Performance Max across 41 UPLIFY projects: a Jan–May 2026 snapshot
This is an internal descriptive snapshot, not a market benchmark or forecast. It reports medians across 41 UPLIFY e-commerce projects for a defined period. Because the accounts used different conversion sets, CPA and conversion rate must be read with the methodology.
v1.0 · 41 projects · 129 Performance Max campaigns
- Source: aggregated Google Ads data in UPLIFY OS.
- Sample: 41 projects, 129 Performance Max campaigns, at least 100 clicks and 10 configured conversions during the period.
- Median unit: one aggregate per project, not one daily row.
- Conversions: sets differ across accounts and may include Purchase, AddToCart, PhoneCall, or FormSubmit.
- Currency: UAH; the earlier USD conversion was illustrative.
Implication: CTR and CPC describe sample traffic. CPA and conversion rate are not purchase-only measures. ROAS depends on how each account sends conversion value and therefore requires value-tracking validation.
1. What the sample contains
| Metric | Per-project median | How to read it |
|---|---|---|
| CTR | 2.25% | click share within the sample |
| CPC | 4.13 UAH | click cost within the sample |
| CPA | 101.46 UAH | across different configured actions |
| ROAS | 9.88× | depends on conversion-value tracking |
| Monthly spend | 34k UAH | sample-scale median |
Weighted overall figures are not used as a reference here: a few large or unusual accounts can disproportionately influence the aggregate. The per-project median describes the sample center more clearly.
2. PMax and Search: description, not ranking
| Channel | Projects | CTR | Avg CPC | Conv rate | ROAS |
|---|---|---|---|---|---|
| Performance Max | 41 | 2.10% | 5.28 UAH | 16.07% | 282× |
| Search | 10 | 6.71% | 6.40 UAH | 6.53% | 9.69× |
These rows do not show that one campaign type is more effective. PMax covers 41 projects and Search 10; campaign sets, goals, and conversion actions differ. Their CTR, CPA, and ROAS cannot be treated as a controlled test or a basis for a universal budget split.
3. How to compare your account
When ROAS is below your break-even point
Compare ROAS with your business's break-even ROAS, not with 5× or 9.88×. Validate margin, variable costs, attribution, and reported revenue.
- Reconcile purchase revenue across Google Ads, GA4, and the CRM.
- Separate brand and non-brand influence where possible.
- Check feed data, prices, availability, and landing-page alignment.
- Use a period that reflects the purchase cycle and conversion lag.
When CPA is above your allowable level
An absolute 200 UAH threshold says little without average order value, margin, and event type. First confirm that CPA refers to the conversion you actually need.
- Separate Purchase, AddToCart, calls, and forms.
- Reconcile order value and cancellations in the CRM.
- Review budget, bidding strategy, and assortment constraints.
- Compare periods with similar seasonality and promotions.
When CTR changes materially
CTR helps diagnose impressions and relevance but does not determine profitability on its own. Read it with CPC, purchase rate, revenue, and traffic composition.
- Review feed, asset, and product-coverage changes.
- Identify categories that gained or lost impressions.
- Compare CTR changes with CPC and purchase share.
- Do not conclude from one short interval.
4. Limits of this snapshot
Sample limitations:
- only UPLIFY-managed projects, not a random sample of Ukrainian e-commerce;
- mixed niches, catalog sizes, budgets, and conversion-action sets;
- the period through May 23 does not cover a full year of seasonality;
- no control group or causal design for comparing PMax and Search;
- aggregate medians do not show dispersion or forecast an individual store.
Budget decisions should use your own purchase CPA, margin, break-even ROAS, purchase cycle, and measurement quality.
5. How the sample was built
UPLIFY OS stores daily campaign metrics from connected Google Ads accounts. For this page, data was aggregated for January 1 through May 23, 2026, with each project contributing one observation to the median.
Only aggregate values are published, without store names or account identifiers. Because conversion configurations differ, the table describes the sample as observed and does not turn it into a purchase-only study.
Review, correction, and accountability rules are described in the editorial policy.
6. How to use the data
Use the medians as questions for your own data, not as a target. First define purchase CPA, break-even ROAS, the revenue source, and an acceptable learning horizon. Then compare only identically defined metrics.
- Check which actions are marked as Primary conversions.
- Reconcile revenue and purchases with the CRM or backend.
- Compare equivalent periods and definitions.
- Document limitations before changing budget or bidding.
Need your own account reviewed? Order a read-only audit: we will separate facts from hypotheses and will not apply a sample median as your forecast.
Frequently asked questions
What is a good ROAS for my store?
One that exceeds your break-even ROAS and leaves the required contribution after variable costs. The 9.88× median describes UPLIFY's sample; it is not a universal target.
Can I use the 4.13 UAH CPC in a media plan?
Only as a descriptive value from this sample. Your CPC depends on assortment, competition, geography, feed quality, bidding, and period. Use your own history or a test with explicit assumptions for planning.
Does this data prove PMax is better than Search?
No. These are different project sets without a controlled design. The comparison describes those accounts but does not establish causality or prescribe a universal budget split.
Why can't conversion rate be compared with a purchase-only benchmark?
Some accounts included AddToCart, calls, or forms as conversions. Mixed actions increase conversion counts and change both CPA and conversion rate.
Will these medians fit my niche?
Not necessarily. The sample mixes niches, budgets, and catalog sizes, and its dispersion is not published. Treat the figures as context and rely on your own economics and purchase data.