The number one thing brands get wrong when building out the data team: the first hire The first data hire is the toughest one to nail - analyst, engineer, or generalist. I've seen it done wrong constantly, and been hired to fix it twice. The analyst is the tempting choice. Someone who can answer questions today, build the dashboard the board asked for, stop the CEO from pulling CSVs at 11pm. Six months later they're the fastest person alive at downloading spreadsheets, because nobody ever built the thing underneath it. So hire the engineer. Except now the engineer disappears into infra for six months, comes back with a beautiful data warehouse, and nobody noticed they were gone. Six months of no visible value is longer than most brands will tolerate for a data engineer you are paying at least $150k and usually longer than that persons political capital lasts. The job is actually one person doing both. An analyst who can engineer, or an engineer who can analyze. I don't think the starting side matters much. What matters is that the foundation gets built by someone who has to use it every week, because that's the only reliable way to find out whether the model is right. Pipeline week 1 > the CFO's margin question week 2 (or maybe month 2), using the thing you just built. The AI tilt: this got much easier. An analyst with Claude can stand up dbt models they couldn't have written alone. An engineer with Claude can write a real narrative around a number instead of handing over a table. The gap narrowed from both direction but still isnt closed. AI closes the syntax gap, not the judgment gap. Knowing a number is wrong before you send and can validate the solution it is still the main job. So the bad news: you're still kind of hiring for a unicorn, at a stage where unicorns have better offers. It is really hard to convince a world class engineer or data scientist to join your startup selling widgets on the TikTok.

Have you ever wondered why shopify & your P&L show different revenue numbers? Neither are wrong, they’re just…different - but because they’re different it can limit your use of AI. For example - you can, and should, calculate revenue 3 ways - using Order to Cash as a bridge. But you need to know each of these numbers along the way and train your ai agents to know the difference What is Order to Cash? It's the accounting process of reconciling orders to actual cash received. Revenue can be calculated in the following ways: 1. Operating. recognized when the order is placed. This is the number marketing celebrates and probably lives on your existing dashboards/reports. 2. Accounting (GAAP). recognized when the order is fulfilled, this is almost always when the order ships but there can be some nuance. Anything sitting at the warehouse waiting to be shipped out isn’t revenue yet. 3. Cash. recognized when the sales channel actually pays you. Shopify settles in days. Marketplaces settle on a cycle and hold a reserve. Wholesale settles on net terms, minus deductions you'll spend a quarter disputing. Illustrative numbers for a month with $1M in orders: - $1,000,000 orders placed - less $60,000 unshipped at cutoff → $940,000 accounting revenue - less ~$100,000 channel and payment fees - less ~$70,000 refunds and returns — and these hit this month's cash against last month's revenue - less chargebacks and retail deductions - less what's still in transit or reserve at cutoff You booked a million but you get paid a fraction of it in a different month. None of the these are wrong, they just answer different questions: - Operating revenue tells you about demand helps with optimizing marketing spend, offers, sales, etc. - Accounting revenue tells you about margin, fulfillment performance - Cash revenue tells you whether you can pay the bills. This one is usually the trickiest to automate, but unlocks accurate cash flow forecasting. Most brands don't discover this until they hire their first controller, usually the same week a lender asks why the bank statement doesn't match the P&L. The good news is that all of the data is available. Every brand should have visibility to all 3.

Who’s going to beanstalk? Come say hi if you see me and break me out of the shell, I’ll be the extreme introvert standing on the outskirts avoiding conversation

Things we got right at gruns #6 - BizOps I hated the BizOps title. It’s Strategy & Analytics. But we tried to hire an Analytics Associate and got nothing, switched it to BizOps and the resumes poured in. When we met with a new vendor or a new brand and had to do the uncomfortable intros I always unmuted and said “I lead Data, my job is to take credit for all the work these guys do”. Our BizOps team was absolutely world class. BizOps: - A centralized data team that was comfortable writing SQL and building with AI but could still build a financial model in Excel - think ex-banker types who are financially oriented but more entrenched in the data - P&L assignments with a company northstar. Each team member worked closely with a specific function but was still tied to the overall KPIs and OKRs for the company - Autonomous. The team ran our Monthly Business Reviews by partnering with their function, interpreting metrics > actions > measuring results. Along the way BizOps was running business case models to make sure decisions were profitable, building ad-hoc reports, maintaining our AI reporting portal, and even helping drive headcount planning. Once the foundational data is in place, every brand should build a BizOps team.

