How to 2x Your A/B Testing Output Without More Budget

Summary

Marketing Operators – Episode 117

How to 2x Your A/B Testing Output Without More Budget
Date: June 23, 2026
Guests:

• Cody Plofker (Host, CEO Jones Road Beauty)
• Connor MacDonald (Host, CMO Ridge)
• Connor Rolain (Host, Head of Growth, HexClad)
• Guest: Michael Ting (GM DTC, Jackson)

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Episode Overview

This episode features a deep-dive conversation with Michael Ting, GM of DTC at Jackson (a major men’s jewelry DTC brand). In this highly tactical and actionable discussion, Michael breaks down his operating mentality, why there’s "no such thing as optimized ad spend," the real drivers behind effective A/B testing, his views on risk management for marketing, and a unique “cost per email” framework that’s allowed Jackson to scale to near-nine-figure revenue.

Listeners will gain insight into advanced ways to scale testing, balance contribution margin and long-term brand growth, and rethink the boundaries of marketing attribution and budgeting.

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Key Discussion Points & Insights

1. Michael Ting’s Unconventional Path to DTC Leadership (07:59-11:03)

• Career journey: From chemical engineering to project management, analytics, product strategy, and finally DTC leadership at Jackson.
  • Quote (Michael Ting, 12:28):
    “At any given point, I’m trying to figure out who was making the decision and become that person and just rinse and repeat.”
• He credits much of his marketing education to listening to podcasts like Marketing Operators during 90-minute LA commutes.
• Unique Blend: Backgrounds in analytics and product gave him an edge in data rigor, but not creative or brand skills—underscoring the diverse backgrounds in DTC marketing leadership.

2. “There’s No Such Thing as Optimized Ad Spend” – Rethinking Marketing Budgeting (15:57-23:57)

• Framework: Spending is always a balance between current contribution margin and future growth—it's never "optimized," only aligned with organizational risk tolerance and time horizon.
  • Quote (Michael, 17:03):
    “If you’re optimizing for contribution margin in the next hour, you’d probably turn off all ad spend. But if you’re optimizing for the next 50 years, you’d pour cash into learning and aggressively test.”
• Finance parallels: Compares ad spend to asset allocation/risk in portfolio management.
• Real-World Application: Recounts Q2 tariffs crisis—when budgets would have forced spend to pull back, Jackson ramped up investment due to favorable CACs, grabbing profitable market share.

3. Balancing Forecasts with Flexibility (24:58-29:03)

• Forecasts help guide when to “lean in” or “lean out” of efficiency (MER) targets, but shouldn’t become rigid anchors that limit adaptability to market conditions.
• Brands must juggle the “capture demand now” vs. “generate future demand” dichotomy.
  • Quote (Connor, 27:12):
    “If you were purely maximizing contribution margin in June, you’d just turn off ad spend. But to make December work, you need to be planting seeds now.”

4. Jackson’s Secret Weapon: Cost Per Email Signup as Leading Indicator (29:03-40:02)

• Why emails? Email signup acts as an unusually actionable metric between top-funnel and purchase, with (roughly) predictable lifetime value.

  • Quote (Michael, 29:03):
    “If you run on email signup, it kind of breaks the trend... I can just take my email list, compare to Shopify, and know exactly how much value each signup gave me.”

• Allows more accurate long-term attribution than last-click or impression-based models.

• The model: Focus on cost per email signup vs. average expected LTV of an email segment.

• Operationalizing: Jackson toggles between “contribution margin focus” (for tentpole moments) and “efficient lead gathering” (off-peak)—using blended, not granular, cost-per-email data.

  • Quote (Michael, 33:04):
    “In a moment of promotion, we’re hyper focused on contribution margin. But let’s say in March, nothing’s going on—it’s a slow season—then we’re hyper focused on getting efficient email leads that we know will be valuable later.”

Tactical Q&A:

• Not optimizing to signup in platform—Meta and other campaigns are still purchase-optimized; email signup cost is used as a leading indicator, not an in-platform conversion event.
• Blended over channel—They look at overall cost per email, not channel-specific breakdowns, for simplicity and because email captures are a natural intent equalizer.

5. Weekly and Daily Execution—Chicken Gendering, Feedback Loops, and Quantitative Instinct (40:41-48:50)

• Daily rhythm: Michael posts a Slack update—data, week-over-week trends, and “narrative” on what’s happening and why.
  • Quote (Michael, 40:41):
    “Like chicken genderers in Japan—who can sort chicks with 95% accuracy without knowing how—I try to build that instinct by reviewing numbers daily and writing down what I see.”
• Core skill for operators: See data every day but avoid knee-jerk reactions; use weekly cycles for major changes.
• Separates decision-making (paid media team) from feedback/narrative (Michael) to reduce bias and develop organizational judgement.

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6. The Reality of Email Payback Periods & Risk Management (48:50-55:00)

• ROI window is unpredictable: Payback for email signups spikes within 7 days but continues at a low, steady rate for years.
  • Quote (Michael, 49:26):
    “…It’s crazy how activated some of those cohorts are. People sign up, disappear for two years, and suddenly buy.”
• Risk in modeling: Build in a “margin of safety” for unknowns—tariffs, product issues, market shocks.
• As businesses scale and earn the confidence of repeat revenue streams, brands can afford to run longer payback windows and take more risk acquiring emails/customers.

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7. Decision Quality: Decoupling Outcome from Decision (56:43-68:32)

• Michael advocates using a 2x2 framework (good/bad decision, good/bad outcome, inspired by the book Thinking in Bets):
  • Important to distinguish high-quality decisions that produce unlucky results from risky/irresponsible decisions that happen to pay off.
  • Sample:
    • Bad decision, good outcome: Rushing a hire with no plan (and getting lucky)—not repeatable.
    • Good decision, bad outcome: Conservative demand planning forecast misses upside—stick with good process over time.
  • Quote (Michael, 57:05):
    “…Can you list a good decision that led to a bad outcome, but if you were given the chance to make that decision again, you would?”
• Building a high-velocity testing culture means expecting (and tolerating) a 20-30% failure rate—buying lots of “lottery tickets.”

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8. 2x A/B Testing Output: Lowering Stat Sig, Smart Test Design & Incrementalism (69:07-73:31)

• Why DTC is slow at A/B testing: The industry standard 95% statistical significance makes test cycles long and expensive, throttling velocity.
  • Quote (Michael, 69:07):
    “How often do you make a marketing decision with 95% confidence? Almost never. So why force a testing velocity that slow?”
• Solution: Run tests to 80% confidence, accepting more “leakage” (false positives/negatives) but dramatically increasing learning and discovery rate (1.9x net output).
• Holdout testing: For smaller brands, shift from trying to stat-sig incremental revenue (which could take 9 months) to using softer metrics—like incremental sessions, add-to-carts, or clicks—for faster directional feedback.
  • “If you can tell me Google is driving a lot of incremental add-to-cart sessions, that’s actionable, even if revenue-based significance is out of reach.”

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Notable Quotes & Memorable Moments

• Michael on obsessing over contribution margin:
  “If I’m trying to optimize for the next hour, I’d turn off all my ads and blast out an SMS with a 50% discount. I’d make a fortune this hour, but good luck in twelve months.” (17:03)
• The chicken gendering metaphor:
  “They see millions of chickens a year and, just by trial and error, pick up on the trend. That’s what I try to do by looking at numbers every day.” (40:41)
• On developing organizational risk tolerance:
  “…We could describe our playbooks in exact detail, but someone else still couldn’t copy it—because they’d probably blink when it got really, really tough.” (55:00)

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Important Timestamps

• 07:59-11:03 – Michael’s detailed career story, from engineering to DTC
• 15:57-17:03 – No such thing as “optimized” ad spend; accordion framework
• 29:03-33:04 – Why cost per email signup is the best leading indicator
• 40:41-44:04 – The “chicken genderers” story and daily feedback loops
• 56:43-57:05 – The decision/outcome 2x2 matrix framework
• 69:07-73:31 – How to 2x A/B test velocity by changing test design

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Conclusion

Michael Ting lays out a refreshingly rigorous, risk-aware, and humble playbook for scaling a DTC brand today. From the value of cross-functional backgrounds, to using leading metrics that actually predict revenue, to the willingness to be “wrong” in pursuit of bigger wins, this episode distills lessons rarely found in textbooks and frameworks.

