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Testing your ideas against reality can be challenging. Not everything will go as planned. It’s about keeping an open mind, having a clear hypothesis and running multiple tests to see if you have enough directional evidence to keep going.
This is the How I Tested That Podcast, where David J Bland connects with entrepreneurs and innovators who had the courage to test their ideas with real people, in the market, with sometimes surprising results.
Join us as we explore the ups and downs of experimentation… together.
David J Bland (0:1.072)
Welcome to the podcast, Deon.
Deon Crasto (0:3.789)
Thanks for having me.
David J Bland (0:5.372)
I'm so excited to have you on because you have this really interesting history in SaaS and pricing and fraud detection and security and all this kind of mixed in together. And it reminded me a little bit of the early start of my career where I was in a fintech startup and I had no expertise in any of that. And I was forced to sort of learn on the fly while, you know, trying to scale a company. And it was, was an interesting ride.
But I wanted you to maybe share some of your background with our listeners and we'll start there and then maybe we can jump into some stories about some of the tests you've seen over the years. I've seen I know you've seen very, very different types of testing from pricing to everything else. So if you could just jump in and give a little bit about yourself before we dive into testing stories.
Deon Crasto (0:58.606)
Sure, happy to. So I started off my early career working as an engineer for platform architectures. Didn't quite enjoy that as much, so I went to grad school for data science. I was interviewing for data science positions and I didn't know what product was, but this gentleman who was interviewing me told me I asked more questions than I gave him answers. And he said, you sound more like a product manager than a data scientist I would want to work with. And I didn't know what a product manager was, but he was generous enough to switch me.
in two lanes and he's like, should try this out. We need this really tech heavy decision science based PM. So yeah, I went through the loop not knowing what a product manager was. That was eight years or 10 years ago. But yeah, I've been a product manager ever since. Started off my career working in decision sciences, product, this company called OnDeck Capital. Then I think when COVID hit, I was very intrigued by what ML and AI could do within the healthcare space. And I was really motivated with that.
So Livongo, which is a company that did remote monitoring for people who live with chronic conditions, was something really cool. So I spent a couple of years working on that. I think somewhere between that experience I really missed working with payments and monetization and all that fun stuff that comes with it. So we switched to this company called Checker, which is an API for the HR platform, primarily focused on making back contracts more seamless. So high volume gig, Uber, Doordash, Instacart, those kind of people.
And then about a year ago, I switched to working in a completely different field, which is employer of record. But I do payments, risk, and global payroll here. So yeah, a very diverse background. I think I was speaking to someone the other day, and they were like, what's your core competency? And I was like, I don't know if I have one. I've just done so many different things. I've been from payments. And then from payments, someone was like, hey, we see payment fraud. Can you do payment fraud? And I was like, oh, OK. And then I was doing fraud. they're like, do you want to do trust and safety? And I'm like, what's that?
have just been, I think, just like you mentioned, the life of a startup company, just trying new things and you're moving around. So yeah, that's been my story.
David J Bland (3:7.740)
So much to unpack there. Let's start with beginning to understand that you're a product manager. I can't tell you how many times I go into companies, either startups or larger companies, and I asked product managers how they became product managers. And the stories vary so much, but the single theme I think I hear time and time again is, well, I'm very curious and I have this curiosity and it's almost like I'm addicted to problem solving.
And therefore, someone told me now I'm a product manager. And they gave me maybe a copy of a Marty Kagan book or something. And I had zero trading whatsoever. And I was just thrown into it. And I can't say your story is exactly like that. But the interviewer recognizing that you're asking more questions is very telling. so what kind of training or any training did you have? Did you just learn? you seek out training like?
Deon Crasto (3:42.442)
Thank
David J Bland (4:6.172)
Explain that a little bit, because I think a lot of people have been thrown into that situation before.
