I got some feedback from a couple of recent conference talks.
Not all of it was positive or even useful but I like to get constructive feedback even if it is negative.
But reading the responses to @laurieontech’s tweet
(the one I responded to above), I also realized that other speakers struggle with parsing feedback. In particular, they appear to struggle with negative ones, that often ding them for nothing to do with their talk “Room too cold 2/5 stars
“. Sometimes, we let the star rankings affect us, like I did here.
I call this effect the “Uber-ization” of speaker feedback – anything less than 5/5 stars is a negative. But when cooler heads prevailed, I was able to make some
sense of the feedback. Let me explain my feedback parsing approach using my latest positive and negative feedbacks as examples:
“Lost your way … halfway through” – in a vacuum by itself this comment would be somewhat vague.
But then one of the other comments had a somewhat similar feel to it
Bingo! Both comments indicate that the talk’s second half or finish had issues. Feedback noted. Thank you for the valuable feedback.
Some of the feedback from my second talk also contained recurring themes.
The first one
The second one
It seems like the topic of this talk resonated with these two attendees, as indicated by “important topic” but they had different suggestions to help improve.
Taking feedback can often be tough. Take this one again for instance.
“Avoid reading them (slides) directly without adding much more information” … ouch. I don’t think I read them from slides directly. In fact, that’s why I started cutting back on the amount of text on slides and use images/diagrams instead. But since someone made a technical suggestion, this is something I will definitely pay attention to in the future.
To repair my bruised ego, I turn to positive feedback.
First the ones with some kind of technical details or actionable feedback.
Also note, the “pacing was perfect” directly contradicts the earlier feedback from a different person in the same talk that “there was time for additional … tips”. Take all feedback with a grain of salt.
I loved this one in particular because it tells exactly what this person exactly liked about the talk, aka, the “examination of …. systemic issues and personal ones”.
And finally, I end with feedback that makes me feel real good – I’m human after all and can use that dopamine kick.
Now I realize that my (experienced 40 something Indian dude with a software professional background) feedback could look different than the feedback of say, a 20 something woman just starting out in the industry. My understanding is that women and other non-majority demographic speakers receive harsher, often even bad faith feedback.
I’ve in the past received feedback like “can’t understand your accent” or “improve your English before you give talks“. I wish I spoke out about that feedback back in the day but better late than never. I took the first one and actually tried to slow down my delivery since I have a tendency to get excited and speak fast. Still, I feel the feedback giver could’ve been kinder and more tactful with the feedback. The second one was just mean feedback which I chose to file away under that category.
Feedback is essential to improvement and completely discarding it (unless it’s all useless or bad faith) doesn’t help. My hope for sharing my experience is to help speakers parse out some useful content from conference feedback.
To sum up:
- Instead of discarding all conference speaker feedback, pick the earnest ones
- Since feedback is individual subjectivity, look for trends across multiple ones
- Ones with technical details (as in about the actual talk content or delivery) are often very helpful
- Always read the good feedback out loud, you worked hard enough to deserve it
I also think conference organizers should do away with star rankings on feedbacks.
Note: the conference referenced in this article is Codemash 2020 at which I gave two talks. My slides are on my GitHub and the video from my talks is supposed to be available on Pluralsight in the near future.