Compare ads management tools by scoring them against the way your agency actually runs accounts, not against the length of a feature list. Use the same criteria for every vendor, then run a controlled two-week trial on a real account to see whether the tool produces accurate data, useful recommendations and changes your buyers can verify and reverse.
An ad software scorecard for agencies should cover platform support, client-specific targets, approvals, rollback, data sources, testing, creative analysis, reporting, access and pricing. The scorecard turns a polished demonstration into an operating decision.
Build the scorecard before you watch a demo
Start with your current process. Write down how a buyer moves from account data to a decision, who approves that decision, how it reaches the platform and how the team checks the result.
This matters because two tools can claim to cover the same task while supporting very different workflows. One might summarize performance. Another might recommend a budget change but leave execution to the buyer. A third might write an approved change to the ad account and verify that it landed.
Those are different products, even if every vendor uses similar language.
Give each category a weight based on your agency. Use a simple scale such as:
| Score | Meaning |
|---|---|
| 0 | Missing |
| 1 | Available with a major workaround |
| 2 | Usable, but limited |
| 3 | Fits the current process |
| 4 | Improves the process without removing control |
| 5 | Fits the process and produces clear evidence |
Do not let every category carry equal weight by default. A team managing Google and Meta may care more about approval controls than TikTok support. An agency with many similar locations may give more weight to grouped account views. An in-house team with several buyers may care more about comparing portfolios.
Your scorecard should also separate present capabilities from roadmap statements. Score what you can use during the trial. Record coming features in a notes column, but do not include them in the current score.
Score the mechanics that affect daily account work
The best questions are operational. Ask the vendor to show each function using a workflow that resembles yours.
1. Platforms covered
List every platform your team manages now and expects to manage soon. Then check whether the tool can do more than display totals.
For each platform, ask:
- Can it read account, campaign, ad group or ad set, ad and creative data?
- Does it recommend changes?
- Can it apply an approved change?
- Does it confirm that the change reached the platform?
- Are important platform features missing?
A combined dashboard is useful, but it is not the same as a combined operating layer. Score reporting and action support separately.
2. Targets and account structure
Agencies rarely judge every campaign by one account-level target. Brand, lead generation, remarketing and prospecting campaigns may have different jobs.
The tool should support the hierarchy your buyers use. Check whether it can set:
- An account target as a fallback.
- A CPA or ROAS target for an individual campaign.
- A rule for a group of campaigns.
- Different targets across clients or similar account groups.
- Constraints that protect campaigns the buyer does not want changed.
Example: Say a campaign has a $50 target CPA. Ask the vendor to show how that target is stored, how it affects a recommendation and what happens when the account-level target is different. Then ask how the tool handles campaigns judged by link cost per click instead of CPA.
A useful tool should explain which target it used. If buyers must infer that from a chart, the recommendation is harder to trust.
3. Approval, execution and rollback
Determine exactly what happens between a recommendation and an account change.
Your scorecard should answer:
| Control | What to verify |
|---|---|
| Approval | Nothing changes before the authorized person approves it |
| Scope | The approval identifies the account, campaign, setting and proposed value |
| Reasoning | The tool shows why it recommends the change |
| Evidence | The supporting numbers are visible in plain language |
| Execution | The system records whether the platform accepted the change |
| Verification | The system checks that the intended value landed |
| Rollback | The buyer can reverse the change without rebuilding it manually |
| History | The team can see who approved, dismissed or reversed it |
Read-only access is a reasonable place to begin. If the tool can write changes, require an approval flow that is specific enough for a buyer to understand what will happen before pressing the button.
4. Data source and freshness
Ask where every displayed number comes from. The answer should distinguish platform data from modeled data, imported business data and AI-generated interpretation.
Check:
- Whether the source is the ad platform or another reporting layer.
- How often data is pulled.
- Whether the values are stored for later comparison.
- How attribution differences are handled.
- Whether the AI can write to the performance database.
- What happens when a platform connection fails.
- How the interface marks incomplete or delayed data.
The cleanest separation is simple: platform data is stored as data, and AI reads it to produce analysis. Recommendations should not be able to rewrite the underlying performance record.
5. Testing and learning
A recommendation is not proven because a buyer approved it. Look for a defined way to test the change and evaluate what happened afterward.
Ask the vendor to demonstrate:
- How a test is created.
- What stays unchanged as the comparison.
- When results are reviewed.
- How the tool handles low volume.
- Whether it can return an inconclusive result.
- Whether it explains why the result is inconclusive.
- How the result affects future recommendations for that account.
Be careful with systems that label every upward movement a win. A useful evaluation considers the account target, the test design and whether enough data exists to make a call.
6. Creative analysis
Creative reporting should follow the asset, not just the campaign name. The same image or video may run in several campaigns or on more than one platform.
Ask whether the tool can identify the same asset across placements and show its combined performance. Then check which fatigue signals it uses, such as rising frequency, CPM or CPC alongside a worsening CPA.
The tool should recommend an action and show the evidence. It should not imply that it can generate or replace creative unless that capability is actually included.
7. Reporting and portfolio views
Test reporting at three levels:
- The individual account.
- The client or account group.
- The agency portfolio.
A portfolio view should let a leader compare properties without forcing every client into the same success measure. Combined metrics can help with oversight, but each account still needs to be judged against its own targets.
Check whether reports can be scheduled, exported as PDF or CSV and traced back to the source data. Also decide whether your team needs written narrative reports now or whether clean tables and explanations are enough.