Listeners will walk away with:

• A clearer mental model for how to balance short- and long-term growth.
• The practical steps to massively increase test output without more budget.
• Tools and mindset shifts to build a high-velocity testing culture rooted in reality—not just theory.

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Final Tip:
Lower your statistical significance targets, focus on leading indicators (like cost per email), and obsess less over “budgeting” and more over learning velocity, risk, and compounding outcomes—for the brand that tests and learns fastest, wins.

Transcript

[A]

Welcome to episode 117 of Marketing Operators. We've got a very special episode for you with Michael Ting. Michael is the GM of DTC at Jackson, which is a men's jewelry brand pushing nine figures in revenue today. We get into a lot. We get into why he believes there's no such thing as optimized ad spend, how he thinks about balancing today's contribution margin with long term growth, how he uses cost per email as a leading indicator of today and future revenue. So much more. What he's focused on going into 2026. This is a very tactical, very actionable episode. Michael, super in the weeds, very data driven and is just a really sharp DTC operator. So really hope you enjoyed the episode today. Thank you to the sponsors Motion Rich panel after cell and House, let's get into it. All right, we're back with another episode of the Marketing Operators podcast. We have Michael Tang on the show today. Michael, welcome to the Marketing Operators.

[B]

Yeah, super excited. I'll mention it later, but really had to learn everything, not exaggerating everything from this show, so it's crazy to be on it. Finally.

[A]

Nice. Excited to, excited to dig in and kind of figure out what was the most and the least valuable. Where are you calling in from today?

[B]

I'm in Los Angeles, Westwood, right next to ucla. I commute down to Newport. So if you're from Los Angeles, you know what that's like.

[A]

Yeah, I don't envy the hexclouds, like half remote, half in person. And I do not envy some of the, the trips that some of those folks are making every single day from like Orange county and like Malibu to the office in downtown LA and back every day. It's crazy.

[B]

Yeah, but like, that's when I listen to the Operator podcast. Right. I got 90 minutes a day to kill. Just put it on 2x feet.

[A]

So. So it's baked into the, it's baked into the morning and the evening commute. All right, that's great. So, Michael, I mean you, you probably know this. We like to start every episode by talking about our, our new favorite protein infused products. And, and David Protein is one that comes up a lot. They just launched their, their David Protein ice cream. Sold out in 28 minutes. I was, I was told that's what I saw from their founder, Peter. And I wanted to know, Connor, did you get your hands on any?

[B]

No, dude.

[C]

And it's so funny because I'm like not much of a consumer. I'm like, not buying the trendy stuff. I'm not, you know I'm never buying anything and this is the first time. I was like, damn it, I really want this ice cream. And I was like, I looked on Amazon, I looked on the site. I'm like, are all the flavors sold out? I'm like, this doesn't seem right. I was like, should I text Conor Connor? You met the like VP of performance marketing at David. I'm like, should I text Connor to ask his friend if I could get the ice cream? I was like really going through it. But no, I, I haven't tracked it down. Hopefully soon. I don't know. I was like, maybe it'll be in grocery stores soon. I. I don't know. But I'm scheming.

[A]

Yeah, I texted. I slacked. Keegan or. Well, hold on. Michael, did you get your hands on any?

[B]

I haven't, but have you guys ever tried Huel? I think most underrated D2C brand out there. H U E L they literally exited the week after Grooms and you know, everyone's social media was all Grooms. I think all of ours, Twitter, everything. They exited for one point, like 2, 1.6 billion. Pretty much the same size the week after, but no one in this space has heard of them. They're UK based, they do like plant based proteins, but they also make Mac and cheese with like a ton of protein. Literally used to live off of it when I was like really grinding at Jackson. I just didn't really have time to take care of myself like breakfast, lunch and dinner. I would just get this Mac and cheese and honestly it was the best shape I was ever in my life.

[C]

Did you, did you ever go through a Soylent phase?

[B]

Never a Soylent phase. Honestly, I'm not sure how I feel about the plant based approach, but I love the Mac and cheese approach personally. Yeah, yeah, yeah.

[C]

No, it's interesting. We're pivoting from David a little bit, but Huell is cool because they like, they were like a fast follow to Soylent, like 2015 or maybe it was like 2017 or something like way back when Soylent was like a hot DTC brand. I basically got in the DDC because of Soylent. I thought it was like the coolest brand. It was like this hat weird, like dystopian brand branding. I'd met the founder at a conference back in 2016. It was really cool. And then Hu Fest followed and then Soylen obviously didn't pan out. They raised too much money. They got sold like for parts basically. And then Huell has a billion dollar exit so it just shows they pivoted away from like the dystopian. All you need in a drink form to like normal things like Mac and cheese that people actually want to consume.

[B]

That's crazy. I never knew the history. I was just a huge fan of the brand. Probably top 1% customer and, and was always just shocked that like no one's talking about it because I think they executed really well with a interesting product.

[A]

They're going it, it's, it's interesting. Hu's going more of the like a lot of these RTD protein drinks are like I like raw. That's, that's my go to. And they're. A lot of them are in that like 170 calorie, 20 grams of protein range hues go in. They're like, no, this is like a full on meal replacement. Like 400 calories. Like you're gonna, you're gonna be full after this. David. To bring it back to David, I have never seen. I mean this is obviously like, this is their trend. They're all about like the maximal maximum calorie to protein ratio. You can get their vanilla bean full pint of ice cream is 210 calories and 30 grams of protein.

[B]

It's just sick.

[A]

It's, it's, it's, it's insanity. It's insanity. You know it's been, I've been following along on, on, on Twitter and a lot of people are coming out of the woodwork on the ingredients. And not that I mean David's never shy that David's not trying to be like a whole food natural ingredient based brand. But like people are really coming out of the woodworks on this one because their flavor system just says natural and artificial flavor. That, that's all it says for the flavor system. And it was funny to see that dude.

[C]

Well, you know it's funny. So actually going back to the, the Soylent point is like their billboards back in 2017 were like pro GMO. That what I like. That's why I like them so much. They like they completely own the artificial nature of it. And at the time they were like natural food is obviously good and like there's, there's no like qualms with that. But at the same time when you think about like feeding tons and tons of people, like I don't think over the, over, you know, the next 100 years we're going to, you know, exclusively produce natural food. I think we're going to have better and better natural ingredients and that's how we end up feeding billions and billions of people over time.

[A]

Yeah. I just love how David's leaning into it. It's. And I also love the. Like, I don't. I think most people know this, but maybe not that. Like, Dave, like, Peter's previous company was the opposite end of the spectrum as where. Where David is. You know, our X bar is all about whole food ingredients, three egg whites, you know, 10 cashews, whatever. Whatever's on the. And then. And then he has this, which is all about maximizing calorie to protein ratio. And in order to do that, he's using artificial ingredients. But it's just. I just think it's so badass that he's been able to, like, take to, like, you know, same market to completely different ends of the spectrum on how he's positioning these products. And, like, the ice cream is just such an exclamation point on that compared to what's out there right now. So. I loved it. I think it's amazing. And I'm gonna try to get my hands on some. I slacked. Keegan. Right. When I saw I was sold out and. And asked. He's like, hey, I'll send you someone.

[C]

Give me the list. Dude, give me.

[A]

No, no. I feel bad because I'm like, I'm not. Send me some. I'll totally buy it. I literally just want to know, when are you restocking so I can be ready to. To buy it? But I. I'll get you on the list for. For the restock. Michael, I want to. I want to dig into your background a little bit. You have a pretty impressive involvement with Jackson's growth trajectory. But I want to. Just before we get too deep into that, can you just give me your. Your background? Like, who are you in the context of digital marketing? How'd you get into this world? And, like, kind of what ultimately got you into your role, which is GM of dtc?