Deon Crasto (4:11.018)
Yeah, I went in not knowing anything about product. I didn't know what product management was. I didn't know what product management did. But I was very curious. And I think a lot of people ask me, how do I break into product? And I've given different answers to different people based on where you're at in life. I think the easiest way to do it or to transition a product is if you're at a company and you're working, try to take on more ownership and see if you can transition, pseudo transition in terms of taking ownership of what a PM does.
to then see if you can make the move. I mean, that's probably a relatively straightforward way to do it if your company's open to it. In terms of training, I did not have any. I was really grateful to have some really great mentors throughout my career and some really patient mentors who've worked with me to sharpen my grasp about a lot of different concepts.
I have this idea that 99 % of things in life can be learned. I think anything you want to learn, you can. I don't think people are born into something. And you have to have some level of curiosity. And if you really want to be a good PM, you have to have the ability to connect people. I think people focus a lot on, like, I need to be a technical PM. need to do all these different things.
I think your biggest problem in my 10 years of product has been connecting people and a lot of diverse stakeholders who probably don't look eye to eye. How do you get them in the room to then just be the mediator? think 90 % of the tough job in most scale-ups and startups is basically how do you make people see your way and get alignment.
David J Bland (6:2.734)
Amazing. you have an ML background a bit. I mean, did you pull that into your product? How did you think about metrics as you got up to speed? What was your thought process there?
Deon Crasto (6:16.130)
Yeah, I've always been pretty quantitative driven. I like numbers growing up. So I always think in terms of what's the ideal way to solve different problems and what's the impact. A lot of people kind of struggle with, oh, I did this and this helped this. But I was like, what's the value proposition they're trying to drive and how do you mesh success? Can you give me a quantitative number? So I've always been really focused on that. I did come from a data science background, so a lot of my focus is like,
All right, you build a hypothesis, you test the hypothesis, you validate it from a p-test, and then you kind of like go into that. Obviously, you can't do that in terms of a strictly product lens, but it really helps understand what a hypothesis is and how do you either prove or disprove it, and then going back to your validation metrics. It's always been helpful for me. I think different companies focus on a lot of different metrics. When I think about...
PMs, I try thinking of two things that people should be focused on. I really think people should skip vanity metrics. Vanity metrics is something you might say, hey, I did ABCD and that kind of like increased a metric that no one really cares about, right? And I like thinking about proxy metrics. A proxy metric in my world is essentially people think like a lot of companies want to increase revenue, like top-line revenue, or like a lot of different things, PMs traditionally
don't have as much power as people or like even we think we have in terms of affecting those numbers. So there's always a better way to influence something called a proxy metric. Like for instance, let's say a revenue is something the company wants to drive, like it's a top line revenue. Let's say it's X and you're working for a social media company. You can't drive revenue automatically, but what you can do is you can drive, let's say the time spent on a site, let's say your Facebooks, you're in charge of the Facebooks.
page on the explore page. How can you make sure that people spend more time on certain actions? So very similar products in terms of aligning my team, because my team is more B2B focused. So a lot of it is how do I make sure we have a metric that we can influence that we know if that goes up, then in 99 % of the cases, the key metric of the North Star metric we're trying to chase goes up. So yeah, that's been a lot of our focus. I worked at OnDeck Capital.
Deon Crasto (8:41.774)
We were giving out SMB loans online SMB loans and a lot of our focus wasn't on Just giving out the loan because a lot of batteries go into it We were focused on how do we make sure the applicant completes the loan? The loan application so a lot of our focus was if the application completes all four pages We have enough information to make a decision and then we he can choose he wants to take the loan or not So a lot of a factor is based on that. So yeah
David J Bland (9:8.890)
One of the frameworks I like, I think I picked up from 500 startups, which was Pirate Metrics, right? So AARRR, which is R. I may have mentioned these in previous episodes, there's acquisition, activation, retention, and then depending on your business, either referral revenue or revenue referral. And I find it still holds up pretty well, you know, because you need to have, maybe it's not a funnel. I think the concept of a funnel might be getting a bit outdated, but a system that...
leads through there. And what I try to do with experimentation is like, okay, where are we focused in this system? You know, like if we have a bunch of people that are turning out, maybe we're not focusing on acquisition right now. You know, we don't need to make that number go up anymore if people are just turning out on activation or retention. So I think early in my career, I would just march down, you know, and just attack each one. And then I realized, kind of depending on the business, you might start in different spots. And, you know,
Deon Crasto (9:59.683)
Mm-hmm.
