Regain Control Of Google Ads: Better Tracking, Clearer Proof Of ROI

Google Ads has steadily automated more of the decisions advertisers once managed directly.

Bidding is an obvious example, but the shift has gone much further. Google now has more influence over which searches ads can match to, where ads appear, how budgets get allocated, and even which creative combinations users see.

For advertisers, that changes where some of our most important decisions happen.

We may have less control over an individual auction than 10 years ago, but we still decide and guide what Google should optimize toward. Even more importantly, how we define success.

Those inputs become much more important when automated systems use them to make thousands of decisions we simply can’t manage individually.

That’s why measurement deserves more attention as Google Ads becomes more automated.

Conversion tracking shouldn’t just stop at counting leads, purchases, or other actions inside the platform. Marketers need to understand whether those conversions actually produce business outcomes they intended to generate.

That requires PPC expertise to extend well beyond campaign settings.

Where PPC Expertise Matters More Today

Having fewer manual controls doesn’t mean PPC teams have fewer decisions to make. Some of the decisions that remain can have a much larger impact on how automated campaigns perform.

Choosing the right conversion goals is one example. If a lead generation campaign optimizes toward form submissions, Google will use that goal to inform bidding. The PPC team still needs to determine whether a form submission is the right signal or whether a qualified lead, booked appointment, or closed sale better represents success.

The same applies to ecommerce. Revenue can tell part of the story, but it may not account for differences in profit margins, new versus returning customers, or which products the business wants to prioritize.

Making those decisions requires more context than Google Ads, or any platform, can provide on its own.

PPC teams need to:

• Understand how leads get qualified.
• What happens after they enter the CRM.
• Which products generate stronger margins.
• Which conversions eventually produce revenue.

That may also require closer coordination with sales, analytics, e-commerce, or other teams that own parts of that data.

This is where PPC expertise becomes especially important as campaign execution gets more automated. Advertisers still need to give Google clear goals and useful business data, then evaluate whether campaign performance matches what happens after the conversion.

A conversion that looks successful in Google Ads may be far less valuable once you evaluate the actual business outcome.

A Conversion Recorded Isn’t Necessarily A Good Conversion

Conversion volume can tell you how many people completed an action, but it doesn’t always tell you whether those actions produced meaningful results for the business.

A lead generation campaign, for example, may report a strong conversion rate, and an efficient cost per lead. If sales later finds that many of those leads are spam, unqualified, or unlikely to become customers, the Google Ads metrics only show part of the outcome.

That gap becomes more consequential when those same conversions guide automated bidding. If form submissions are the primary goal, Google can optimize toward generating more form submissions without knowing which leads eventually become qualified opportunities or customers. It’s not taking into consideration quality of any sort.

Google recommends using qualified or converted leads as conversion goals when advertisers can send that data back into Google Ads. Enhanced conversions for leads can also connect offline outcomes with the ad interactions that generated them.

Ecommerce advertisers can run into a similar problem when conversion volume or revenue doesn’t reflect the full value of a sale. Product margins, customer type, returns, and other business factors can change how valuable two purchases are, even when both look successful in the platform.

This doesn’t mean every advertiser needs to pass every possible business outcome into Google Ads. The goal is to identify which signals give automated bidding a better representation of the results the business wants to generate.

That requires measurement to do more than report what happened after someone clicked an ad. The data advertisers choose to measure and send back to Google can also influence how campaigns optimize going forward.

Measurement Is A Key Component Of Guiding Automation

As Google takes on more campaign execution, advertisers still have influence over the goals and data that guide those decisions.

That starts with defining what success looks like for the business. A primary conversion action tells Google which outcome to prioritize, while conversion values can provide additional context about which outcomes are worth more.

The measurement setup also determines what advertisers can evaluate on their end.

For example, cost-per-lead (CPL) may be the primary key performance indicator inside a Google Ads account, but qualified lead rate and customer acquisition cost (CAC) provide a better view of whether those leads produced results. Ecommerce advertisers may need to look beyond simple return on ad spend to understand profitability, new customer acquisition, or repeat purchase behavior.