[B]

Yeah, it's been, honestly, a pretty wild, unpredictable journey, like, I think most of us. But I would say what defined my career was essentially, at any given point, I'm trying to figure out who was making the decision and become that person and just rinse and repeat. So actually graduated chemical engineering. So my first job was R D. I was trying to make memory foams and mattresses more comfortable, completely different from what I do today. I felt like the project manager was always telling me what to do, so I was like, okay, how do I become a project manager? Went through the ringer, kind of went into product development, and a lot of lean manufacturing stuff as a project manager, program manager. Then I was like well now just the category or product manager keeps telling me what to do. So how do I become product manager? So then jumped to ruggable back in 2021 and I was kind of category management there. Felt like same thing where analytics was the one actually finding the opportunity and I just felt like I was executing it. So then I was like okay, now I gotta go to analytics. So for a little while as manager of Analytics, I then felt like, well I'm finding opportunity but strategy gets to define where I'm actually looking for opportunities. So jumped to strategy at Ruggable and then finally I was like well I'm doing strategy at rugby. I'm just making PowerPoints for the, you know, execs. So how do I figure out how to become an operator? So kind of made the move to Jackson. Really just saw like we had a crazy brand presence shout out bear who just absolutely crushes it with partnerships and building like these authentic relationships. But then I saw the sales numbers and I was like oh, there's a massive gap between the name recognition among like I'm probably target demographics of the name recognition between like my cohort and the actual sales dollars. So I was a little, little cocky and thought I could jump over and try to make the company what I thought it could be. So it's really just been a story of trying to take it from low or I'm not low but like let's say eight figures and just a journey towards hitting like nine figures which I would say we're mostly successful on. I think the crazy part though is that like you know my background, analytics, project management, little bit of data science, a little bit of strategy. Never a hands on performance marketing and about two and a half years ago right now just organizational gap resulted in me and my buddy Ricky who leads our marketing effort really having to make all the decisions and I had no marketing experience, I had no idea, I didn't even know what CAC was, I didn't know what an MER was and I was given a mid 8 figure budget to try to grow the company. So like I mentioned earlier, just really had to rely on the operators podcast to try to crash course myself into being able to execute things like advertising. Budgeting was the main focus of mine. Merchandising, just pricing, strategy, all those aspects. Yeah and honestly it felt like learned a lot, learned from the mistakes, learned from the good parts. But it's been a wild ride. Love D2C

[C]

Motion just dropped their 2026 creative benchmarks report. And it's been getting shared everywhere. Slack channels, LinkedIn, Twitter, sharing it in our private group chats. And it's great because everybody's been asking the same four questions forever. What is normal? How many ads should we actually be shipping? What is a healthy hit rate? And which formats really win? The report analyzes over 575,000 creatives from 6,000 advertisers and over a billion dollars in ad spend to answer these exact questions. And the report has some really interesting findings, like the fact that only 4 to 8% of ads actually become winners and over half of ads actually lose. And for Motion customers, this report is especially helpful. You can upload it into your Motion dashboard with their runneth AI chat and compare it directly against your vertical benchmarks. Hit the link in the show notes. I promise you won't regret it. And as always, go to motionapp.com and tell the marketing operator sent you.

[A]

That's interesting. I think that's a cool way to. That's a, that's a very interesting. I don't know if I've heard someone position it that way before, but I think it's very. It makes a lot of sense. You've, you've let like, you want to be in the seat as the decision maker and you've let that guide your career. And that's, it sounds like that's kind of led you to this point and every, every step along the way was how do I become the decision maker? And, and that's. What. Is that true? Would you say that's kind of been like the lens you've been viewing your career and like, each step through?

[B]

Yeah, definitely. And I feel like you guys can empathize where right now I feel like I can make the decisions, I can make the big calls, but I'm spread over such a large surface area of just different areas that I don't actually feel like I'm making the big calls anymore. Where it's really just trying to build up the team to be able to make the decisions that I would inside their shoes. So now that I finally feel like I've made it, I get to, like, look at myself as an operator. I'm like, wow, the good old days, right?

[A]

Yeah, well, and that's what I think, that's what a good leader does is they, they empower their team to make. Make decisions as well. So not everything, you know, ends up with them being the bottleneck. And I also love your, your background. I think that's the cool part about D2C performance marketing. If you, if you put 10 performance marketers in a room, heads of growth, CMOs, what have you, they'll all come from different backgrounds. There's no, there's not like a clear path to getting into this E Comm. Performance marketing world, which I think is really fun because it just, it means there's such a diverse group of people that are in these, like, you know, performance marketing roles now, which is. Which is exciting. And it just creates a lot of different opinions and types of people. And I think it's. It's good for our, our industry as a whole that there's so much diversity and background of the people that get into these roles.

[B]

Of course, head to head, if I can hire one person, I'm gonna hire one with experience. But inside performance marketing. But it's just been interesting where some things that are abundantly obvious to me, like testing methodologies, how to run a, you know, robust testing program, how to do things with like, a lot of statistical rigor, how to get creative with numbers to achieve set goals, they come naturally to me. But then some things that are just basic to others, like having an eye for creative or figuring out what a great partnership is just completely foreign to me. So I think that a lot of the value I added where Jackson was, a lot of people who are amazing at brand, amazing at creative, just really able to go out there and go out there with an entrepreneurial spirit, essentially just adding some level of quantitative rigor to it. And then it just been kind of the best of both worlds since I joined.

[A]

What I want to get into Jackson and what you're focused on there in a second. But before we do that, you started in product. You started, you started in like product as an engineer. How do you feel like that's helped you as you've moved into marketing roles like your. Your origins, your base and product development and just understanding what goes into that. Like how. How has that helped you or not in. In your current role?

[B]

Yeah, it's been interesting. I think that one big area product teaches you how to do is a lot of times you're launching something just without a lot of data and trying to make a decision when you just don't have. You don't have clear numbers to look at. A lot of the times you just have to make a call and you have to kind of both think through. You really have to think through the problem versus trying to calculate an answer. I think that's an interesting part about product. I think the other part was Just an appreciation for the challenges of supply chain, for project management, how everything is always there that you expect. For a while at Jackson was owning the demand planning aspect and talk about an absolute mess when I was running it, but you really just learn how to appreciate the other side and the challenges they're going through, which I feel like helps you integrate the team slightly better.

[A]

I sometimes wish I had that background because I don't understand it. And I find myself at times in the past have found myself thinking, what do you mean we don't have, we don't have more knives, just order more. It's like it's not that easy of course. So yeah, that's, that's cool that you have that perspective and you're able to like bridge the gap between different parts of the org because of that. So I have written down here in our notes that you have a core thesis that there is no such thing as optimized ad spend. It's all about risk tolerance and time horizon. Optimizing for all to optimizing for results right now is a completely different exercise than optimizing for a 10 year timeline. So I, I definitely resonate with this, this note. You know, we're a highly considered product at Hexclad and the ad spend that we're deploying today often doesn't really fully materialize until months or years down the road. So we're always trying to strike that balance of like, you know, capturing demand and generating demand at the same time. So a few questions on this. Can you walk me through how you actually think about ad spend as, as a risk management exercise? Like what does that framework look like in your day to day?

[B]

Yeah, so I think a lot of, kind of what's helped me find success at Jackson is just these 90 minute commutes to OC where I'm just stuck in my head and just running these like thought experiments pretty much combined with listening to the experience of other people. So essentially the thought experiment that had helped me with this one was the guy named Brad Jacobs and fascinating guy, founded $8 billion companies. Not 8 billion, but 8 separate billion dollar companies. Crazy entrepreneur. And he does this exercise where he tries to like accordion his mind back and forth to the really small picture and the really big picture and back to the really small picture. So then when we're talking about trying to optimize your advertising spend, like what is the optimal advertising spend? Well, I think if you accord into the really small picture and if you're trying to optimize for contribution margin, the Next hour, what would probably all of us do? Especially high consider process would probably turn off all of our ad spend, hit SMS and drop our prices 50% into a massive discount. And that's probably what would optimize contribution margin for the next hour. If you go accordion back to the really, really big macro big picture and you're like, okay, how would I optimize contribution margin over the next 50 years? Well, assuming that we compound growth for the Next, let's say 50 years, this year is going to contribute a fraction of a fraction of a percent where probably what I would do is I would focus on learning, I would focus on getting as many insights, figuring out whether we had the right product and aggressively testing and I wouldn't care at all about contribution margin for this year. It would just be a learning exercise where I would spend as much as my cash flow could allow. So then if you go back and forth, that just means that essentially on one hand, one extreme you essentially have optimal ad spend is zero and the other extreme is optimal ad spend is as much as my cash flow allows. Which just makes the problem that we always discuss about trying to how much incrementality, what are we trying to achieve? It's less so like an optimization problem and just a problem definition where it sounds kind of hootie flutie but it really turns into, it brings an aspect, an internal aspect of personal risk management where there are probably two ways I can improve contribution margin and the expected value. And the number one way is that I just get better at growth hacking. I just improve that as an operator. Number two, a lot of times is just taking on more risk. And I think that like an intuitive example that we can all understand is that like, you know, when we're building a personal finance portfolio, you're buying some stocks, some bonds, so you keep, you know, you keep some money in the savings account. You're not fully optimized to maximize your expected value. You're not trying to fully optimize to maximize your return. There's an aspect of risk management where I don't think the money in my savings account is compounding at the rate I don't think it's getting the maximal value. But what I think is that given my risk tolerance, that's how much money I want to have on hand just in case everything hits the the wall. There are like lotteries out there where for example, if I want to maximize my expected value, I put all of my net worth into the lottery when it gets to a certain point. And my Expected value is positive but we don't do that. It's just risk management. So then it just brings into perspective of like essentially I feel like we don't ask ourselves as operators often enough like what are we trying to achieve? Are we really honestly trying to maximize our contribution margin for let's say a three year horizon even if that would require us essentially sacking a quarter? Or is there a better balance where we are actually optimizing for let's say 80% for next quarter and maybe 20% for next year. Hopefully that resonates slightly. But I definitely feel like being high OB brands, you guys can definitely understand that like feeling where I could just cut spend and make a little extra contribution margin today. But I know long term that would really bite me.

