Some campaigns cannot be included in optimisation because they fail one or more eligibility checks. These checks exist to ensure the Budget Optimiser only generates recommendations when there is enough evidence to produce reliable results.
Even when a campaign is not optimisable, it may still contain useful signals. Alvie will always explain what is preventing optimisation, and the guidance below outlines what would need to change for the campaign to become eligible.
1. Low data sufficiency
Data sufficiency refers to whether a campaign has accumulated enough recent and usable activity to support reliable modelling of its performance behaviour.
When there is not enough historical data available, the Budget Optimiser cannot fit a response curve with confidence, as there is insufficient evidence to describe how the campaign behaves under different conditions. This situation often arises when a campaign is newly launched, has been paused for periods of time, receives very low traffic, or has gaps in tracking or platform reporting.
In practice, this means the model does not yet have a stable enough foundation of observations to distinguish signal from noise.
What can help:
• Ensure the campaign remains active during the reporting period
• Increase the number of days with measurable activity within the lookback window
• Verify that spend and click data are being collected consistently
• Encourage additional traffic where the campaign is expected to scale
2. Low fit quality
Fit quality describes how well the modelled response curve aligns with the campaign’s observed historical performance.
In cases where the relationship between spend and performance is not sufficiently consistent, the model is unable to identify a reliable pattern that it can safely generalise from. This does not imply that the campaign is performing poorly, but rather that the underlying behaviour is too irregular, or too influenced by external factors, to be modelled with confidence.
This typically occurs when performance is highly volatile, when significant campaign changes have been made during the modelling period, when tracking has shifted, or when a small number of atypical periods disproportionately influence the data.
What can help:
• Build a longer and more stable performance history
• Identify and account for outlier periods where possible
• Avoid frequent or major structural campaign changes during the modelling window
• Review tracking setup and data consistency
3. Low spend variation
Spend variation refers to the extent to which a campaign’s spend has changed over time, and whether those changes are sufficient for the model to learn how performance responds at different budget levels.
When spend remains largely constant, the Budget Optimiser has limited ability to observe how changes in investment affect outcomes. As a result, it cannot reliably estimate the relationship between spend and performance, because the necessary variation in input conditions is missing.
What can help:
• Allow spend to fluctuate more naturally over time
• Collect performance data across a wider range of budget levels
• Avoid holding the campaign at a fixed spend level for extended periods
4. Low spend variation after search demand adjustment
For search campaigns, spend variation is adjusted to account for changes in available search demand. This ensures the model distinguishes between true budget changes and shifts driven by fluctuations in market demand.
After this adjustment, some campaigns may still show insufficient effective variation in spend, meaning that most observed changes can be explained by underlying changes in search volume rather than deliberate budget movement.
In these cases, the model still does not have enough independent variation in spend to learn how performance responds.
What can help:
• Collect longer history across different effective spend levels
• Allow budgets to vary more independently of demand fluctuations
• Ensure impression share and search demand data are complete and reliable
5. High impression share
High impression share indicates that a campaign is already capturing a large proportion of the available search demand.
When this happens, the campaign is often operating close to saturation, meaning there is limited remaining demand to expand into. This makes it difficult for the Budget Optimiser to estimate additional upside from increased spend, since incremental budget is less likely to translate into additional impressions or conversions.
What can help:
• Expand targeting where appropriate, such as keywords, geography, or inventory
• Assess whether the campaign is already near saturation
• Consider that scalability may be naturally limited in this state
6. Unsupported channel
The campaign is assigned to a channel that is not currently supported by the Budget Optimiser.
This usually occurs when a campaign is categorised as “Other” or when its configuration does not clearly map to a supported channel structure, making it unsuitable for modelling within the current system.
What can help:
• Review and correct channel mapping where appropriate
• Assign the campaign to a supported channel if applicable
• Check configuration and naming consistency to ensure correct classification
7. Excluded channel
The campaign belongs to a channel that has been explicitly excluded from optimisation.
Unlike other cases, this is not driven by data quality or modelling limitations, but rather by configuration choices that define which channels are included in the Budget Optimiser.
What can help:
• Confirm whether exclusion is intentional
• Update configuration if the channel should be included
• Review whether the campaign should now fall within scope
8. Not enough conversion volume
The campaign does not currently generate enough conversion activity to support reliable optimisation.
The Budget Optimiser relies on sufficient conversion data to estimate value accurately, ensuring that recommendations reflect meaningful relationships between spend and outcomes. Without enough conversion evidence, particularly in attribution based setups, the model cannot confidently assign value to changes in budget.
What can help:
• Accumulate more conversion history over time
• Validate conversion tracking implementation
• Review attribution settings for completeness and accuracy
• Consider optimisation at a higher aggregation level if appropriate
• Focus spend where it is expected to generate measurable conversion volume