A beginners guide to coding in Market Research
Unless you've worked in the Market Research industry, you will probably have no idea what "Coding" is all about, In a nutshell, Coding is the process of converting unstructured survey data into a form that can be analysed quantitatively. In this article we're going to take a look at why this is necessary, how it's done and to what extent this process can be automated.
The Market Research process, like many activities, often involves weighing up some tricky trade-offs. For example, when designing a questionnaire there are a number of standard question types you can use. You can ask a "closed question" and offer the respondent a fixed set of responses they can pick from.
e.g. "What did you like most about your visit to the Restaurant?"
This approach has the advantage that it's quick and easy to administer and allows us to direct the respondent to specific themes we're interested in.
There are, however, some downsides to this approach:
So, instead, you can simply as the respondent an open-ended question: "What did you like most about your visit to the Restaurant?". You allow them to type or speak and give you an answer in their own words. This avoids the problem above and allows you to capture detailed feedback - making sure you capture all the issues, themes and comments that are important to respondents.
The main downside of this approach is that you will receive hundreds, or thousands of customer comments in unstructured text form that you need to make sense of. In order to analyse the data it needs to be categorised and converted to a form that can be analysed quantitatively. This process, in Market Research, is called Coding.
The traditional approach to Coding, which has served us well for decades, is a manual process performed by, you guessed it: coders.
As a first pass, a coder will read through a sample of survey comments looking for common themes or topics. This is a task that requires skill and judgement because, it's not always straightforward to decide which theme a comment belongs too. For example, if a customer says: "The food was cold by the time it arrived" - is this a comment on the food, or the service? Once an initial set of themes are extracted, these are added to a list - with each item in the list being assigned a unique identifier or code. This list is known as a "Codeframe". The coder then proceeds to read through each survey response assigning codes to each to denote which themes apply to the text. As the coder works through the data, they might find additional themes arising and so add new codes to the codeframe. This process continues in an iterative, and fluid manner until the coder is satisfied that the list of themes is complete and representative and that all responses have been tagged with a correct set of codes.
Yes, this process can be time consuming. Many people argue that this fully manual, human driven approach is the only way to achieve good quality, accurate, insightful data. Surely, these days, technology has advanced to the point where we can just do all this automatically?
The truth lies somewhere between these two extremes. Technology, including our software "codeit" can massively speed up the time it takes to transform unstructured data into coded data. However, it's unlikely you will want to exclude people from the process altogether. People have many skills that are still useful and important to this process - the skill lies not in the extremes, but in finding the correct balance between these extremes.
We will be returning to this idea and exploring it in more depth in this blog over the coming weeks.
Read the next instalment - Coding with Context
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