Inna Leitner didn’t plan on becoming a Google Ads expert. With a background in journalism and business administration, she moved from Ukrainian newsrooms to Germany’s performance marketing scene.
Today, she runs a thriving consultancy that helps brands navigate an ad ecosystem that’s increasingly driven by automation, data, and AI.
Her success comes from structured experimentation. Inna blends curiosity with precision—testing Google’s machine learning campaign types like Performance Max (PMAX), refining data quality to improve conversion modeling, and adapting to updates such as consent mode to keep tracking reliable.
Her approach is to treat automation as a framework for smarter decisions—a playbook for marketers aiming to master Google Ads in 2025 and beyond.
Key takeaways:
- High-quality inputs—data, feeds, and creative assets—have a significant impact on AI-powered campaigns.
- User behavior is shifting in AI-driven environments, making context, conversation, and semantic relevance increasingly important.
- Planning for seasonality and algorithm learning cycles allows campaigns to maximize long-term ROAS instead of chasing short-term metrics.
- As AI takes on a bigger role in execution, success depends on translating signals into decisions that drive performance.
The unconventional path to Google Ads mastery
From journalism to numbers
Before becoming a Google Ads expert, Inna worked as an editor and writer for business magazines in Ukraine, interviewing CEOs and shaping stories about growth and innovation. However, as print media declined, she began looking for a more data-driven path.
When she moved to Germany about 12 years ago, her limited language skills narrowed her options.
For Inna, that’s Google Ads for paid search. That realization marked her transition from storytelling to performance marketing, blending her business background with a growing fascination for analytics.
Working in Germany exposed her to strict GDPR and data compliance standards, which shaped her privacy-first mindset.
The freelance evolution
Inna’s move to freelancing was strategic. She had been managing high-volume e-commerce accounts in-house when her employer shut down.
“I thought: it’s now or never. I already had two clients before going full-time freelance—so it didn’t happen overnight.” That early foundation allowed her to refine her services and build trust before scaling.
Today, she runs a boutique consultancy that specializes in e-commerce performance marketing. Her work combines hands-on campaign management with strategic planning.
Challenges beyond Google Ads: the Merchant Center challenge
The problem: a disapproved Merchant Center
Inna recalls a project with a veterinary pharmacy client—a case that highlights how the role of marketers is evolving.. “The company had already had Google Ads running for years, but they were convinced it wasn’t working,” she explains.
An audit quickly revealed the real issue: the client’s Google Merchant Center—the platform that powers product listings for Google Shopping—had been disapproved. Without it, they couldn’t run shopping or PMAX campaigns that were essential for e-commerce success, effectively cutting off a core growth channel.
“Having your Merchant Center disapproved is like a dead case,” Inna explains. “You can fix individual products, but when the whole account goes down, you’re stuck.” Restoring it quickly became mission-critical.
Navigating healthcare regulations and compliance
The issue traced back to a tangle of compliance errors. The client’s catalog fell into a gray zone—veterinary medicine—subject to different rules than human healthcare. Typically, Google’s automation handles such nuances correctly, but in this case, it failed—flagging the products for missing licenses and approvals that didn’t actually apply.Because each new account inherited the same violation record, every manager hit the same wall.
To break the loop, Inna spent two weeks speaking to Google support. “It was back and forth—cleaning up product descriptions, submitting certifications, and explaining the healthcare nuances,” she recalls.
Her persistence paid off. After she clarified the categories and aligned the documentation, Google reinstated the Merchant Center.
The subscription product puzzle
Another obstacle surfaced: several of the client’s products were sold through monthly subscriptions, which Google Shopping didn’t permit in markets like Germany. Today, this policy has evolved, allowing subscriptions for certain product categories in specific regions such as the United States.
To comply, Inna reimagined the offer—creating dedicated landing pages that turned subscriptions into prepaid annual packages.
It wasn’t obvious, but it reflected the clever, data-informed experimentation at the core of Inna’s paid search approach.
Why Shopping ads are non-negotiable for e-commerce
This experience reinforced one of Inna’s strongest beliefs: Google Shopping placements—including PMAX campaigns—are essential for any business selling physical products.
“Any company selling physical products shouldn’t start Google Ads without their Shopping channel running,” she stresses.
Shopping ads work differently from text search ads. They show products visually in search results, often above traditional ad listings. Many users engage with them without realizing they are ads at all.
