$24K a year
misallocated at 10% wrong
Numbers you can
bet the budget on.
Galton is honest measurement for Shopify operators spending $20k a month or more on paid. Add up what every platform claims it drove and the total comes out bigger than your bank account. Galton starts from the one number that can't lie, your actual sales, reads your Shopify, Meta, and Google data nightly, and answers the questions that decide whether you scale, on contribution margin, not revenue.
Galton never writes to your accounts. Connect once, your history backfills on its own, and cohorts land the same day.
What being wrong costs.
Every operator misallocates some share of paid budget. The question is how big the share is, and whether anything you own can show it to you. Platform-reported ROAS can't: it is the same system marking its own work. Here is the annual bill at 10% wrong.
$60K a year
misallocated at 10% wrong
$120K a year
misallocated at 10% wrong
Ten percent is a conservative guess. One client paused Meta for three weeks to settle a claim like this, and the readout came back clean, precise, and worthless: they had cut spend on the control channel two weeks earlier, and the test sat on their annual summer decline. Both were bigger than the test itself. Galton now checks a test's validity before reading it and calls that one invalidated, because a test broken by something you changed elsewhere is not evidence a channel does nothing, and reporting it that way is how a channel gets killed for the wrong reason.
One client, one three-week test, their numbers removed. Your gap is your own, and finding it is the job.
The answers you need to scale.
Ask any question you'd take to a data scientist. If the answer is in your data, Galton will surface it. And if not, it will help you design the experiment that will.
What moved overnight, and what's my highest-priority action?
Home →
How much does each channel over-claim?
Channel Intelligence →
Which channel earns the next dollar?
Channel Intelligence →
How are my paid channels affecting my unpaid channels?
Channel Intelligence →
When does a new customer pay us back?
Customer Health →
Is growth coming from new or repeat buyers?
Customer Health →
Do our free sample packs bring in long-term paying customers, and how do they differ from the rest?
Customer Health →
Which products deserve the budget?
Product Insights →
Which products are trending up, and which are trending down?
Product Insights →
Every question routes to one of four modules. Below, what each one shows you.
Know what to look at first.
Better answers, better decisions, in the order they matter. When a number moves, Galton brings it to your attention immediately. When nothing moved, it stays quiet. Two numbers lead: blended MER, revenue per ad dollar across every channel, and CMER, the same figure after product costs. MER tells you what came in. CMER tells you what you kept.
Blended MER
3.2×
3.9%
revenue per ad dollar
CMER
1.4×
5.2%
profit per ad dollar
Net revenue
$412K
8.1%
last 30 days
Ad spend
$129K
4.0%
last 30 days
Which channel actually creates demand?
Meta claims 2.1× the revenue GA4 grants it, and branded search co-moves with Meta spend. A holdout would settle it.
Recommendation waitingWhat can you afford to pay for a customer?
Max CAC on the candle line fell to $22 as rebuys softened to 19%. Blended break-even sits at $27.
Changed overnight
When does a cohort pay you back?
Payback shortened to 81 days, from 94, as the post-purchase upsell took hold.
Where is the next dollar still working?
Meta prospecting campaigns show signs they can keep scaling profitably.
New vs returning mix
Returns and shipping
Product mix
Discount load
Illustrative. Your questions, your numbers.
Does each channel earn what it claims?
Every platform claims as many sales as it can. Solving attribution is the wrong approach. Galton starts from your actual sales, the one number that can't lie, then applies a range of statistical methods to reach a verdict per channel, and marks where only an experiment can settle it.
Galton's read
Full history
Observational
verified today
Meta likely earns close to two thirds of what it claims. Google's brand line is the soft spot.
- Meta claims $268K. The model reads it incremental at roughly two thirds of that, and GA4 grants it $128K.
- Google claims $181K. Brand search co-moves with demand you already had; nonbrand reads incremental.
- Claimed together: $449K. Revenue that exists: $412K.
Observational. A holdout is what would settle the Meta gap.