Building that fuller picture often requires coordination outside the PPC team. Sales and CRM data can show which leads become qualified opportunities or customers, while ecommerce and analytics data can add context around profitability, returns, and customer behavior.

PPC teams don’t need to feed every piece of that data back into Google Ads. Some data can improve optimization, while other data helps advertisers evaluate whether the automation is producing the intended results.

Google’s upcoming migration of Local Services Ads into Performance Max provides a timely example of where both become important.

Advertisers who rely on LSAs should understand how those leads perform beyond the initial conversion. That includes whether leads are qualified, turn into booked jobs, and ultimately generate revenue.

Having that context becomes especially useful before the campaign environment changes. If lead volume or cost per lead shifts after the migration, advertisers will need their existing performance data to determine whether lead quality and business outcomes changed along with it.

Building that reference point before a major automation change gives advertisers something meaningful to compare against once the change takes effect.

See also: How To Measure PPC Performance When AI Controls The Auction

Build A Measurement Baseline Before Automation Changes

A useful baseline needs to capture more than just the metrics available in Google Ads.

Before a major campaign change or account launch, document enough performance data to understand both what the platform reports and what happens after the conversion. The exact metrics will vary by business, but the baseline could include:

• Conversion volume, conversion rate, and cost per conversion.
• Qualified lead rate and cost per qualified lead.
• Appointment, booking, or close rates.
• Revenue, conversion value, and ROAS where applicable.
• Performance differences across meaningful segments, such as location or service category.

For advertisers affected by the upcoming LSA migration, this means capturing those metrics before accounts move into Performance Max. It also means making sure the data covers a long enough period to account for normal fluctuations in lead volume, cost, and quality.

The comparison shouldn’t stop at whether Performance Max generates more leads or lowers cost per lead after the migration. Advertisers should also watch whether lead quality, booked jobs, close rates, and other downstream outcomes move in the same direction.

Give the new campaign enough time and data before drawing conclusions, especially if performance naturally varies from week to week. Comparing the same metrics before and after the migration can help separate normal volatility from changes that may warrant a closer look.

Search Engine Journal’s upcoming webinar, Google Local Services Ads Are Moving To PMax: What To Check First, will go deeper into that process with a before-and-after audit checklist, including what to verify before migration and what to monitor once LSAs move into Performance Max.

A baseline won’t prevent performance from changing, but it gives advertisers a more reliable way to understand what changed and whether those differences are showing up in actual business results.

Know What Success Looks Like Before Google Optimizes For It

Google will likely continue automating more of the decisions that go into running campaigns. As that happens, advertisers need to be clear about what they want those campaigns to accomplish.

That means looking beyond whether Google Ads hit a target CPA, ROAS, or another platform goal.

PPC teams need to know whether those results are turning into qualified leads, customers, revenue, or whatever the business ultimately cares about. They also need enough historical context to tell when performance has truly changed, especially when Google changes how a campaign works.

The upcoming LSA migration is yet another good reason for affected advertisers to take a closer look at their measurement now, but this won’t be the last Google Ads change that puts it to the test.

As more campaign decisions become automated, advertisers may have fewer opportunities to adjust every lever themselves. Knowing what a good result looks like, and having the data to measure it, gives them a much better way to judge whether that automation is working for the business.

More Resources:

• Surviving The Impression Squeeze: How Agentic Commerce Is Changing Google Ads In 2026
• Why Your AI Ad Strategy Is Only As Good As Your Data
• Google Ads Rolls Out Journey-Aware Bidding And New Pacing Controls For Advertisers

Featured Image: Summit Art Creations/Shutterstock

Many Google Ads accounts have a conversion tracking problem that is disguised as a strategy problem.

The ad account in this case has every action labeled as a “conversion.” The conversions are form fills, key button clicks, page views, cart adds, and checkouts started, which are all flowing into the same column, and all weighted equally. This less-than-ideal conversion setup is training Google’s Smart Bidding to optimize toward a vague composite of “engagement” instead of what matters for the advertiser.

