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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesConversion tracking tells you which conversions Google Ads attributes to ad interactions under your account settings. Conversion Lift estimates how many additional conversions the advertising caused by comparing an ad-exposed treatment group with a holdback control group. Those are different measurements, so an attributed-conversion total is not interchangeable with an incremental-conversion result.
Tracking is the day-to-day system for reporting, bidding and optimization. Lift is a controlled study for the counterfactual question: what would have happened if comparable people or regions had not received the ads?
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The difference at a glance
| Aspect | Conversion tracking | Conversion Lift |
|---|---|---|
| Question answered | Which valuable actions are reported under the conversion actions and attribution rules configured in the account? | How many more actions occurred with advertising than under a comparable control condition? |
| Method | Records conversions and associates them with ad interactions according to the account’s settings. | Runs a controlled comparison between an ad-exposed treatment group and a held-back control group, or between regions with and without ads. |
| Primary outputs | Attributed conversion count and value. | Incremental conversions or value, relative lift, incremental CPA and incremental ROAS (iROAS). |
| Best use | Ongoing campaign reporting, bidding and conversion optimization. | Estimating causal, incremental impact for budget and channel decisions. |
| Main limitation | Attribution alone does not show how many actions would have occurred without advertising. | Eligibility, study design, budget, uncertainty, measurement gaps and conversion lag affect whether a result is available and how precisely it can be interpreted. |
Google’s documentation treats these as complementary systems rather than competing reporting modes. Keep conversion tracking configured even when you plan to run a Lift study.
What Google Ads conversion tracking measures
Conversion tracking starts with a conversion action: a valuable event such as a purchase, lead or other business outcome that you tell Google Ads to measure. The account then records qualifying events and associates them with ad interactions using the selected conversion-action settings and attribution rules.
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The result is an attributed view of performance. A conversion appears in campaign and account reports when the configured system assigns credit to an ad interaction. The reported conversion value follows the values and counting choices attached to those actions. That makes tracking useful for routine questions such as:
- How many reported conversions did each campaign generate?
- What conversion value is being attributed to the spend?
- Which campaigns should automated bidding optimize toward under the current account configuration?
- How are reported results changing from one reporting period to the next?
Those answers are operationally important, but they are not a controlled estimate of the counterfactual. A person may have converted after seeing an ad even if the ad did not change that person’s decision. Conversely, an ad can influence a decision without the conversion being directly linkable to the original interaction. Attribution rules decide how credit is assigned; they do not, by themselves, observe the outcome that would have occurred without the ad.
What Conversion Lift measures
Conversion Lift is an experiment. Google divides eligible users or geographic areas into conditions, exposes the treatment condition to the measured advertising and holds back the control condition from those ads. The difference between the conditions is used to estimate incremental impact.
User-based studies
In a user-based study, people are assigned to treatment or control. The treatment group can receive the ads included in the study; the control group is intentionally prevented from seeing those measured ads. Google defines incremental conversions as:
Incremental conversions = treatment conversions − control conversions
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The comparison is meant to approximate the conversions caused by the advertising rather than all conversions that happened after an ad interaction. The study’s estimate therefore follows its experimental assignment, not the attribution model used for normal campaign reporting.
Geography-based studies
A geography-based study assigns regions, such as geographic markets, to treatment and control conditions. Treatment regions receive the measured campaigns while control regions provide the comparison. This approach can support a wider range of campaign types and offline data, but regional splits are noisier than user-level assignment and do not provide the same demographic slices. Google also says geography-based studies tend to require a higher budget.
How the key metrics are calculated
| Metric | Calculation or meaning | How to read it |
|---|---|---|
| Attributed conversions | Conversions credited to ad interactions under the account’s configured conversion actions and attribution rules. | The standard reporting total; not a counterfactual estimate. |
| Incremental conversions | Treatment conversions minus control conversions in a user-based Lift study. | The estimated net increase associated with the advertising condition. |
| Relative lift | Incremental conversions divided by control conversions. | A percentage relative to the control baseline. A small control count can make the percentage appear unusually large. |
| Incremental CPA | Total ad spend divided by incremental conversions. | The spend required for each estimated additional conversion. |
| Incremental ROAS (iROAS) | Incremental conversion value divided by total ad spend. | Return based on estimated additional value, not all value attributed to ads. |
| Standard ROAS | Attributed conversion value divided by spend. | Return under the normal conversion-reporting configuration. |
For example, if a study observed 1,200 treatment conversions and 1,000 control conversions, the estimated increment would be 200 and relative lift would be 20%. If spend were $20,000, incremental CPA would be $100. If the estimated incremental value were $30,000, iROAS would be 1.5. Those figures are an illustration of the formulas, not a benchmark or expected result.