“My mother-in-law clicks on 20 shopping ads she sees without realizing they’re ads,” Inna laughs. That natural interaction builds trust and drives higher-quality clicks.
The Google Ads landscape in 2025
The March 6 “black day”: consent mode version 2
Inna calls March 6, 2025, a “black day” for Google Ads professionals, when Google enforced consent mode version 2—a policy update limiting how much user data advertisers could collect without explicit consent.
The update hit many marketers hard, shrinking available conversion insights and making optimization trickier overnight. “If you’re missing data, you’re flying half blind,” she says.
The shift pushed marketers toward modeled conversions—Google’s privacy-safe way of estimating results. “If you’re missing data, you’re flying half blind.”
The year of full AI integration
AI now drives every layer of Google Ads: real-time automated bidding, dynamic creatives, and predictive audience modeling. PMAX is the full expression of this shift—letting Google decide when, where, and how ads appear.
For Inna, this evolution demands curiosity and experimentation. “If Google introduces something new, I want to test it. Some things work. Some don’t. But at least you have data rather than assumptions.”
Her approach reflects a broader trend among advanced marketers: treat automation as a partner, not an opaque system. Use it to learn faster, but keep human oversight to interpret the “why” behind the results.
Performance Max: when it works and when it doesn’t
PMAX campaigns use AI to automatically serve ads across Search, Display, YouTube, and Shopping from a single setup. For e-commerce brands, Inna says, PMAX and product feeds are a natural match.
The more signals—product details, pricing, conversion data—the system gets, the smarter it optimizes.
But not every product is a good fit for PMAX. She recalls a client selling bulk flooring with nearly identical visuals: “Search campaigns performed better because PMAX struggled to match them correctly.”
PMAX shines when it has strong conversion data to learn from and strong product data to match the correct product user needs. Without it, automation doesn’t have the context it needs to perform.
Letting Google lead the keyword research
One of the biggest shifts in Inna’s workflow is letting Google take the lead on keyword discovery.
Marketers once spent hours buried in keyword tools, competitor analysis, and search volume reports—but Inna now starts small and lets automation do the heavy lifting.
From there, she refines targeting—keeping the winners and filtering out what’s irrelevant with negative keywords. Faster, iterative, and smarter with each cycle.
This approach shines when budgets generate enough data for Google’s machine learning to learn quickly. Smaller accounts take longer. But, as Inna notes, “for most decent accounts, this approach outperforms traditional keyword research.”
The psychology of Google Ads management
Why linear thinking fails in advertising
One of Inna Leitner’s biggest lessons from years in digital marketing is that linear thinking doesn’t work in advertising.
“Business owners often hope that if they spend $10,000, they’ll get $20,000 back—and if they spend $20,000, they’ll get $40,000. But Google Ads is not linear.”
Performance fluctuates constantly, influenced by countless variables—seasonality, competitor actions, market trends, and even Google’s learning cycles. Reacting to short-term dips can do more harm than good. “You can’t make decisions based on one or two days of data. That’s the biggest mistake I see,” Inna warns.
Quick fixes, like pausing campaigns or slashing budgets, may offer temporary relief, but often reset valuable learning data and slow long-term growth. Successful advertisers, Inna emphasizes, learn to ride the waves instead of fighting them.
The 30-day rule: resisting knee-jerk reactions
To counter impulsive decision-making, Inna applies what she calls the 30-day rule. While Google’s algorithms can adapt quickly—sometimes within a week—human judgment should take a longer view.
“Before doing anything drastic, especially pausing or cutting budget, look back 30 days. Compare monthly and yearly trends,” she advises.
This broader window reveals patterns that daily fluctuations obscure, helping separate normal variability from real performance issues.
Planning for seasonality and peak periods
Seasonality is another factor Inna urges marketers to plan for. Every industry has its rhythm. “August might be slow for one business, while Black Friday can be huge.” The key is anticipating these cycles and adjusting budgets proactively.
For example, campaigns targeting November sales should start ramping up in October to capture early signals and train algorithms before demand spikes. This often means accepting a lower short-term ROAS to maximize long-term returns.
Even when clients can’t plan an entire year, acknowledging these cycles helps set realistic expectations and reduces pressure to chase daily metrics.
Managing client expectations through market cycles
Perhaps the most human side of Inna’s work is managing client expectations. Advertising spend can feel like “burning money,” especially for startups and small businesses. However, this perceived waste is what ultimately drives future revenue.
