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The Incrementality Test Design Template

Prove your ads are driving real sales, not just claiming credit for ones that would have happened anyway.

Most ad platforms will tell you your campaigns are working. They have every incentive to. This template gives you a structured way to run your own test and find out whether your ad spend is creating real lift or whether your business would have gotten those sales regardless. Fill in each section before you run a single pound or dollar of spend on your next campaign.

What is inside
  • Test Overview
  • Baseline Definition
  • Test Group and Control Group Design
  • Success Metrics and Lift Calculation
  • Confounding Variables and Test Integrity Checks
  • Results Read-Out and Decision Framework
Section 1

Test Overview

Define the boundaries of your test before anything else. Vague tests produce unreadable results.

1.1

Test Overview Card

Test Name: [SHORT DESCRIPTIVE NAME, e.g. "Meta Q3 Incrementality Test"]

Business Question Being Tested: [Write the exact question in plain English. Example: "Does running Meta ads cause more people to buy our [PRODUCT/SERVICE], or do those buyers find us anyway through organic search, word of mouth, or direct?"]

Channel Being Tested: [e.g. Meta Ads / Google Search / LinkedIn / Email]

Campaign or Ad Set Name: [Exact campaign name in your ad account]

Test Start Date: [DD/MM/YYYY]

Test End Date: [DD/MM/YYYY]

Minimum Test Duration: [Recommended: at least 14 days. Note here if your purchase cycle is longer and adjust accordingly.]

Person Responsible for Running This Test: [NAME]

Person Responsible for Reading the Results: [NAME]
Section 2

Baseline Definition

You cannot measure lift without knowing what was happening before. This section captures your pre-test reality.

2.1

Baseline Metrics Card

Baseline Period: [DD/MM/YYYY to DD/MM/YYYY] (use the same length of time as your test period, from directly before it starts)

Baseline Revenue From This Channel: [£/$ AMOUNT over baseline period]

Baseline Conversion Volume: [NUMBER of purchases / sign-ups / leads over baseline period]

Baseline Conversion Rate: [% calculated from your own data, not platform benchmarks]

Baseline Average Order Value or Deal Size: [£/$ AMOUNT]

Organic and Direct Conversion Volume During Baseline (no paid ads running): [NUMBER]

Notes on Anything That Was Different During Baseline: [e.g. seasonal events, promotions, website changes, competitor activity that could skew the comparison]

Data Source Used for Baseline: [e.g. Google Analytics 4, HubSpot, Shopify, CRM. Be specific. Do not use platform-reported numbers as your baseline.]
Section 3

Test Group and Control Group Design

The reliability of your result depends entirely on how cleanly you separate who sees ads from who does not.

3.1

Group Design Card

Test Group Definition (exposed to ads):
[Describe who will be in the test group. Example: "All users in [GEOGRAPHIC REGION or AUDIENCE SEGMENT] matching [TARGETING CRITERIA]. Estimated size: [NUMBER of people]."]

Control Group Definition (not exposed to ads):
[Describe who will be withheld from ads. Example: "A [X]% holdout of the same audience, excluded at the ad account level using [PLATFORM HOLDOUT FEATURE / GEO SPLIT / MANUAL EXCLUSION LIST]." Estimated size: [NUMBER of people].]

Control Group Size as % of Total Audience: [Recommended: 10 to 20%. Note your choice here and why.]

Method Used to Create the Control Group: [e.g. Meta Conversion Lift holdout / Geo split / CRM suppression list / Platform-level audience exclusion]

Risk of Contamination Between Groups: [Describe any ways the control group might still see your ads, e.g. via a partner, retargeting, or organic social. State what you have done to reduce this.]

Are Both Groups Exposed to the Same Seasonal Conditions? [YES / NO. If NO, explain.]
Section 4

Success Metrics and Lift Calculation

Decide what you are measuring and what result will change your decisions, before you see the data.

4.1

Metrics and Lift Card

Primary Metric: [The one number that determines whether this test is a pass or a fail. Example: "Number of confirmed purchases attributable to incremental lift."]