Things we got right at gruns #5 - The data stack…for now A managed data ingestion platform, dbt Cloud (big proponent of dbt and owning your transformation logic), and a BI tool can easily cost a brand at scale $125k+. We picked all of these tools for the same reason: ease of use and speed to value. Given where we were and how fast we were growing, they were the right decisions. They were not the cheapest ones, and they were probably not the optimal ones. Here's the part the vendors leave out. Buying the managed stack did not remove the team. We had 4+ engineers focused on data: 1. custom Python orchestrated on Airflow, for everything the connectors didn't cover. We had a big ETL budget and still needed engineers writing custom code. 2. Data modeling. Dbt is an amazing tool, but the tool runs the models, it doesn't know your business. There are some great open source packages out there for ecomm businesses but still had to to touch code for every model. 3. Building the reports. The opposite of dbt. Point to click BI tools are a pain, and at this point very antiquated. The contract we signed before I joined didn’t hold its weight by the time I left So six figures gets you managed pipes and you still have to build the layer that makes the numbers true, because that layer is your business and no vendor has it. That's the trade I'd make again, if I was in the same position. but the paradigm is shifting quickly. AI tools are quickly replacing the Modern Data Stack. A foundational data warehouse + semantic layer is still massively important, but how brands get there is evolving.

Things we got right at gruns #4 - Reporting & DKM Every morning we sent a report in Slack called DKM - Daily Key Metrics. We had a similar report emailed out at Tecovas. ~15 metrics, each at a daily, weekly and monthly grain, one for the company and one for every brand individually. It went to everyone, not just finance or leadership. CAC, rev by channel, fulfillment lead time, First Orders, CVR, and a dozen or so other metrics that the company ran on. Most brands have this as a dashboard nobody opens, our difference was push vs pull. If the number has to be pulled, it gets pulled when someone is already worried. If it gets pushed at 8am, everyone starts the day discussing the same reality. Three things made it work: 1. same grain every day - daily, wtd, mtd side by side, so you can tell a bad day from a bad trend. one bad day is noise, three is a decision 2. high visibility. You didnt need to bug the data team to know what CAC was or if fulfillment time was slipping, everyone could go view the same report with the agreed upon definitions 3. per brand, not just consolidated. consolidated numbers hide the brand that's quietly funding everyone else's CAC It also killed a meeting, nobody needs a Monday recap of numbers everyone already read on Friday. The hard part wasn’t building a fancy dashboard or report, it was the data underneath it. Nailing down the foundational data warehouse was what allowed all of the reporting and AI work to run smoothly, getting that right allowed the report to write itself. Get it wrong and you've automated a lie.

Things we got right at gruns #3 - Hire only the Best This one isnt going to be about data and tech, fair warning in advance. CEO of your domain - this was the phrase that Chad constantly hammered home, hire people who are amazing at their jobs and get out of their way AND hold them accountable for results. The people team at Gruns drove culture with inspiration from Netflix and their No Rules Rules, great book if you havent read it. but this only works if you actually do hire the absolute best people. Not the best in your city, or the best in the industry, but go find the absolute best person you possibly can to do the task you need done and then just let them do it. Holding teams accountable becomes alot easier when your KPIs are agreed upon and everyone understands how they drive profitability, democratized, automated, and broadcast for the entire company to see. Then you give people the proper tools, systems, and data they need to hit those KPIs. Maybe this was about data afterall, everything is in the end. Shameless self promotion - we need the best people at Iris. If you can ship production code, have lead marketing for an AI product, or have worked as a chief of staff and you find these CPG/ecomm posts interesting then you should go apply.

Things we got right at gruns #2 - Unit Economics Everyone thinks they know their unit economics, but they really don’t - not at the level they need to. LTV:CAC is the primary metric, and it has a million inputs costing SKUs at the PO level, knowing the exact impact on your LTV of that gift with purchase campaign, understanding how retail affects gross margin and CAC tolerance, all of it - down to the literal penny. This is where data and AI really unlock tremendous value inside brands. when you achieve that level of clarity and understanding around your unit economics, you can scale with open eyes and make decisions that increase the value of the business. Doesn’t hurt having a world class mktg and ops Org like we did that can execute Blending an average gross margin across your entire assortment and eyeballing LTV:CAC as you grow is ok for the early days, but when you start to scale past 8, 9 figures - eyeballing becomes extremely expensive. Integrate your product margin and costs of delivery across all of your cohort analytics at the SKU level and be mindful of your LTV:CAC ratio. When that ratio gets to be closer to 2 or 3 than 1, you are ready to scale. Until that number gets there, you have work to do on your margins and retention - don’t try to scale things that don’t work

Things we got right at gruns #1 - Data & The Office of the CFO Every brand I've worked with eventually has the same argument: who owns data? They usually pick one way, hire a jr analyst and then contract out the engineering. Then they change who that person reports to 5 times before deciding to build out a proper team and function. - Finance says it's theirs because the numbers end up in the P&L. - Operations wants it cuz they’re the “backbone” - ceo sometimes wants it as a direct report because dashboards feel strategic… I've now run it all three ways, and I have a strong view👇 Data belongs in the office of the CFO. Not because finance is smarter, but because the entire point of leveraging data is to build a more valuable company, and when data is integrated into the financial reporting and forecasting systems at a brand, that’s when it really unlocks enterprise value. My recommendation: 1/ Have the CFO own the data function 2/ Implement the proper infrastructure that gives you as much granularity and flexibility as possible 3/ Build an AI harness around this data in order to unlock speed and insights 4/ Report on results vs plan religiously 5/ Iterate and optimize proactively instead of reactively - pull the levers you need to pull