Every argument clarity score on this site is built from rows on this page. Each
question and answer was assessed with names hidden, the host's own answers included, on
four things from 1 to 5:
directness (does it answer the question asked), coherence (do the ideas follow),
precision (concrete details and clear references), compression (says a lot per word). The weighted
mix (30/30/25/15) is the exchange score. A person's published score averages their exchange
scores on raw tape only, at least 8 of them, shrunk toward the cohort mean.
Full method →
Answered produced feed
D 5 · C 5 · P 5 · Cm 4 4.85
Q So what are some of the things that people can then do to move those users up that ladder of engagement?
A Step one is It's really segmenting your users into this kind of engagement map. Oftentimes you'll see this visualized as a kind of state machine where you have folks that are new, you have folks that are casual, and you want to track how much they're moving up or down in each one of these steps. And then once you have that, then the question is, okay, well, great. How do you actually get them to move from one place to the other? First, there's like content and education. They need to know kind of like in context that they can actually do something. So for example, if you can get your users to set their home and work, For transportation product, then you can maybe, like, figure out, you know, okay, should I prompt them in the morning to try their ride based on what the ETAs are, right?
AI assessment note: “First, there's like content and education. They need to know kind of like in context”
Answered produced feed
D 5 · C 5 · P 5 · Cm 4 4.85
Q Right. It's like counting impressions and being able to sell that to advertisers, right?
A Exactly. Their products have historically been 60% plus daily actives over monthly actives, and that's very high. You know, you're using it, um, more than half the days in a month. On the flip side, what I was talking about in my essay about this is that DaoMao can tell you if something's really high frequency and if it's It's working. But a lot of times products are actually lower down now for very good reason, because they're sort of just a natural cadence, you know, to the product. Like you're not going to get somebody who is using a travel product to use it more than a couple times per year. And yet there are many valuable travel companies, obviously.
AI assessment note: “Exactly. Their products have historically been 60% plus daily actives over monthly actives”
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q So what are some of the things that people can then do to move those users up that ladder of engagement?
A Step one is It's really segmenting your users into this kind of engagement map. Oftentimes you'll see this visualized as a kind of state machine where you have folks that are new, you have folks that are casual, and you want to track how much they're moving up or down in each one of these steps. And then once you have that, then the question is, okay, well, great. How do you actually get them to move from one place to the other? First, there's like content and education. They need to know kind of like in context that they can actually do something. So for example, if you can get your users to set their home and work, For transportation product, then you can maybe, like, figure out, you know, okay, should I prompt them in the morning to try their ride based on what the ETAs are, right?
AI assessment note: “Step one is It's really segmenting your users into this kind of engagement map.”
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q So sometimes you have to optimize it based on how you're monetizing it. What are some of the metrics for retention? I mean, is it just, should I stay or should I go? Like, is that the retention metric?
A And I think the big thing is the concept of churn is a tricky one. In some cases, like subscription, Hulu, Netflix, and then also in the SaaS world, whether or not you're still continuing to pay or not, right? And that's really obvious. The thing that's tricky for a lot of these consumer products, especially episodic ones, and it's actually less whether they've quote unquote churned or not. It's actually just whether or not they're active or inactive. And whether or not that's happening at a rate that you in your business strategy have decided is acceptable or not. If every Halloween, you know how there's those costume stores that like open all over the place. If every Halloween you go back and you buy a costume, but you're inactive the rest of the time, have you churned or not? Like it's not clear. And I would argue you've not churned because you're doing exactly what they want, which is to buy a costume every, you know, Halloween.
AI assessment note: “It's actually just whether or not they're active or inactive.”
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q I was about to actually ask you, what are the buckets of cohorts? Are they all demographic data?
A For a bunch of hyperlocal type businesses, the reason why segmenting it based on market geography, why that's so valuable, is because then you can compare markets against each other. You can say, well, you know, this market, which has much more density in terms of the number of scooters, behaves like this, and you can start to draw conclusions, you know, sort of a natural A-B test in order to do that. And I think the similar kind of analysis you can do for B to B companies is, For products that have different size teams using it. If you have a really large team that are all using a product, well, are they all using the product more as a result? And let's compare that to something that maybe only has a couple, right? And so this way you can start to kind of disassemble, you know, the structure of these networks, and do they actually lead to higher engagement?
AI assessment note: “segmenting it based on market geography, why that's so valuable”
Answered produced feed
D 4 · C 5 · P 4 · Cm 4 4.30
Q come in to, is there really a magic number or aha moment for a product to who are the power users and what is the power user curve for measuring them? But first, we begin with what happens after the initial acquisition phase as different kinds of users join a product or platform over time? What does that mean for engagement, and how do you analyze them using cohort analyses?
A One of the things that you see is that people end up using these products very differently because the kinds of users that you're getting are changing over time. You know, when you look at something like rideshare, you know, all the early cohorts are basically people in urban areas. And then these days, all of rideshare is more like suburban or rural folks because, you know, you've saturated all of the center. And so what you tend to see is as you acquire your You know, folks, you know, your core demographic out, that actually ends up showing up in the engagement. And so, you know, going back to the natural, like, kind of gravity to the whole thing, this gravity also hits the engagement side of things as well, and then ultimately the LTV, because your users are typically getting, you know, kind of less valuable. It may take years to see this kind of play out, but that's kind of the natural, you know, law of things.
AI assessment note: “ends up showing up in the engagement... gravity also hits the engagement side of things”
Answered produced feed
D 4 · C 4 · P 4 · Cm 4 4.00
Q I was about to actually ask you, what are the buckets of cohorts? Are they all demographic data?
A For a bunch of hyperlocal type businesses, the reason why segmenting it based on market geography, why that's so valuable, is because then you can compare markets against each other. You can say, well, you know, this market, which has much more density in terms of the number of scooters, behaves like this, and you can start to draw conclusions, you know, sort of a natural A-B test in order to do that. And I think the similar kind of analysis you can do for B to B companies is, For products that have different size teams using it. If you have a really large team that are all using a product, well, are they all using the product more as a result? And let's compare that to something that maybe only has a couple, right? And so this way you can start to kind of disassemble, you know, the structure of these networks, and do they actually lead to higher engagement?
AI assessment note: “segmenting it based on market geography, why that's so valuable”
Answered produced feed
D 3 · C 5 · P 4 · Cm 4 4.00
Q Right. It's like counting impressions and being able to sell that to advertisers, right?
A Exactly. Their products have historically been 60% plus daily actives over monthly actives, and that's very high. You know, you're using it, um, more than half the days in a month. On the flip side, what I was talking about in my essay about this is that DaoMao can tell you if something's really high frequency and if it's It's working. But a lot of times products are actually lower down now for very good reason, because they're sort of just a natural cadence, you know, to the product. Like you're not going to get somebody who is using a travel product to use it more than a couple times per year. And yet there are many valuable travel companies, obviously.
AI assessment note: “Exactly. Their products have historically been 60% plus daily actives over monthly actives”
