Performance marketer calls hardcoded UTMs "structural flaw" in Google Ads tracking – PPC Land

Ilja Zonov advocates dynamic ValueTrack parameters over manual UTM strings, arguing static tracking breaks campaigns and creates attribution failures.
Performance marketing specialist Ilja Zonov is challenging widespread industry practices for Google Ads tracking, arguing that hardcoded UTM parameters represent “a structural flaw and an ineffective tracking method” that undermines campaign measurement and scalability.
Zonov, who leads performance marketing at Klareo.agency in Barcelona, published his critique on LinkedIn, providing detailed guidance on implementing dynamic ValueTrack parameters at account level. His intervention addresses persistent tracking infrastructure problems as advertisers navigate Google’s expanding automation features including AI Max for Search and Performance Max campaigns.
“Relying on manual strings, such as utm_campaign=summer_sale, makes your growth infrastructure fragile,” Zonov explained. “Strings don’t scale. Systems do.” The specialist’s assessment challenges common practices where marketing teams manually configure UTM parameters across thousands of campaigns, creating tracking systems that require constant maintenance and troubleshooting.
The timing coincides with growing complexity in Google Ads measurement infrastructure. Advertisers adopting AI Max for Search campaigns face new technical requirements for tracking templates that must accommodate dynamic landing page selection through Final URL expansion. Google’s documentation specifies particular ValueTrack parameter patterns for compatibility with automated campaign features.
Zonov identifies three critical failures in hardcoded UTM approaches that affect campaign measurement accuracy and operational efficiency. The first problem involves zero robustness – when advertisers rename campaigns or duplicate tests for scaling, manual tracking strings become outdated immediately. Campaign IDs and ad group IDs remain constant in Google’s systems even when naming conventions change, but static UTM parameters require updating across every affected asset.
“The moment you rename a campaign or duplicate a test, your tracking becomes outdated,” Zonov stated. “Your IDs should stay constant, even if your naming conventions change.” This consistency requirement becomes particularly challenging for marketing teams managing rapid iteration cycles or launching scaled campaign tests where dozens of campaign copies require identical tracking infrastructure.
The second failure stems from human error inherent in manual processes. “Manual entry is inconsistent by design,” Zonov explained. “One typo or missed update during a launch can break your attribution data.” Marketing teams coordinating campaign launches across multiple team members cannot reliably ensure every tracking parameter contains correct syntax, proper values, and consistent naming conventions.
The specialist characterizes the third problem as “the Silent Failure” – tracking errors that remain undetected until post-campaign analysis. “You are very likely not to notice the problem until the campaign is finished,” he stated. “By the time you pull the report, the data is already incorrect.” Unlike technical errors that generate immediate alerts or prevent campaign launch, attribution gaps from tracking parameter mistakes only become apparent when marketers attempt performance analysis and discover missing or incorrect data.
These problems compound as campaign complexity increases. Advertisers managing hundreds of ad groups across dozens of campaigns face exponentially growing maintenance requirements when using manual tracking strings. Each campaign rename, each scaling test, each organizational restructure requires systematic updates across all tracking parameters to maintain attribution accuracy.
Zonov advocates comprehensive migration to dynamic ValueTrack parameters implemented exclusively at account level. “Stop typing names. Use placeholders that Google automatically populates when someone clicks on an ad,” he stated. “This ensures 100% consistency across the entire account.”
The specialist provides specific tracking template configuration that advertisers should implement in account settings: {lpurl}?utm_source=google&utm_medium=cpc&campaign_id={campaignid}&adgroup_id={adgroupid}&ad_id={creative}&keyword={keyword}&match_type={matchtype}&device={device}.
This template uses ValueTrack parameters as placeholders that Google’s system populates dynamically when users click advertisements. The {campaignid} parameter inserts the actual campaign ID regardless of campaign name. The {adgroupid}parameter provides ad group identification independent of ad group naming. The {creative} parameter identifies specific advertisements, while {keyword} captures the matched keyword and {matchtype} records whether the match was exact, phrase, or broad.
The device parameter {device} returns ‘m’ for mobile, ‘t’ for tablet, and ‘c’ for computer, enabling device-level performance analysis without manual device-specific campaign structures. These parameters function consistently regardless of how advertisers organize or name campaigns, creating what Zonov characterizes as “immutable data” that persists through campaign reorganizations.
“Result: Immutable data. Total consistency. Zero manual updates,” Zonov summarized. The approach eliminates entire categories of tracking maintenance work – updating parameters after campaign renames, ensuring consistency across scaled test duplications, and troubleshooting attribution gaps caused by manual entry errors.