Ask a follow-up →
Blended MER
3.2×
revenue per ad dollar, all channels
CMER
1.4×
contribution margin per ad dollar
Meta
claims $268K
Likely · 75 to 90%
Reads incremental at near two thirds of the claim. GA4 grants it
$128K. A holdout would settle the gap.
Google
claims $181K
Mixed · 40 to 60%
Brand search co-moves with demand you already had. Nonbrand reads
incremental.
Claimed together: $449K. Revenue that exists: $412K.
Observational verdicts cap at "likely". Only an experiment earns causal language.
Illustrative numbers.
Which channels bring in new customers, and at what cost?
A channel can read badly on margin and still be worth running if it keeps buying first-time buyers cheaply. Association, not attribution.
Google's contribution-margin read is unresolved, but about 44 new customers a day move with its spend at roughly $32 implied cost each, above your ~$27 break-even ceiling on acquiring a customer.
Channel
Reads
New customers/day
Implied CAC
Last 28d vs prior
Last 28d vs last year
Meta$2.6K/day spend
Likely · 75 to 90%
~118
~131 last 28d
~$22
under ~$27 ceiling
+12%
+34%
Google$1.4K/day spend
Mixed · 40 to 60%
~44
~41 last 28d
~$32
above ~$27 ceiling
−6%
+9%
Scroll the table sideways for every column.
These are associations across your history, not measured acquisitions. A holdout is what turns them into a number you can bank on.
How implied CAC is computed
Implied CAC is the channel's mean daily spend divided by the new customers a day that move with it. It is not a measured cost per acquisition and not the platform's own reported number. All paid spend together implies ~$25 per new customer, and your store actually acquires ~183 first-time buyers a day across every source, paid and unpaid.
Illustrative numbers.
Everything you need to know about the health of your customer base.
Lifetime value, retention, and payback, cohort by cohort, on contribution margin. You see when a cohort crosses break-even before the next campaign commits the cash.
New-Customer LTV
$63
12-mo: first order + predicted
CAC
$24
7.7%
acquisition cost
LTV : CAC
2.6×
health ratio
Repeat rate
28.4%
2+ orders
New customer %
61.2%
3.1%
share of revenue, last 30 days
90-day churn
41.0%
recent cohort
New customer AOV
$58
5.5%
first-order value
Days to reorder
47 days
median 1st to 2nd
Why only three tiles carry a comparison
Repeat rate and days to reorder are taken over every customer you have, LTV:CAC inherits a cohort-age confound, and churn is anchored to the window's end date. A pill on those would be a period claim the number cannot make.
Galton's read
Last 30 days
Observational
verified today
A new customer now pays you back on day 81, thirteen days sooner than last month.
- First-order AOV rose to $58 from $55 as the post-purchase upsell took hold.
- CAC eased to $24 from $26, so there is less to earn back in the first place.
- Repeat rate held at 28.4%, so the gain is in the first order, not in retention.
Observational. Cohorts under 90 days are projected.
Ask a follow-up →
M1
M2
M3
M4
M5
Feb
9%
16%
21%
25%
27%
Mar
8%
15%
20%
24%
Apr
10%
17%
23%
May
9%
16%
Open cells are months the cohort hasn't lived yet. Galton leaves them blank rather than guessing.
Blended payback crossed at day 81 this month, from day 94.
Illustrative numbers.
You asked
What can we afford to pay for a new customer?
Galton
Between $22 and $31, depending on the line.
- Serum line: $31, on a 34% rebuy rate.
- Candle line: $22. Rebuys at 19%.
- Blended break-even sits at $27.
Contribution margin, not revenue. Cohorts under 90 days are projected.
Illustrative conversation. Your questions, your data.
Which products deserve the budget.
Every product ranked on the metrics that decide budget, with the customers it recruits and what they go on to be worth. Click a product and the row opens into its SKUs.
Galton's read
Last 30 days
Observational
verified today
Skincare recruits your most valuable customers, on 34% of the budget.
