Quantitative data is numeric and countable. Qualitative data is verbal, visual, or observed, and it captures meaning rather than measurement. One answers how many or how much. The other answers how and why.
The argument about which is better wastes time. The useful question is which one should lead, because the order changes what you learn.
Numbers let you compare groups, track change over time, and test whether a difference is larger than chance. If you want to know whether episode length correlates with retention across 300 uploads, you need quantitative data and a fair amount of it.
The weakness is context. A retention drop at minute eight is a fact. It tells you nothing about what viewers disliked, because a number cannot explain itself.
Interviews, open-ended answers, and session recordings explain the number. Watch five people hit the same confusing moment and the retention curve stops being a mystery.
The weakness is scale. You cannot safely say how common a theme is based on depth interviews. Ten people can describe a problem beautifully and still not tell you whether one percent or forty percent of viewers share it.
Quantitative tells you the size of the problem. Qualitative tells you what the problem is. Start with the one you are missing.
Use qualitative work first when you do not yet know what to measure. The themes become survey options or tracked events. Use it after quantitative work when a result needs explaining, such as a sudden dip in comments-per-view.
When both run at once, decide in advance which one settles a disagreement. Saying 'we listen to the interviews' or 'we trust the numbers' after the fact is how mixed methods become mixed signals.
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