Zonov emphasizes a technical specification that many advertisers overlook during implementation: removing tracking templates at lower hierarchy levels. “You should remove any old, static templates at the campaign or ad group levels,” he explained. “Lower levels always override account settings.”
Google Ads implements tracking templates at multiple hierarchy levels – account, campaign, ad group, asset group, keyword, and individual ad levels. When templates exist at multiple levels, lower-level configurations override higher-level settings. An advertiser might implement perfect account-level tracking templates but see no effect because campaign-level templates continue governing actual tracking behavior.
This inheritance pattern creates common failure scenarios. Marketing teams implement systematic account-level tracking following Zonov’s guidance, verify the configuration appears correct in account settings, launch campaigns, and later discover tracking parameters don’t match expectations. The root cause typically involves forgotten campaign-level or ad group-level templates from earlier implementations that override the account-level standard.
“Important: You should remove any old, static templates at the campaign or ad group levels. Lower levels always override account settings,” Zonov stated in his guidance. The cleanup requirement means advertisers must audit existing campaign structures, identify all tracking template configurations across hierarchy levels, and systematically remove lower-level implementations before account-level standards can govern behavior consistently.
This auditing work reveals broader infrastructure problems in accounts managed over years by different team members. Campaigns accumulate tracking parameters implementing different naming conventions, parameter sequences, and URL structures. The consolidation process forces architectural decisions about standardized tracking infrastructure that improve campaign management beyond attribution accuracy alone.
Marketing professionals responding to Zonov’s analysis confirmed experiencing the exact problems he describes. “Switched an account to ValueTrack macros and the data consistency alone saved hours of debugging every month,” commented one advertising specialist who completed the migration. The practitioner emphasized that proper tracking structure directly reduces operational workload by eliminating manual maintenance tasks.
Marty Taylor, who specializes in performance marketing, validated Zonov’s emphasis on account-level standardization. “Don’t get me started on companies that use these tracking parameters inconsistently across the ad level, ad group level, campaign level and they often conflict,” Taylor stated. His experience confirms that tracking template hierarchy problems remain widespread across accounts of all sophistication levels.
Other practitioners highlighted specific use cases where dynamic parameters prove essential. “You can also use campaign name there, so it’s easier to identify the campaign without looking through IDs,” noted Yuri Baranovski, who manages campaigns requiring both machine-readable identifiers for automated systems and human-readable labels for CRM integrations.
The discussion revealed that tracking challenges extend beyond simple implementation. Morgan Fabre, a web analytics consultant, questioned how ValueTrack parameters function when AI Max’s Final URL expansion substitutes dynamic landing pages. “UTM Tracking in Google Ads allows to track user when auto-tagging is not working due to user preferences. gclid can’t be used for Google Ads x GA4 sync. What about the campaignid value track?” Fabre asked, highlighting measurement complexity in automated campaign environments.
MetaCrawl, a provider of SEO tracking tools, characterized Zonov’s explanation as particularly valuable for practitioners unfamiliar with tracking architecture. “This makes a lot of sense. Static UTMs sound fine in theory, but in practice they break way too easily. Dynamic macros feel like a much cleaner and safer way to keep tracking consistent,” the company stated.
Brian Lasonde, who works with e-commerce brands, confirmed the operational efficiency gains. “Switched an account to ValueTrack macros and the data consistency alone saved hours of debugging every month,” he commented, echoing the broader pattern where systematic tracking implementation delivers ongoing labor savings through reduced troubleshooting requirements.
Zonov’s systematic approach addresses technical requirements that Google specifies for AI Max compatibility. The platform’s documentation explains that tracking templates must use specific ValueTrack parameter patterns when campaigns employ Final URL expansion – the feature that directs users to dynamically selected landing pages rather than advertiser-specified destinations.
Google identifies acceptable LPURL tag patterns that ensure proper functionality: {lpurl}? for tracking parameters following the landing page, {lpurl}& when URLs already contain parameters, {lpurl}# for fragment identifiers, and standalone {lpurl} when no tracking parameters exist. The dynamic parameter template that Zonov recommends uses {lpurl}? syntax, ensuring compatibility with Final URL expansion’s dynamic landing page selection.
The documentation warns that static tracking URLs without {lpurl} tags prevent AI Max from directing users to optimized landing pages. In these configurations, users reach hardcoded destinations specified in tracking templates regardless of AI Max’s optimization logic – exactly the inflexibility problem that Zonov characterizes as fundamental failure of manual tracking approaches.
Non-standard LPURL tag usage presents additional compatibility problems. When tracking templates use LPURL tags as portions of URL parameters rather than complete values, the system cannot properly substitute dynamic landing pages. Google cites foo={lpurl}value as problematic syntax that causes 404 errors when AI Max attempts Final URL expansion.