Inna finds that sometimes, less change can be better. The best-performing accounts are those given space to breathe. Staying consistent through volatility allows algorithms to learn and gains to compound over time.
Her approach blends data discipline with empathy—educating clients to trust the process, stay patient through fluctuations, and focus on strategy over emotion.
It’s this mindset, she believes, that separates sustainable success from short-lived wins.
Practical strategies for maximum performance
Focus on what you can control: inputs over outputs
“You need to work on inputs. That’s your data, your feed, and your creatives. Those are the levers a company can control,” Inna explains. She stresses prioritizing these elements over reacting to every algorithmic fluctuation or chasing daily metrics or short-term ROAS swings.
Strong inputs build a foundation for consistent, long-term performance.
The three pillars: data quality, feed optimization, and creatives
Inna highlights three areas that consistently move the needle in Google Ads campaigns. When these are strong, every other optimization works better.
- Data quality: Reliable, privacy-compliant tracking is the foundation. Inna works with certified specialists to ensure GDPR-safe setups and enhanced conversions, giving Google’s algorithms clean data.
- Feed optimization: For e-commerce, the product feed is the algorithm’s strongest signal. Optimizing titles, descriptions, and images helps Google match products to user intent—crucial for strong PMAX results.
- Creative assets: Polished copy and visuals give AI better inputs. When your assets stay consistent with your brand, Google automation produces more engaging, higher-converting ads.
The declining role of manual campaign management
“More of the manual tasks have been replaced by Google’s built-in bidding strategies and campaign automation,” Inna explains.
Today’s most effective marketers act less like technicians and more like strategic operators—managing inputs, interpreting machine-generated insights, and aligning campaigns with business goals.
This shift requires new expertise. Data literacy, campaign architecture, and cross-channel strategy now outweigh the value of constant manual tweaking. For Inna, mastering these strategic skills represents the next frontier of Google ad optimization.
Solving tracking and data challenges
Why you need a certified tracking specialist
Inna highlights the importance of collaborating with certified tracking specialists to navigate the growing complexity of Google Ads tracking and privacy compliance.
“I’m not a tech specialist,” she admits, “so I always collaborate with certified experts who know GDPR, consent mode, cookie banners, and enhanced conversions.”
This expertise is especially crucial in regulated markets like Germany, where misconfigured tracking can lead to data loss, fines, and poor campaign performance.
Certified specialists ensure proper implementation, ongoing maintenance, and troubleshooting of tracking codes—providing advertisers with high-quality data for effective campaign optimization.
Understanding enhanced conversions and modeled data
Even with accurate tracking, data gaps remain. Inna notes that due to stricter consent rules, Google can’t track up to 40% of conversions.
Enhanced conversions help recover some of that lost data by using anonymized, hashed first-party signals—like emails or phone numbers submitted at checkout.
Google can still collect certain signals and, instead of recording them directly, uses them to build predictive models that generate modeled conversions.
Measured conversions come from actual user interactions. Modeled conversions rely on aggregated data to estimate results when tracking isn’t possible. Predictive algorithms fill the gaps, giving marketers a more complete picture than direct observation alone.
Knowing the difference helps you set realistic expectations and optimize campaigns based on observed and modeled results.
GDPR compliance in the German market
These tracking fundamentals become even more critical under strict privacy laws. Germany’s rigorous GDPR enforcement shapes much of Inna’s operational discipline.
“Sometimes clients’ webmasters try to set up tracking, but they don’t understand the latest German requirements or consent mode enforcement,” she explains.
To protect performance and compliance, Inna insists on working only with partners who understand the whole regulatory and technical picture. This ensures campaigns remain effective and reliable.
The importance of first-party data
For Inna, performance starts with the data you feed into the system. Fresh, reliable first-party signals help Google model conversions more accurately and reveal results that would otherwise be lost in attribution gaps.
The fresher the data, the more precise the optimization. You can think about first-party data as a performance lever.
Feeding recent purchase activity, subscription data, or lead interactions into your ad platforms helps algorithms recognize high-value users faster—improving audience quality and conversion tracking across campaigns.
The future of digital advertising
AI-driven experiences redefine user journeys
Digital advertising is moving beyond isolated channels. Ads increasingly follow users across AI-powered environments—chatbots, voice assistants, and immersive AR or VR spaces—creating continuous, personalized interactions.