When accounts are set up like this, the outcome is unfortunately predictable.

The campaigns look healthy inside Google Ads and on reports, the conversions look high, and the return on ad spend appears strong and efficient, yet none of it matches what the advertiser is actually experiencing inside the business. When the business team looks at their financial statements or the money in the bank, the story is different and doesn’t match. The advertiser isn’t growing, and doesn’t feel successful, and the internal reality doesn’t line up with what the Google Ads team claims.

The fix isn’t here isn’t to test another bid strategy.

The solution is to examine the conversion architecture. By using a primary‑versus‑secondary conversion framework, the ads manager can control what Google’s machine learning is allowed to learn from and, more importantly, what data the platform should ignore. When the conversion framework is applied correctly, primary and secondary conversions become an additional lever that actively shapes algorithm behavior and brings the account back into alignment with real business outcomes.

Here’s an example, and while the numbers are fictional, patterns like this show up in real accounts all the time.

A Performance Max campaign generated 4,000 clicks and produced 37 purchases, yet the ads platform reported a 62% conversion rate. That math only works when roughly 90% of the “conversions” are button clicks, form interactions, and abandoned checkouts being treated as equal to revenue. In practice, this looks like:

• Add‑to‑cart events counted as conversions, even when the user never returns.
• Checkout‑start events weighted the same as completed purchases, inflating return on ad spend.
• Button clicks or page scrolls logged as “micro‑wins,” overwhelming the real signals.

That is a signal‑to‑noise ratio of roughly 9:1 against the algorithm.

The Smart Bidding Signal Crisis

It’s important to reset how we think about Smart Bidding in Google Ads. Smart Bidding is not just a bidding tool; it’s a pattern‑matching engine. Google’s own documentation makes this clear when it explains that Smart Bidding evaluates audiences, the queries a user searched before and after, and a wide range of signals we can’t see. In other words, the bidding system isn’t optimizing for keywords in isolation; the bidding algorithm is optimizing for patterns in user behavior. And the patterns that are learned look at conversion architecture you feed it.

Every primary conversion you record teaches the algorithm what an “ideal customer” looks like. The model uses signals like device, time of day, audience cluster, query intent, landing page behavior, and more to find more users who match that pattern.

When the ad account is set up and mixes high-intent actions with low-intent micro-actions in the same primary pool, the model loses contrast. The bidding algorithm cannot distinguish a buyer’s pattern from a browser’s pattern, because the ad manager told it those two users represent the same outcome.

Many in the industry will say that Google Ads chases the easiest conversion. However, taking a step back, it is more than the system that does what it is designed to do. Yes, Google Ads takes the path of least resistance. This is because button clicks are vastly easier to generate than purchases. Cart adds are vastly easier than completed transactions. So the bidding algorithm aggressively hunts for users who do the easy things unless it is guided differently by the human in charge.

This is not a bug in Google Ads. It is the algorithm executing the instructions perfectly.

The Architectural Fix: Signal Engineering, Not Tag Management

The Primary vs. Secondary framework reframes conversion tracking from a reporting concern into an algorithmic training concern.

Two settings, two completely different jobs:

• Primary (Optimization): Populates the “Conversions” column. Actively used by Smart Bidding to train, predict, and bid. This is the algorithm’s curriculum.
• Secondary (Observation): Populates the “All conversions” column. Strictly ignored by the bidding strategy. This is the ad manager’s diagnostic layer.

The mistake many ad managers make is ignoring the secondary conversions. These are switches that determine which data the machine learning model is actually allowed to see during training.

Think of primary and secondary conversions as data architecture, not data management. When the account is set up, it’s important to consider what gets fed into the model and what gets stored in the warehouse for human review later. Those are two distinct surfaces with two distinct audiences.

