes to research communities, improving data quality lies at the heart of every successful research community panel. That’s why, when discussing community panels, I can’t help but turn the spotlight onto the opportunities that good data quality creates. By putting data quality at the forefront of community panel research activities, insight professionals guarantee captured insight is robust and add confidence to strategic decision making.

While community research panels have long been known for their agile research results and ability to deliver on speed, improving data quality is a step that is often overlooked in both newly-created and mature research communities. There are several dimensions involved in improving data quality, including your data’s uniqueness, accuracy, timeliness and completeness.

Using Complete Data

Community panels are known for their agile research results and ability to deliver insights at speed, but improving data quality is a step often overlooked to the detriment of insight generation.

In your community panel, an efficient way of ensuring that you have access to complete data is to encourage your members to complete a profiling survey when they join your community. A profiling survey is used to identify your customers’ demographics, psychographic, geographic and behavioural characteristics. Once this data is appended to your community’s database, insight professionals have access to information about your members to ultimately segment members within the panel. By regularly sharing profiling surveys with your long-term community members, insight professionals also benefit from having profiling data that is kept up-to-date. By embracing the use of complete data, insight professionals identify differences in subgroups of your brand’s community and confidently report the variation in these groups’ behaviours.

Accurate and Relevant Data

One way to improve the relevance and accuracy of data quality is to determine the proportions of the community members in your database as this will reduce any anomalies within your research activities. This is done by identifying the populations that you are targeting and introducing criteria that your members must meet to qualify for joining your community. This criteria can then be added into your community panel’s recruitment screener and ensures only qualifying participants are added to your database. Then, when it comes to conducting quantitative activities in your community, margins of error and confidence levels will tell you how accurately the results represent your target population and successfully inform your brand’s decisions.

Combining Data Types

By using both quantitative and qualitative data types, insight professionals have access to more diverse types of data which can be leveraged across various business functions and improve its appeal amongst stakeholder audiences. Together, different data types create a compelling data story as quantitative data highlights statistical proportions, and qualitative data captures emotionally driven data.

Timeliness of Data

As the insight industry continues to evolve, good data quality will remain an important consideration for improving the reach, scale, and timeliness of data.

The timeliness of data is a particularly important dimension when it comes to continuous research, such as customer satisfaction and brand tracker surveys. This is because data is used to track changes in consumers’ attitudes and experiences over a period of time. As a beneficial research approach for when key data points, such as inflection points, occur, historic data enables insight professionals to identify turning points in brands’ success. Therefore, by sharpening the timelines of your data, you will encourage stakeholders to spot trends early and have access to timely insights to evaluate brand performance.

As the insights industry continues to evolve, good quality data will remain an important consideration for improving the reach, scale and the timeliness of data used. By making considerations for improving your community panel’s data quality early, dimensions, such as using complete and accurate data, will weave their way into research activities and benefit the long-term goals of both research and brand strategies.

This article was originally published on the FlexMR Insight Blog, and can be accessed here.

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