Inna predicts that ads will become a continuous conversation across devices and AI touchpoints, making integrated, multi-platform strategies essential for reaching users across their digital lives.
From search to answers: How LLMs are changing discovery
At the same time, LLMs are transforming how people search and consume information.
Queries are becoming conversational, driven by context and intent rather than specific keywords. Users increasingly expect direct, AI-generated answers instead of scrolling through results pages. This means shifting from keyword-centric tactics to optimizing for context, conversation, and semantic relevance—aligning content with how AI understands and responds to intent.
This evolution demands a new discipline for marketers: answer engine optimization (AEO). Rather than competing for rankings, it’s about structuring content to be factual, clear, and context-rich so that AI systems recognize it as a trusted answer source.
As Inna puts it, “You need to think about how AI interprets your message, not just how humans read it.”
Continuous monitoring and refinement are crucial to safeguard brand integrity as AI increasingly shapes how information reaches audiences.
Ads aren’t disappearing—they are evolving
Despite these shifts, Inna remains confident in advertising’s resilience.
Even if dominant platforms change, Inna believes monetization through ads will persist. “If Google ever loses its position to ChatGPT, I guarantee there will be ads inside ChatGPT. They’re already starting to appear.”
The same applies to Google’s AI-generated answers, which already include ads in the US and are expanding globally.
As every major AI model develops its monetization layer, marketers will face new frontiers—contextual, LLM-based advertising tailored to how users express intent inside conversational systems.
AI Max: The next evolution of search campaigns
Building on PMAX campaigns, the emerging AI Max model taps into advanced AI capabilities—multi-channel orchestration, real-time learning, and deeper signal processing.
“The goal is that you still give it signals, like keywords, but it adapts to different types of searches—especially this new LLM-style behavior,” Inna explains. “Google promises it can adjust to changing search patterns.”
Advertisers can’t control exactly where their ads appear, but AI Max interprets the signals to optimize search, display, video, and shopping channels. Some of Inna’s peers are already testing it and seeing results that outperform traditional search campaigns. These early adopters hint at the potential for AI Max to reward advertisers who experiment early and learn fast.
Building a sustainable solo practice
Why small can be strategic
For Inna, running a solo practice is a deliberate choice. She doesn’t see it as a limitation.
“I still aim to stay small, so there will be no classical agency,” she explains. “That would be the next logical step, but working solo with trusted contractors—for tracking, creatives, and data analysis—lets me focus on the actual work, not on overhead.”
This model allows her to deliver premium, personalized services and nurture strong client relationships without the complexity of managing a large team. Inna treats side projects as low-risk testing grounds for new tools, strategies, and ideas. Lessons learned from these “guinea pig” experiments often feed back into her main practice, keeping campaigns innovative and strategies fresh.
Continuous experimentation is valuable when structured in a way that doesn’t jeopardize client performance.
Finding satisfaction in direct impact
For Inna, one of the biggest rewards of running a solo practice is the clarity of cause and effect. Seeing that direct link between strategy and results keeps her focused on e-commerce performance.
“I really enjoy seeing products sell and revenue grow—that motivates me,” Inna says. “There’s that dopamine hit when something you’ve calculated and set up actually works—it’s the same satisfaction people get from ticking off tasks on a to-do list.”
The immediate feedback loop fuels constant improvement and sharper decisions. It builds ownership and purpose—proof that focus and accountability can rival sheer scale.
Thriving at the intersection of strategy and automation
Inna’s journey shows that mastery in performance marketing comes from combining strategy, technical expertise, and continuous experimentation. Success comes when you focus on what you can control, using AI and automation wisely and keeping privacy at the forefront.
From resolving complex Merchant Center issues to optimizing product feeds and running AI-driven campaigns like PMAX, Inna’s experience offers a practical blueprint for marketers seeking sustainable results. Her lean, agile setup—working solo while collaborating with trusted contractors—delivers high-quality execution without the overhead, while side projects fuel ongoing learning and innovation.
Even as AI chat interfaces and conversational platforms evolve, ads remain a core channel for reaching audiences at scale, with monetization opportunities following wherever users engage.
Inna’s experience shows that lasting marketing success comes from mastering the fundamentals, adapting to change, and using tools and automation to amplify human strategy.
If you want to adopt similar approaches, tools like Bïrch can help transform those disciplined insights into actionable performance—bridging human strategy with automation precision.