Why tracking and Lift can show different numbers
Attribution credit is not causal incrementality
Tracking asks which conversions the configured system can credit to ad interactions. Lift asks whether the treatment condition produced more conversions than the control condition. A campaign can have many attributed conversions but little measured incrementality when most exposed users would have converted anyway. It can also show fewer directly attributed conversions than its estimated incremental effect when measurement cannot connect every influenced conversion to an ad interaction.
Lift uses its own experimental estimate
Google states: “Conversion Lift intentionally ignores the standard conversion tracking settings and attribution rules.” This is why changing an account’s attribution setting does not simply transform an attributed-conversion report into a Lift result. The study uses treatment and control outcomes to estimate the difference.
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Some conversions are modeled or not directly linkable
Google’s user-based reporting can model conversions that cannot be directly linked to ad interactions. Browser restrictions and cross-device behavior are examples of reasons a direct link may be missing. A Lift result can therefore contain modeled components. Label modeled or projected portions when presenting results to stakeholders instead of treating every figure as a directly observed ad click-to-conversion path.
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Conversion lag changes what a study can see
A conversion that happens days or weeks after an ad exposure may arrive after a short study window closes. Google’s setup guidance says study duration should reflect the conversion lag, distinguishing short purchase cycles from longer sales cycles. Eligible Demand Gen studies can also include supplementary conversion reporting and delayed incremental conversions. Check the current setup guidance for the account’s recommended duration and reporting windows.
Unmeasured campaigns can weaken the contrast
If the control group is held out from only part of an advertiser’s activity but can still see other campaigns serving the same audience, the treatment and control conditions become less distinct. Google recommends including campaigns that share campaign type, audience and targeting strategy. Measuring only a subset can make incremental impact harder to detect.
Eligibility, access and current Google requirements
Conversion Lift is not available to every Google Ads account. Google’s current user-based setup documentation directs some advertisers to contact a Google account representative and lists the following figures for the described setup:
| Requirement or guidance | Current published figure or qualification |
|---|---|
| Observed conversions | At least 1,000 observed conversions. |
| Campaign budget | At least $5,000 USD minimum campaign budget. |
| Conversion action | At least one compatible conversion action. |
| Most reliable certainty target | Aim for 90% or greater certainty. |
| Directional range | Results from 50% to 90% certainty are described as directional. |
These are Google-published setup figures accessed on October 13, 2026, not universal guarantees of eligibility or statistical power. Availability, thresholds and campaign support can change, so verify the current requirements for the specific account before committing budget or promising a study date.
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Google documents user-based Lift for Video, Discovery and Demand Gen in Google Ads and advises consulting a representative about some other campaign types. Geography-based measurement supports more campaign types and offline data, but it normally needs more budget and trades user-level detail for regional analysis.
Choosing between user-based and geography-based Lift
| Decision factor | User-based study | Geography-based study |
|---|---|---|
| Unit of comparison | Individual users assigned to treatment or control. | Geographic regions assigned to treatment or control. |
| Campaign availability | Google lists Video, Discovery and Demand Gen in Google Ads; ask a representative about other types. | Supports a wider set of campaign types. |
| Offline data | Not identified as the broad advantage in the setup comparison. | Can support offline data. |
| Demographic analysis | Can provide user-level slices when the study and reporting support them. | Does not provide the same demographic slices. |
| Statistical trade-off | Finer assignment, subject to account eligibility and user-level measurement limits. | Regional split is noisier. |
| Budget tendency | Uses the user-based thresholds and study requirements that apply to the account. | Required budget tends to be higher. |
Use the method that matches the decision you need to make and the data you can legally and operationally use. Do not select geography-based measurement merely because it is available to more campaign types; the extra regional noise can make a modest effect harder to distinguish.
How to plan a user-based Conversion Lift study
- Confirm access before planning around the result. Check whether Conversion Lift is available in the account and contact a Google account representative if the setup flow or documentation directs you to do so.
- Check volume and budget. Compare the account’s recent observed conversion volume with the current 1,000-conversion guidance and verify that the measured campaigns meet the documented $5,000 USD minimum budget for the described setup.
- Choose a compatible conversion action. Decide which business outcome the study should measure and confirm that the action is supported. Keep ordinary conversion tracking active; Lift does not replace it.
- Define the campaign set. Include campaigns that share campaign type, audience and targeting strategy when they could otherwise expose the control group. Document exclusions so stakeholders know exactly what the result covers.
- Set the study window around conversion lag. Use a period long enough for the relevant purchase or sales cycle, and follow Google’s current minimum-duration and account-specific recommendations. A short window can undercount delayed outcomes.
- Review the assignment and holdback design. Make sure the control condition is genuinely protected from the measured ads and that other campaigns will not erase the treatment-control contrast.
- Monitor certainty and modeled components. Plan to report the confidence or credible interval, any modeled conversions and any delayed or supplementary reporting separately from directly observed components.
- Decide in advance how the result will be used. Define whether the decision concerns budget, channel mix, campaign expansion or a pause. This prevents a statistically uncertain estimate from being treated as a binary winner-or-loser verdict.