[C]

When you think about balancing short term contribution margin, cash flow and then where you want to invest that long term it feels to me like if I think about how that gets captured at Ridge, it's like in our forecast, right? Like our, our, our growth team is not allocating our ad dollars versus some form of capex or you know, hiring or, or you know, retail expansion. Right. Like the, the grow, the, the marketing team is not making that decision. That's coming from a much higher level and where that should get captured is like next month we want to do $1 million at a forex M is like that's all that, that's what the marketing team knows and is optimizing towards. And then it is like the group of executives, whoever that may be to then allocate, hey, based on these results, based on this forecast, we will generate this cash and we're going to invest in these ways. Is that how you would describe it? And then how do you decide like, like what are some of the thought processes that you go through to then decide where are you allocating that capital so that you're maximizing the value over time?

[B]

Yeah, so I think that it kind of gets into the nitty gritty methodology. I think on one thing where probably contrast is I hate budgets. I hate forecasts. I think that there are times where forecasting budgets have been useful. But more often than not what they caused me to do is they caused me to anchor to a result. So one of the stories was back when last year Q2 tariffs hit. Obviously they tariff. There was a period of time where they terrible China, Europe, everything got tariffs. We all went through it and a lot of our cogs started increasing and then we knew we were going to probably miss a gross margin on a budget on a forecast. But what we saw was that everyone was pulling back spend so far that we were seeing record low cacs. So essentially the equation was that like, oh well, we have a really low CAC that's way below budget or really high or our lower gross margin, which is missing budget. I feel like that if we really fixated on the forecast, we wouldn't have done what we ultimately did, which is spend really aggressively into the cat, low cat and allow us to kind of like take a lot of market share, which it was probably the best period we've ever had. I think that it just causes us to anchor and that every day what we do at Jackson is pretty much telling the team right now, given our risk tolerance, you need to be optimizing for contribution margin or right now you need to be optimizing for some of the leading indicators and can talk about the leading indicators. I think we have a really interesting one which is like a cost per email sign up can go through the whole thought process of that. But that's really been the major unlock is just kind of disconnecting spend from your prior assumption from two months ago, from your reforecast or just from anchoring to some number that you're trying to hit at all costs, whether or not there's a better way around that number.

[D]

We talk about incrementality a lot, but how do you actually operationalize it to make your business better? That is one thing that I've been really leaning in with my team recently and House has played a tremendous role. We use it for all of our experiments, all of our geolift testing, but we now use it for our MMM as well. I've been a design partner, I've been one of the early design partners on Houses. CMM C stands for causal. So it's one of the only MMMs, if not the only MMM that I've seen that's actually using your causal experiments to build the model. And so that allows me to trust

[B]

the data so much more.

[D]

So it's not a black box, but actually informs our roadmap and has been so crucial for allowing us to operationalize around incrementality. The House team is world class. I can't speak highly enough about them. They've also built a really amazing community with some of the best DTC growth operators out there. They have a few exciting events coming up soon that they call the Houzz Growth Lab. One is in LA on May 19th and the other is in New York on May 21st. So highly recommend checking it out if you're in the area. If you want to check it out, learn a little bit more about cmm, go to house IO operators to start making better data driven decisions today.

[A]

I think Connor, I agree with your point though. I think, I think like the, the, I think the forecast can, if it's set up the right way, be a guiding force on which moments in time you're doing. Like you're leaning more or less into the ends of the spectrum that Michael just talked about. Like this is, this is something that we had to figure out a hexclad is for. It was hard for us. It's like all right, our MER goals, let's just say it's a 5x for the year but we might run the business at a 3x in some of the slower months in the interest of hitting our revenue target and then, and then coming in at a 5 or a 5.5x and like that's that balance of all right. In the dog days of summer we're less efficient. We're obviously not optimizing for contribution margin as much because our M is lower. But we have to keep spending, we have to keep driving, driving traffic. We have to keep generating demand if we're going to hit those, those bigger revenue targets, those higher efficiency, those larger contribution margin moments. But that, that is guided by our forecast. Whereas, whereas before we like had that learning on like the seasonality of our business. It felt like every month we were having these conversations on like whether or not we should pull back or spend more or, or what have you and now we, we have that baked in. But I think I agree with your note on like letting the forecast drive that because if you can set more like month to month level then your, your marketing team just has a really, they're, they're really unlocked to make good decisions and not you're not having these conversations with finance every week on like whether or not we should pull back or, or, or spend more. And yeah, I think that's like a very tough thing to figure out for your business. But Michael, what's, I want to, I want to ask you a question about like some tactics on this. Do you, do you have any examples? Because in reality all brands are doing a little bit of both at, at any given time. Right? Like we're running a summer sale right now that is helping us optimize for contribution margin right now. But we're also not pulling, it's like we're not spinning all the way to that end of the spectrum. If we were, we would put all of our budget into meta in search. Right now we're still investing into YouTube and linear TV in podcasts. So we are running an offer that's optimizing for contribution margin today. But we're also still investing in these upper funnel channels like we're.

[C]

Can I say one thing quickly just because like I want to hit this point because I, I, I like the point about forecast. I made that one. I'm super down to reforecast all the time. Which is maybe closer to the spectrum Michael's on where it's like hey, let's not like, let's not be like too, too you know, anchored to any one like specific number because I think that's extremely problematic too. But the forecast, there's all sorts of just like light constraints that a brand has to give themselves. And the forecast is one because Connor, you're saying it now. You guys are running a summer sale. If you were optimizing for contribution margin, frankly, like you would probably turn off ad spend or like a lot of it. And Michael made that point earlier if you were purely like in the month of June, we need to maximize contribution margin. We're just going to reduce a lot of ad spend because you, I'm sure there are people that you're prospecting today who are not going to convert until October or whatever, right? Like, you know, there's a ton of like latent. It might be, the consideration might be period might be as short as it's going to be during the summer sale, but there's still a consideration period and there's still people that are going to convert over like a very long tail. So one of the reasons you have to spend today is that your forecast says that you need to do like $400 million in December or whatever it's going to do. So like, so all of those things are just, I would even say, yeah, so anyway, I'll leave it at that. But like I feel like that kind of, it's not even a marketing thing at that point. That's why I say it's like an org wide perspective of like what does the business want to do from a contribution margin perspective, from a cash flow perspective and like a future growth perspective. And all of those are, I think the forecast is a great way for it to get captured and then can provide constraints on like a day to day, week to week basis so that nobody's running around being like I'm going to run at a 10x mer right now, blast the SMS list like Michael mentioned and just generate a bunch of cash right now and then. And then I'm going to worry about the September forecast when I get there because like ultimately that month will be smaller if you do that.

