The Ultimate Guide of The Best Marketing Measurement Practices For $1M+ Annual Paid Media Spend
Eight measurement strategies to optimize marketing measurement, reduce wasted ad spend, and make data-driven decisions at scale.
When managing over $1M in annual paid media spend, proper measurement stops being a nice-to-have and becomes critical for business success. Poor measurement infrastructure impacts every aspect of marketing performance and can lead to significant waste in advertising spend. And, the bigger the budget, the bigger the waste.
Similarly, marketing measurement can become a business advantage. When you measure campaigns that are hard to attribute, you identify strategies that your competitors overlook. This is where you find the most cost-effective CPAs.
But how do you get there?
This guide outlines eight marketing measurement strategies to understand campaign performance better and answer difficult budget allocation questions.
First of all, not every $1M is the same
In this guide, I assume that if you’ve hit $1 million annual spend, you’re spreading your eggs between many baskets. At this point, you might have YouTube, Google Ads, and Meta running concurrently.
If your $1M is allocated to a single channel—a common example being Paid Search—you don’t need all that much. MTA (multi-touch attribution) could be all you need.
Multi-touch attribution can be all you need, depending on your marketing strategies.
In fact, I find important to highlight that having all budget allocated in a single strategy is great if that strategy has a positive ROAS and a comfortable payback period. The wider the channel mix, the more complex it is to manage campaigns and measurement. So, do “squeeze all the juice” of your working strategies before expanding.
Now, let’s get to those best practices.
The 8 Measurement Best Practices To Implement
1. UTM and Ad Platform Naming Conventions
Even in 2025, click-based attribution is a crucial part of all marketing measurement. I don’t need to go into the importance of UTM naming conventions. Dynamic UTM values—from Meta’s to Linkedin—have made implementing and maintaining consistent UTM structures very, very easy. So please use and abuse them. Lukas Oldenburg has a great article on designing UTM naming conventions if you want guidance.
However, many teams focus solely on UTM naming conventions. In fact, effective naming conventions must work seamlessly across advertising platforms, analytics tools, and CRM systems. Your naming framework should enable data two things:
Data consolidation: joining data between platforms, e.g. Meta and Mixpanel.
Granularity: segmenting insights meaningful analysis.
I’ve written in-depth on how naming conventions enable this.
Poor naming conventions can prevent joining first-party data with Google Analytics data, severely limiting your ability to understand customer journeys and make data-driven decisions. This impact compounds as marketing operations scale and more channels are added to the mix.
Market information, audience segmentation, and creative elements all need consistent representation across platforms. You’re unlikely to make decisions based on CTRs reported on Day of the Week. But you could learn valuable audience information on CTR per Creative Messaging. Naming conventions will enable you to segment reporting at the level you require.
2. The Critical Importance of Early Data Transfer Setup
The GA4 UI interface has significant limitations that many teams don't realize until it's too late. Due to cardinality constraints, you cannot report things very granularly through the UI. Even concepts like user-level attribution (super important for subscription products!), where you report all revenue dependent on the campaign that brought in the user, are impossible without querying.
GA4's BigQuery integration only stores data from when you set up the integration. If you set up the integration in month six, you've permanently lost granular data from months one through five. The data simply cannot be recovered retroactively.
Google has made the BigQuery integration process smooth because it's one of their main monetization strategies for Google Analytics today after deprecating Universal Analytics. Despite the easy setup, many companies still postpone this critical integration, not realizing the permanent loss of granular historical data they're incurring.
This is also relevant if you’re not using GA4. Queryable data from your CDP (e.g. Segment or Rudderstack) or event analytics (e.g. Mixpanel and Amplitude) can also be crucial if one of these tools is your product’s data source of truth.
3. Build and Maintain a Testing Roadmap
A testing roadmap should help you achieve three things simultaneously: 1) maintaining a high testing cadence, 2) ensuring actionable next steps, and 3) making insights easily understandable across the organization.
Creating a roadmap can be as simple as a well-structured spreadsheet in Google Sheets or a complex layout in Miro, Notion, or Jira. The key is choosing a tool that's easily accessible by the whole team—pick something that’s already used in their day-to-day work.
Your testing roadmap should function as a structured database of various tests, organized by team or section—whether that's product marketing, growth marketing, lifecycle, paid media, or onboarding. It keeps everyone aligned, maintains testing cadence, and helps prevent losing valuable insights.
Documentation becomes crucial in maintaining a comprehensive record of your tests. Think of it as leaving a trail of breadcrumbs that can guide future marketing strategies. Even tests from years ago can provide value, so you don't want to lose them through employee turnover.
Every test requires several key elements: test name, hypothesis, methodology, metrics, and timeline. Running tests without defining your hypothesis and methodology beforehand is strongly discouraged—you risk being unable to assess results due to incorrect setup or missing necessary metrics. This article is a handy place to start thinking about testing roadmaps.
Always define the test’s hypothesis and methodology beforehand.
