Chris

building @irisfinanceco - before led Data @ Grüns, Tecovas, Farfetch

[Chris @cdolan04]

Well done @seanjagermann

[Chris @cdolan04]

Include data deep dives so @markoivanik has to reply

[Drew Fallon @drewfallon12]

iris is about to cross $10B in tracked GMV

This feels like a big enough number to start to post insights from our aggregated data. There is data across virtually every source, from openAI ads to target to 3PLs

What industry insights would you want to see us post about?

[Chris @cdolan04]

Please respond to this so Drew has someone to talk to and leaves Eng alone.

[Drew Fallon @drewfallon12]

As you can imagine, I get probably 25 messages a week from CFOs, founders, or finance professionals at brands all looking for the same 3 things:

- Jobs
- people to hir
- industry data

As a former beauty CFO, I intimately understand the lack of community and shared knowledge out there - its a huge reason I started iris and got into content. Given my social media efforts and iris' nearly 200 customers, i think its possible to finally connect all these people

I am considering starting some sort of finance professionals community. If you're a brand CFO, founder who wants to learn more about finance/capital markets, or finance resource and this sounds interesting to you - shoot me a DM

[Chris @cdolan04]

@drewfallon12 @DerekLauermann Neither of these people are in B2B ai but I don’t know how to work twitter so I can’t delete. But go follow Derek anyway

[Chris @cdolan04]

@drewfallon12 @DerekLauermann and someone should go drag Claire from Gruns out of her farm life

[Chris @cdolan04]

@steeze_0 Yes, but driven by past experience and doesn’t mean that’s the only way. Would love to see what you are building

[Chris @cdolan04]

Brands get your data cleaned up, even if you don’t use iris (you probably should) you are leaking CM. Like Peter Drucker said - you can't improve what you don't measure

[Drew Fallon @drewfallon12]

You can't even make this up

After some number crunching we discovered that on AVERAGE a brand adds 3.2 full POINTS to their contribution margin ratio after joining Iris Finance

The top quartile of users add 12.8 points to contribution margin. POINTS!

Typically there are quick wins immediately that brands don't even realize they are leaking margin on until iris shows them the light. This is the cost of bad data in consumer

[Chris @cdolan04]

@steeze_0 Survey responses, CX interactions, and review parsing can all be very valuable and predictive when looking at churn, LTV, etc. New products like Outersignal are also great for persona type stuff at the customer level.

[Chris @cdolan04]

@drewfallon12

[Chris @cdolan04]

Go add up your fivetran bill + dbt bill + snowflake bill, don’t even need to include Eng headcount.
Then realize @markoivanik and crew did it better anyway

[Drew Fallon @drewfallon12]

would agencies be interested in the iris data warehouse product?

settle a debate with me and @cdolan04

[Chris @cdolan04]

Iris is a complete, out of the box data warehouse and analytics platform, but our website used to only show like 5 connectors. @drewfallon12 finally let us update the site, only took 3 months

[Chris @cdolan04]

Took a lot for me to not steal this chart and post as my own when Drew shared internally. Great read and top of mind after my time at Gruns

[Drew Fallon @drewfallon12]

https://t.co/vlQadTzllr

[Chris @cdolan04]

@jacob_posel We got $100k on GCP at Gruns which was kind of crazy as a nontech company.

[Chris @cdolan04]

I hate the new MLB playoff format, play 162 games and decide it on a best of 3 … but that Harper homer, baseball is pretty great

[Chris @cdolan04]

The number one thing brands ask me: How should my framework for LTV:CAC change once I’m selling in retail?

It's easy when you're just on Shopify or TikTok Shop because the data is all there. Amazon changed their data model recently but you can still do this analysis. Retail is a totally different exercise.

Typical Pattern:

CAC goes down. A big Costco launch puts you in front of a ton of customers who have never seen your brand and the retailer is doing most of that acquisition work for you. You dont see it in your marketing spend but they are charging you for it in margin, so the cost quietly moves off your marketing line and into COGS. There are still marketing costs in retail, handing out samples on a Saturday costs money, so you still need a full view of the marketing costs too.

LTV will probably drop too and is the bigger issue because the data isnt clear. A retail customer might be buying every three weeks with perfect loyalty and you'd have no idea. Syndicated and panel data take a swing at repeat behavior, and it's worth buying, but it's directional and expensive. Subscription brands generally get hit harder because some of your existing subscribers will start buying in store instead. That shows up in your data as churn when it's really a channel shift.

Model retail repeat off whatever panel data you can get, then back-test it against the DTC cohorts you actually own to see if the shape is believable. It won't be exact, there are some assumptions you need to make based on your business, and the math can get a little funky.

It also becomes more important to understand the halo effect of all of your marketing. A FB ad drives someone to Target, and someone who picked you up at Walmart goes to your site to learn more. Incrementality testing and MMM are huge here. Throw in unit economics / price pack architecture across sales channels, different expiration rules, logistics costs and retail gets really fun

Tactically the ratio is still the thing to manage. If CAC drops further than measured LTV the ratio improved and you have room to spend more. Most brands see LTV falling, get nervous, pull back on the channel that just got cheaper.

[Drew Fallon @drewfallon12]

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Like this post, Reshare, and Comment "COMPS" and I will DM you the link!

Plus, subscribe to my newsletter to get my analysis along with the last several years of data all totally for FREE. link in profile

[Chris @cdolan04]

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.

[Chris @cdolan04]

@asturnerr Would love to learn more, would be super helpful for the brands we work with

[Chris @cdolan04]

@herrmanndigital *with consistent sizing
Every brand mentioned in the comments is good…until you buy the same size you’ve been buying and it fits completely different

[Chris @cdolan04]

@johnhickey1970 We were told the same, pivoted to a Defender and love it