Latest / Leaders In Payments / Stopping Subscription Churn with Charles Rosenblatt, CEO of Butter | Episode 478

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Stopping Subscription Churn with Charles Rosenblatt, CEO of Butter | Episode 478 · Leaders In Payments

• 0:00
  SPEAKER_00: Welcome to the Leaders in Payments Podcast, where we talk to sea-level leaders from across the payments landscape.
• 0:07
  SPEAKER_00: We'll be discussing the products and services that impact the payment space today, as well as trends and predictions for the future of payments.
• 0:14
  SPEAKER_00: We will also hear stories from our guests about their journeys to the top.
• 0:18
  SPEAKER_02: Hello, everyone, and welcome to the Leaders in Payments Podcast.
• 0:20
  SPEAKER_02: I'm your host, Greg Myers.
• 0:22
  SPEAKER_02: Today's special guest is Charles Rosenblatt, the CEO of Butter.
• 0:26
  SPEAKER_02: So, Charles, thank you for being here and welcome to the show.
• 0:28
  SPEAKER_01: Thanks for having me, Greg.
• 0:29
  SPEAKER_01: Nice seeing you again.
• 0:30
  SPEAKER_02: Yeah, you too.
• 0:31
  SPEAKER_02: It's been a while.
• 0:32
  SPEAKER_02: Actually, Charles was on the show in a different capacity a few years ago, so it's great to see you again.
• 0:37
  SPEAKER_02: But before we dive into your career and a little about the company, can you give us a quick snapshot of your personal background, maybe where you grew up, where you call home today, a few things like that?
• 0:47
  SPEAKER_01: I grew up in northern New Jersey, right outside of New York City.
• 0:51
  SPEAKER_01: Spent a little bit of time back in the city after that.
• 0:54
  SPEAKER_01: Now I am in Richmond, Virginia, after 15 years in San Francisco and married.
• 0:58
  SPEAKER_01: I have four boys.
• 1:00
  SPEAKER_01: Keep them busy on that side of the house as well.
• 1:02
  SPEAKER_02: Awesome.
• 1:03
  SPEAKER_02: Can you walk us through your professional journey and how you ended up getting to Butter?
• 1:07
  SPEAKER_01: I sort of lumped my professional journey in a few buckets.
• 1:10
  SPEAKER_01: I guess when you're old, you get to put things in buckets as to rather than just continuous.
• 1:14
  SPEAKER_01: So I started out in the hedge fund world at a firm called DESHAW, which is a statistically arbitrage-based fund, moved to Capital One, where I led different divisions there, as well as then moving to Washington Mutual as sort of the first executive hire post-Pravidian acquisition with the Providian team.
• 1:32
  SPEAKER_01: That's sort of the first phase of my career when Washington Mutual was quote unquote acquired by Chase.
• 1:38
  SPEAKER_01: I decided to leave that sector of the world.
• 1:41
  SPEAKER_01: I then moved on, did some consulting in the loyalty space, but then spent the next 10 years or so running global cross-border payments companies, co-founding one, as well as being on the executive team at Hyperwallet, Payoneer, and PayQer, where we had met previously.
• 1:57
  SPEAKER_01: After I left PayQer, I was doing some work for some venture capital and private equity funds.
• 2:02
  SPEAKER_01: And a woman who actually worked for me 20 years ago, but I've kept in great relationships with, who was the former COO of build.com, became the CEO of Butter.
• 2:12
  SPEAKER_01: And she called me and said, Hey, what do you think about this company?
• 2:15
  SPEAKER_01: I just became CEO.
• 2:17
  SPEAKER_01: And she's one of the most brilliant operation and technical people I've ever met in my life, was one of the best hires I ever made.
• 2:24
  SPEAKER_01: But go to market is not her strength, same way technology is not mine.
• 2:28
  SPEAKER_01: And so she said, Hey, take a look at this.
• 2:31
  SPEAKER_01: I looked at it, I saw the product.
• 2:32
  SPEAKER_01: I said, This is amazing.
• 2:34
  SPEAKER_01: And I decided to join as the chief commercial officer to work with Sophia to help run the company.
• 2:39
  SPEAKER_01: Sadly, Sophia had some illness in her family towards the end of last year.
• 2:44
  SPEAKER_01: And she had to leave, and the board asked me to become the CEO of Butter.
• 2:48
  SPEAKER_01: And so that's how I'm here.
