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How Dapresy Pro lets you have your cake and eat it…

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When it comes to the topic of stacked versus unstacked data, it’s easy to get lost in the details and technicalities. For the marketing professional, however, the question should always be ‘how is understanding this going to improve my business?’. In the simplest terms, unstacked data is great for understanding respondents, while stacked data is great for understanding their answers. The challenge then, is that both are useful – here’s how Dapresy Pro can help you get the best of both worlds.

 

Stacked vs. Unstacked – an overview

Typically, brand and campaign studies have used ‘looped’ questionnaires, where the respondents answer the same or similar questions for a number of brands or campaigns. This data type of looped questionnaire usually generates a large number of variables. For example, in a global brand study of 200 brands with a loop of 50 questions for each, there will be 10,000 columns/variables in the data set.

Looped questionnaires have traditionally generated a flat, ‘unstacked’ data format, where there’s a column/variable per brand/campaign and ‘loop’ question. This is a great format for many types of market research, including customer experience and employee experience studies.

 

It does, however, present certain problems. The time it takes to produce research reports is strongly correlated to the number of columns/variables in the data set and also increases the risk of errors. Since large brand studies often have a high number of respondents, the data sets become huge, which can make the reporting even more difficult.

 

By making the data response (rather than respondent) driven and stacking it, we can significantly reduce the column/variable count. This has a number of advantages – fewer variables reduce the computational workload, while maintenance hours are also reduced. New brands can be added to the questionnaire with the minimum of structural changes, cutting back on unnecessary costs.

 

Dapresy Pro features/benefits

Import

Dapresy Pro allows for the import of both stacked and unstacked data formats. Stacked data makes numerous tasks easier and more efficient, especially the handling of brand tracker questionnaires, where the same questions (such as brand statements or campaign follow-up questions) are present multiple times. This is common when the respondent evaluates multiple brands or different campaigns.

The example of the global brand study given above, with 10,000 variables, results in a data file where the size and structure makes reporting complicated. The solution for creating a friendlier data format for easier reporting is to pull out these ‘looped’ variables into a separate stacked data file which results in a vastly reduced number of variables.

So instead of only importing one data file containing all data for all respondents the data can now be split up into multiple files  (a ‘normal’ unstacked file and one or more ‘stacked’ files) without losing any filter capabilities across variables located in different imported data sets. The system will automatically connect these files via the Respondent ID for achieving cross file calculation and filtering.

Filter

We’ve spoken in previous articles about making data ‘meaningful’ for business purposes, always asking the questions ‘how does this process add value?’ Dapresy Pro includes advanced filtering options, allowing the user to correlate data from both unstacked and stacked sources. By doing so, the user can get the best of both worlds, cross-referencing respondent and response driven questionnaires to arrive at actionable conclusions.

In addition, Dapresy Pro now has the ability to include filters based on open ended variables such as email addresses, making it easier to clean and recode data for specific persons. This is crucial for compliance with GDPR anonymization regulations.

The ability to recode data permanently, in combination the option of automatic recoding of data after a certain time period, makes it easy to permanently remove personal information from the data. This is a requirement for data minimization and data retention GDPR policies. Respondents can also be permanently deleted in line with the GDPR’s ‘right to be forgotten’ rules.

 

Conclusion

By allowing native, intuitive import and filtering of both stacked and unstacked data, Dapresy Pro radically reduces the time between question and action. Processing bottlenecks are eliminated and the data (from all sources) can be cleaned and presented in a useful, manageable format for reporting, quickly and efficiently.

To learn more about Dapresy Pro’s data handling functionality, please read our detailed release notes here.

Market research professionals are busy people, but taking 5 minutes to understand the difference between stacked and unstacked data could radically transform your project delivery time. Find out why

Curious about how this works in the real world? Find out how Dapresy helped one customer transform their project delivery speed…
Read the case study

 

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