A Representative Example Of How This Breaks Inside The Ad Platform

Here is another fictional scenario that can help illustrate how this failure shows up inside Google Ads accounts. Imagine a Performance Max campaign with healthy spend and what appears to be performing well. In this setup, “begin checkout” and “button click” are both designated as primary conversions alongside the actual purchase event. On the surface, the ad platform reports strong results. Underneath, the data tells a very different story:

• Reported conversion rate: 62%
• Composition: roughly 90% of “conversions” are button clicks and initiated checkouts.
• Actual purchases from 4,000 clicks: 37.
• True purchase rate: 0.9%
• Spend: $5,400.
• Revenue: $11,000.
• Effective ROAS: 2.04 (well below the 4.0+ target typical for the category).

In this scenario, the Smart Bidding system is not malfunctioning. It is performing exactly as instructed: finding more users who click buttons. The model has been trained on signals that do not correlate with revenue, so it optimizes toward the wrong pattern.

Correcting this issue is not instantaneous. Moving the micro‑conversions back to secondary status forces the system into a relearn phase because the model has been shaped almost entirely by false signals. Performance then becomes volatile and often depressed for several weeks while the algorithm rebuilds its understanding from cleaner data.

The broader lesson is that poor conversion architecture compounds quietly and recovers loudly. The cost of a flat, noisy setup is not paid in the first month; it is paid in the 30‑day relearn that follows the cleanup of the conversions.

The Technical Layer: Optimization Vs. Observation

Up to this point, the article has shown that the mechanics of a conversion framework matter because misconfiguration compounds over time. The next layer is understanding how Smart Bidding interprets the signals it receives.

How Primary Conversions Train The Algorithm

Every primary action recorded in the ad platform is treated as a successful outcome. Smart Bidding then works backward to identify the conditions that produced that outcome and increases bids to replicate those conditions. This is why the criteria for primary conversions must be strict. Only true macro goals belong in this category: a completed purchase, a submitted lead form, a booked consultation. These are actions that map directly to revenue rather than actions that merely correlate with revenue.

If a direct line cannot be drawn from the action to a dollar of pipeline, it does not belong in the primary pool.

How Secondary Conversions Inform Without Polluting

Secondary conversions operate in observation mode. The bidding system does not optimize toward them, but they still populate the “All conversions” column for reporting. This separation is the core value of the framework. It allows as many secondary actions as needed to map the funnel without contaminating the training data.

Examples include:

• Pricing page view.
• Add to cart.
• Begin checkout.
• Shipping page view.
• Account creation.

Each of these steps provides diagnostic insight into where users fall off. None of them instructs the algorithm to pursue low‑intent traffic. The result is a full picture of funnel behavior without sacrificing data quality.

There is one nuance worth noting. While Google’s documentation states that secondary actions are ignored for bidding, the system likely still uses them as predictive indicators of intent. This means even observation‑only events should represent meaningful steps in the buyer journey. Filling secondary slots with vanity actions risks creating false positives in the prediction layer.

Tracking legitimate funnel steps is the best advice.

The Hidden Override: Custom Goals

Custom goals, on the other hand, override the Primary vs. Secondary tagging entirely.

If you build a custom goal and add a secondary action to it, that action will be used for bidding in any campaign assigned that goal, regardless of how it is tagged at the account level.

This is a powerful feature, and a frequent landmine in Google Ads accounts. Strategists who assume “secondary is always observation” miss that custom goals re-promote those actions back into the bidding signal. A best practice is to audit every custom goal in the account before assuming the framework is intact.

How This Architecture Affects The Learning Phase

Smart Bidding’s learning phase typically runs 7 to 14 days after a strategy change (though this window extends significantly for campaigns with low conversion volume). During this window, the bidding algorithm is actively building (or rebuilding) its model of what success looks like.

A clean Primary vs. Secondary architecture compresses learning. Fewer, higher-quality signals mean faster convergence. The algorithm has clearer contrast between “buyer” and “non-buyer” patterns and can stabilize bid logic more quickly.

A polluted setup does the opposite for the account. The bidding algorithm grinds against contradictory signals, extending the learning phase and degrading early performance over the long-term. Worse, when the eventual cleanup happens or a restructure, the system enters a forced relearn and that 30-day window where revenue dips while the model unlearns the bad pattern.