• 2:49
  SPEAKER_02: Thanks for that background.
• 2:51
  SPEAKER_02: So let's tell the audience what Butter is all about.
• 2:54
  SPEAKER_02: What does Butter actually do?
• 2:56
  SPEAKER_01: Butter was built to solve the problem of involuntary churn.
• 3:00
  SPEAKER_01: Our founder worked at both Microsoft and Scribd and realized hold on, if you put a machine learning strategy behind payment failure, and this is card failure.
• 3:10
  SPEAKER_01: So subscription comes up for$99, Joe Smith gives his debit card, he doesn't have enough money in his account, or there's a fraudulent transaction, et cetera, the payment fails.
• 3:20
  SPEAKER_01: How can we recover that money?
• 3:22
  SPEAKER_01: And so the company was basically built on recovering that payment failure for subscription-based companies.
• 3:29
  SPEAKER_01: We have been doing that successfully using machine learning and AI for the last six years as the leader in the category, actually creating the category in some ways.
• 3:37
  SPEAKER_01: And now we're on to actually leveraging some of my background that I just explained to you, which is really about modeling and statistics and predictive models to build some new products around predictive models that get ahead of that payment failure as opposed to waiting for that payment failure overall.
• 3:54
  SPEAKER_01: So we cure things after the payment fails.
• 3:57
  SPEAKER_01: We also now are able to predict if the payment's going to fail.
• 4:01
  SPEAKER_02: Are there specific verticals within that kind of subscription industry that you focus on?
• 4:07
  SPEAKER_01: So that's actually the beauty of the business.
• 4:09
  SPEAKER_01: We have clients in the AI space, in the gym and fitness space, in the e-learning space, like a masterclass as an example.
• 4:18
  SPEAKER_01: We have physical products like a Pros Hair or a Bobby that are clients.
• 4:22
  SPEAKER_01: The reality is the best products for us are AOV,$120,000,$125 or below.
• 4:30
  SPEAKER_01: Because if your credit card fails for an$8,000 transaction, machine learning can only do so much if you're never going to have$8,000 again.
• 4:38
  SPEAKER_01: But in essence, the beauty of it is we work across any industries.
• 4:42
  SPEAKER_01: We do work best in high margin industries because the way we charge is we actually take a fee of the incremental money that we save.
• 4:51
  SPEAKER_01: And so clearly higher margin industries have a better way for us to do that incrementality with us.
• 4:57
  SPEAKER_02: Okay.
• 4:58
  SPEAKER_02: So it's not a SaaS model.
• 5:00
  SPEAKER_01: Ironically, that is one of the shifts I've done since I got here, is actually we probably have 50% of our clients now on SaaS.
• 5:07
  SPEAKER_01: A lot of our clients say, show us the value before we're going to sign on to a fee.
• 5:12
  SPEAKER_01: And so what we do actually is usually in the first three or four months, we'll start on that incremental model.
• 5:18
  SPEAKER_01: And if the client and when the client gets comfortable that we're driving a lot of value, we'll look at that and move to a SaaS-based pricing.
• 5:25
  SPEAKER_01: Our longstanding clients, people who have been with us a year or more, the majority of them have shifted to a SaaS pricing because they see their invoices, it also creates some steadiness for them, right?
• 5:36
  SPEAKER_01: So holiday seasons, we may recover more than non-holiday seasons, or for gyms, a lot of subscriptions come up in January and February and they change.
• 5:46
  SPEAKER_01: And so rather than paying us$22,000 one month and$58,000 the next, and then 13, the CFOs like say, well, if we're paying you$300K over the year, let's just make it$25,000 a month and create that smoothness once they've seen that we've been able to produce the results we do.
• 6:02
  SPEAKER_02: What's the biggest challenge that your company is solving for your customers right now?
• 6:07
  SPEAKER_01: The biggest challenge we're solving for our customers is as a product person, having run product at multiple places, you're always looking to ways to make your product better.
• 6:18
  SPEAKER_01: And you also want to make sure it works in the market.
• 6:21
  SPEAKER_01: And so you look at churn, right?
• 6:23
  SPEAKER_01: That's just a common characteristic people look at.
• 6:26
  SPEAKER_01: What is my customer retention?
• 6:28
  SPEAKER_01: What is my customer churn?
• 6:29
  SPEAKER_01: And so let's say hypothetically you have 30% churn.
• 6:32
  SPEAKER_01: You may say, oh my God, my product is terrible.