To explore more of Inna’s work, you can connect with her on LinkedIn or explore more of her thinking on her website: innaleitner.com.
FAQs
Inna Leitner didn’t plan on becoming a Google Ads expert. With a background in journalism and business administration, she moved from Ukrainian newsrooms to Germany’s performance marketing scene.
Today, she runs a thriving consultancy that helps brands navigate an ad ecosystem that’s increasingly driven by automation, data, and AI.
Her success comes from structured experimentation. Inna blends curiosity with precision—testing Google’s machine learning campaign types like Performance Max (PMAX), refining data quality to improve conversion modeling, and adapting to updates such as consent mode to keep tracking reliable.
Her approach is to treat automation as a framework for smarter decisions—a playbook for marketers aiming to master Google Ads in 2025 and beyond.
Key takeaways:
- High-quality inputs—data, feeds, and creative assets—have a significant impact on AI-powered campaigns.
- User behavior is shifting in AI-driven environments, making context, conversation, and semantic relevance increasingly important.
- Planning for seasonality and algorithm learning cycles allows campaigns to maximize long-term ROAS instead of chasing short-term metrics.
- As AI takes on a bigger role in execution, success depends on translating signals into decisions that drive performance.
The unconventional path to Google Ads mastery
From journalism to numbers
Before becoming a Google Ads expert, Inna worked as an editor and writer for business magazines in Ukraine, interviewing CEOs and shaping stories about growth and innovation. However, as print media declined, she began looking for a more data-driven path.
When she moved to Germany about 12 years ago, her limited language skills narrowed her options.
For Inna, that’s Google Ads for paid search. That realization marked her transition from storytelling to performance marketing, blending her business background with a growing fascination for analytics.
Working in Germany exposed her to strict GDPR and data compliance standards, which shaped her privacy-first mindset.
The freelance evolution
Inna’s move to freelancing was strategic. She had been managing high-volume e-commerce accounts in-house when her employer shut down.
“I thought: it’s now or never. I already had two clients before going full-time freelance—so it didn’t happen overnight.” That early foundation allowed her to refine her services and build trust before scaling.
Today, she runs a boutique consultancy that specializes in e-commerce performance marketing. Her work combines hands-on campaign management with strategic planning.
Challenges beyond Google Ads: the Merchant Center challenge
The problem: a disapproved Merchant Center
Inna recalls a project with a veterinary pharmacy client—a case that highlights how the role of marketers is evolving.. “The company had already had Google Ads running for years, but they were convinced it wasn’t working,” she explains.
An audit quickly revealed the real issue: the client’s Google Merchant Center—the platform that powers product listings for Google Shopping—had been disapproved. Without it, they couldn’t run shopping or PMAX campaigns that were essential for e-commerce success, effectively cutting off a core growth channel.
“Having your Merchant Center disapproved is like a dead case,” Inna explains. “You can fix individual products, but when the whole account goes down, you’re stuck.” Restoring it quickly became mission-critical.
Navigating healthcare regulations and compliance
The issue traced back to a tangle of compliance errors. The client’s catalog fell into a gray zone—veterinary medicine—subject to different rules than human healthcare. Typically, Google’s automation handles such nuances correctly, but in this case, it failed—flagging the products for missing licenses and approvals that didn’t actually apply.Because each new account inherited the same violation record, every manager hit the same wall.
To break the loop, Inna spent two weeks speaking to Google support. “It was back and forth—cleaning up product descriptions, submitting certifications, and explaining the healthcare nuances,” she recalls.
Her persistence paid off. After she clarified the categories and aligned the documentation, Google reinstated the Merchant Center.
The subscription product puzzle
Another obstacle surfaced: several of the client’s products were sold through monthly subscriptions, which Google Shopping didn’t permit in markets like Germany. Today, this policy has evolved, allowing subscriptions for certain product categories in specific regions such as the United States.
To comply, Inna reimagined the offer—creating dedicated landing pages that turned subscriptions into prepaid annual packages.
It wasn’t obvious, but it reflected the clever, data-informed experimentation at the core of Inna’s paid search approach.
Why Shopping ads are non-negotiable for e-commerce
This experience reinforced one of Inna’s strongest beliefs: Google Shopping placements—including PMAX campaigns—are essential for any business selling physical products.
“Any company selling physical products shouldn’t start Google Ads without their Shopping channel running,” she stresses.
Shopping ads work differently from text search ads. They show products visually in search results, often above traditional ad listings. Many users engage with them without realizing they are ads at all.