Secondary Metrics (optional, do not let these override the primary): [e.g. Revenue per user in test vs control / Cost per incremental conversion / ROAS calculated on incremental revenue only]

Minimum Detectable Lift to Be Considered Meaningful: [X%. Choose a number that would actually change your spend decision. If 5% lift would not change anything, set the bar higher.]

Lift Calculation Formula:
Incremental Lift = (Conversion Rate in Test Group) minus (Conversion Rate in Control Group)
Incremental Revenue = Incremental Lift x [TOTAL AUDIENCE SIZE] x [AVERAGE ORDER VALUE or DEAL SIZE]

Target Incremental ROAS (the ROAS calculated only on incremental revenue, not total attributed revenue): [X.Xx]

Note: Platform-reported ROAS includes conversions your control group would have made anyway. Incremental ROAS is always lower. If incremental ROAS drops below [YOUR THRESHOLD], the campaign is not earning its budget.

Statistical Confidence Threshold Required Before Acting on Results: [Recommended: 90% minimum. Note your threshold here.]
Section 5

Confounding Variables and Test Integrity Checks

List every external factor that could make your results unreliable, and decide in advance how you will handle each one.

5.1

Integrity Checklist Card

Planned Promotions or Sales During the Test Period: [LIST ANY. Promotions inflate both groups and can mask or exaggerate lift. If a promotion is unavoidable, note how you will account for it.]

Seasonal Events That Overlap With the Test: [e.g. bank holidays, school terms, industry events, competitor launches]

Website or Product Changes Planned During the Test: [e.g. pricing changes, new landing pages, checkout updates. If possible, freeze these during the test.]

Other Active Campaigns Running in Parallel: [LIST ALL. Cross-channel exposure can make it impossible to isolate this channel's effect. Note how you will separate them.]

Data Collection Method and Verification: [Where will you pull results from? How will you verify the data is not double-counting or pulling from the platform's own attribution model?]

Test Pause Conditions: [Describe the specific conditions under which you would pause or cancel the test early. Example: "If spend in the test group exceeds [£/$X] with zero incremental conversions recorded by day 7, pause and review."]

Sign-Off Required Before Pausing or Extending the Test: [NAME and ROLE]
Section 6

Results Read-Out and Decision Framework

Fill this section in after the test ends. Write your decision before you see the data, so the result drives the action rather than the action driving the interpretation.

6.1

Results and Decision Card

Pre-Committed Decision Rules (fill in BEFORE the test ends):

IF incremental lift exceeds [X%] AND incremental ROAS exceeds [X.Xx] THEN: [STATE YOUR ACTION. e.g. "Scale budget by [X]% and expand to [NEW AUDIENCE or REGION]."]

IF incremental lift is between [X%] and [X%] AND incremental ROAS is between [X.Xx] and [X.Xx] THEN: [STATE YOUR ACTION. e.g. "Hold budget flat and run a second test with revised creative and targeting."]

IF incremental lift is below [X%] OR incremental ROAS is below [X.Xx] THEN: [STATE YOUR ACTION. e.g. "Reduce budget by [X]% and reallocate to [CHANNEL] where lift has been demonstrated."]

---
Fill in after the test:

Test Group Conversion Rate: [X%]
Control Group Conversion Rate: [X%]
Measured Incremental Lift: [X%]
Statistical Confidence Level Achieved: [X%]
Incremental Revenue Generated: [£/$ AMOUNT]
Incremental ROAS: [X.Xx]
Platform-Reported ROAS (for comparison): [X.Xx]
Difference Between Platform ROAS and Incremental ROAS: [X.Xx]

Decision Made Based on Results: [WRITE THE DECISION EXACTLY AS STATED IN YOUR PRE-COMMITTED RULES ABOVE]

Anything That Compromised Test Integrity: [DESCRIBE or write "None identified"]

Next Test Recommended: [DATE and BRIEF DESCRIPTION]
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Lilach Bullock has spent 21 years in marketing. Forbes Top 20 (twice), Oracle Social Influencer of Europe, and ranked the number one digital marketing influencer in the UK. She now builds AI-powered marketing systems for entrepreneurs, service businesses, and founders. The Sunday newsletter goes to 15,000 readers at a 70%+ open rate.

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