• 6:35
  SPEAKER_01: I need to change the formula.
• 6:37
  SPEAKER_01: I need to move Coke to new Coke or something along those lines.
• 6:40
  SPEAKER_01: When the reality is, if 25% of that 30% is actually cards not working or failing because a payment isn't going through, your product is actually pretty good.
• 6:51
  SPEAKER_01: You have 95%, or you only have 5% retention that's not happening because the product's bad.
• 6:56
  SPEAKER_01: And so that idea of having a false negative or a false positive, depending on how you look at it, is a major problem that we solve.
• 7:05
  SPEAKER_01: That's probably not the first thing people look at.
• 7:07
  SPEAKER_01: The first thing people look at is I have a million dollars going out the door every month because of failed payments.
• 7:12
  SPEAKER_01: You guys can come and save 300,000 of it.
• 7:15
  SPEAKER_01: It goes straight to my ARR, it goes straight to my bottom line.
• 7:19
  SPEAKER_01: I think that's the true problem that people are coming to us for us to solve.
• 7:23
  SPEAKER_01: I actually believe the other one probably has equal value in the grand scheme of things.
• 7:28
  SPEAKER_02: Yeah.
• 7:28
  SPEAKER_02: And they keep the customer longer.
• 7:30
  SPEAKER_01: That's right.
• 7:31
  SPEAKER_01: So the average customer stays three and a half months after we save them.
• 7:36
  SPEAKER_01: So if it's a hundred dollar a month subscription, you're not just saving a hundred dollars.
• 7:40
  SPEAKER_01: You're saving four hundred and fifty dollars, the hundred plus an additional three and a half months at a hundred each.
• 7:45
  SPEAKER_01: By the way, we only charge on that first hundred.
• 7:47
  SPEAKER_01: It is a huge ROI for you for every$450 person dollar that you're saving.
• 7:53
  SPEAKER_01: Let's say we charge 20%.
• 7:55
  SPEAKER_01: We're charging$20.
• 7:57
  SPEAKER_01: You're getting a 23x ROI on that math.
• 8:00
  SPEAKER_02: So is it mainly in the US or is it global?
• 8:03
  SPEAKER_01: Completely global.
• 8:04
  SPEAKER_01: It is card mostly, but we work with things like PICs and Alipay.
• 8:09
  SPEAKER_01: What's interesting is we are an abstraction layer on top of your current payment platform.
• 8:15
  SPEAKER_01: So what we don't do, some competitors of ours do, they just buy the transactions or they take the transactions and then they go run it.
• 8:22
  SPEAKER_01: Let's say you're on Stripe, they'll go run it on their deal on Audien on the other side.
• 8:27
  SPEAKER_01: We keep your payment through your payment methods and payment schemes.
• 8:31
  SPEAKER_01: We're just sending an API signal to you saying this is the right time, this is the right place, this is the right method in which to do it, especially if you have multiple methods, but you're still running it through Stripe.
• 8:43
  SPEAKER_01: So Stripe continues to get their payment revenue and cost.
• 8:46
  SPEAKER_01: You have no extra cost than actually just retrying another payment, which is cents on the dollar.
• 8:52
  SPEAKER_02: Well, what would you say differentiates butter from your competitors out there?
• 8:56
  SPEAKER_01: You know, it's interesting.
• 8:57
  SPEAKER_01: I think at the very beginning, machine learning really did it.
• 9:01
  SPEAKER_01: And still, by the way, today, 90% of our competitors are heuristic-based.
• 9:06
  SPEAKER_01: So they do what we call a woodpecker strategy.
• 9:09
  SPEAKER_01: They'll try on day one, they'll try on day five, they'll try on day nine, they'll try at day 13, they'll try at 3 p.m.
• 9:15
  SPEAKER_01: each of those days.
• 9:16
  SPEAKER_01: What we do is a dynamic process because of our ML and AI.
• 9:20
  SPEAKER_01: And so we actually look at every single invoice and create a path or a method for each individual invoice.
• 9:26
  SPEAKER_01: So the example I give that's best that differentiates us from a lot of our competitors are let's say a Chase debit card fails on the 13th.
• 9:35
  SPEAKER_01: Well, if you use that puristic or woodpecker strategy, you're gonna try again on the 14th.
• 9:40
  SPEAKER_01: I don't know about you, but most people who don't have money in their bank account on the 13th probably don't have money again on the 14th.
