PPC reporting has always been both essential and frustrating. It’s essential to keep clients engaged by informing them of the results you’re driving.
But it’s also frustrating because of data discrepancies, cumbersome analysis, and the time required to share understandable, jargon-free reports with different stakeholders.
Fortunately, AI is turning these obstacles into opportunities by filling in gaps left by privacy-compliant tracking, surfacing insights hidden in overwhelming data sets, and automating reporting so it meets the needs of every stakeholder.
In this article, I’ll walk you through some of the technology used by modern marketers and share examples of how I’ve used AI to streamline my PPC reporting.
1. Collect Complete And High-Quality PPC Data
We need data to guide us before we can optimize accounts and share our wins, so let’s start there.
The Problems With Data Before AI
Inconsistent and missing data plague PPC efforts.
Google, Meta, Microsoft, and Amazon operate in their own silos, each taking credit for all conversions that have any touchpoint with their platforms. This leads to double counting, making it difficult to decide where to allocate budgets for optimal results.
In other words, the data between the various ad platforms is inconsistent. Specifically, the conversion value advertisers see in their business data may be lower than the sum of all conversion values reported by the ad platforms.
Add to this the challenge of missing data. Privacy regulations like GDPR and Apple’s iOS changes limit tracking capabilities, which causes data loss, incomplete conversion paths, and gaps in attribution.
Marketers who rely heavily on pixel-based or third-party cookie tracking, both of which became unreliable due to browser restrictions and user opt-outs, see a continuous decline in the quality of the data they need to operate.
While AI can’t magically give us perfect data, it can fill in gaps and restore insights, so let’s take a look at some of the solutions in this space.
AI-Driven Solutions For Data Hygiene And Compliance
1. Data Clean Rooms And Privacy-First Measurement
Clean rooms like Amazon Marketing Cloud (AMC) and Google Ads Data Hub allow advertisers to securely analyze anonymized cross-channel performance data without violating privacy laws.
These platforms aggregate data from multiple sources, giving marketers a comprehensive view of the customer journey.
Example:
A retail brand can use AMC to evaluate how its Google and Facebook ads influence Amazon purchases. Based on what they find, they can re-allocate budgets between platforms to maximize overall return on investment (ROI).
Clean rooms themselves aren’t an AI innovation; however, they benefit significantly from several AI capabilities.
For example, Meta’s Advantage+ uses clean room insights to build lookalike audiences while staying privacy-compliant.
2. Modeled Conversions
While clean rooms are great for unifying cross-platform data, their usefulness is predicated on data completeness.
When privacy regulations make it impossible to get all the data, clean rooms like Google Ads Data Hub and Amazon Marketing Cloud use AI-powered modeled conversions to estimate user journeys that can’t be fully tracked.
Modeled data is also used by tools like Smart Bidding, which leverages machine learning to predict conversions for users who opted out of tracking.
For users who opt out of tracking, Consent Mode still allows the collection of anonymized signals, which machine learning models can then use to predict conversion likelihood.
Example:
Google’s Smart Bidding leverages machine learning to optimize bids for conversions or conversion value.
In cases where conversion data is incomplete due to user consent choices or other factors, Smart Bidding can use modeled conversions to fill in gaps and make good bidding decisions.
The models do this by identifying patterns and correlations between user attributes, actions, and conversion outcomes.
While modeled conversions offer significant benefits in their ease of use (they’re basically provided without any extra effort by the ad platforms), it’s important to remember that they are only estimates and may not be perfectly accurate in all cases.
Advertisers should consider using modeled conversions in conjunction with other ways to get a more complete picture of campaign performance.
For example, advertisers can use Media Mix Models (MMM), a Marketing Efficiency Ratio (MER), or incrementality lift tests to validate that the data they are using is directionally correct.
3. Server-Side Tagging And First-Party Data Integration
Server-side tagging lets marketers control data collection on their servers, bypassing cookie restrictions.
Platforms like Google Tag Manager now support server-side implementations that improve tracking accuracy while maintaining privacy compliance.
Server-side tagging captures anonymous pings even when cookies are declined, feeding better signals into Google’s AI models for more accurate conversion modeling.
This gives AI more complete data when doing things like data-driven attribution (DDA) or automated bidding.
Example:
An ecommerce company transitions to server-side tagging to retain high-quality data even when technologies like Safari’s Intelligent Tracking Prevention (ITP) break JavaScript-based tracking.
As a result, the advertiser sees a complete picture of all the conversions driven by digital marketing and can now justify higher bids, which makes them more competitive in the ad auction and boosts total sales for their brand.
Actionable Tips:
- Implement GA4 Consent Mode and server-side tagging to maintain accurate performance data.
- Leverage data clean rooms to analyze cross-platform conversions securely.
- Use modeled conversions to fill tracking gaps caused by privacy restrictions.
2. Extract Data Insights And Make Smarter Decisions
Now that we’ve covered technologies that can stem the decline in access to data, let’s examine how AI can help make sense of it all.
The Problem With Data Analysis Before AI
Marketers may struggle to extract actionable insights when looking at a mountain of PPC data.
Humans simply aren’t as good as machines at detecting patterns or spotting anomalies in large data sets.
While statistical methods have long been used to find these patterns, many marketing teams lack the expertise to do it themselves or have no access to a qualified analyst to help them.
As a result, teams miss opportunities or spend more time than they can afford looking for signals to guide optimization efforts.
AI Solutions For Data Analysis And Attribution
1. Data-Driven Attribution Models (DDA)
DDA isn’t the newest solution in attribution modeling, but it exists largely because AI has become cheaper and more accessible.
It solves the problem of assigning values to different parts of the consumer journey when users take a multitude of paths from discovery to purchase.
