When you analyze marketing performance with AI, the accuracy of the results depends on how the data was prepared. Google Ads, Meta Ads, GA4 and Search Console can each report the same performance using different measurement methods. If those differences are ignored, the conversion value reported by an ad platform can be confused with actual sales revenue. When preparing data for AI, AdsLuma makes it easier to access data from connected marketing accounts, so you can review the performance of different channels in one place.
To get better marketing insights from AI, start by defining the right account, date range and metrics. Performance results in the AdsLuma dashboard can be interpreted with Luma AI, while the MCP connection lets you query advertising, analytics and SEO data in compatible AI clients. With either method, you need to check data freshness, account for measurement differences and define your analysis question clearly.
Which Marketing Data Do You Need for AI Analysis?
Before you start the analysis, decide which question you want answered. Why ad costs are rising, why conversions are dropping, or which pages have lost organic traffic are all investigated with different data. The sources you use in AdsLuma can be chosen based on these questions.
When examining campaign costs, you can evaluate spend, clicks, conversions and CPA together. To find the search terms that drain your Google Ads budget, you can use the cost and conversion data of the queries. To understand shifts in traffic sources, you can look at GA4 sessions, user counts and revenue figures.
On the organic side, Search Console's click, impression and average position data helps you investigate which pages are losing visibility. For Shopping and Performance Max campaigns, you can examine sales performance through product-level spend, conversion and revenue data.
How Do You Prepare Data for Analysis with AdsLuma?
When running an analysis in AdsLuma, you first select the relevant ad account and date range. For agencies managing multiple client accounts, working from the correct workspace prevents data from different brands from getting mixed up.
The next step is to define the metrics you will compare. For example, if you want to compare the last 14 days with the previous 14 days, it is more reliable to work with completed days. You should also keep in mind that recent conversions may be updated later.
Conversion and revenue values reported by different platforms should not be treated as the same measurement. The differences between Google Ads and GA4 usually stem from attribution and reporting methods. You can find the details in our guide on Google Ads and GA4 conversion differences.
How Do You Analyze Campaign Performance with Luma AI?
Luma AI can answer performance questions by taking into account the campaign, channel and date range you are viewing in the AdsLuma dashboard. When you work with the metrics on screen, you can start an analysis without preparing a separate CSV or Excel file.
Say the last 14 days of campaign results are open on your Google Ads screen. To find out which campaigns have seen a rise in cost per conversion, you can ask Luma AI directly.
Example Luma AI question:
Identify the Google Ads campaigns that saw a CPA increase in the last 14 days. Compare the changes in spend, clicks and conversions. Show which metrics accompanied the cost increase. Also point out possible causes that the data does not explain.
The goal here is to see the impact of changes in spend and conversions, rather than interpreting the cost increase through CPA alone. After reviewing Luma AI's findings, you can decide which campaigns need a closer look.
How Do You Pull Marketing Data with AdsLuma MCP?
With the AdsLuma MCP (Model Context Protocol) connection, supported data from sources such as Google Ads, Meta Ads, GA4 and Search Console can be accessed through compatible AI clients like ChatGPT or Claude. This lets you query the performance of specific periods without downloading manual reports from different platforms.
You can use query_metrics to get the overall results of connected accounts and list_campaigns to list Google Ads and Meta Ads campaigns. Teams that want to compare campaigns by spend and conversion results can narrow their queries by defining the relevant date range.
On the Google Ads side, the get_search_terms query lets you investigate the searches users make. With this data, you can see searches that spend budget but generate no conversions. When preparing negative keyword suggestions, you should also check the search intent of the query and whether it has enough data volume.
Teams that want to evaluate website traffic can access GA4 source and medium information through get_traffic_sources. To examine the Search Console and GA4 performance of organic pages, you can use get_landing_pages. The latest sync status of your integrations can be checked with the get_sync_status query.
Example: Comparing Ad Performance with MCP
Imagine a marketing manager who wants to compare Google Ads and Meta Ads results for the last 28 completed days. After selecting the relevant workspace through the AdsLuma MCP connection, you can check the data sync status. Then you can query the campaign performance of both platforms.
Example MCP prompt:
From my AdsLuma connection, pull the Google Ads and Meta Ads campaign data for the last 28 completed days. First check the data sync status. Compare the campaigns by platform in terms of spend, clicks and conversions. Also compare against the previous 28-day period and identify the campaigns with the largest cost increase. Explain the possible causes using the relevant metrics. Do not directly add up the conversion values of different platforms.
