Design a statistically powered test for any list you can target, run it on your channel, match outcomes back, and get incremental CAC, ROAS, lift, intervals, and saturation in the app.
Direct mail, CRM, email, SMS, or any audience you can match back to outcomes.
Power sized first
Know the audience, holdout, and minimum detectable iCAC or iROAS before launch.
Balanced assignments
Use stratified random sampling so treatment and control look comparable.
Matchback results
Upload outcomes and get lift, iCAC, iROAS, intervals, and a clear decision.
Saturation curve
See how marginal CAC changes as you reach deeper into the audience.
From sizing and assignment to results and MMM calibration. You bring the audience and the outcomes. We do the math.
Size the test before you spend. See the smallest iCAC or iROAS you can reliably detect, or let us recommend the holdout for the audience you already have.
Split treatment and control with stratified random sampling so the two groups are balanced on what matters and your test is well powered.
Upload outcomes and get incremental CAC or ROAS, lift, confidence intervals, and a clear decision. No spreadsheets, no extra tools.
Score your users and trace the diminishing-returns curve. Know your all-in campaign iCAC and the marginal cost of one more person.
Send the measured effect and the saturation curve straight into your MMM to calibrate the channel and validate the model.
Every test, assignment, and result is stored by brand. Search it, reference it, and reuse it as evidence later.
Four steps, no spreadsheets, nothing extra to wire up.
Bring the list of users you can reach. Add an optional 0 to 100 score for each person to unlock depth analysis.
Run the power analysis, then split treatment and holdout with stratified random sampling. Download the assignment to launch.
Send to the treatment group on your channel. Upload outcomes and we match exposed users back to what happened.
See incremental CAC or ROAS, lift, and the saturation curve in the app, then feed them into your MMM in a click.
A coin flip can hand you a lopsided test. We split your audience with stratified random sampling, so treatment and holdout look alike on the things that drive your outcome. The result is a more powered test and a cleaner read.
Stratify on revenue, propensity, or any pre-test score so high and low value users are evenly represented in both groups.
Stratify on region, customer type, lifecycle stage, or channel so no segment skews one arm of the test.
Every assignment is reproducible and logged, so you can stand behind the split.
Balanced within every segment
Diminishing returns
Campaign iCAC
all-in, across everyone you reach
Marginal CAC
the cost of one more person
Give each user a 0 to 100 score for propensity, value, or whatever you model, and targeting depth becomes a dial. Target your best users first. Go deeper and each additional person costs more. We trace the whole diminishing-returns curve from a single test.
See your all-in campaign iCAC and the marginal CAC of reaching one more person, so you know exactly how far down the list to go.
That same curve calibrates your MMM's saturation, and the headline iCAC or ROAS calibrates the channel. One experiment makes the model smarter.
User-level testing works on any channel where you control the audience and can match exposed users back to outcomes. Person, household, or account level.
Prospecting, win-back, and reactivation. Use clean treatment and holdout files to measure real incremental response and profit, not attributed conversions alone.
Holdouts on promotions, lifecycle journeys, and offers. Measure revenue and conversion by assignment, with guardrails on fatigue and unsubscribes.
Build the segment, target the treatment group, and match conversions back by id. If you can upload an audience, you can test it.
Direct mail, CRM, or paid social. Person, household, or account. Any reach with a matchback works.
Every user-level result becomes evidence for the MMM we are launching next, calibrated at the exact point in time you ran the test.
Lock in the measured incremental CAC or ROAS as the channel's effect, straight from a real holdout.
Feed the diminishing-returns curve in so the model learns where the channel starts to saturate.
Check that your top-down MMM agrees with what the holdout actually measured.
One experiment, a smarter model.
Designs, assignments, and results are stored per brand. The app runs every calculation for you, keeps a full history you can search and reference, and turns published results into reusable MMM evidence. No spreadsheets to maintain, nothing to wire up.
All your user-level tests and results live together, scoped to your brand and ready to reference.
iCAC, iROAS, lift, confidence, and the saturation curve are computed in the app on every run.
Publish a result and it is ready to calibrate and validate your MMM, now or later.
You don't need to be a statistician. The hard math runs under the hood; you point, click, and make the call with the full picture in front of you.
Geo, user-level, and platform tests all feed one central database. That shared truth calibrates and validates your MMM, while attribution keeps everything pointed in the right direction. Run any piece on its own — or run them together and let each one make the others stronger.
Inform. Attribution gives the MMM a fast, directional read on what's working between tests.
Calibrate. Incrementality tests anchor the MMM to causal ground truth, not just correlation.
Validate. Holdout tests check that attribution and the model agree with what really happened.
Connect any AI agent through MCP/API to read-only user-level test context: audience assignments, stratification choices, matchback results, lift, iCAC/iROAS, intervals, and saturation curves. Shako Stats computes the deterministic readout; the AI helps your team understand what it means.
Meet your AI Marketing ScientistOne-click OAuth for Claude & ChatGPT, read-only, anyone on the team can ask
Size it, split it, measure it, and calibrate your MMM in one place.
Paid B2B plans built for brands & agencies.