• 9:46
  SPEAKER_01: We know, based on the 128 attributes of the card, that it's a Chase debit card.
• 9:51
  SPEAKER_01: We know most people get paid the 15th.
• 9:53
  SPEAKER_01: We know Chase usually puts money in the bank account at 5 a.m.
• 9:56
  SPEAKER_01: So what are we gonna do?
• 9:57
  SPEAKER_01: We're gonna go back in at 5.02 a.m.
• 10:00
  SPEAKER_01: on the 15th as our first try.
• 10:02
  SPEAKER_01: Again, that's a very simple example, but it's one that shows the difference between sort of this heuristic-based model that a lot of our competitors use and sort of the ML-based model that leverages billions of transaction points over six years to be able to create a model to do that.
• 10:18
  SPEAKER_02: I like that you're using the word ML and you're not trying to use the buzzword of AI, AI like it's this new thing, right?
• 10:26
  SPEAKER_02: Because ML's been around forever.
• 10:28
  SPEAKER_01: It's funny you say that.
• 10:29
  SPEAKER_01: So DESHAW, which I mentioned, is a statistical arbitrage fund.
• 10:33
  SPEAKER_01: I mean, we did as much as 15% of the trades on the New York Stock Exchange in the day back in the 90s when I was there.
• 10:39
  SPEAKER_01: And it was probably the most sophisticated modeling I've ever seen in my career.
• 10:43
  SPEAKER_01: And I spent time at Cap One, which is known for that.
• 10:45
  SPEAKER_01: That's ML.
• 10:46
  SPEAKER_01: The only thing that AI does from a modeling perspective is it does it faster.
• 10:51
  SPEAKER_01: Don't get me wrong.
• 10:52
  SPEAKER_01: It's not necessarily going to make it better.
• 10:53
  SPEAKER_01: Now, we use ML a ton.
• 10:55
  SPEAKER_01: We've built MDP connections.
• 10:58
  SPEAKER_01: Our tech team uses Claude and Claude code and all that all the time.
• 11:02
  SPEAKER_01: I think that helps get rid of some of the more menial tasks on a day-to-day basis.
• 11:06
  SPEAKER_01: So our ML team and so our development team get to focus on the things where they can really be truly value add with their knowledge.
• 11:14
  SPEAKER_01: We are an AI company as well.
• 11:16
  SPEAKER_01: I think everyone wants to or has to say that.
• 11:19
  SPEAKER_01: But the ML is really what drives our decision making.
• 11:22
  SPEAKER_01: And I think ML candidly was just AI before AI became cool.
• 11:27
  SPEAKER_02: Yeah.
• 11:27
  SPEAKER_02: Well, let's talk a little bit about the future.
• 11:30
  SPEAKER_02: So, where do you see the biggest growth opportunity for you and your segment of payments?
• 11:35
  SPEAKER_01: I mentioned the predictive modeling that we're now doing, and I can't tell you how excited about it.
• 11:40
  SPEAKER_01: We just launched our first client.
• 11:41
  SPEAKER_01: We have a few more coming on.
• 11:43
  SPEAKER_01: And so I'll give the example again.
• 11:45
  SPEAKER_01: So there are three uses of it.
• 11:47
  SPEAKER_01: One use of it is for folks in the AI industry, in the telehealth industry that we're using, which we call cancel the subscription.
• 11:56
  SPEAKER_01: So normally people know 20% of subscriptions will fail on payment failure.
• 12:01
  SPEAKER_01: Great.
• 12:01
  SPEAKER_01: Well, that doesn't help.
• 12:02
  SPEAKER_01: 80-20 really doesn't get you there.
• 12:05
  SPEAKER_01: We can tell you which 20 are going to fail and not go through.
• 12:09
  SPEAKER_01: By doing that, we can have you shut off your subscription for those folks rather than have them free ride you.
• 12:14
  SPEAKER_01: So with our telehealth client, let's say the average person does three telehealth visits if their payment fails and they keep people on.
• 12:21
  SPEAKER_01: That has a true cost of hundreds of dollars to them.
• 12:24
  SPEAKER_01: By being able to shut that off, they're saving hundreds of dollars of costs.
• 12:28
  SPEAKER_01: In the AI space, the computing power that's used during the seven or 10 days that people allow you to stay on, we can decouple that what we call dunning window or subscription and the dunning window, which is the collection period.
• 12:41
  SPEAKER_01: So that's one use case.