These technical specifications reinforce Zonov’s broader argument about systematic infrastructure. Advertisers using manual UTM strings typically implement them without LPURL tags, creating templates incompatible with automated landing page selection. The migration to dynamic ValueTrack parameters that Zonov advocates inherently addresses Google’s compatibility requirements by using proper parameter syntax.
The systematic tracking infrastructure that Zonov advocates becomes essential for evaluating AI Max performance claims that independent testing suggests may not match Google’s projections. While Google claims 14 percent conversion improvements and 27 percent uplifts for exact match campaigns, independent analysis shows AI Max delivering conversions at approximately 35 percent lower return on ad spend compared to traditional match types within identical campaigns.
Advertisers cannot accurately measure these performance discrepancies without consistent tracking infrastructure. AI Max attribution challenges compound measurement complexity – the system claims credit for conversions that would have occurred through existing exact and phrase match keywords, treating all keywords as broad match regardless of specified match types.
Without reliable campaign IDs, ad group IDs, and match type parameters, advertisers cannot distinguish AI Max traffic from traditional keyword matches. When manual UTM strings contain errors or become outdated through campaign renames, determining whether poor performance stems from AI Max algorithms versus measurement infrastructure failures becomes impossible.
The accurate attribution that dynamic ValueTrack parameters enable proves particularly valuable for testing AI Max against traditional campaign structures. Advertisers can implement identical conversion tracking across AI Max and non-AI Max campaigns, confident that parameter consistency enables valid performance comparisons. This measurement reliability matters as industry practitioners evaluate whether AI Max’s aggressive Search Partner Network expansion delivers acceptable returns.
The tracking infrastructure challenges that Zonov addresses intersect with ongoing privacy-driven measurement changes that further emphasize systematic implementation importance. iOS tracking restrictions have limited gclid parameter transmission since iOS 14.5 introduction, requiring Enhanced Conversions and server-side tagging to maintain attribution accuracy when traditional identifiers become unavailable.
Google’s click identifier faced additional constraints when Apple implemented App Tracking Transparency requirements. The platform stopped sending gclid parameters for traffic from certain Google applications on iOS devices, forcing advertisers to implement first-party cookie solutions and enhanced conversion tracking methodologies.
These privacy-driven changes create measurement fragility similar to the problems Zonov identifies with manual tracking. When gclid parameters become unavailable due to user privacy settings or platform restrictions, advertisers relying exclusively on Google’s automatic identifiers lose attribution capability. The systematic approach using multiple ValueTrack parameters provides measurement resilience – campaign IDs, ad group IDs, and keyword parameters continue functioning even when gclid transmission fails.
Enhanced Conversions supplements parameter-based tracking through first-party data matching, correlating conversion events with campaign data using hashed email addresses and phone numbers. Server-side tagging moves tracking functionality from browser-side JavaScript to server environments, reducing dependence on client-side parameters. Both approaches require coordination with ValueTrack parameter infrastructure to deliver complete attribution.
The interaction between tracking templates, Enhanced Conversions, server-side tagging, and first-party data collection creates technical complexity that systematic implementation helps manage. Zonov’s account-level standardization ensures all campaigns use consistent parameter structures that integrate properly with supplementary measurement systems.
Zonov frames proper tracking implementation as infrastructure investment rather than compliance exercise or technical overhead. “Immutable data. Total consistency. Zero manual updates,” he stated, positioning the work as creating operational efficiency that persists across campaign lifecycles.
Marketing teams managing legacy campaign structures accumulated over years face particular challenges. Campaigns created before modern automation features often use tracking template patterns incompatible with current requirements. Templates scattered across keyword, ad group, and campaign levels create inheritance complexity that prevents straightforward standardization.
The migration work that Zonov’s systematic approach requires forces confronting accumulated technical debt. Advertisers must audit existing configurations, evaluate compatibility with dynamic parameter requirements, and remove incompatible implementations. This cleanup reveals broader infrastructure problems – different team members implementing different conventions, historical experiments leaving orphaned configurations, and organizational changes creating inconsistent account structures.
Industry practitioners who completed these migrations report benefits extending well beyond tracking accuracy. The systematic implementation eliminates entire categories of troubleshooting work – tracking down broken UTM strings, reconciling inconsistent parameter naming, debugging attribution gaps from manual entry errors. These operational improvements persist regardless of whether advertisers enable AI Max or other automation features.
The alternative involves ongoing technical debt accumulation. Each new Google automation feature introduces additional compatibility requirements. Performance Max campaigns completed feature rollouts in August 2025 with comprehensive controls requiring proper tracking infrastructure. Customer lifecycle targeting introduced in April 2025 demands new conversion tracking parameters. Value-based bidding requirements for Demand Gen campaigns specify particular conversion tracking configurations.