[B]

Oh, I was just going to say I actually really like that because that is something we sometimes struggle to do is the times forecasts have been useful is when we say that hey, we're going to do something that's not going to feel great in the moment. Whether it's like serve spend in October ahead of bfcm. That's when it's really been a cool. That's when it's been a useful tool. On one hand though, I feel like we've been talking about this but haven't shared our methodology, which I feel like is kind of the missing piece of the puzzle because we're not just really running around choosing random numbers. I think the missing piece of the puzzle is we have a main KPI which is a cost per email sign up. And just describing why we see it as super valuable, shockingly valuable, and I don't think talk about it enough is essentially you have a spectrum of metrics. You got impressions, clicks, add to carts, checkout order, ltv. And I would argue that the nuance of the spectrum is that one side it's really easy to attribute value. So if you tell me how you got an order, I could tell you exactly how much revenue you got, but it would be really hard for me. It would be really hard for me to tell you how much it costs to get that order accurately, especially for a brand like ours. On the other side, if you tell me you got an impression, I could tell you easily how much it costed because it's pretty much immediate yet. But the challenge is that it's really hard to attribute like what value did you get out of that one impression, one incremental impression, probably really hard and essentially go so on, so forth. So you got impressions then like I said, you got click add to cart checkout order ltv. The one interesting metric though is if you run on an email signup, it kind of breaks the trend where it's easier to attribute revenue to an email signup than a checkout or add to cart order, even though it's earlier in the funnel. And the reason is I can just take my Shopify list of emails, I can take my email list and I can tell you exactly how much value we got out of every single email. And the other advantage is that it's not immediate that I pay $1, get an impression and get an email, but it's much faster than the purchase cycle for a. It's much faster than a purchase cycle for a, let's say like high ALV brand where it might pull forward the sales or the value like the point where you the marketing spend have an impact or by let's say like months. So then if you essentially you view your business as these durable businesses, you view it as like a freemium model where my goal is to get an email subscriber. And based on a lot of analysis, I can roughly estimate the lifetime value of the email subscriber. Unlike cpg, the timing of that value is very different. Where if I get an email subscriber in October, I'm probably going to realize most of the value late November. Whereas if I'm getting a night email subscriber on bfcm, I'm probably realizing most of the value on that day. You can do those kind of calculations and then almost run these durable brands. It's a LTV over CAC where you just focus on the email subscribe as the main moment instead of an order. It sounds, there are a lot of like pitfalls that you can fall in. And we've had a trial and error essentially to figure out how to do this. But the really nice part about it is that lets you find these pockets of moments. We always talk about it like I feel like every brand feels like they underspend in the lead up to bfcm and I feel like it's because they don't have this leading indicator that they can attribute future sales to at an earlier moment. But it's allowed us to find these random pockets where the buying intent might not be there, but the marketing spend and the impressions is definitely there and you're getting high value leads is essentially the goal. So it's just so essentially how our business runs is in a moment of like promotion. We're really hyper focused on contribution margin. But let's say in March, nothing's really going on for the business, it's a slow season, then we can hyper focus on getting efficient email leads that we can roughly estimate the value for at an efficient cost. Does that make sense?

[A]

I love that. So that's. So that's an example of you're optimizing more on the, the, the broader picture end of the spectrum versus the today's value. So you're. I just want to repeat this back to you and, and correct me if I'm if I'm miswording how you guys have this setup. So you're basically saying you are calculating like an average, on average revenue per email sign up. Correct. And not every, not every email turns into that. Some are more, some are less. But on average, you know that every email you capture is gonna, is gonna lead to $75 of value or 150, $50 of value, whatever it is. So you're basically using that. You're basically, you have a cost per email target that is related to that average revenue per email and that's what you're using to like optimize your ad spend. And as long as you're like in line with what you know will, as long as the cost per email is in line with what the average email will back out to revenue wise, you, you're kind of like keep, continue spending or pulling back spend. Is that how you operationalize around that?

[B]

Yeah. And it's really just two aspects. The first that helped me like kind of find this metric. The first one is looking over our shoulders at our friends in CPG and just how nice their LTV over CAC model felt watching it. Where they can spend unprofitably short term, but they can have a high confidence in the long term benefit. And for a high decision cycle brand like most of ours that just didn't feel possible until we had this metric. And we kind of shifted the focus to finding when finding a moment other than the moment of order that we could attribute spend to. The second one was actually listening to Ridge and you guys. So when I took over our marketing budgeting we, I would listen to the podcast and our goal at the time was just to win on contribution margin every day probably led to significant underspend. And I was listening to Connor just talk about like how low of an MER they would run in the lead up to a sale and how they would essentially rake in the contribution margin during their quarterly seasonal promotions. And I was like, maybe there's something there, but that feels kind of ridiculous. Why not win contribution margin every day? When we started mapping out the lifetime value though of these like email signups, we did see very, very clearly that if you're able to get a lot of email signups prior to a promotion, you're able to monetize them depending on during the promotion. So even though it might really, really dent your contribution margin in let's say October, or for us another one is like Father's Day, so it might dent April, you can act with some confidence that you will eventually make back the value. I think the challenge for us versus like a CPG brand is the timing of the value is really is just completely unpredictable for us. So I can fairly confidently tell you how much a email subscriber will get us unless we kind of do something weird like dilute it. But I can tell you how much an email subscriber will get us over let's say a one year period. I cannot tell you how much it would be over 30, 60 or 90 day period because it's pretty unpredictable when exactly they purchase. But the stability of the aggregate, the stability of the metric over a longer term just enables you to make these decisions and have confidence that like well I'm not getting as much value within 30 days as I expected but I also understand that that's probably just noise and that overall big picture, the stability I'll make it probably back in 60 days and 70 days or so on so forth.

[C]

I have two questions. One is if you are like within meta, are you optimizing for email signups, are you optimizing for purchases and then monitoring cost per email? And then two, I'm curious how that's changed or if it's changed if you guys have diversified channels because I would assume that like an email captured from a Snapchat is just going to be worth less than an email captured from like a super high intent meta campaign, something like that. So as as like the the email, the quality of the email changes by channel or by person or whatever else. How do you guys factor those things in?

[B]

Yeah, I, I the good question. I feel like that's kind of thrown people off where definitely would never optimize for anything but conversion unless you're doing some like reach campaigns which is a completely separate topic. So this is more just a canary in the coal mine where we're not trying to get the canary to squawk, we're using it as a scale leading indicator of how our spend is doing even though we're not necessarily optimizing for it. We do sometimes optimize created for it though, which is interesting. The other part about channel is also something I've talked about a lot and then tried to really dig into data wise. The nice part is that email subscribe is kind of an equalizer of intent. So if I get a lot of really low quality traffic, they're probably not subscribing to the email as much as it kind of filters through the really bad traffic. We can roughly estimate, let's say we get the number of emails north theme from a certain channel compared to the revenue they generate and then do a gut check to see the attributed revenue over the number of Emails kind of makes sense and checks out and we do see some fluctuation, but by and large I would say that it does help equalize essentially the buying intent. Whether someone signs up because they saw a Facebook ad or whether someone signs up because they saw a TikTok ad kind of evens out.

[A]

So you're just blending it. You're really looking at a blended cost per email sign up. You're not getting too granular with like, here's all of our email signups with a meta utm, here's Snapchat, TikTok. And like you, you have different models. You're basically kind of going one level up and saying, all right, blended cost per email sign up, blended average lifetime value of an email. And that's, that's kind of like what you're using for the model.

[B]

Yeah, and I think that it's not that like we wouldn't be better off by getting very granular, but we're both a small team and I think that like when we get really into the day trading aspect of trying to like make every channel perfect, sometimes we, sometimes we as a company definitely lose sight of the bigger picture and really focus that. Focusing on the blended metric really just keeps us honest, keeps us simple. Where the additional granularity in theory would help, but I feel like in practice ends up being kind of a distraction from the main goal.

[C]

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[A]

Today, can you speak to how you're. How this is integrated into like a weekly workflow? So on a, on a weekly basis are you like meeting with your paid team and like, do you guys have a cost per email sign up goal? And then on a weekly basis are you like monitoring where you're at relative to that goal? And then let's say Your goal is $2 cost for email sign up. Are you then saying, hey, we're at a buck 75, let's spend more because we're not at or we're below target and we have some, some like volume upside here or can you like maybe that's just an example, but can you just walk us through how you're actually like in a day to day, week to week basis, you're actually like making decisions and interacting with the team and like just overall like operationalizing around this KPI.