David J Bland (10:5.946)
you can't, you gotta be careful like locally optimizing for one part of that and then the whole thing suffers. And I think one of the sayings that really stuck with me was measure what you wanna go up, but also measure what you don't want to go down. And I think sometimes we forget about the what we don't want to go down part and we get very, very focused on what we wanna go up. But I love your experience with OnDack. That was a company I was following kind of early on when it was first founded.
Deon Crasto (10:17.922)
Mm-hmm.
David J Bland (10:34.938)
Maybe if you could share with us what kind of experiments or what kind of assumptions were you testing there and kind of explain your process a bit. Because I think a lot of people would love to know what was it like being a product manager running experiments on a deck.
Deon Crasto (10:53.570)
Yeah, this was circa 2017, 16, 2017. Yeah. So for your listeners who don't know, OnDeck, it's an SMB lender, online SMB lender, primarily focused on making credit more accessible. We look at a lot of different factors beyond just FICO scores and how long you've been in business. So it's the idea is how to make the holistic picture of a company's creditworthiness. And we use a lot of machine learning, a lot of heuristics to determine that.
I think one very interesting project that I worked on was trying to figure out how we can have an applicant complete the online application with all the required documents that were required in order for adjudication. So for instance, the application process was four pages. First page is about your business, second page is about you, third page is some business information, and fourth page is where we collect information about your
Bank details, right? So give us your bank data. Traditionally, when we were doing this, we would ask you for PDFs. So go to your Bank of America or Chase, download three months, give it to us. Someone would manually look at those numbers and then process it. Somewhere around 2017, I think APIs and Plaid and all those fun companies were like retaking off and someone came up with the idea. I don't remember who, probably it was someone on our team.
We said we should really integrate with this because we think that we have a lot of a drop off on this fourth page, which is where they give us bank data because it's like such a tedious task of like going and downloading PDFs and you will forget about it and you never complete it. So we should just integrate with like a third party bank aggregator and our entire funnel would like skyrocket for like application completion rates. And I was very early in my career so I didn't really ask a lot of questions.
Probably should have. But we did build this project. We built this integration. And long story short, we ran an A-B test. the test was basically, the first three pages were the same, right? But on the fourth page, we made a 90-10 split. So 90 % of customers would see the traditional, please upload your PDFs. But at the bottom, would be a small electronic button which would say, if you would like to sell it like...
Deon Crasto (13:18.222)
Submit your bank account electronically, then please click this button and it will be much quicker, like in 12 hours or something. But the longer route is what you're going. And for the 10 % of the cases, the electronic one was provided as the primary option and there was a small button which you would rather select as PDF. Go through, click this button. What we learned was very, very fascinating because everyone was so...
everyone felt strongly that the B test would be like so much higher, like ridiculously high because we'd be like nothing other that. And what we found was the A group was like so much better, which is the traditional solution that we had. And everyone's like, oh, this is just like, you know, a couple of days. We gave it a week. We gave it two weeks. And it was still the same. It was a margin of like 10 or 12%, which is significant. And we were like, what is happening to the point where we shut it down? We shut down the B test.
And we just showed everyone the A test. And then we sat down with a lot of these merchants who presented the option as the primary option. we said, we had a really good design and research team. And we sat down with a lot of these customers. said, what's happening? Walk us through your thought process. And a lot of them came up with very, very valid concerns that we just hadn't thought about. One of them was like, we've never seen the idea of connecting my bank. Again, this was 2016, 2017.
