Ask a market researcher why they chose their career, and you won't hear them talk about prepping sample files, cleaning data, creating tables, and transferring those tables into a report. These tasks are all important parts of creating accurate and compelling deliverables, but the real value and fun is deriving insights, finding the story, and connecting that story to meaningful decisions.
So, what’s a researcher with a ton of data and not a lot of time to do? Hello, automation!
Automation is awesome.
There are a ton of examples of automation in market research, but for these purposes I'll keep it simple. As a data manager at CMB, part of my job is to proofread banner tables and reports, ensuring that the custom deliverables we provide to clients are 100% correct and consistent. I love digging through data, but let’s be honest, proofing isn’t the most exciting part of my role. Worse than a little monotony is that proofing done by a human is prone to human error.
To save time and avoid error, I use Excel formulas to compare two data lists and automatically flag any inaccuracies. This is much more accurate and quicker than checking lists against one another manually—it also means less eye strain.
As I said, this is a really simple example of automation, but even this use case is an incredible way to increase efficiency so I have more time to focus on finding meaning in the data.
Other examples include:
- Reformatting tables for easier report population using Excel formulas
- Creating Excel macros using VBA
- SPSS loops and macros
I’m a huge proponent of automation, whether in the examples above or in myriad more complex scenarios. Automation helps us cut out inefficiencies and gives us time to focus on the cool stuff
Automation without human oversight? Not awesome.
Okay, so my proofreading example is quite basic because it doesn’t account for:
- Correctness of labels
- Ensuring all response options in a question are being reported on
- Noting any reporting thresholds (e.g. only show items above 5%, only show items where this segment is significantly higher than 3+ other segments, etc.)
- Visual consistency of the tables or report
- Other details that come together to create a truly beautiful, accurate, and informative deliverable.
Some of the bullet points above can also be automated (e.g. thresholds for reporting and correctness of labels), but others can’t. On top of that, automation is also prone to human error—we can automate incorrectly by misaligning the data points or filtering and/or weighting the data incorrectly. Therefore, it’s imperative that, even after I automate, I review to catch any errors—flawless proofing requires a human touch.
When harnessed correctly, automation maximizes efficiency, alleviates tediousness, and reduces error to free up more time for insights. Before you start arming yourself against a robot takeover, remember: insights are an art and a science, and machines haven’t taken over the world just yet.