Definition
Impression share is the % of times your ads were shown out of total eligible impressions (search).
How to use it
- Lost impression share usually comes from budget limits or low rank (bid/quality).
- Increase impression share only if marginal ROAS/profit supports it.
Why this matters
This term matters because it affects how you interpret performance and make budget decisions. If you use inconsistent definitions or windows, ROAS/CPA can look "better" while profit gets worse.
Practical checklist
- Write a 1-line definition for "Impression Share" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Sanity-check with a related calculator from the same category on MetricKit.
- Read the related guide (e.g., Marginal ROAS: how to scale ads with diminishing returns) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- Break-even CTR Calculator: Compute the CTR required to break even (and hit a target) given CPM, CVR, AOV, and contribution margin.
- A/B Test Sample Size Calculator: Estimate sample size per variant for a conversion rate A/B test given baseline CVR, MDE, significance, and power.
- CPL to CAC Calculator: Convert cost per lead (CPL) into CAC using lead-to-customer rate (and compute targets).
- Break-even CVR Calculator: Compute the CVR required to break even (and hit a target) given CPM, CTR, AOV, and contribution margin.
- Click-through Conversion Rate Calculator: Calculate click-through conversion rate (click-to-conversion CVR) and estimate required clicks for target conversions.
Guides
- Marginal ROAS: how to scale ads with diminishing returns: A practical guide to marginal ROAS: why average ROAS misleads at scale, how diminishing returns work, and how to pick a profit-maximizing spend level.
- Attribution vs incrementality: what to trust, when, and how to test: A practical guide to attribution vs incrementality: common attribution models, window pitfalls, how MER/marginal ROAS fit in, and how to run holdout/geo tests.