Survey data processing is the crucial step that follows the collection of any survey data. The aim of data processing is to manipulate or transform raw data into meaningful results which can be analyzed in any statistical software tool, and ultimately presented in a way that answers the intended research question.

Quality assurance

A central component of processing customer feedback survey data is quality assurance - ensuring that the data is of high quality, and therefore presents valid results. This can take a number of forms, including logic checking and data cleaning. Logic checking ensures data has been collected correctly and there is no missing or erroneous data. That is, no individual has answered a question they were not supposed to answer and no one has missed a question that they should have answered.

Data cleaning, on the other hand, deals with identifying outliers and removing respondents who have given contradictory, invalid or dodgy responses, or are potential duplicate records. Common forms of cleaning include identifying speedsters, flatliners, and nonsensical or obscene open-text responses.

  • Speedster: A respondent who completes the survey in a fraction of the time they should have. Therefore, it is believed that they could not have possibly read and answered all the questions properly in this time. As each survey has a set expected length, you can measure this against the individual duration of the entire survey, or specific sections (if recorded), to identify speedsters.
  • Flatliner: A respondent that answers the exact same way for each item in a series of ratings (such as “On a scale of 1 to 5 where 1 means ‘Not very satisfied’ and 5 means ‘Very satisfied’, how would you rate each of the following supermarkets?”).
  • Nonsensical: Someone may also write random letters or numbers instead of giving a legitimate answer to an open-text response question. Similar repeated behavior on key questions will often be used as justification for removing individual records from the data set. Examples of these can be found highlighted below:

Data preparation and production

The final step in the data processing stage is to ensure the data is usable for analysis. This may require adjustments and transformations, including data entry, editing, rebasing, filtering and reconstructing. If verbatim responses (open-ended questions) have been collected, they may need to be coded down to a more manageable number of themes and comments.

Data weighting may also be required in order to correct issues with sampling inconsistencies or to ensure the data is representative of the target population. Once the data is prepared, it can then be presented through statistics in tables, charts, or dashboards.