[B]

Yeah, I'm about to go totally off the rails and then bring it back, but I just love telling the story. I think it's applicable. So in Japan they have these chicken genders. Perfect. Yeah, it's really out there. But I love telling this on any excuse to tell. They have these chicken genders. And what's fascinating about them is that in order to, you know, farm chickens, farm eggs, you need to be able to tell the gender of the little chick hatchling. And they can do it with about 95 to 99% accuracy within 1 to 3 seconds. The crazy part about it though is if you ask them how they're able to tell there's no physical sign, they just say like, I know. And even crazier is even with all this crazy AI, all the technology, machine learning still can't do it as well as these people. And the reason they're able to do it so well is because they see probably millions of chickens a year and just by trial and error they pick up on the trend. And it's essentially building a feedback loop for themselves that they just hyper optimize for to a point that not even this fancy AI tool could do. I feel a lot about. That's kind of how I feel about performance, where not even a weekly basis, but I think my team's going to kill me. But I used to do it myself. Every single day I would go through the numbers numbers and write a couple bullet points about, you know, the trends. I was seeing what a contribution margin was, what cost per lead was, cost per add to cart. Trying to put together and then trying to form it into a cohesive narrative where if, let's say we see great cost per add to car, great cost per lead, contribution margin is lagging. But we're ahead of a major seasonal moment. The narrative would be essentially be that like, hey, we're building a funnel for this large moment, we're being efficient with our spend even though we're not realizing the value. Whereas let's say it's our shipping cutoff and we see a huge spike in conversion rate. Cost per lead is super competitive. It's more like, okay, let's lay low with spend. We're realizing value. So I think that's kind of the nuance where I talk about it a lot with Austin from North Beam. But being able to look at the number every day but not react to it, I feel like is one of the hardest parts of the job. Because it's terrifying sometimes that like when you look at the big picture for the year, it's very clear to say, like, oh, I kind of overspent here. I understand here. But in the moment it is kind of nerve wracking. And it's really hard to see when you see your cost per lead jump down, it's like, do I spend into it? Well, maybe the second day you see it while you do spend to it. So I think that's kind of not even a weekly basis, but I think us personally, we like doing on a daily basis. And then just to kind of step back from the day to day noise, probably once a week I have to put together a deck kind of on the performance. And that kind of consolidates my thinking, lets me take a step back.

[A]

So it's. So it's daily monitoring and then would you say weekly making of moves? Hey, we're. It's if like, hey, cost per lead's down a bunch today, but it's only a single day. We're not gonna, we're not gonna go and action this because it might shoot back up tomorrow. And like there's so many daily fluctuations, as we all know. But if you zoom out over the course of the week and it's still looking good, then that's when you're going to go and say, all right, we can go and spend more. Because over the course of a week our cost per leads below it's trending down or something like that. Is that accurate?

[B]

Yeah, I would say a very slight twist where kind of at the point in the organization, thanks to just hiring some killer people inside our paid marketing team, no longer really giving them numbers on how much to spend more. So just giving them guidance of like, this week we need to focus on cost per lead or this week we need to focus on contribution margin and just trying to build that feedback loop where they're making the decision. We have not really an ad spend budget, to be honest. We have targets we're trying to hit and their goal is to allocate the ad spend to hit the target. We actually didn't have a finance team for a very long time, which probably is why we had the permission to be able to do these kind of weird stuff. But their goal is essentially to hit some kind of target and then kind of what I see my goal in the organization is, is to tell them like which target to focus on and when they make these decisions. I like separating the person who makes the decision versus the person who is like giving the feedback. Because I feel like too often when you're making the decisions yourself, you can tunnel vision into seeing the numbers one way because you really want something to work and it's just human. They've done the double blind testing to show that like it's just a statistical thing, that if you're making the decision, you're going to be biased towards it. I think that is why like they're making the ad spend decisions independently of kind of like what I'm doing, which is kind of giving feedback and recording and posting every day on like what we're seeing, what the narrative is, whether what leading indicators are clean, which ones are messy. Sometimes everything just works out where you have a great cost per lead, great cost per checkout, great cost per add to cart, bad contribution margin. But you're ahead of a seasonal moment. And those are the moments where we've done a really good job of as an organization of taking advantage of and really raking it in those moments. More often than not though, it's nuanced where two out of three metrics might be great, or one out of three of the metrics might be great, but that one is amazing. And then just trying to, just trying to be able to iteratively figure out whether you're correct. The other part about like just posting every day is that it gives you a lot of times up at that. Like I talked about the chicken gendering. A lot of it is just trying to build some kind of instinct. And I think the best way to build the instinct is to write down what you actually think and then be able to see if you were correct. Like to make these kind of predictions. We have a public one where my team does it and I think it's been a great tool to see like if we made a mistake, what were we saying in the lead up. That's been a really good retroactive exercise for us. There have been times where we underspent, there have been times where we overspent. And it's kind of funny reading these old posts because you can just somewhat feel the delusion where I'm like, oh, like it's going to happen. We just really need to believe. We just need to run aggressively for one more week and it'll be fine. And then the week after it's like, well, that, that didn't work out, so let's learn from that. But I think that it helps it.

[A]

Sorry to interrupt you. You've mentioned this post a few times and I just want to clear up what that is. Are you saying like every week or every month or every two weeks? Are you doing some sort of like, are you writing a memo like on, on the same day every week or every two weeks or something? And that's like the post and that like is looking at the data and like you talked about deep diving in the data, creating a narrative on like what's happening in the business. Is that what you're talking about? This post that you've mentioned a few times, like, what is that?

[B]

Oh, it's a slack post. So we have a slack channel with all the leaders in the company and posting it every single day, actually. So we'll post the numbers on a daily basis, week over week, year over year, and describe what we think is happening. Describe, like what. How whether it aligns with our plans. The thing is that it's not about trying to micromanage the spend budgets every single day. It's more about just trying to. It's more about just making sure you're really rigorous and looking at the numbers every single day. So definitely should not be reacting. And I think that's really the hard part though, is just sometimes posting the numbers saying like, hey, there are some signs that this might be happening, but let's sit back and wait and see. Makes you get a little more evidence. That's really just been the challenge. But also I think it's been what's developed us as performance marketers. And also me, myself, I had no experience in this before. I think that being able to have that retrospective retroactive look on when I succeeded, when I failed, what I was saying when I succeeded, what I was saying when I failed has really just developed me and just my personal skill set in that respect.

[C]

Can I ask a very. To jump back quickly to a very tactical question around optimizing for cost per email. What is the variance in the window of time in which you realize value from those emails? You kind of alluded to it earlier, but I'd love just like a ballpark of like, is it really. Is it super short in November and is it really long in September? Because I'm just curious like over what time frame? Yeah, because that ultimately comes down to like that risk tolerance and budget allocation. It's like how much are you willing to invest today and how long are you willing to wait to capture revenue from that email? So just like the, the, the, the band of oscillation would be interesting to hear about.

[B]

Yeah. So you definitely get a spike on the first day. There are some impulse purchasers out there and I would say for the first seven days it's a pretty steep curve. Unlike kind of lifetime value for like maybe a CPG brand though it doesn't really level out where you can continue to realize value from emails from a year ago or two years ago. It's kind of crazy how, how activated a lot of those cohorts are where I'm like, who signed up for our email list and not interacting with the brand for two years and suddenly decides to purchase. But I feel like as marketers it's hard for us to believe, but as consumers, like I got a hexclad. I love it. But my purchase journey for that product was essentially I heard about it on the podcast, actually. I saw a ton of ads and then after seeing all the ads I got saw it everywhere organically where every single influencer chef was using a hexclad pan and that's kind what sold it on me. But then I waited probably two months and then I happened to get more than two months, probably nine months. And then someone gave me a Visa gift card. I didn't know what to buy. It was like $300. I was like, oh, this is perfect. I'll get a hexcloud pan. The problem though is that like inside your northbeam model, inside your, inside your post purchase attribution, no data point is really telling that story of how I bought where you wouldn't be able to say what was the CAC to acquire me. It's just such a vague number. But then it's like one of those sayings that every model is wrong, but some are useful. So then it just trying to find something that is a representation of reality that can take this whole story and distill it into something actionable, knowing that the metric is incorrect, but it has value because it tells you something like the law of averages where eventually you'll get the value from it.

[C]

In this case, I think we use a first touch attribution model. I think it's a, I think it's a podcast. I think it's a podcast purchase.

[A]

Connor?

[B]

Yeah, I'm trying to remember what I put on it. But I don't think that you had podcasts as an option. Well, I think it was podcast generic but not podcast operators.

[C]

We need marketing operators. Specifically in the post purchase survey we

[A]

have a write in. So I'm going to go check out the write ins after this and see how many are attributing to marketing operators. That's really, that's. Do you. What's like the average? Do you know the average? Like if you were to blend it out over the course of a year and you were to say hey I want to know what's the average time from email sign up to order. Like do you know what that number is? Or is it so variable that that's not even that and is it so seasonal and variable that that's not even that helpful?