There is also a default-state trap that catches even experienced ad managers. When you import conversions from Google Analytics into Google Ads, they are set to secondary by default. If your macro-goal lives in GA4 and you assume the import handled the optimization tag, you have just disconnected your bid strategy from your true revenue signal. A best practice is to verify the status manually after every import.

Edge Cases The PPC Manager Must Architect For

Phone Calls

Phone calls are the most context-dependent action in the framework.

For some businesses, calls are pure informational requests, with questions like “What time do you close?” These belong in secondary. For others, calls are the macro-goal because they result in booked consultations, demos, or sales conversations. These belong in the primary.

The decision is not based on the action label. It is based on the post-call data. If you cannot evaluate call quality, you cannot configure this correctly. Pull a sample of calls and talk to the humans answering the phones. Then categorize the calls and make an informed decision.

Imported Google Analytics Events

GA4 events imported into Google Ads default to secondary. This is intentional because Google does not want imported actions inadvertently changing your bid strategy.

But it means every macro-goal sourced from GA4 must be manually promoted to primary. This step is missed constantly, and the symptom when it is missed is subtle. A campaign that “should be” optimizing toward purchases is actually optimizing toward whatever else was already tagged primary in the account.

Low-Volume Accounts And The Cold Start Problem

For accounts that have not yet reached Smart Bidding’s data threshold (typically 30 to 50 conversions in a 30-day window, although this varies by strategy type), the framework in this post still applies, but the secondary layer becomes more strategically valuable for the account to perform well.

While Google does not officially support using secondary actions as bidding signals, many practitioners infer that the algorithm uses them as predictive indicators of intent, though Google has not confirmed this. For a low-volume account, that prediction layer can offer the algorithm enough texture to begin pattern-matching even before the macro-goal hits volume.

This is why secondary action quality matters more than secondary action quantity. Every meaningful step in the funnel, from a pricing page view to a demo video watch, or configurator interaction, is data that gives the algorithm a directional signal during the cold start.

Of course, garbage actions in this slot, however, create false positives the model may build a flawed early model around. So, it is important to apply discernment when adding secondary conversions.

When To Promote A Secondary Action To Primary

Almost never.

The framework in this article has limited tolerance for upgrading secondary actions to primary. Cart adds, checkout starts, and page views and chats should not be promoted, regardless of how much volume they generate. Volume does not equal intent. A high-volume “begin checkout” or “chat” action still represents users who did not buy.

The legitimate scenarios for status changes are narrow:

• Phone calls reclassified. When the business validates that calls are higher-intent than initially assumed (or vice versa).
• Imported GA4 events corrected. When an imported macro-goal lands in secondary by default and needs to be promoted to primary.
• Lead quality redefined. When the business shifts from “all leads” to “qualified leads only,” and the qualification action becomes the new macro-goal.

That is the entire list. If a strategist is regularly promoting micro-actions to primary, the framework is not being used, but it is being eroded.

Tactical Checklist: Auditing Your Primary Vs. Secondary Architecture

Before any Smart Bidding strategy test, walk the ad account through this audit:

• One macro-goal per campaign objective. Confirm a single primary conversion that maps directly to revenue.
• All micro-actions tagged secondary. Cart adds, button clicks, checkout starts, page views, and chats, none of these should be primary.
• GA4 imported events verified. Confirm any macro-goal imported from Analytics has been manually promoted to primary.
• Phone calls evaluated by quality. Sample recent calls and tag the action based on actual business value, not assumed business value.
• Custom goals audited. List every custom goal and confirm which secondary actions are being force-promoted into bidding.
• Funnel coverage in secondary. Every meaningful step between the click and the conversion has its own secondary action, which is not for bidding but for diagnostics.
• Reporting columns verified. “Conversions” column equals bidding signals. “All conversions” column equals full diagnostic layer.
• No vanity events in secondary. If an action is not a legitimate buyer-journey step, remove it. Quality over quantity is critical here.
• Learning phase respected. After any framework change, allow seven to 14 days for the algorithm to recalibrate before evaluating performance. Make sure to document the change in order to explain dips in conversion volume or efficiency.
• Relearn budget accounted for. If the account is being cleaned up from a flat setup, plan for a 30-day relearn period of depressed performance.