Like, are you going to do with my bank data? Why am I giving you my username and password? Like, who is this, whatever this electronic thing is, how do I know my data is going to be secure? And we also noticed when we dug a little deeper, a lot of these were returning customers, the customers who already had a loan with ONDAC and maybe for some reason, like we went through this thing and this thing gave us to learn for the first time. Well, actually, we tried something. And then we saw a lot of...
Geo data like a lot of our audience catered to more mature businesses a lot of older folks and they just didn't like the idea of like connecting to this bank data So a lot of them spoke to us and they're like, are you gonna do with my data? How do I know it's secure? Where is this going? I don't like this. Give me the first option that I've always had So we learned a lot about That experiment and we made a lot of changes But yeah, that was one very interesting part of that I worked on and I didn't really build the hypotheses
Deon Crasto (15:38.606)
because the hypothesis built itself up towards the end of the project. yeah, that was a really interesting project I worked on.
David J Bland (15:45.572)
I like that you talk to customers, right? I think sometimes we feel the quantitative is going to tell us everything we need to know, but there are people behind those metrics and talking to them will yield these sort of insights that you're not going to find through quantitative results. I feel as if there's probably something about segmentation there in your answer about there is maybe a sub segment.
that would be ideal for this, but maybe it skews younger and more tech savvy and a first time applicant. And it doesn't make sense to show this to everyone. And that's something I see with startups. I also see this with larger companies where we try to do A-B tests and we try to run these experiments, but we really don't have the ability to segment as well as I would like to see. And therefore,
We show it to folks where it's not going to be received well, mostly because they're not the ideal target customer for this feature or this service. So I think there's something about segmentation there. I don't know, what are your thoughts?
Deon Crasto (17:1.070)
No, 100%. And I think I was getting to this. What you said was 100 % true. So what we learned was a lot of tech savvy younger businesses were a lot more receptive to go with that idea. And we learned that through A, the user study, but also looking at the data. So a lot of these customers that we collect information from, and we have something called industry classification, which is we, through a Nix code, we classify which business, what type of business you are in.
If you are something in a more tech savvy industry, we notice that even if you presented the A option, you would still click the B like try to check out the electronic button. So what we did was we kind of built personalization into the workflow. So on the first page and second page, we collected information about you and your business, who you knew, how old you were, what business you were kind of going for, what kind of loan sizes you wanted if you were first time returning customer. And we used that to then build in personalization in terms of showing you what kind of
workflow like what experience you should like. So if you're a first-time business owner, you're tech savvy, you're younger, we said, hey, here's a screen that we think might work for you. And if you're a returning customer, said, here's the option, and let's see if you like this or if there's another one, then click at the bottom and go from there. So that really helped in terms of segmentation of customers. And just to gather a little mindset.
And we did that a few times to kind of see what the sweet spot was in terms of like what the ideal customer segment. But yeah, I think towards the end we said there's a lot more nuance that we kind of like thought about. yeah, speaking about security and bank data is still a conversation. At back then was a conversation where like, we don't know what these things are. Don't show is connect my bank data to you guys. So yeah, we learned a lot from that experience.
David J Bland (18:53.532)
So you said you were pretty new, right? You were thrown into this role and you didn't ask a lot of questions maybe because you weren't comfortable enough. As you were maybe at the end of your stint at On Deck, what kind of questions were you asking or what were you feeling more comfortable with as far as challenging ideas? Because I'm sure, I mean, the startups I've been at, ideas come from everywhere. And it can be...
It's not a lack of ideas. It almost feels like there's too many ideas and we just spin. And so maybe compare and contrast at the end of your tenure. What kind of questions were you asking or what was your thought process as you gained some experience?