• 12:43
  SPEAKER_01: Another use case is someone like a blue apron.
• 12:46
  SPEAKER_01: They have to make a decision in 36 hours whether to send the box or not send the box.
• 12:51
  SPEAKER_01: We can tell them with 90% likelihood that this person, even though their payment failed, we will be able to get that payment in the next 14 days.
• 12:59
  SPEAKER_01: So send the box.
• 13:00
  SPEAKER_01: Don't cancel the subscription.
• 13:02
  SPEAKER_01: Don't lose your customer that you spent all that money for in order to do that.
• 13:07
  SPEAKER_01: And let us just take the next 14 days and we'll get that money for you.
• 13:10
  SPEAKER_01: Don't worry about it, and you'll be set the next time.
• 13:13
  SPEAKER_01: That's a huge savings to the consumer.
• 13:15
  SPEAKER_01: And the final one's probably the one I'm most excited about.
• 13:18
  SPEAKER_01: We have a gym that we're partnering with.
• 13:21
  SPEAKER_01: So with 96% accuracy, we can tell them that 26% of the people who made that first payment for the gym, the first month of membership, the second month's payment will not go through.
• 13:32
  SPEAKER_01: So if you think about that, you're spending hundreds of dollars on CAC and acquisition.
• 13:37
  SPEAKER_01: 25% of the people you just acquire are 26 in this case, but 25% of the people you're going to acquire aren't even going to get to month two because their payment's going to fail.
• 13:46
  SPEAKER_01: So what kind of actions can you take?
• 13:48
  SPEAKER_01: You can ask for a second card up front.
• 13:50
  SPEAKER_01: You cannot pay, you can tell people you're now paying the CAC on third membership, not first membership, because those first memberships have a lot of false positives.
• 14:00
  SPEAKER_01: So leveraging the ML, what we're doing is actually taking those basically tranches of accounts or segments that we've created over time that we use to make those back end decisions, we're able to actually push that forward and show that to people.
• 14:14
  SPEAKER_01: So that's what I'm really excited about.
• 14:16
  SPEAKER_01: I think that's where the future is heading, and that has a ton of value on top of the 5% ARR we're growing on the back end.
• 14:23
  SPEAKER_01: It lets people make the right business decisions up front.
• 14:26
  SPEAKER_02: So are you getting that prediction, that information from just the card information and the history, or are you like plugging in at the beginning of the subscription process and watching that as well?
• 14:39
  SPEAKER_01: The beauty of this is our first model only uses card.
• 14:44
  SPEAKER_01: So we are literally doing this from looking at the 128 variables of a card transaction that comes in.
• 14:50
  SPEAKER_01: So the easiest example, which doesn't make up the 26% to be clear, otherwise you wouldn't have much of a business, is if someone gives you a prepaid card up front, that has a huge likelihood of failing on the second transaction.
• 15:01
  SPEAKER_01: So that makes up a small portion of the 26%.
• 15:04
  SPEAKER_01: But there are a whole lot of other factors that we see.
• 15:07
  SPEAKER_01: We are now working with some partners to bring on additional data.
• 15:11
  SPEAKER_01: So for instance, in the case of the ship the box example that I gave with Blue Apron, we're looking at how many months they've been a client already.
• 15:21
  SPEAKER_01: Has this happened before?
• 15:22
  SPEAKER_01: Has there been a payment failure before?
• 15:24
  SPEAKER_01: Have they switched cards?
• 15:26
  SPEAKER_01: Things along those lines.
• 15:27
  SPEAKER_01: So we're beginning to bring in other factors outside of that, but we use no PII at all.
• 15:33
  SPEAKER_01: So today it is solely the anonymous card data that we're making our decisions based off.
• 15:38
  SPEAKER_02: What does success look like for your company, say over the next three, five years?
• 15:43
  SPEAKER_01: Yeah, I think success is taking our core product of recover, which is what I explained to you before.
• 15:50
  SPEAKER_01: Payment fails, we recover that money for the merchant and adding on additional complementary products like these.
• 15:56
  SPEAKER_01: So one's the predictive score.
• 15:58
  SPEAKER_01: The other thing we just launched is something called outreach, where we actually use email, SMS, and AI call center and use a machine learning layer for all four channels, the one we do already plus those three, to communicate in the best possible way.
• 16:13
  SPEAKER_01: So, for instance, again, I'll give a prepay card.
• 16:15
  SPEAKER_01: Prepaid card fails on day one.