Each enhancement that advertisers attempt to adopt requires verifying tracking compatibility, potentially updating parameter implementations, and troubleshooting failures. Manual tracking approaches multiply maintenance burden with each new feature, while systematic dynamic parameters adapt automatically.
Zonov’s framework positions advertisers to accommodate Google’s ongoing automation expansion without repeated infrastructure overhauls. The platform plans additional documentation updates for early 2025 explaining AI Max matching behavior technical mechanics. These updates will likely clarify how autocomplete suggestions trigger inferred intent matching and how keywordless matches appear in reporting.
Google’s pattern involves incremental feature releases followed by technical specification refinement. The company introduced AI Max in May 2025, added API support in August 2025, integrated functionality across Google Ads Editor in July 2025, and continues expanding reporting capabilities through quarterly updates. Each enhancement potentially introduces new compatibility requirements with tracking infrastructure.
The systematic approach using dynamic macros creates resilience against ongoing changes. When Google introduces new ValueTrack parameters or modifies existing parameter behavior, account-level templates automatically incorporate changes. Manual tracking strings require updating across every affected campaign, creating the fragility that Zonov identifies as fundamental flaw.
Future automation capabilities will likely expand beyond current AI Max features. Web-to-app marketing requires UTM parameter mapping between Google Ads and attribution systems. Enhanced measurement tools for iOS campaigns introduce gbraid parameters and on-device conversion measurement. Cross-channel budgeting features in Google Analytics require proper conversion tracking configurations.
These parallel developments validate Zonov’s argument that tracking requirements will grow more complex rather than simpler. Advertisers must maintain expertise across multiple interconnected systems while adapting to frequent platform updates introducing new specifications. The systematic infrastructure foundation creates capacity for absorbing ongoing complexity without proportional increases in maintenance burden.
The choice facing advertisers extends beyond AI Max compatibility or immediate measurement accuracy. Manual tracking strings might function adequately in static environments where campaigns never rename and tests never scale. But Google’s automation trajectory moves consistently toward dynamic optimization across creative, landing pages, audiences, and bidding strategies.
Zonov positions this as fundamental infrastructure choice rather than incremental optimization decision. “Strings don’t scale. Systems do,” he stated. The assessment challenges advertisers to evaluate whether manual processes can accommodate future platform capabilities or whether systematic automation provides necessary foundation.
Campaign management complexity increases as advertisers adopt Performance Max, AI Max, automated bidding strategies, and dynamic creative optimization. Each automation layer introduces technical requirements that manual tracking approaches struggle to satisfy reliably. The human error, zero robustness, and silent failure problems that Zonov identifies compound across multiple automation features operating simultaneously.
Industry practitioners evaluating AI Max report significant attribution challenges even with proper tracking infrastructure. The system’s tendency to claim credit for conversions that would have occurred through existing keywords creates measurement complexity regardless of parameter implementation quality. But without accurate tracking foundation, distinguishing actual AI Max performance from measurement artifacts becomes impossible.
The operational efficiency gains from systematic tracking implementation deliver value independent of automation adoption decisions. Marketing teams reducing debugging hours, eliminating parameter update workflows, and preventing attribution gaps through infrastructure investment realize returns even if they choose not to enable AI Max or other automated features.
Zonov’s intervention reframes tracking parameter implementation from technical compliance task to strategic infrastructure decision. Whether advertisers adopt his systematic approach or continue manual methods will increasingly determine campaign measurement reliability as Google’s platform automation expands across all campaign types and advertising objectives.
Who: Ilja Zonov, performance marketing specialist at Klareo.agency in Barcelona, addressing advertisers using Google Ads tracking infrastructure.
What: Zonov argues hardcoded UTM parameters represent “a structural flaw” creating three critical problems – zero robustness when campaigns rename, human error from manual entry, and silent failures undetected until post-campaign analysis – advocating instead for dynamic ValueTrack parameters implemented at account level using specific template configuration that ensures immutable data and zero manual updates.
When: Zonov published his analysis on LinkedIn in February 2026, following Google’s expansion of AI Max automation features throughout 2025 and ongoing platform complexity increases requiring systematic tracking infrastructure.
Where: The guidance applies to Google Ads accounts across all campaign types, particularly affecting advertisers adopting AI Max for Search campaigns, Performance Max campaigns, and other automation features requiring proper tracking template configurations for compatibility.
Why: Manual tracking strings fail to scale as Google’s platform automation expands across creative optimization, landing page selection, and bidding strategies, while systematic dynamic parameter implementation creates operational efficiency through reduced debugging burden, eliminated parameter maintenance workflows, and consistent attribution regardless of campaign organizational changes.
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