[C]

I would also bet you know, like Michael, you know, just from a cash flow perspective, there has to be some breaking point for the time in which you're willing to wait, right? Like even if, even if you found out like every email you, you acquire today, 20% of revenue comes two and a half years in the future. It's like you just can't invest. That can't be your break even point. Just two and a half years in the future if you spend a dollar today. So kind of to Connor's point, is there an average? And then, and then how have you thought about the tolerance of time to

[B]

the question of like is there an average? It's hard to say because like I said, their lifetime value, the lifetime value of an email subscriber continues to appreciate over multiple years. So then what is the lifetime value? Well, it's probably how long the person lives. Right. So I think that makes it a little bit hard to find exact timing. I know that we realize a disproportionately large amount of value within let's say like 60 to 90 days potentially. And then past there it's a very slow but actually surprisingly steady gain. In terms of like the risk or in terms of like the cash flow aspect. I think that loops it back to the first point which is just this is just a risk management exercise, right? Where at some point you need to draw a line in the sand. And it makes sense a lot. Where things happen that are unexpected. There are known unknowns and unknown unknowns. And unknown unknown might be like tariffs. Unknown unknown might be a quality issue. There's always something that might disrupt your ability to get value from these email subscribers. So you need to hedge against it. I think you just build in a margin of safety where you say you build in a large enough margin of safety where even if everything goes wrong over a reasonable time frame, you'd be able to monetize it. And let's say hypothetically you make the call in January and you realize that like, hey, we didn't, we weren't able to monetize this segment as expected and maybe for a couple days, if not like weeks we spent unprofitably, then you just bring in the feedback loop and then you just really try to learn from that and you better understand next time you're in those shoes. Like this is what the reality is versus just the basic model. Right? Because I think the other thing that

[C]

I think is interesting in this conversation is like as your business grows, your risk tolerance can obviously go up or like the, the, the break even point for which you want to capture value from a lead or a customer. If you're doing like a more traditional LTV to CAC model can go down. Right. And it's like, you know, hims, I haven't seen this talked about in the timeline in a while, but hims runs at a 20% am for every dollar they generate in new customer revenue, they spend $5 on advertising. It's like the only reason they can do that is because they've been doing it now for nine years and they have millions of customers, so they have a ton of returning customer revenue. So they can be way far out on the risk curve for acquiring that next customer in a way that like no startup today would be able to unless you just wanted to like light money on fire forever. So as Jackson's gotten bigger, I would also just assume if you want to continue growing and prioritize growth, you can get even more aggressive with your cost per email and the payback in which you expect to get a return on that. The payback period. Yeah.

[B]

And I think that's like we all brands are talking about the moats that their brand has. I feel like that experience one is in surprisingly big moat where I honestly I look at you guys at Ridge a lot for it where some of the decisions you make, I'm like, I know it's more optimal, I know that we should be doing that. And honestly I just, I don't have it in me at this point to do it. But it's something that, you know, you, I'm sure you guys had the same experience where gradually, gradually through trial and error and seeing the successes, you're able to act more aggressively when you see an opportunity. I think that's Something that like every brand kind of has to do within themselves. Where we could describe all of our playbooks in exact detail, exact numbers, and someone else still wouldn't be able to copy it just because they would probably blink when it got really, really tough. And I think that's the hard part is that like these kind of decisions, when you take on risk, it gets tough. And even right now in the organization, I think that's something that like we're internally trying to do is go from a mindset. We used to be try to win every day, then every, we were at win every week. I feel like right now we've reached the level of we're trying to win every month. But I think that ultimately what's going to grow the brand is being able to sacrifice entire months, being able to sacrifice October, being able to sacrifice April, being able to sacrifice, I don't know, July in order to drive future value for the quarters, and then shifting the mindset of being able to optimize every quarter. Maybe someday we're even thinking about sacking entire quarters to optimize a whole year. Not sure we get to that point, but it's something that like we can all describe, but in the moment it's just hard to execute. Right.

[A]

I want to, you're talking, you're kind of, you're kind of dancing around a little bit. So I want to ask you some questions on this. I understand you have a decision quality framework, a two by two of good and bad decisions cross with good and bad outcomes and that this is what guides a lot of the decisions that you're making on a day to day basis. So can you just like explain what this framework is first and then I have a few follow up questions.

[B]

Yeah, I was going down a rabbit hole reading a lot about probability and just two really good books. Highly recommend. Number one is Thinking in Bets was written by a professional poker player. Number two is just Michael Malvesan. He writes a lot about, he's a big investor, writes a lot about the impact of luck. As an aside, one of his really interesting points about like you can tell whether something takes a lot of skill or a lot of luck by asking can you intentionally fail? Where I could not intentionally fail a coin toss. I could not, but I could intentionally fail, let's say like launching a new product probably. They did a study where they took a bunch of hedge fund analysts, had them choose 10 stocks that they thought would intentionally fail and 73% of them still beat the S and P. So it's just Like a weird little aside about the impact of luck, but essentially what the framework is, and it's just another interesting thought exercise, is that we all know that luck has an impact to our decisions. We can all talk about really easily what are some good decisions we've made that led to good outcomes. We can all talk about what are some bad decisions we made that led to bad outcomes. I think the really hard one that I spent a lot of time thinking about personally is can you guys list a good decision that led to a really bad outcome but that if you were given the chance to make that decision again, you would make that kind of decision again and can you do the opposite? Which is what is a bad decision you made that led to a good outcome, but if you could go back in time, you would never do it again. It's something that I kind of puzzled over a surprising amount.

[A]

What's an example of that? Like what's a, what's a bad decision that led to a good outcome and what's a good decision? Because I would, I could see that the, the, the contrarian here would say, well how could it be a bad decision if it drove a good outcome? Doesn't that by default mean it's a good decision? But I want to hear like what's an example of, of bad decision that led to a good outcome and good decision that led to a bad outcome per the, the framework you just mentioned.

[B]

Yeah. So I think bad decision that led to a good outcome. Really Hiring for me. So probably back in 2020, 2023, we were hiring a bunch of media buyers and we found some great candidates. Really, really loved one candidate. And as soon as I saw us moving in a different direction within 24 hours. I wasn't hiring at the time. I just made up a role, got it approved and had in an offer. Had no idea what he would do, had no idea, no organizational framework, had no list of responsibilities, just had a salary and offer letter and it turned out that worked out really, really great. Where he's a great culture fit specifically for the organization and within my team. Shout out to Joe. But in hindsight it was a completely irresponsible decision that could have blown up in my face. Like hiring someone without any idea what they would do without any buy in from the organization, probably would not do that again.

[D]

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[A]

So you're calling that, you're calling that a bad decision because you weren't really, you weren't really prepared. Like you didn't have the, the roles and responsibilities outlined. You didn't have a 30, 60, 90 plan. You just in your head were like, I think we need media buyers. I'm just going to like make this happen and hire this person. But you weren't really ready for it. So you're saying that's a bad decision. But they're saying over time this person like figured out their role alongside you and like they're providing a ton of value now. So like it led to a good outcome. Is that the, is that what you're saying?

[B]

Yeah, even simpler. I think that if I made this decision, if I made this decision 10 times in a row, it would probably worked out two times and that I was essentially saved by the bell where it happened. The one time I made this decision, it worked out. The flip side is good decision, bad outcome, like I said I did a lot of demand planning and we would make investments into like a new category, a new product and we would make a bet on it where sometimes we're just trying to grow existing products that existing categories that were a small percentage of revenue. We predicted we could 2x the product and we had the appropriate inventory buy for that in reality at like 3 to 5x, which sounds great, but it just meant that we sold out within like 30 days. It sounds like it's signing that ridge with all of your suitcases. But I wouldn't, I need to keep expressing to the team where that's a situation where it's a, it was a bad outcome, us not having a key new hero in stock. But on the flip side, to go into demand planning and say we can buy any category is ultimately just going to lead to like a disaster on your inventory list. So even though that was a bad outcome in that one instance, we shouldn't learn to suddenly aggressive, hyper aggressively forecast every product that was just two out of 10 times. We'll end up buying something and those two out of 10 times will run out of inventory. But by and large, if we have to keep making the decision, we should keep making that same decision.