- Customers whose first order contained a Skincare product go on to be worth $84 on average, 1.9× the $44 from Home fragrance.
- Home fragrance takes 51% of allocated spend against Skincare's 34%, and its CMER sits at 0.8×.
- Inside the Serum line, the refill SKU is the rebuy engine: up 31% while the lead SKU grew 9%.
Observational. Lifetime value looks backwards, and spend follows available demand as well as value.
Ask a follow-up →
Product
Revenue
Margin
Margin %
Δ
Units
Orders
COGS coverage
Serum DuoSkincare
$88.4K
$46.9K
53.1%
▲ 13.0%
2,140
1,880
96.4%
SKU / variant
Revenue
Margin
Units
Orders
SRM-DUO-30 30 ml
$41.2K
$22.1K
980
910
SRM-DUO-50 50 ml
$33.6K
$18.1K
690
640
SRM-DUO-RF refill pouch
$13.6K
$6.7K
470
330
Candle TrioHome fragrance
$61.0K
$22.6K
37.0%
▼ 8.0%
1,910
1,640
91.2%
Wick + Trim KitAccessories
$24.1K
$10.1K
41.9%
▲ 3.0%
760
700
88.5%
Scroll the table sideways for every column.
Top 3 of 24 products. Margin is contribution after product costs, from the COGS you keep in Shopify; COGS coverage is how much of the revenue carries a measured cost rather than your assumed rate.
Where the ad dollars go · campaign spend classified by category
Category
Revenue
Margin
Direct spend
Brand
Catch-all
Total spend
ROAS
CMER
Skincare
$188.0K
$96.5K
$38.2K
$6.1K
$0
$44.3K
4.2×
2.2×
Home fragrance
$142.5K
$51.3K
$61.8K
$4.6K
$0
$66.4K
2.1×
0.8×
Accessories
$52.4K
$21.5K
$9.4K
$1.7K
$3.1K
$14.2K
3.7×
1.5×
Gift sets
$29.1K
$11.3K
$3.1K
$0.6K
$0.4K
$4.1K
7.1×
2.8×
Scroll the table sideways for every column.
How spend is allocated across categories
Brand spend spreads across all categories, catch-all only across categories without a dedicated campaign, both revenue-weighted. ROAS and CMER here are blended and observational: they say what a category's ad dollar coincided with, not what it caused.
Illustrative numbers.
Decisions you can defend.
What actually changes once the numbers are honest.
You decide on margin, not revenue.
Galton reads the COGS you keep in Shopify, so max CAC, payback, and LTV are all contribution margin. Top-line ROAS can look healthy while the P&L says otherwise.
You find the ceiling before you spend into it.
A max CAC per product line and a payback date per cohort. You know when to push, and when the next dollar stops working.
You stop paying for orders you'd have gotten anyway.
Harvesting demand versus creating it, platform claims versus GA4, and a holdout to settle it when the stakes are big enough.
- Nightly reads on your business’s most important questions
- MMM, incrementality, and holdout design
- Customer LTV and payback cohorts
Every answer shows its work.
Observational, and says so.
When the data only shows correlation, Galton says "associated" and "co-moves". It never dresses a model up as proof.
Causal, when earned.
Causal language arrives only after an experiment: holdouts Galton designs and reads. When it says "drives", there was a test.
Fresh or silent.
Answers are dated and re-verified nightly. When cohorts are too young to read, Galton waits rather than guessing.
Also from Finch Labs
Status
Finch, the creative strategist: which ads win with which buyers, and what to make next. See Finch →
Currently in closed beta.
We onboard hand-invited brands and agencies, a few at a time, and we work the first weeks with you. Write to us and this is what follows: a call to check that your spend and order history can carry the reads, then a connect that takes minutes, then your first answers while the backfill is still running.
hello@finchlabs.ai →- Connect Shopify, Meta, and Google. Minutes, not a project.
- Your order history backfills on its own
- Galton never writes to your accounts