The Strategist’s Role In 2026

Smart Bidding will continue to absorb tasks that used to be human-controlled. This will happen from bid management to creative and audience targeting. The visible surface of paid search keeps getting smaller but it is important to remember to keep humans in the loop.

What does not absorb is signal architecture and how humans think through problems and outcomes. The algorithm cannot decide what data it should learn from, rather that is a business decision that needs a rational decision and not an optimization decision.

Doing this work requires understanding pipeline math, sales cycles, lead quality, and revenue attribution.

The Primary vs. Secondary framework is where that judgment lives at this point in paid search. If it is configured well, and the algorithm scales in the direction of the right outcomes. If it is configured poorly, and the algorithm scales the wrong outcomes faster and finds more pockets of “wrong.”

The framework is the strategy. The bid is just the output of the setup that has been outlined.

More Resources:

• Is Your Conversion Data Misleading You? 7 Common Google Ads Tracking Issues
• Google Ads Bid Strategy Testing: What Changed In 2026
• What Marketers Need To Know About Micro Conversions In Google Ads – Ask A PPC

Featured Image: Master1305/Shutterstock

You’re finalizing a monthly PPC report, excited to show improvements you’ve seen in the account. You highlight an A/B test that yielded an improvement in CPA, along with a new Meta campaign that is driving marketing qualified leads.

Yet when you present the report to leadership, you still get questions like, “How are these actions helping us grow revenue?”

Knowing your audience is a fundamental principle of marketing, and it applies equally so when creating marketing reports. A report geared to a marketing director who is more in the weeds of individual campaigns will look different from a report geared to a C-level executive.

When creating a report that will be viewed by senior leadership, consider what they are held accountable for. A CFO may answer to shareholders or VC firms, but ultimately, their main concern is increasing revenue. If a report doesn’t clearly answer the question of profitability from an investment in PPC, you’re setting yourself up for failure.

In this article, we’ll consider the metrics your CFO actually cares about when reviewing reporting on paid media campaigns.

A Note On Tracking

Before kicking off any ad campaigns or reporting, make sure that you’ve set up proper conversion tracking on your website to measure key actions in ad and analytics platforms. If you’re not confident in your data measurement approach, you can’t trust the numbers you put in your reports.

Agree On Shared Goals

Before building your first report, you should talk to key stakeholders about what internal revenue goals are and where PPC fits as part of those. For instance, a business may have set an annual goal to grow revenue by 10% or to increase the customer base by 20%.

When considering the metrics you include and how you talk about them, think about how measurement relates to the shared business goals. For instance, you may be able to show not only that Meta had a 10% increase in conversions but that it was the largest contributing channel to the previous month’s growth goal.

It may also be helpful to include a section in your reporting where you highlight overall goals, such as a graph showing total new accounts or revenue vs. planned.

CPA, But Consider The Conversion

Cost Per Acquisition (CPA) is a foundational metric for PPC campaigns. However, one common question faced when presenting performance and including this measure is: “What is a conversion?”

Microconversions, such as form fills and asset downloads, can be helpful for optimization in the right instances, but particularly for higher-level executives, you need to be very clear about what you’re reporting on when sharing a cost tied to a conversion action.

Ideally, CPA in this case should be tied as closely as possible to a customer. While, particularly for long lifecycle businesses, it may not be viable to report on actual customers signed in a monthly PPC report, you may be able to report on sales qualified leads.

In turn, if you have proper CRM tracking to monitor leads through the lifecycle of initial contact to customer, you can include CPAs for customer acquisition over a longer period. For instance, if your average time to final sale is 90 days, show a view of the past 90 days, including total cost and CPAs broken down by marketing qualified leads, sales qualified leads, and final sales.