• 16:17
  SPEAKER_01: A typical Dunning strategy will take 28 days and then they'll do something.
• 16:21
  SPEAKER_01: We'll move them straight to SMS or email on day two, because why waste those 27 days?
• 16:27
  SPEAKER_01: And so when you ask about the future in the next five years, I think it's very much around growing this suite of products to help people recover and retain revenue, as opposed to sort of a single-threaded product that we'd been in the past.
• 16:41
  SPEAKER_01: Although we did have a disputes product, which is a chargeback product as well.
• 16:45
  SPEAKER_01: So we'll layer that in.
• 16:46
  SPEAKER_01: But the idea of having a suite of products to solve this same churn problem is, I think, what we're excited about for the future.
• 16:54
  SPEAKER_02: When you step back and kind of zoom out, what are some of the biggest trends that you think are reshaping the payments industry right now?
• 17:01
  SPEAKER_01: Part of me wants to go back to my cross-border payment days to talk about that.
• 17:06
  SPEAKER_01: And if I say stable coin, will I be the 50th guest in 50 days to have said stable?
• 17:11
  SPEAKER_02: Yes, you will, but that's okay.
• 17:13
  SPEAKER_01: I think actually this trend and understanding, I'm not sure it's much a trend.
• 17:18
  SPEAKER_01: We talked about AI.
• 17:20
  SPEAKER_01: How much can AI do in all of this and how much can't?
• 17:23
  SPEAKER_01: We just saw the announcement about ChatGBT after meeting with 10 merchants pulling the payment side out of it from where they were headed.
• 17:32
  SPEAKER_01: I think there's a trend around moving quickly, but I think what we're gonna find out in the next year are which things can be replicated through either AI or a fast-moving technology change, and which things are sort of here to stay.
• 17:47
  SPEAKER_01: I mean, Visa Mascara both have done a great job extending their global cross-border rails.
• 17:51
  SPEAKER_01: They bought companies in the ACH space.
• 17:53
  SPEAKER_01: But the reality when push comes to shove, what's the most valuable rail in the entire world right now as far as payments?
• 18:01
  SPEAKER_01: Visas and MasterCards swipe and pay.
• 18:04
  SPEAKER_01: And so I think what I'd say is the trends in the future are all sexy and they will change things.
• 18:11
  SPEAKER_01: But that underlying railroad track is going to remain, I think, key to all of this, no matter how far we go.
• 18:18
  SPEAKER_02: Talking about the AI thing and directly related to your business.
• 18:22
  SPEAKER_02: So if we have an agent or a bot out, someone gives it authority to go look for the blue aprons of the world and says you have the authority to go pay for it, whatever, whatever.
• 18:33
  SPEAKER_02: I mean, that's kind of what everyone says the future's gonna be, consumers are gonna do that.
• 18:36
  SPEAKER_02: Does that affect your business really at all?
• 18:39
  SPEAKER_01: Well, in subscriptions, it tends to be a little more set it and forget it.
• 18:44
  SPEAKER_01: So it's not really an interactive payment process for most consumers, right?
• 18:49
  SPEAKER_01: When you have your Netflix subscription, when's the last time you've looked at what card is on your Netflix subscription?
• 18:55
  SPEAKER_01: Maybe if your card expired and you had to change all of them or you had a lost and stolen card and Visa account update can solve most of that anyway.
• 19:03
  SPEAKER_01: So I don't view that as much in the subscription space as causing a problem.
• 19:09
  SPEAKER_01: Maybe they'll help get smarter on the front end.
• 19:11
  SPEAKER_01: And again, the way we're selling our payment score, which is what we call that product that we talk about Blue Apron, is sort of like a plaid score.
• 19:18
  SPEAKER_01: So we don't actually need to do anything.
• 19:20
  SPEAKER_01: We're giving you a number or a score that you can then put in your own models.
• 19:24
  SPEAKER_01: For us, if Blue Apron builds an amazing AI solution to solve this, my hope is that we just are an input into that model that AI picks up or their modeling team picks up.
• 19:34
  SPEAKER_01: And that's fine for me because I'm charging on a per score basis.
• 19:38
  SPEAKER_01: So this is a move for us as well is not needing to be absolutely the people doing everything.
• 19:44
  SPEAKER_01: We can just be an input into your models to make your models better.
• 19:47
  SPEAKER_02: Great.
• 19:48
  SPEAKER_02: A couple of final questions.