Deon Crasto (19:38.413)
Yeah, was very, very, I think towards the end of my tenure there, I think we were really focused on building the best SMB at the lock line of product, like a lot of different products on there. I always thought we could do a little more in terms of like building an ecosystem. I was very, very intrigued as SMBs were growing, right? There's a lot of different products that they needed.
you know, tax or like compliance or like payroll, a lot of different things. And my idea was, hey, why aren't we doing more? And there are different takes on this. think a lot of us like, let's be lazy, focus on one thing, right? And let's not do too many things. But then my philosophy on that, think towards the end of us, maybe we should do something because at the end of the day, if we don't do that, you're just becoming a company that you're becoming a bit of a commodity, right? People are going to shop around for what's the best terms and
they're gonna look for as opposed to if you build an ecosystem then instead of them coming to you to give a loan like to ask for a loan you could just have enough information you can say hey do you want this this loan right now right like how Stripe does it I guess right now with or at least back then when they said we've been collecting a lot of prepayments we think there's an upcoming seasonal bump you might require capital here's an automated product that you can just click in and we learn right away some of the information we have so there's there was a lot of different things that I was like hey we could have done something different and we could try something different
Deon Crasto (21:7.479)
I think it's always good to ask questions. We had really good leadership in terms of answering them and keeping us on track and explaining why decisions were being made. So I really enjoyed that. It also pushed you to be a little more creative in terms of what you can and cannot do.
David J Bland (21:25.574)
So beyond on deck, what kind of assumptions were you testing at other companies? How did that experience maybe shape how you approach things up until today?
Deon Crasto (21:28.430)
Mm-hmm.
Deon Crasto (21:42.604)
Yeah, I learned some really interesting things at a bunch of companies. One that...
I'll talk to you little bit more about them. think we offered an online promo. This is a very simple. Here's a $40 coupon. Sign up for our product, like a meal kit delivery product. And a lot of the metrics that the growth team was focusing on was like, look at the number of people that have used our $40 coupon and the number of accounts that have been created. I think someone's looking at the number of accounts created as a metric. We have X thousand accounts.
that created, right? But when we dug into the... I was in charge for the monetization piece of that. And when I was looking at it, I was like, there's like 8,000 accounts created, but the revenue from those 8,000 accounts is like probably $8,000. Doesn't make a lot of sense to me. It's probably a little higher, but it didn't make a lot of sense to me in terms of what numbers they were pulling. I started looking at the numbers a bit more, and I noticed that a lot of these customers were just using $40 promo coupon and then never using the account again.
Um, so it taught me, it taught me a lot about asking, do we have that? And I went back to like, grew up things like, why do we have this? It's like creating a lot of accounts on the platform that I don't, that don't really monetize themselves. And they just like, they're phantom accounts. You use that one coupon and they're of like gone. Um, and they're like, well, at least it gets them in the door. And I said, okay, cool. Let's just like not add that coupon. Let's take it off completely. Right. Let's see what happens. And we took it off and, and actually I don't think.
I do think the number of accounts created probably went down a little, the revenue or the the average order size, I think like quadrupled because you won't often have $40 and that was where the money was coming from. So that was one interesting question in terms of don't focus on vanity metrics, focus on what you're trying to like increase in this point in time. And if your accounts created is the metric that you're really targeting on, and I don't think that's what you should be looking for. So yeah.
David J Bland (23:47.833)
I gave a talk at a conference this year at Prodacity and Alastair Kroll gave a talk. He was one the co-authors of the Lean Analytics book and his whole thing was the one metric that matters, right? So like focus on one metric. And it was really interesting hearing him talk today because that book is several years old now. He's like, well, here's how people misuse that. It's, you know, they'll do all kinds of creative things to make that number go up to the detriment of the business.
And you do have to be careful about how you approach that. And I think we've seen that play out in especially in the financial sector. We've seen various examples of, know, we have this one number we want our account managers to focus on. And, you know, it sometimes ends up being, you know, fraud and other things in extreme cases. So how do you balance this? You know, obviously, we don't want to measure just vanity metrics. But we also don't want people so focused on one metric that they're actually hurting the company.
So you mentioned about connecting people and alignment, kind of fast forward to where you are today. How do you start approaching that or how's your approach changed about what people measure and how to get them aligned?