Compare access, ownership and pricing without surprises
Access design tells you a great deal about how a product expects agencies to work.
The client should retain ownership of the ad account. The agency or software provider can be added at the required access level, then removed without taking the account history with it. Confirm the process for Google, Meta and every other supported platform because connection methods differ.
Ask these questions before connecting anything:
- Who owns the account and its historical data?
- What permission level does the software require?
- Can the trial begin read-only?
- Who can approve changes?
- Can approval rights differ by property or buyer?
- What happens when an employee leaves?
- How is access removed?
- Does removing the software affect the original ad account?
Then compare the complete pricing model. Common structures include fixed subscriptions, tiers based on usage and charges tied to media spend. None is automatically right or wrong.
Use a cost grid based on your own portfolio:
| Pricing check | Vendor A | Vendor B | Vendor C |
|---|---|---|---|
| Base subscription | |||
| Cost as clients are added | |||
| Cost as spend grows | |||
| Platform or connector fees | |||
| Reporting fees | |||
| Onboarding or support fees | |||
| Expected cost at current use | |||
| Expected cost after growth |
Do not compare the entry price alone. Model the cost at your current portfolio and at the next likely stage of growth. For Ads Arsenal specifically, current terms belong on the pricing page.
Run a two-week trial on a real account
A sandbox can teach you the interface. A real account shows whether the software understands the decisions your buyers face.
Choose an account that is active but not unusually volatile. Get the appropriate client approval, define the trial scope and record the starting state. If the vendor supports read-only access, begin there.
Use this two-week plan for your ad software scorecard for agencies.
Before the trial
Document:
- The account target and any campaign-specific targets.
- Campaigns or settings the software must not touch.
- The buyer who can approve changes.
- The current reporting process.
- Known data or tracking limitations.
- How rollback will be tested.
- Which outcomes count as a useful recommendation.
During the first week
Review the data connection first. Compare the tool's account totals and campaign values with the source platform. Investigate any mismatch before judging the recommendations.
Then evaluate the queue:
- Does each recommendation identify the correct account and target?
- Is the reasoning specific?
- Can the buyer see the supporting numbers?
- Does the tool distinguish an urgent issue from a minor opportunity?
- Does it avoid repeating the same recommendation?
- Can the buyer dismiss a recommendation and explain why?
Approve only a small, reversible change after the team understands the proposed action. Confirm that it reached the correct account and setting. Then test the rollback process.
During the second week
Look for consistency. The tool should continue applying the account's targets and constraints instead of treating every pass as a new problem.
Review what happened to approved, dismissed and unchanged recommendations. Check whether the tool tracks an approved change after execution and whether its evaluation is clear about uncertainty.
At the end, have the buyer score the tool without the vendor on the call. The person doing the work should answer a direct question: did this make account decisions clearer, safer or easier to follow?
Turn the trial into a buying decision
Add a trial evidence column beside each original score. This prevents a strong demonstration from outweighing what your team observed.
A completed ad software scorecard for agencies might look like this:
| Category | Weight | Demo score | Trial score | Evidence |
|---|---|---|---|---|
| Platform coverage | ||||
| Account and campaign targets | ||||
| Approval controls | ||||
| Execution verification | ||||
| Rollback | ||||
| Data provenance | ||||
| Testing | ||||
| Creative analysis | ||||
| Reporting | ||||
| Access and ownership | ||||
| Pricing model |
Record the evidence in plain language. “Recommendation used the campaign target and showed the source numbers” is useful. “AI features looked good” is not.
Also list the workarounds your team would inherit. A feature can exist and still create extra steps. The final decision should account for workflow fit, buyer confidence, access requirements and total cost.
For a broader list of agency requirements, see PPC management software for agencies.
Frequently asked questions
How do you compare ad management tools?
Define your operating requirements before viewing demos, then score every tool against the same criteria. Test the leading option on a real account and require evidence for data accuracy, recommendation quality, approvals, execution and reporting.
What features matter most in an ads management platform?
The most useful features are the ones that support your actual workflow, including platform coverage, account-specific targets, clear recommendations, approval controls, rollback, testing and reporting. Data provenance and access controls matter as much as the interface.
How do you trial ad software on a client account safely?
Start with read-only access or require approval before every account change. Use a suitable real account, define protected settings, record the starting state and confirm that every approved change can be verified and reversed.
Should ad software charge a percentage of spend?
A percentage of spend can fit some service models, but it can also make software costs rise without a matching increase in work or value. Compare it with fixed and tiered pricing using your current spend and expected growth.
What questions should you ask an ad software vendor?
Ask where the data comes from, how often it refreshes, what the software can change and how approvals and rollback work. Also ask about platform coverage, testing, account ownership, onboarding, reporting, support and the complete pricing model.
Where Ads Arsenal fits today
Ads Arsenal runs Google, Meta and TikTok from one login. Each client can have its own property, or one property can cover a group of lookalike accounts, with several properties shown in one portfolio. Each account is judged by its own fallback, campaign and campaign-group targets.
Approve a change and Ads Arsenal writes it to the account, checks a few minutes later that it landed and tracks whether it worked. Nothing changes until a person approves it, every change has one-click rollback, and read-only access is available to start. Its numbers come from the ad accounts and are stored in a database each pass. The AI reads that data but does not write it.
Ads Arsenal is in a free design-partner beta for agencies and in-house teams running real spend on Google, Meta and TikTok. Become a design partner.