• 19:49
  SPEAKER_02: So if you could go back and give yourself advice at the start of your career, what would that be?
• 19:54
  SPEAKER_01: I think a lot of it would be that every interaction matters.
• 19:59
  SPEAKER_01: And yes, fintech's a very small space.
• 20:01
  SPEAKER_01: The fact, Greg, that we're doing a second podcast is me at two different companies shows that.
• 20:06
  SPEAKER_01: But it's a very small space.
• 20:08
  SPEAKER_01: And I think I learned very, very, very early in my career I had a negative interaction with someone.
• 20:14
  SPEAKER_01: And it plagued reviews and stuff like that of mine for like two years afterwards.
• 20:20
  SPEAKER_01: When I think back at it, it was stupid.
• 20:23
  SPEAKER_01: It was a stupid interaction that I probably shouldn't have had or shouldn't have acted in the way I did.
• 20:28
  SPEAKER_01: I'm not saying it was other folks' fault.
• 20:30
  SPEAKER_01: I'll take accountability.
• 20:32
  SPEAKER_01: But every interaction and every conversation matters.
• 20:36
  SPEAKER_01: I try to use that as I think about things going forward in my career.
• 20:42
  SPEAKER_01: Business is more about people than it is about product.
• 20:45
  SPEAKER_01: Nine months ago, I'd never heard of Dunning before, or I heard of it, but very little.
• 20:50
  SPEAKER_01: Everyone's smart enough to pick up business if you're in this business.
• 20:54
  SPEAKER_01: But having those relationships and having those positive interactions with folks, no matter what you're doing, are absolutely critical in whatever you do.
• 21:02
  SPEAKER_01: And I think that was an early lesson learned for me and one I try to live by.
• 21:07
  SPEAKER_02: Well, one final question for the folks out there listening today, what's the key takeaway you want them to take away from this episode, you know, mainly about butter and what you do?
• 21:16
  SPEAKER_01: I would say that if you're servicing the subscription space, I'll go back to what I said earlier, which is make sure you're looking at everything in a level playing field.
• 21:27
  SPEAKER_01: Make sure you're finding out and digging in.
• 21:30
  SPEAKER_01: If you have 20% or 30% retention issues, make sure it's actually your product that's the retention problem.
• 21:37
  SPEAKER_01: We can come in at butter and help you get the money back.
• 21:41
  SPEAKER_01: But we can't help you make a decision not to launch new Coke and take billions of dollars of value off of your brand.
• 21:48
  SPEAKER_01: Have us take a look.
• 21:49
  SPEAKER_01: We do a free study for folks.
• 21:51
  SPEAKER_01: We take a look at their churn.
• 21:52
  SPEAKER_01: We tell them how much more money we can graze.
• 21:55
  SPEAKER_01: Or to be honest, I've had multiple people in the last three months where I'm like, you know what, you guys do.
• 22:00
  SPEAKER_01: A great job already.
• 22:01
  SPEAKER_01: Or you don't actually have this problem.
• 22:02
  SPEAKER_01: Of the 30%, 27% leave because your product stinks.
• 22:06
  SPEAKER_01: You have a different problem than the one we're sharing.
• 22:09
  SPEAKER_01: And so I'd welcome them to reach out, come to Butterpayments.com and sign in, do what they need to do, and welcome to speak to folks at upcoming conferences or through LinkedIn.
• 22:19
  SPEAKER_02: Well, Charles, thank you so much.
• 22:20
  SPEAKER_02: I know your time is very valuable.
• 22:22
  SPEAKER_02: So I really appreciate you coming on the show again and explaining what Butter does.
• 22:26
  SPEAKER_02: So I really appreciate your time.
• 22:27
  SPEAKER_02: Again, thanks for being here.
• 22:29
  SPEAKER_01: Greg, I appreciate your time and your channel.
• 22:31
  SPEAKER_01: It's always a great listen.
• 22:33
  SPEAKER_02: I appreciate that.
• 22:34
  SPEAKER_02: And to all you listeners out there, I thank you for your time as well.
• 22:37
  SPEAKER_02: And until the next story.
• 22:39
  SPEAKER_00: Thank you for joining us this week on the Leaders in Payments Podcast.
• 22:42
  SPEAKER_00: Make sure you visit our website at leadersandpayments.com where you can subscribe to the show and where you'll find our show notes.
• 22:50
  SPEAKER_00: If you enjoyed listening, please share on your social channels as well.