Deon Crasto (24:59.438)
Yeah, I think in kind of like, in some ways it has stuck from the top in terms of like people giving us a clear idea of what they care about. A lot of companies will be like, hey, here's the three things we care about in 2025. These are the three top line metrics of the company we care about, we care about nothing else. How you influence that and how you get there should be up to you and all teams should be aligned with that. I kind of like that idea because it gives you something towards
It gives you something to aim towards and more importantly, it get teams aligned. So let's say a company says, hey, we really want to reduce fraud. We don't really care about like top line revenue this year. We're a mature company. We really care about increasing the bottom line. We don't want to lose to like fraud. Maybe that's like a big use case. If you get a lot of people in the room and someone's like, hey, we could do this as a growth driver. Like that's a really cool idea, but we don't care about growth at this point in time. We care about this respective metric.
I think that really helps navigate, like get people in the same room and like see eye to eye. Because I feel if we, if you don't set that as a goal, right, as the top line, a lot of you are going to come up with a lot of different opinions and a of different ideas and the conversation doesn't go anywhere because everyone's going be like, this item is important and someone's going be, I'm going to think this is important. But if you give someone a not star goal, then you can quantify everyone's projects, either hypothesis driven or otherwise.
How does this project and this initiative help towards goal A or goal B or goal C? And then you can be like, well, this project increases 8 % and this project drives 12%. So you want to go to 12 % one, we'll see 8 % one unless they're quick wins, right? So I think that generally helps in terms of getting people aligned. I like being in places that companies are like leaders say, here's what we care about as a company for 2025. Some people always talk about top down versus bottoms up.
I think top down in this case for like defining what we care about should matter. And then individual teams should be empowered to making sure that how we get this our way is up to us, right? But we will make sure that we get there in terms of delivering, but we need some kind of like direction. So I think that really helps at least from like the teams and the stakeholders that I've worked with to kind of like drive a line.
David J Bland (27:20.984)
I like that. And you had so much experience in risk and in fraud detection. And I know we have some listeners working in heavily regulated industries. And what would you say to them if they're listening this and saying, well, this sounds great, but because of the regulations, I can't run experiments or I can't run tests that I would like to do. And there's some truth to that. What kind of advice would you give them if they're in a highly regulated
environment and they're trying to find ways to test things to make it to make improvements.
Deon Crasto (27:56.547)
Yeah, I think that's fair. I've worked in areas of finance and healthcare, which again, actually regulated and even background checks, which have a lot of regulation behind them. I think in those cases, it's really useful to kind of like take a step back and like analyze the data that you have to kind of give you a better sense of what predictive modeling would look like. Like for instance, I have this...
Sometimes I'm very, very impressed by how creative bad actors can get. It's ridiculous use cases that I come up with and I'm like, I know I'm supposed to be on the other side, but I'm so impressed by like the use cases you're driving and the ways you're trying to like defraud us. So it's a very interesting world to kind of like think about. We've used, in lot of different cases, we've used machine learning to kind of like look at patterns and pattern recognition to kind of indicate bad actors. And we've used human in the loop.
protocols. a lot of these different companies, so a lot of these different products that we built are used for flagging. So we've used machine learning models to say, hey, here's an anomaly detected and uncertain factors. can be, let's use a human in the loop to like double check, be certain if this should be a bad, if the account should be like deauthorized or not. But there's also been cases where there's such, such obvious fraud that we can just like tell that will just have the model be like, it was going to like automatically
disabled as a con based on these factors. I think there are some industries where having tools kind of like works, there's automated tools that you can like help with. If you work in finance, Stripe will be like, hey, we already like bunch of sweet autumn products, go use them. But your business or your use case might be completely different where that might be layer one, you'd have to build layer two, three and four on top of it. So always be open to seeing what kind of data you get.