Further reading: Why Do Budgets Overspend Even With A Target ROAS or CPA? – Ask A PPC

Customer Acquisition Cost

This leads to a more comprehensive metric: Customer Acquisition Cost (CAC), which represents total sales/marketing expenses divided by the number of customers obtained in the same period.

CAC can be shown as an overall metric, as well as attributed at the channel level if your CRM has the ability to measure customers by the source they came from. You should report on trends over time, which will show both how particular mixes of campaigns and channels are performing, as well as pointing to seasonal trends.

Return On Ad Spend

ROAS may be easier to attribute in some types of accounts than others (for instance, ecommerce vs. B2B products with long sales cycles), but can help point to return on investment from PPC campaigns. If revenue values are being properly measured, ROAS can help to answer the question, “How much did we make from this campaign?”

Of course, be ready to provide context and answer questions about how ROAS is being calculated for your brand. For instance, ad platform numbers may or may not include added costs such as shipping and taxes.

As with CPA and CAC, you should report on ROAS at a blended (cross-account and cross-campaign) level, as well as more granularly, where it makes sense to call out specific efforts.

Lifetime Value

Gaining new customers is great, but what if a customer buys one cheap product and never purchases from your brand again? Or if they pay for a one-month subscription but then cancel?

Incorporating LTV into reporting allows you to see not only which channels and campaigns are driving the most customers but which are contributing to the most valuable ones.

For an ecommerce business, you may look at data for how much revenue is generated from items any one person has purchased over time. For a SaaS business, you may look at the total subscription cost over time. For an industrial equipment supplier, you may look at both how much revenue has come from product purchases and if a customer uses your business for ongoing servicing.

Incremental Growth

When testing a new channel, campaign type, or offer, a key justification for funding is proof that it can drive new revenue that would not otherwise have come from existing efforts. Showing new customer counts and revenue amounts that are incremental to your testing efforts will help in maintaining funding for the future.

Relying solely on in-platform tracking can be tricky here, as a new Google campaign may readily take credit for conversions that are also being tracked in and influenced by Meta. While ad platforms are adding their own methodologies for tracking incremental attribution, these are ultimately still siloed at the platform level.

Using a Media Mix Modeling (MMM) tool here can provide a broader view of how adding to or subtracting from your paid media portfolio is impacting revenue. You can also run incrementality tests either by isolating campaigns to specific geographies and comparing against similar regions, or by comparing two periods of time. Looking at the results at the end of the test can show if a new initiative helped to lift customer and revenue growth.

When presenting on incremental performance, be transparent about the testing methodology, but explain concepts in a way that keeps in mind your leadership team’s level of technical knowledge. Lead with results, and include more technical documentation in an appendix.

Anticipate The Why

When including the above measures that are relevant to your client or stakeholder, think proactively about what questions will come from the data. If conversions are down, you’ll likely be asked why that is the case.

You’ll build rapport with executives reviewing the report if you can be transparent about negative performance, but also be able to provide a reasonable explanation why that is the case. For instance, you might compare seasonal performance to last year at the same time and note that business generally dips. Or a technical issue with a form on the website may have interfered with the ability of prospects to contact you.

Ideally, include brief bullet points in the report addressing these potential concerns up front, and be prepared to talk through deeper explanations if asked.

Start Building Better Reports

Think through the metrics surfaced in this article, along with tactics for presenting them to your CFO and other executives. Now think through how you’ve previously been reporting and any pain points you might have had in reaching shared understanding of performance.

Build on a foundation of solid tracking to highlight the numbers that will resonate the most with the individuals accountable for the purse strings. Be open to hearing out questions you’re asked and additional data that stakeholders might request, and continue to tweak your reports to meet your stakeholders where they are.

More Resources:

• The 8 Most Important PPC KPIs You Should Be Tracking
• How To Measure PPC Performance When AI Controls The Auction
• Should Your PPC Strategy Focus On The Lead Pipeline Or Revenue?

Featured Image: Natalya Kosarevich/Shutterstock