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BUILT FOR SHOPIFY STORES

Which discounts
actually create
a sale?

A discounted order tells you what you spent. It cannot tell you whether that shopper would have bought at full price.

Randomize comparable shoppers. Observe real Shopify orders. Build evidence before changing your strategy.

demo-store.myshopify.comSHOPIFY STORE
THE EDITSHOP   /   CART (1)
Illustrative ivory sneaker in a Shopify storefront
NEW ARRIVALClassic Sneaker
$180.00
10% OFF−$18.00
SHOPIFY ORDER #2841Customer paid $162.00
Did the $18 create this sale?UNKNOWN
Follow the $18

01 / THE QUESTION

One order.
Two possible stories.

We saw a purchase with 10% off. We cannot see what the same shopper would have done without it.

One receipt cannot show causality.

SAME SHOPPER
OBSERVED
10% OFFPaid $162Purchase happened
UNOBSERVED
NO OFFERPrice $180?

To find out, compare many shoppers assigned before purchase.

02 / THE CONTROLLED TEST

Same store.
Random assignment.

Eligible shoppers enter one of two conditions: your normal 10% offer or no offer. Assignment happens before the outcome.

Comparable groups. Different offers.

SHOPIFY STOREFRONT EVENTSEligible shopper sessions
10% OFFNormal eligible discount$162
NO OFFERNo experiment discount$180

ILLUSTRATIVE EXPERIMENT · NOT LIVE STORE DATA

03 / THE REAL COST

Every purchase counts.
So does every discount.

Shopify order outcomes fill both groups. When a discounted order lands, the cost lands with it.

ILLUSTRATIVE DISCOUNT SPEND$4,20710% OFF group · $0 in the no-offer group

Early conversion differences are not evidence. Keep measuring.

CONTROL10% OFF6,421 eligible shoppers
ORDER #2841Purchase recorded−$18
ORDER #2849Purchase recorded−$18
ORDER #2856Purchase recorded−$18
Illustrative conversion5.1%
TREATMENTNO OFFER6,421 eligible shoppers
ORDER #2844Purchase recorded$0
ORDER #2850Purchase recorded$0
ORDER #2862Purchase recorded$0
Illustrative conversion4.8%

ILLUSTRATIVE EXPERIMENT · NOT LIVE STORE DATA

04 / THE EVIDENCE

Now ask what
actually changed.

Compare purchase behavior and discount spend after a sufficiently mature randomized test. A prediction alone cannot answer this question.

The experiment decides what the discount was worth.

INVISIBLE OFFER / SHOPIFY ORDER OUTCOMESExperiment report
ILLUSTRATIVE
MEASURE10% OFFNO OFFER
Eligible shoppers12,84212,842
Conversion5.4%5.3%
Discount spend$8,421$0
OBSERVED DIFFERENCE+0.1 percentage points

No statistically meaningful lift detected in this illustration. Similar rates do not prove equivalence or realized savings.

MADE FOR SHOPIFY MERCHANTS

Your storefront.
Actual outcomes.

Invisible Offer observes Shopify customer events, records randomized assignments, and connects them to Shopify orders. The result is evidence about the discount, not a guess about the shopper.

SHOPIFY STOREFRONTCustomer eventsView · cart · checkout
INVISIBLE OFFERRandomized comparison10% OFF / NO OFFER
SHOPIFY ORDERSPurchase and refund outcomesEvidence before policy

A PERSONAL ECONOMIC ESTIMATE

What do discounts cost your store?

Estimate your monthly discount spend. The incremental effect is still unknown until it is measured.

$
%
%

ESTIMATED MONTHLY DISCOUNT SPEND

$17,640/ month

Illustrative estimate based on your inputs. This is not actual waste or savings; Invisible Offer measures the real effect.

Now let’s measure it Four inputs. No assumption about how many discounts were unnecessary.

QUESTIONS FROM SHOPIFY MERCHANTS

Before you change
an offer.

What the test can answer, what it cannot, and what happens before anything shopper-facing changes.

Explore the discount field guide
01What does Invisible Offer actually measure?

It compares eligible shoppers who keep the normal 10% offer with shoppers who receive no experiment offer. Purchase and refund outcomes—not a prediction about one shopper—support an estimate of whether the discount creates incremental behavior. Read the guide to discount incrementality.

02Why can't I tell from a discounted order alone?

The receipt shows the discount and the purchase, but not what that same shopper would have done at full price. Random assignment lets you compare groups instead of guessing about an individual order. See discount spend versus incremental revenue.

03Will connecting Shopify change my storefront discounts?

Connection sets up measurement and requires Shopify permissions, including pixel and discount capabilities. It does not itself start a shopper-facing experiment. A controlled test is a separate, deliberate step, with readiness checks before it starts.

04Does this depend on my theme's Add to Cart button?

Primary behavior measurement uses Shopify standard customer events through a Web Pixel, not button text or theme-specific selectors. Storefront intervention still has setup and readiness requirements that should be checked on your store before a test.

05Does AI decide who gets a discount?

No. Experimental assignment is randomized before the purchase outcome. Merchant constraints and deterministic policy govern the permitted action; semantic signals are not proof that a discount caused a sale.

06How soon will I know whether the discount works?

There is no universal time or order count. Both groups need enough eligible shoppers, valid outcome attribution, and a pre-defined evaluation window. Early differences are labeled as measuring, not a win. See how a Shopify holdout test works.

07Does a no-offer purchase mean I saved 10%?

No. A single purchase cannot establish what would have happened with the offer. Avoided discount spend is not automatically realized profit: conversion, revenue per eligible shopper, refunds, and the full experiment result must be considered together. Learn about refund-adjusted outcomes.

MEASURE FIRST. CHANGE LATER.

Keep discounts that change behavior.
Question the rest.

Connecting Shopify provisions measurement. It does not, by itself, start a shopper-facing discount experiment.

Estimate your discount spend
  1. 01 Connect Shopify
  2. 02 Observe baseline behavior
  3. 03 Run a controlled test
  4. 04 Review mature outcomes

Shopify permissions include customer-event pixel and discount capabilities; installation is not read-only. Offer changes require an experiment to be started.