And I'd say always test because you, it's, with fraud, there's never a solution that you build and it's done. You build a solution, it'll work for a while, someone will figure out a way out and you build again on top of it. I was joking with someone the other day, was like, you really wanna like have a job that's secure, get into fraud as a PM or something, like is there always gonna find a way to like be to the punch so you'll always be busy? But yeah, I think it's a constantly a whopping field and I think it's a mix of.
Deon Crasto (30:19.630)
At least right now it's a mix of technology and humans to get the place that we need to get.
David J Bland (30:25.084)
Yeah, I can relate to that. worked in a defense startup for a little bit and, you know, just watching how creative people with no moral compass whatsoever, but very creative. You kind of have to you can't move very slowly in a big, large batch process kind of way. You have to be agile. You have to be quick and iterative and responsive because things change so quickly. You know, like your threat maps change and.
I like your comment about job security there in a way, because that is so much true. So what gets you excited now? mean, you have this great background from data to product to risk and all these different SaaS companies. And I know AI is now being sort of pushed everywhere. What gets you excited now? What's some of the big assumptions? They don't have to be company specific, but.
What are some big assumptions you're excited to test in the near future?
Deon Crasto (31:25.486)
I am speaking to someone the other day. I get really excited about ambiguous problems as weird as that may sound. I feel I do very well in unstructured worlds where there's not a lot of data or there's not a lot of work that's been done before this. It's a completely novel product, a completely different novel problem. No one's looked at it or someone's looked at it and given up. I think those are areas that I like coming in because
A, no one really cares about it if I mess up, which I like doing in terms of testing. And B, it really helps set the lens for what you can do, because there's no one there before. There's no robot trying to follow. So you're not following someone's footsteps. It's just completely open blanket space. So how do you go about doing it? I think those kind of problems or those areas excite me. Thinking about
Hypotheses are like what I like testing. like testing in terms of like testing if the problem that people Think is a problem Actually matters as much to them or to the company Well, I'm gonna say hey, we really need like solve a b c or d and I'm like, okay I'm out of an ideal world. I solved a b c and d what happens in that world? Like what what happens and they're like, well, we do it we do this and that might be my office efficiency
increases right and i was like okay let's say your office efficiency increases what does that look like and they're like well my people will spend less hours and i was like how many hours then you like cumulatively like 20 hours in a month and i'm like it will take me probably 200 hours of engineering effort to just build this and if it saves you 20 hours a month you have to tell me what's worth it right so i always got like going back to the idea of like what do think this is going to solve
So like, someone's like, hey, this is a really important problem. We really need to solve this. And if we solve this, our world is going to get so much easier. And I always come back with, imagine this is an ideal world. I build this. I build the ideal solution. Everything goes away. What happens? Talk to me in terms of what that world looks like. And then tell me what quantifying impact it has on our lives collectively. And you'll get a lot of really interesting answers.
Deon Crasto (33:53.390)
because sometimes you find people trying to like fudge numbers and be like, well, you could do all these different things. And I'll be like, well, the entire bucket we're trying to solve is like X. And you told me doing this one thing will be X plus four. It doesn't really add up. So it's very interesting. But also people get alignment in terms of, is this really an important problem? I was just going on bar for like the last six months saying it's important problem. So yeah, I like problems like that. I like asking questions. like making people ask questions for themselves and going from there.
David J Bland (34:23.664)
Yeah, staying curious. I like it. Well, I want to thank you so much for your time. If folks were listening to this and they have questions about maybe a topic you mentioned, what's the best way for them to reach out to you?
Deon Crasto (34:38.126)
You can reach out through email. It's first name, lastname.gmail.com. I am also on LinkedIn, Dion Crasto on LinkedIn.
David J Bland (34:47.908)
Okay, so we'll include those on the product detail page for the podcast and make sure people can get in touch with you. I want to thank you so much for hanging out with us. We learned everything about how you became a product manager and started asking questions, some tests that went well and didn't go well. I just appreciate you being very candid and sharing some of your experiences with us today.
Deon Crasto (35:7.704)
Thanks for having me.