How to plan a geography-based study
- Verify that regional measurement fits the business. Confirm that conversions can be compared across regions and that offline outcomes are available in a form the study supports.
- Select comparable regions. Treatment and control regions should be sufficiently similar for a regional comparison; document major differences in market size, seasonality or media availability.
- Include the relevant campaign coverage. A region should not be labeled “control” if other unmeasured campaigns continue reaching it and materially change exposure.
- Budget for greater noise. Because regional splits are noisier and Google says geography-based studies tend to require more budget, allow enough spend and volume for the estimated effect to be distinguishable.
- Interpret at the regional level. Do not assume that a geography-based result supplies the same demographic detail as a user-based study.
How to interpret a Lift result responsibly
Read the interval, not only the point estimate
Google describes confidence or credible intervals around Lift results. A point estimate such as “200 incremental conversions” is incomplete without the uncertainty range and certainty level. A result near zero with a wide interval may not support a strong budget change even if the point estimate is positive.
Put relative lift beside its baseline
Relative lift divides incremental conversions by control conversions. When the control count is small, the percentage can look dramatic even though the absolute number of additional conversions is limited. Report the control count and incremental count next to the percentage, and avoid comparing lift percentages from studies with materially different baselines.
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Separate incremental and attributed economics
Use incremental CPA and iROAS for decisions about the estimated additional outcomes. Use standard CPA and ROAS for decisions tied to the account’s normal attributed reporting. Mixing the two can make a campaign appear profitable under one definition and unprofitable under the other without any arithmetic error.
State what the study did not measure
A user-based study covers the selected campaigns, audience and conversion action during its study window. A geography-based study covers the selected regions and their conditions. Neither result automatically generalizes to campaigns, markets, conversion actions or seasons outside that design.
Common failure modes and fixes
| Failure mode | Why it causes trouble | Better practice |
|---|---|---|
| Calling attributed conversions “incremental.” | Attribution does not observe the no-ad counterfactual. | Reserve “incremental” for the controlled Lift estimate. |
| Changing attribution settings to explain a Lift result. | Lift intentionally ignores standard tracking settings and attribution rules. | Analyze the experiment and the tracking report as separate measurements. |
| Running below the documented volume or budget guidance. | Low volume reduces the ability to detect a difference and may prevent access. | Check current eligibility and seek account-specific guidance before setup. |
| Ending the study before delayed conversions arrive. | Long-lag outcomes may be missing from the observation window. | Match duration and post-study reporting to the conversion cycle. |
| Leaving overlapping campaigns outside the study. | Control users or regions can still receive similar advertising. | Include campaigns sharing audience, campaign type and targeting strategy where appropriate. |
| Reporting a large percentage with no denominator. | A small control count can inflate relative lift. | Show control conversions, incremental conversions and the uncertainty interval together. |
| Hiding modeled or projected components. | Readers may assume every conversion was directly linked to an interaction. | Label modeled, supplementary and delayed components explicitly. |
| Assuming Lift must be positive. | An experiment can estimate no measurable increase or a negative result. | Let the interval and study design determine the decision. |
How to use both systems in one measurement program
A practical sequence is to use tracking continuously and Lift periodically for causal validation:
- Maintain tracking as the operating system. Keep conversion actions, values and reporting settings stable enough for bidding and trend analysis.
- Use tracking to identify a decision. For example, a campaign may receive substantial attributed value, but the business may still need to know whether the spend creates additional demand.
- Run Lift when the account qualifies. Select a study design, campaign set, conversion action and duration that match the decision.
- Compare like with like. Put attributed CPA/ROAS in one column and incremental CPA/iROAS in another; do not substitute one for the other.
- Apply the result within its scope. A result for selected Demand Gen campaigns, for example, should not automatically be applied to every channel, region or future season.
- Document uncertainty and timing. Preserve the interval, certainty level, modeled share and any delayed-conversion update alongside the headline estimate.
This division of labor prevents a common measurement error: using an attribution report to make a causal claim, or using one Lift study as if it were a permanent replacement for routine conversion reporting.
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- The exact conversion action and campaign set measured.
- Whether the study was user-based or geography-based.
- Treatment and control conversion counts.
- Incremental conversions and relative lift, with the control denominator.
- Incremental CPA, iROAS and the spend and value definitions used.
- The confidence or credible interval and certainty level.
- Any modeled, supplementary or delayed incremental conversions.
- Study dates, conversion-lag assumptions and the regions or audiences covered.
- Eligibility or design limitations that prevent generalizing the result.
Bottom line
Conversion tracking reports attributed performance under the rules configured in your Google Ads account. Conversion Lift uses a treatment-versus-control experiment to estimate the additional conversions and value generated by advertising. Keep tracking for everyday optimization, and use Lift—when the account qualifies and the study is designed correctly—to answer whether the ads changed outcomes that would otherwise have happened without them.
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