During the query, get_sync_status returns the integration information, while list_campaigns returns the campaign results for the selected periods. This way, the cost and conversion changes of each platform can be examined within its own measurement conditions.
The MCP connection is read-only. Any campaign changes recommended as a result of the analysis are not applied automatically to your ad accounts.
What Is the Difference Between Luma AI and MCP?
The key difference between Luma AI, which works inside the AdsLuma dashboard, and the MCP connection is where the data is accessed. Luma AI uses the performance screen the user is viewing as its context; for example, while examining a specific Google Ads campaign, you can ask about a cost increase or a change in conversions.
MCP, on the other hand, provides access to supported data from sources connected to AdsLuma through external AI clients. In ChatGPT, you can retrieve the results of specific campaigns, compare different date ranges, or run a separate analysis of GA4 traffic sources.
You can use Luma AI to examine performance in the dashboard and MCP to query data in an external environment. The choice depends on what data your team needs and where it wants to run its analysis.
How Should You Check Data Quality in AI Analysis?
When pulling data through AdsLuma, check that the reporting period and sources are up to date. The get_sync_status query in MCP shows the latest sync information for your integrations. That said, a successful sync does not guarantee that all conversions in the ad platforms are final.
When working with manual reports, missing or duplicated records can also lead to faulty conclusions. For example, if a campaign's 5,000 TRY spend is added to a file twice, CPA is calculated higher than it actually is. You can query the cost for the same period through AdsLuma MCP and compare it with the figure in the manual report.
When interpreting performance changes, you should also account for developments inside the company. If stock issues, price changes or promotion dates are not present in the queried data, you can provide that information to the AI separately. This way, possible causes of the campaign change other than ad performance can also be explored.
Checklist Before Starting AI Analysis with AdsLuma
Completing the basic checks before running an analysis with Luma AI or MCP helps prevent reporting on the wrong account or period, especially for teams that manage multiple clients.
Are the AdsLuma workspace and ad account correct?
Have the periods and metrics to be compared been defined?
Has the latest sync status of the connected data sources been checked?
Are Google Ads, Meta Ads and GA4 conversions being evaluated according to their own measurement rules?
Are there any missing or duplicated records in manual reports?
Have stock, price and promotion changes been included in the analysis?
Have the numerical results provided by the AI been compared against the source reports?
When working with external AI clients, your company's privacy and security rules on sharing customer data should also be taken into account.
From Marketing Data to Insight with AdsLuma
To evaluate marketing data with AI, start by selecting the right account, period and metrics. While monitoring the performance of different ad channels in AdsLuma, you can ask Luma AI questions about the campaigns you are reviewing. The MCP connection also gives external AI clients access to supported data in the same ecosystem.
For a first run, you can review the last 28 completed days of your Google Ads account's performance with Luma AI. When you need a more detailed comparison, you can pull campaign data through MCP and compare it with the previous period. Once the results are verified, you can set optimization priorities for the campaigns with cost increases or conversion losses.
Frequently Asked Questions
Do I need to prepare data manually for AI analysis with AdsLuma?
Supported performance data from ad accounts connected to AdsLuma can be viewed in the dashboard. Luma AI can answer questions about the metrics on the screen you are reviewing, while the MCP connection lets you query data from external AI clients. Business information that is needed for the analysis but not available on the platform can be shared separately.
Which marketing data can Luma AI use?
Luma AI can include the campaign, channel and date range the user is reviewing in the AdsLuma dashboard as analysis context. Its answers are based on the information on the relevant screen. For different analysis needs, you first need to select the right performance screen and period.
Can I run analysis in ChatGPT with AdsLuma MCP?
Once the MCP connection is authorized, AdsLuma data can be queried in supported ChatGPT environments. Specific performance information from Google Ads, Meta Ads, GA4 and Search Console sources can be analyzed. Because the connection is read-only, no changes are made to campaign settings.
How do I check data freshness with AdsLuma MCP?
You can get the latest sync information for connected sources through MCP with the get_sync_status query. From there, you can review the success or error status of your integrations. You should also keep in mind that recent conversions may change later when comparing periods.
What is the difference between Luma AI and AdsLuma MCP?
Luma AI is used to examine performance results in the AdsLuma dashboard. MCP provides access to supported data from ChatGPT or another compatible AI client. You can choose one of the two methods depending on whether the analysis will be done inside the dashboard or in an external environment.
