Phil Carter
speaker
228 appearances
1 recordings
1 series
first heard Sep 2024
last heard Sep 2024
Phil Carter’s voice in public audio — every appearance, attributed to the second.
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Appearances
I think one perfect example of this is Jason Vandermeer, who now is the Director of Growth Engineering at Strava. He was an early engineer at Strava. He didn't join to build their growth team. But it turned out he was very good at figuring out, along with others on the team, how to run growth experiments that accelerated the growth of the business or certain parts of the product.
And so then he just became a natural fit to be one of the founding members of their growth team. I think that's often what you can see.
I think the degree to which you should be taking big swings that maybe have lower confidence but much higher impact if they hit versus smaller optimizations is largely a function of the maturity of your company. Or maybe said a different way, you often hear within the growth community this idea of an S-curve.
So you launch a product, you're at the early part of the S-curve, it's just starting to get adoption, you find your early adopter group, then you hit a tipping point growth takes off, you're in the steep part of the S-curve where you're getting hyper growth. And then at some point, you start to saturate that market and you hit the upper end of the S-curve where it flattens out and growth stalls.
So if you're in the early part of that S-curve, which by definition means any seed or series A startup, you should not be focusing on small optimizations. It's a waste of time for a couple of reasons. One, because it's not going to move the needle enough to make a difference. Two, you should still have lots of low hanging fruit.
If you're still that early, you're either working on the wrong product, you don't have product market fit, you're working on the wrong solution. Or you should have lots of low hanging fruit left to optimize, which means you should be taking bigger swings.
And then the third reason is, oftentimes, even for consumer businesses, and certainly for B2B businesses, if you're that early, you simply don't have enough users to be able to measure a statistically significant difference in a small optimization type of an A-B test. And so you're much better served taking these big swings.
Now, on the other end of the spectrum, if you are a more mature company, especially if you're still in that hyper growth steep part of the S curve, then it can make a lot of sense to do small paywall optimizations and optimizing every last screen in your onboarding flow, or even changing the color of your CTA button in the case of Amazon.
Because a tiny decimal point movement in things like subscriber conversion or checkout conversion can be millions of dollars in incremental revenue. So it really just depends where you are on that curve.
Yeah, well, one of them I can speak to because I made this mistake personally. So I led the core product team at Ibotta and then I moved over to Quizlet and helped them build and scale their growth team, their product growth team. And when I first started to hire product managers, and this was not just true of growth, it was true of core product as well.
I had this tendency to focus too much on specialist skill sets. I want to hire somebody to work on the core product experience who has a design background and who's just really, really good at understanding user psychology and building the perfect aesthetics for an end user experience. Or I want to hire somebody to work on growth who comes from an extremely analytical background, ideally like
has already had 3 to 4 years of growth PM experience or comes from a quantitative background like investment banking or whatever the case may be. Well, it turned out that was just misguided because I think product managers need to be strong generalists. They need to be able to flex to whatever the demands of the business dictate in any given quarter.
And then also, I just think with growth PMs in particular, I think that the traits that are most correlated with success are deep intellectual curiosity, a desire to move fast and deliver impact as quickly as possible for the business, and a willingness to take smart risks. And those things don't necessarily relate to specific experiences or skill sets.
But I learned to focus less on these sort of like specialized skill sets and more on just finding really smart, hungry generalists who are ready to roll up their sleeves and figure it out.
Yeah, there are a few things I like to do. So one is, I do often like to assign a homework assignment. And I'm very careful about this for a couple reasons. I guess to step back, there are lots of ways to introduce bias into a homework assignment like this.
Number one, if you give an assignment that is specifically about your company, then especially if you're an early stage company that not everybody has heard of, or not everybody deeply understands, you're going to get a lot of bias just based on who already knows your product and who doesn't.
Number two, and this is particularly true as you start to be looking at people who have families or they have other demands on their time. If you give an assignment and oftentimes it'll be like, don't spend more than a couple hours on this. Well, of course, the best candidates are going to want to roll up their sleeves and do everything it takes to deliver the best possible output.
But different people have very different limitations on their time, especially if they already have a full-time role, they've got a family at home. And so what I found works best is pick an assignment that truly shouldn't take more than an hour or two before you hit diminishing returns on spending more cycles on it.
And that number two is either more of like a hypothetical scenario or it's a company that everybody's heard of. So Uber, Airbnb, companies that are like ubiquitous at this point in the American tech mindset.
And then focus on a question or a prompt that really gets to a person's ability to think critically about a problem from first principles and ideally, apply some sort of quantitative rigor to that question. But that isn't going to lead to hours and hours of going through a long list of questions or packaging up a perfectly polished presentation because that just introduced a lot of bias.
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