“My mother-in-law clicks on 20 shopping ads she sees without realizing they’re ads,” Inna laughs. That natural interaction builds trust and drives higher-quality clicks.
The Google Ads landscape in 2025
The March 6 “black day”: consent mode version 2
Inna calls March 6, 2025, a “black day” for Google Ads professionals, when Google enforced consent mode version 2—a policy update limiting how much user data advertisers could collect without explicit consent.
The update hit many marketers hard, shrinking available conversion insights and making optimization trickier overnight. “If you’re missing data, you’re flying half blind,” she says.
The shift pushed marketers toward modeled conversions—Google’s privacy-safe way of estimating results. “If you’re missing data, you’re flying half blind.”
The year of full AI integration
AI now drives every layer of Google Ads: real-time automated bidding, dynamic creatives, and predictive audience modeling. PMAX is the full expression of this shift—letting Google decide when, where, and how ads appear.
For Inna, this evolution demands curiosity and experimentation. “If Google introduces something new, I want to test it. Some things work. Some don’t. But at least you have data rather than assumptions.”
Her approach reflects a broader trend among advanced marketers: treat automation as a partner, not an opaque system. Use it to learn faster, but keep human oversight to interpret the “why” behind the results.
Performance Max: when it works and when it doesn’t
PMAX campaigns use AI to automatically serve ads across Search, Display, YouTube, and Shopping from a single setup. For e-commerce brands, Inna says, PMAX and product feeds are a natural match.
The more signals—product details, pricing, conversion data—the system gets, the smarter it optimizes.
But not every product is a good fit for PMAX. She recalls a client selling bulk flooring with nearly identical visuals: “Search campaigns performed better because PMAX struggled to match them correctly.”
PMAX shines when it has strong conversion data to learn from and strong product data to match the correct product user needs. Without it, automation doesn’t have the context it needs to perform.
Letting Google lead the keyword research
One of the biggest shifts in Inna’s workflow is letting Google take the lead on keyword discovery.
Marketers once spent hours buried in keyword tools, competitor analysis, and search volume reports—but Inna now starts small and lets automation do the heavy lifting.
From there, she refines targeting—keeping the winners and filtering out what’s irrelevant with negative keywords. Faster, iterative, and smarter with each cycle.
This approach shines when budgets generate enough data for Google’s machine learning to learn quickly. Smaller accounts take longer. But, as Inna notes, “for most decent accounts, this approach outperforms traditional keyword research.”
The psychology of Google Ads management
Why linear thinking fails in advertising
One of Inna Leitner’s biggest lessons from years in digital marketing is that linear thinking doesn’t work in advertising.
“Business owners often hope that if they spend $10,000, they’ll get $20,000 back—and if they spend $20,000, they’ll get $40,000. But Google Ads is not linear.”
Performance fluctuates constantly, influenced by countless variables—seasonality, competitor actions, market trends, and even Google’s learning cycles. Reacting to short-term dips can do more harm than good. “You can’t make decisions based on one or two days of data. That’s the biggest mistake I see,” Inna warns.
Quick fixes, like pausing campaigns or slashing budgets, may offer temporary relief, but often reset valuable learning data and slow long-term growth. Successful advertisers, Inna emphasizes, learn to ride the waves instead of fighting them.
The 30-day rule: resisting knee-jerk reactions
To counter impulsive decision-making, Inna applies what she calls the 30-day rule. While Google’s algorithms can adapt quickly—sometimes within a week—human judgment should take a longer view.
“Before doing anything drastic, especially pausing or cutting budget, look back 30 days. Compare monthly and yearly trends,” she advises.
This broader window reveals patterns that daily fluctuations obscure, helping separate normal variability from real performance issues.
Planning for seasonality and peak periods
Seasonality is another factor Inna urges marketers to plan for. Every industry has its rhythm. “August might be slow for one business, while Black Friday can be huge.” The key is anticipating these cycles and adjusting budgets proactively.
For example, campaigns targeting November sales should start ramping up in October to capture early signals and train algorithms before demand spikes. This often means accepting a lower short-term ROAS to maximize long-term returns.
Even when clients can’t plan an entire year, acknowledging these cycles helps set realistic expectations and reduces pressure to chase daily metrics.