Static attribution models lack the sophistication to account for this and cause advertisers to bid incorrectly.
Google’s data-driven attribution (DDA) uses machine learning to analyze conversion paths and assign credit based on a more complete analysis of a user’s consumer journey.
Unlike static models, DDA dynamically adjusts credit allocation to reflect the many ways consumers behave.
Machine learning, a form of AI, is what enabled Google to make this more advanced attribution model available to all advertisers and what has driven the steady improvement in results from Smart Bidding.
2. Automating Auction Insights Visualization
Generative AI is not only enhancing attribution but also automating repetitive tasks.
Recently, I tested GPT Operator to streamline several PPC reporting workflows.
Operator is OpenAI’s tool that lets the AI use a web browser to achieve tasks. It goes beyond searching on the web; it allows you to follow links, fill in forms, and interact intelligently with websites.
In one task, I asked Operator to download auction insights, visualize the data using Optmyzr’s Auction Insights Visualizer, and email a report.
It handled the data transfer and visualization steps flawlessly, though it struggled with taking a clean screenshot instead of attempting to attach HTML.
Illustration by author, February 2025This illustrates how AI agents can help when data lives in disparate places. There are no APIs available to move it, as is the case with auction insights data from Google.
While Operator still needs too much hand-holding to be helpful today, it seems likely that we’re less than a year away from when it can do many tedious tasks for us.
3. Advanced Statistical Analysis Available To Anyone
Before AI advancements, conducting a statistical analysis could be a labor-intensive process requiring specialized software or data science expertise.
But today, generative AI enables marketers to explore these areas that were previously firmly outside their realm of expertise.
For example, GPT can explain and execute a process like a seasonality decomposition. AI can quickly write Python code that breaks down campaign data into trend, seasonal, and residual components, helping marketers uncover patterns they can act on.
How AI Automates Seasonal Analysis
In one of my PPC Town Hall podcast episodes, Cory Lindholm demonstrated how GPT can handle complex seasonality analysis in minutes.
Inspired by this, I used GPT’s Advanced Data Analysis feature to upload weekly Google Ads data and run a full decomposition.
GPT efficiently cleaned the data, identified issues like formatting errors, and generated a breakdown of trends, seasonal variations, and residual fluctuations.
In the analysis, GPT flagged recurring trends, allowing me to pinpoint peak demand periods and optimize bid strategies ahead of time. Tasks that previously took hours now take just a few minutes.
On a side note, I have found large language models (LLMs) so helpful with coding that I am now using v0.dev almost weekly to create apps, browser extensions, and scripts on a weekly basis.
3. Communicate Results Effectively Across Teams
With solid data in place and AI-fueled ways to speed up analysis, we should have some great results to share with stakeholders.
But sharing results through reports has traditionally been one of the most time-consuming and least loved tasks that fall on the plate of the typical account manager. And there were other problems, too.
The Problem With Sharing Reports Before AI
Reports were often static, one-size-fits-all documents that failed to meet the needs of different stakeholders.
Executives required high-level summaries focused on ROI, marketing strategists needed cross-channel insights, and PPC specialists required detailed campaign data.
Customizing reports for each audience was time-consuming and prone to error.
AI Solutions For Tailored Reporting
1. LLM Report Summarization
LLMs such as Claude, Gemini, and ChatGPT can quickly generate different explanations of reports from the same underlying data, enabling efficient customization for each audience.
For example, ChatGPT can produce a concise executive summary alongside a more detailed keyword-level report for PPC teams.
But that customization can and should be taken even further. In OpenAI, it’s possible to create custom GPTs, each with its own instructions. This can be used to create a different ChatGPT flavor for every client.
Whereas today, agencies depend on their people to remember how each client likes to get their reports, GPT can be trained to remember these preferences.
Things like how well they know PPC, what jargon they tend to use at their company, and even what the year’s strategic initiatives are.
Then, the LLM can word the summary in a way that resonates with the reader and even explain how the search marketing campaign’s results are key to the company’s strategic objectives for the year.
2. Interactive Dashboards For Real-Time Transparency
AI-driven dashboards provide live, customizable views of campaign performance. Stakeholders can explore data interactively, filtering by date ranges, platforms, or key performance indicators (KPIs), reducing the need for frequent manual report updates.
And while dashboards have been around for a long time, AI can be used to quickly highlight the most salient insights.
For example, AMC lets marketers use AI to generate SQL to explore the data by using natural language.
At my company, Optmyzr, we deployed Sidekick, which can instantly answer questions about data in any account, for example, the biggest optimization opportunities or wins in the last month.
Before AI, these insights might have remained hidden in the data.
Actionable Tips:
- Set up custom GPTs for every client you work with.
- Implement reporting tools that use natural language to explore the data.
Conclusion: From Reporting To Strategic Decision-Making With Generative AI
Generative AI has redefined PPC reporting, transforming a once fragmented and time-consuming process into a streamlined, insight-driven workflow.
It doesn’t just automate data collection and report generation; it also surfaces hidden trends, correlations, and anomalies that might otherwise go unnoticed.
This enables marketers to make smarter, faster, and more strategic decisions based on real-time insights.
With AI-driven tools, marketers can see beyond surface-level metrics, discovering patterns and opportunities that traditional reporting might take hours or days to uncover.
This improved understanding of performance empowers teams to refine budget allocation, creative strategy, and campaign targeting more effectively, leading to more substantial outcomes and greater profitability.
The conclusion is simple. With Generative AI, PPC managers have more complete data, leading to better insights and better decisions – all of which can be shared more meaningfully with all involved stakeholders.
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Featured Image: Igor Link/Shutterstock