[C]

I don't have a good like example off the top of my head. I love both the ones that you just gave. What it does remind me of, and it's ironically because I also read Thinking in Bets a couple of years ago. But like I have had the conversation with my team where we need to decouple results from the quality of the work that we're doing and trying to make that distinction a little bit more. There's a period in, of time in 2024 where you know, the business was doing fine. I felt like we were doing great work and the business was doing fine. What I felt would have been the wrong decision to make at the time was to like, you know, all of a sudden change our processes, lose confidence in our internal strategy, our internal processes, our team, et cetera, and say, and instead say, hey, I'm gonna, I think I'm gonna decouple the results from the work here a little bit. I think we were doing extremely quality work. I think we're making extremely high quality decisions. Let's continue down this path. And ultimately it's a, it's a matter of luck to some degree where at some point, whether it's, you know, the meta algorithm or consumer confidence or whatever else, these things that are out of our control will turn back in our favor and we will begin benefiting again from the quality of the work that we're putting in. So I do, I do like that line of thinking. I think it's an important sort of

[A]

concept for people to consider. I think a lot of it's like the decision versus the execution. Like you have to decouple that too because I think a lot of times you actually. It's not the, it's not the decision that was a bad decision. If the outcome wasn't great. It's often the decision might have been good but the execution didn't, didn't drive. So I think like we've made a few influencer bets that I like we did this Haley Bieber activation, I mean this was years ago. This is probably three years ago at this time. And I just don't think like, I don't now. I think in that situation maybe it wasn't, I think it actually was a good decision. I don't think we activated, executed the right way because I think what we ended up doing was we basically sponsored this, this YouTube series that Haley was doing. We were integrated in the show and what. So the, I think the decision to activate with her was a good one. But the way we executed in that integration was bad because we ended up just like integrating in a show that had a bunch of like, you know, 17 year old girls kind of gushing over Hailey Bieber. In reality what we should have done is led with like a paid deal and had the organic integration be part of the deal. So I think that's a, that's an example. Like I don't think the decision was necessarily bad. I just think the way that we decided to activate with her was probably not the right way for us to extract value. And, and we see this all the time. It's like, hey, we have a new offer idea on paper and we even have some data points that suggest it, it worked really well in some aspects. But we also have other data points suggesting that there was parts of the offer that were clunky and didn't work as well. So it's like good decision execution wasn't quite there. I think that's important because otherwise you end up like telling yourself you made the wrong decision when reality, it's like you might just need to iterate on the execution of that decision and then that's the unlock to because it's like decision, execution, outcome. So like that middle point though, I think can sometimes get glossed over. It's like, oh, you shouldn't have done that partnership. Oh you shouldn't have done that offer. Oh, you shouldn't have done that ad? Well, it's like, no, not necessarily. Maybe we should have just done that partnership, that offer that ad a little differently than we did and that would have given us the outcome. So it's, I think it's interesting to, to hear this like, decoupling of the two from you, Michael, because I think it can very easily get connected to a bad decision when it might not have been.

[B]

So was that a variety of company sizes? Was that like 5 billion? Was that 100 million? Was that, I don't know, almost half a billion. Just a variety of different company sizes. And I think as like leaders in the company, part of the challenge is as you scale, suddenly people start going for the 100% probability of a 15% return. Projects where they're afraid of being wrong and they're looking essentially at their accuracy, but trying to shift them to focus on impact versus accuracy, where a 50% chance at a 3x return, if you keep making that bet over and over again, will beat out 100% probability at 15 return long term. But then trying to give your permission, trying to give your team essentially the permission to fail. I try to set the expectation that like I, I told our team that, like, hey, if you're not failing 20 to 30% of the time, you're not taking big enough risks where you're expected to do things that do not work out and you're not going to be held accountable for it if in the big picture these things are having that net benefit. So trying to have that fine line of you still want to hold people accountable to results, but whether you should hold people accountable to every decision, I feel like it's just going to drive that small incremental bet constantly versus trying to give them the permission to take bigger risks but also have a asymmetric outcome, essentially that's been something that we've been working on a lot within the brand and just trying to encourage these senior manager direct at level people to make these big calls that might blow up in their face but also have the comfort to do it and also the expectation that they should be doing that.

[C]

Do you have any tips for teams that want to be testing at a higher velocity, like with this in mind? And this is a super important point. I've talked about it on the podcast a bunch. I heard it from someone at L. Catterton years ago where they were like, we decided we have no ability to determine what's going to work or what's not. So Our main KPI is just volume of testing. And it sounds like you're maybe more in that camp, albeit like thinking a little bit about expected value. So for, for people who want to be testing at a higher velocity, what are some of the things that you guys have implemented that could be helpful?

[B]

Yeah, I love that question because it's one of my favorite topics and I think it comes from background in like R and D where was running essentially the testing program for these like different memory phones. So I think that let's say like Ecom or holdout testing, I'm going to focus a little more on Ecom A B testing. I think the standard is to run things to 95% confidence. And that always struck me as odd because as a performance marketer, how often do you actually make a decision with 95% confidence? Pretty rarely. Like we're pretty used to making decisions with 60, 70, even 51% confidence. We just make the call. So then ultimately what happens though is that just because you have the ability to do something with 95% confidence on Ecom AP tests, the cost is you just slow down your test velocity where let's say you go from 95% confidence to 80% confidence, that would literally 2 to 3x your test output. And sure you would have more leakage, you'd have more false positive, more false negatives, but the resulting net benefit would be 1.9x for your total testing program. There are certain conditions that you need to meet in order for this to work out where essentially the quality of your test has to. The quality of the test in the backlog have to be the same as the current test you're running. So if I think I got five winners and 10 losers in my backlog, I should spend more time on the winners to make sure that I realize the full benefit from them. You need to have the development resources. But essentially it's like the cost of. The idea of a cost of quality I think is eating into a lot of Ecom AB brand is eating into the testing program for a lot of Ecom A B tests because we're essentially just thinning the velocity just so that we can have 100% confidence in something that like we definitely do not really fully need. 100% confidence in. Holdout testing is another one of my favorite ones where I would really love if a SaaS did this. I did the math recently and let's say for like a 30 to $50 million brand, they probably need to be running like a 9 plus month holdout test in Order for statistical power. That's just how the math works out. Even if you hit statistical significance before then, it's probably just noise that makes it challenging. And then it's like, well I'm like a $50 million brand. I probably should be doing some kind of holdout testing to get results like what can I do? Same idea of a spectrum of KPIs where you get essentially clicks, add to cart sessions, checkout sessions, orders, revenue, just shifting the holdout test and all of these in Shopify. Instead of looking at your incremental orders where you're never going to hit statsig within a reasonable amount of time, why not look at the incremental checkout sessions or the incremental add to cart sessions or the incremental clicks. That's really been an enabler for us for helping test these small channels where I look at the revenue and just complete noise. I look at the graph and one person bought a solid gold chain and just completely made the test irrelevant. But for example, if you could tell me that Google is getting me a lot of incremental traffic that's somewhat actionable and if you could tell me that the add to cart rate for that traffic is comparable to the add to cart rate for just normal traffic, then that's also actionable. And then essentially the benefit of this is that suddenly you're enabling yourself at regardless of scale essentially to get actionable findings out of these holdout tests. Of course I would rather have the full like gross profit contribution margin impact with a lot of granularity, but sometimes just the time isn't worth it. The time to actually do the proper test is just ultimately not worth it. And by the time you actually finish it, the insight has probably shifted. So like I said, would really love if someone started making a SaaS platform to do holdout tests on different metrics. Because right now I'm doing it manually.

[A]

That's a great example of like the art of triangulation, right? It's, it's not perfect but it's, it's data driven and it's, it's going to allow you to make most of the right decisions most of the time. As long, as long as you're confident in what those like soft metrics are that, that you're comping to. Right? But if you know meta is like your number one revenue driver and you're confident in that and you have a solid cost per add to cart, well yeah, if, then you go launch Snapchat and you're getting a dollar better cost per cart. Like I'd be confident scaling Snapchat based on that, right?

[B]

Yeah. And I think like, Even something like YouTube, not a great click through channel. But if you can tell me that we're getting a lot of incremental traffic from YouTube, I can probably do the math in the back of my head of like, okay, what if the revenue per click is about the same as meta? Is that like a good channel for us to be on? It just unlocks a lot of different aspects. But then, you know, we don't want to be spending $100,000 per day on YouTube in order to get statistical significance within like 30 days. So then you just end up having to kind of find these roundabout ways to kind of like massage the numbers into a way that you form a narrative where the goal isn't to publish your results in an R and D paper. The goal is like, okay, I've seen enough that I'm willing to make this bet because I think that disproportionately it will pay off.