For example, quarterly incrementality tests on YouTube and Meta prospecting campaigns help teams optimize budget allocation while accounting for seasonal variations. These regular cadence tests build a valuable repository of knowledge about channel performance over time. We’ll go deeper into this on the next section.
4. Measure True CACs Through Incrementality Testing
Incrementality refers to the additional outcomes—be it sessions, conversions, or other metrics—that result from a specific marketing strategy. It represents the difference between the outcomes with a particular marketing activity versus the outcomes without it.
Most companies start their incrementality journey with Paid Brand Search. The hypothesis is straightforward: would someone who saw (or clicked on) a brand search ad have converted anyway? The methodology involves turning off Paid Brand Search campaigns for a set period and comparing results with Organic search data (with Google Search Console).
Beyond Brand Search, prospecting initiatives like YouTube and Meta campaigns should undergo regular incrementality testing. One e-commerce client discovered their Meta prospecting campaigns were driving 40% more revenue than what click-based attribution suggested. When they reallocated that budget, the impact on overall sales was significant.
5. Channel-Specific Measurement Approaches Instead of a “One Source of Truth”
Meta's conversion tracking provides crucial signals for algorithm optimization, while Google Analytics offers a deduplicated view of cross-channel customer journeys. That’s why their conversion numbers don’t match. Neither source represents absolute truth. Both contribute to understanding marketing performance.
Different channels serve different purposes in the customer journey and require tailored measurement approaches. This doesn't mean working in silos, but rather understanding how various data sources complement each other to build a complete picture of marketing performance. The answer here is to educate stakeholders so they understand the value of a “triangulated” approach to attribution.
6. Server-Side Tracking for Top Funnel or Cross-Device Initiatives
Server-side tracking is considerably more complex to set up and maintain than client-side tracking. My rule of thumb is to avoid data complexity until it’s absolutely necessary. However, there are use cases where implementing server-side tracking can change campaign performance from night to day.
Meta CAPI, in particular, is the prime example here. The cross-device journey that happens on Meta ads is simply not measured effectively with click-based tags. By exporting PII (like emails and names) from converters from your backend to Meta, you can increase the number of Meta-attributed conversions. Meta can use this precious data signal to identify who to target with your ads and improve your campaign performance.
Whether this is worth to implement for your brand will depend on how much you’re spending on Meta or other top of funnel initiative that can be improved with server-side measurement.
7. Implement Discrepancy Monitoring to Identify Errors Proactively
The last thing you want is to waste your analysts’ time chasing down rabbit holes to find out why different data sources are reporting different numbers. Unfortunately, there's no universal threshold for acceptable discrepancies.
I've seen Meta and Google Analytics conversions with a 90% discrepancy, and everything was working fine! In other cases, a 10% discrepancy indicated serious tracking issues. This variability makes it crucial to monitor trends rather than focus on absolute values.
Regular monitoring helps establish what "normal" looks like for your specific implementation. Once you understand your baseline, you can more easily identify when something requires investigation.
One client saved significant ad spend when their monitoring system detected a discrepancy between their brand safety tool (IAS) and ad server (Campaign Manager), revealing geo-targeting setup errors in their programmatic campaigns. This discovery prevented continued waste of advertising budget and highlighted the importance of systematic monitoring.
In most cases, this will require a Data Warehouse, but it can also be done with Modern Spreadsheets. Evaluate the right time to invest in this based on your spend level and data quality.
8. Leverage Marketing Mix Modeling (MMM) for Confident Budget Allocation
At $1M+ annual spend across many channels, Marketing Mix Modeling (MMM) can help you understand true marketing impact. MMM's primary value lies in analyzing strategies that perform poorly under click-based attribution, like TV or YouTube campaigns. Traditional click-based measurement often fails to capture their true impact— not because these channels don't work, but because the measurement approach doesn't match how they influence customer behavior.
But not every strategy that is a fit for MMM. You need certain levels of volume and variation. If you’re wondering when to use MMM or click-based attribution, this article can shed some light.
Building Your Measurement Foundation
Proper measurement infrastructure isn't built overnight, but each element described above forms a crucial part of a comprehensive measurement strategy. Start with the fundamentals:
Implement consistent naming conventions across platforms
Set up your GA4 or CDP data warehouse integrations
Create and maintain a structured testing roadmap
Begin incrementality testing. Paid Brand Search is a good start
Monitor discrepancies systematically if you’re suffering from poor data quality
Review whether server-side tracking can drive the performance of your campaigns
Consider MMM for budget allocation as you scale
Remember that poor measurement doesn't just affect reporting–-it impacts every decision your marketing team makes. It means leaving money on the table. And the cost of inadequate measurement compounds as your marketing spend grows.
But what do you really need?
What should you start with? What do you prioritize? With marketing, unfortunately, the answer is always: it depends. It depends on your spend level, your channel mix, your sales journey, your skillset, your data stack and more.
I’m a marketing measurement consultant who can help you identify the biggest data opportunities for your marketing campaigns. I usually start a project like this with a Marketing Analytics audit to identify what your team and your campaigns need.
If you’re interested in that, do reach out: