How can you balance qualitative and quantitative data in marketing performance measurement?

Leveraging quantitative data in marketing is critical for providing a tangible north star for not only performance, but also contribution to shared business goals. Before launching any campaign, it's important to ask 'what are we trying to accomplish' and 'how are we going to know if we've succeeded'. Understanding your quantitative KPIs allows you to set up your scorecard, measure success, and communicate results easily. For many marketers, it's the communication component that is often overlooked. In my career I've made it a habit to create 'scorecards' early on and recommend every leader do the same. This allows you to easily track progress over time and spot trends. It also allows you to seek out the red - metrics that need attention.

With the current trend of marketing becoming to dependent on quantitative data, it's important to find a proper balance in your measurement and include qualitative data. While quantitative data is of the moment and typically easy to acquire, qualitative data often require more time to acquire, examine, and extract insight. The best marketing organizations understand that proper measurement requires both types of data - without one or the other, you'll never have a full picture. And in the instances where the quantitative data is showing underperformance (or overperformance), it will most likely be the qualitative data that tells you why.

One of the most powerful uses I have found for AI, is to feed it a large amount of qualitative data and ask it to summarize it and/or categorize it. By categorizing it, you are effectively turning it into quantitative data that can be used in reports. Example: We ask the question "How did you hear about us?" on our forms. This is an open text field, so the responses are qualitative. We use AI to categorize that data and find the best match with options from our Lead Source drop-down. AI can also recommend that we create a new category when there are a large number of responses that don't align with existing options.

Categorizing qualitative data to make it more easily reportable is simple in concept, but automating that process can be arduous, especially if you have a large amount of data. Take self-attribution data as an example: When contacts submit an open text response to "How did you hear about us?" we should ideally be able to categorize those responses into pre-determined Lead Source buckets. However, automating that has historically relied on us guessing keywords that, when included in a response, could indicate that contact belongs to a specific Lead Source. Hardly fool proof. But AI is making this process much easier. It can look at the whole of qualitative data and recommend categorization much more efficiently and accurately.

In the last 2 years, I've interviewed ~50 candidates for open roles on my teams. With every candidate I've asked the same question - "where do you see yourself on the art vs science spectrum of marketing"? It's amazing that ~90% of candidates swung heavily to the science or data side of the scale. Whereas I consider my greatest marketing tool a pencil & sheet of paper, a majority of new marketers that I've spoken to consider a spreadsheet to be their weapon of choice. This is concerning, as this same cohort of marketers seemed to only talk about quantitative metrics when discussing how they measure their success. If this is indeed a trend, I believe it's ever more important for marketing leaders to emphasize the need for both data sets.