Did You Miss the Quirks NYC Event?

So many great presentations, so little time!?We can’t call this a top 10, because Matthew Seltzer and I couldn’t possibly attend all the great presentations.?(Sorry if we missed yours.)

For everyone who couldn’t make the trip to the Big Apple (or didn’t buy a pass before they were SOLD OUT), here’s a (human written) summary of some of the best insights and new ideas we encountered.

Measuring the Unmeasurable: INSPIRATION ( Daron Sharps, PhD , Pinterest )

We like to measure things that are easy to measure even if they aren’t all that valuable (*cough* brand tracking *cough*).?Pinterest grabbed the bull by the horns and went right after measuring the critical stuff at the heart of their platform – the inspiration that is created by powerful images. But how do you even define inspiration? They think it’s a mix of insights and movement. Deep stuff.

And Pinterest, if you really want to do awesome imagery-based surveys (with seamless voice and video at scale and top-notch AI), call us.?Seriously.

Die NPS Die!?( Stephanie Scalice and Allison (Slotnick) Schoer at LinkedIn )

LinkedIn took the stage to put a bullet through the head of the zombie that is Net Promoter Score.?(Am I biased against NPS??Nah…)?Turns out that NPS did such a bad job of predicting product and feature success for LinkedIn that they replaced it across their organization.?After a year of work, 20,000 survey responses, 600 interviews, and a couple million dollars, they settled on a two-factor score combining feature value and ease of use.?I’m 10 out of 10 to recommend their approach to a friend or colleague.

ChatGPT and Video Qual-At-Scale ( 微软 , Voxpopme )

When Jamin Brazil joined VoxPopMe I expected great things and he did not disappoint!?They’re plugging in ChatGPT to search and query dozens (or hundreds) of video transcripts for qualitative level insights in near real time, with summaries and AI-built showreels to back them up.?Analyze hours of video transcripts in minutes and query it interactively. Cool stuff!

Real Data, Real Time, Real People ( Nihal Advani , QualSights )

QualSights launched an easy-to-deploy smart sensor that looks like a coffee coaster and can track real life usage patterns for shampoo, dogfood, and more.?A great example of a device tech changing MR and a huge leap over recall based data for when you really want to know how often someone brushes their teeth.???

Narrative Economics and Contextual Worldview ( Susan Fader?? )

If I were hiring someone to deliver a master class in moderation, Susan would be a top choice.?6 veteran tips to better engage people in qual (and quant) that will help you not get replaced by ChatGPT. ???My biggest takeaway: FRAME your research (qual and quant) with a narrative open ended question to set the participant’s mindstate.? At Intuify, we use voice or video narrative, but even text narrative helps.

Using ChatGPT for Open Ended Follow-Up Prompts ( Kathy Cheng and Steve Male from Logit Group)

Simply asking a couple ChatGPT follow-up prompts to your open-ended questions gives you more/better data. ?I expected that, BUT it also improves the data quality for the rest of your survey too. Plus, even though it takes longer, people say it feels shorter. ?(Quick plug: We see a similar effect for Intuify voice prompts and gamified user interfaces, but Intuify’s effect is bigger and our surveys actually are faster. Not that we’re counting…?OK, we’re counting.)

The In-Person Difference: Mirroring??Affective Presence??PHEROMONES??( Clint Jenkin from Vanguard )

Zoom meetings are great, but we’re losing a lot of depth and nuance in our qualitative research (and job relationships) when we go digital. People have a presence and it affects conversations.?And even simple things like Liminal Space (the transition time to and from a meeting) profoundly affect conversations.?BUT, for some situations (and people) online is actually better.?(Short stature? Nervous? Bad pheromones?)

Predicting out TEN YEARS? ( Kim M. , Chris Pagnucci , Molson Coors Beverage Company )

I was skeptical, but it’s a compelling case. Coors outlines the difference between mere insights and foresights.?Foresights are an amalgamation of current trend datapoints that, when taken together, can help predict future trends or habits. An example of this is the “hard seltzer” craze, which data from 10 years ago clearly points to (in hindsight, of course).?This synergized with another presentation from Beam Suntory on why they do research with a 4-to-20 year lifespan because it takes that long to age good whiskey.

Long Term Pipeline Building ( Mike Wojcikiewicz from 百事 , and Roos Hofland from Black Swan Data )

PepsiCo thinks big, and this is their take on using data to predict several years ahead to build out innovation pipelines. They showed how they pull together various data sources (including observational trend data) to systematically identify opportunities and fill their innovation pipeline.?(I’d call it machine learning, not artificial intelligence, but that’s me on my analytical high horse.)

Cultural Relevance and GenZ ( Lauren Leahy and David Pangilinan at Paramount Pictures )

Every generation has its tribal mentality.?75% of GenZ expect brands to work to maintain cultural relevance, and a third consider themselves fans of specific brands.?This reminds me of some great research at an IA conference that shows that GenZ are also far more likely to buy brands that are endorsed by TikTok influencers vs. ones that are advertised on TV.?

So that was Quirks for us – good stuff!

Did you like another presentation that we missed??Tell us about it in the comments.

Also, shout out to my old Affinnova, Inc. friends.????Please forgive my terrible selfie-skills.

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