Feeling spicy 🌶️ during this cold week 🥶 so here's another nerdy take from me 🤓 - All push back and comments welcome! #MaxDiff #TURF have been the tried-and-true approach for years when teams need to prioritize claims, benefits, or features. And as the (self-crowned) queen of MaxDiff 👑 🤣 I know why! It’s really great for answering: “How do we make sure there’s something for everyone?” But lately I've been having a lot of conversations where the real business question isn’t reach or breadth it's... “What’s the best combination of things to say together to actually drive purchase?” And that is what makes Token-Based Conjoint (#TBC) so powerful! It can tell you the best 1 claim, 2 claim, 3, 4, 5, 6 claim bundle AND how purchase intent changes as you add more claims to the bundle. And we know that for some consumers (think "enthusiasts / early adopters") we might not need to offer too much to drive conversion. They'll subscribe with 1-2 strong benefits. But for others (think "pragmatics / skeptics / late adopters"), they might need more reassurance...and even then you still might not win them! TBC is built to answer: - If you only got 1 benefit, would you subscribe? - If you get these 3 together, how much does intent jump? - When do extra benefits stop helping and just start getting more expensive for you to offer? MaxDiff TURF - while fabulous - just can't answer that conversion question in the same way. My rule of thumb? 👍If the job is to maximize how many people see something they like → TURF 👍If the job is to optimize the story the pack (or offering) tells under constraints → TBC TL;DR TURF = coverage / breadth TBC = conversion / depth The screenshot below shows you "how" we capture TBC data. Want to learn more about TBC? Check out the recording of our webinar at the link here lnkd.in/g2gnPWif

Hunkering down in this freezing 🥶 cold weather and catching up on some reading by the fire and thought I’d share this interesting thought piece in Barron's bit.ly/49OK7FC On one hand, I’ve spent years helping companies uncover willingness to pay through #conjoint analysis. But…typically my clients use #WTP at the segment, channel, or regional level. Setting prices that maximize profit for “students” versus “enterprises” or at “Walmart” versus “Target” or in an “urban” area versus a “rural” area. This article talks about (what many of us probably already assumed was happening) which is that AI is making it possible for two people to buy the exact same basket of groceries, from the exact same store, but one pays $114 while the other pays $123. 🤯 Dynamic, individualized pricing. And sure, maybe sometimes it’s just pennies but those swings can add up, especially for loyal customers! And thats where I get anxious. It starts to feel like a brand is prioritizing a short-term gain with severe long-term risk. What happens when a customer realizes they paid 10% more than their neighbor? Not because of a promo or timing but literally because an algorithm decided they would. 🫣 You can absolutely optimize for willingness to pay. But you can also quietly erode trust. And losing a loyal customer can be far more expensive than winning a new one. As someone who loves pricing optimization and cares a LOT about how it’s applied, I’m watching this closely. Curious how this plays out. 👀 WDYT? I got time to chat in the comments…because I’m certainly not going outside 🤣

MaxDiff: The Halloween Candy Edition x.com/i/broadcasts/1ynKODjlN…