Managing client expectations through market cycles
Perhaps the most human side of Inna’s work is managing client expectations. Advertising spend can feel like “burning money,” especially for startups and small businesses. However, this perceived waste is what ultimately drives future revenue.
Inna finds that sometimes, less change can be better. The best-performing accounts are those given space to breathe. Staying consistent through volatility allows algorithms to learn and gains to compound over time.
Her approach blends data discipline with empathy—educating clients to trust the process, stay patient through fluctuations, and focus on strategy over emotion.
It’s this mindset, she believes, that separates sustainable success from short-lived wins.
Practical strategies for maximum performance
Focus on what you can control: inputs over outputs
“You need to work on inputs. That’s your data, your feed, and your creatives. Those are the levers a company can control,” Inna explains. She stresses prioritizing these elements over reacting to every algorithmic fluctuation or chasing daily metrics or short-term ROAS swings.
Strong inputs build a foundation for consistent, long-term performance.
The three pillars: data quality, feed optimization, and creatives
Inna highlights three areas that consistently move the needle in Google Ads campaigns. When these are strong, every other optimization works better.
- Data quality: Reliable, privacy-compliant tracking is the foundation. Inna works with certified specialists to ensure GDPR-safe setups and enhanced conversions, giving Google’s algorithms clean data.
- Feed optimization: For e-commerce, the product feed is the algorithm’s strongest signal. Optimizing titles, descriptions, and images helps Google match products to user intent—crucial for strong PMAX results.
- Creative assets: Polished copy and visuals give AI better inputs. When your assets stay consistent with your brand, Google automation produces more engaging, higher-converting ads.
The declining role of manual campaign management
“More of the manual tasks have been replaced by Google’s built-in bidding strategies and campaign automation,” Inna explains.
Today’s most effective marketers act less like technicians and more like strategic operators—managing inputs, interpreting machine-generated insights, and aligning campaigns with business goals.
This shift requires new expertise. Data literacy, campaign architecture, and cross-channel strategy now outweigh the value of constant manual tweaking. For Inna, mastering these strategic skills represents the next frontier of Google ad optimization.
Solving tracking and data challenges
Why you need a certified tracking specialist
Inna highlights the importance of collaborating with certified tracking specialists to navigate the growing complexity of Google Ads tracking and privacy compliance.
“I’m not a tech specialist,” she admits, “so I always collaborate with certified experts who know GDPR, consent mode, cookie banners, and enhanced conversions.”
This expertise is especially crucial in regulated markets like Germany, where misconfigured tracking can lead to data loss, fines, and poor campaign performance.
Certified specialists ensure proper implementation, ongoing maintenance, and troubleshooting of tracking codes—providing advertisers with high-quality data for effective campaign optimization.
Understanding enhanced conversions and modeled data
Even with accurate tracking, data gaps remain. Inna notes that due to stricter consent rules, Google can’t track up to 40% of conversions.
Enhanced conversions help recover some of that lost data by using anonymized, hashed first-party signals—like emails or phone numbers submitted at checkout.
Google can still collect certain signals and, instead of recording them directly, uses them to build predictive models that generate modeled conversions.
Measured conversions come from actual user interactions. Modeled conversions rely on aggregated data to estimate results when tracking isn’t possible. Predictive algorithms fill the gaps, giving marketers a more complete picture than direct observation alone.
Knowing the difference helps you set realistic expectations and optimize campaigns based on observed and modeled results.
GDPR compliance in the German market
These tracking fundamentals become even more critical under strict privacy laws. Germany’s rigorous GDPR enforcement shapes much of Inna’s operational discipline.
“Sometimes clients’ webmasters try to set up tracking, but they don’t understand the latest German requirements or consent mode enforcement,” she explains.
To protect performance and compliance, Inna insists on working only with partners who understand the whole regulatory and technical picture. This ensures campaigns remain effective and reliable.
The importance of first-party data
For Inna, performance starts with the data you feed into the system. Fresh, reliable first-party signals help Google model conversions more accurately and reveal results that would otherwise be lost in attribution gaps.
The fresher the data, the more precise the optimization. You can think about first-party data as a performance lever.
Feeding recent purchase activity, subscription data, or lead interactions into your ad platforms helps algorithms recognize high-value users faster—improving audience quality and conversion tracking across campaigns.
The future of digital advertising
AI-driven experiences redefine user journeys
Digital advertising is moving beyond isolated channels. Ads increasingly follow users across AI-powered environments—chatbots, voice assistants, and immersive AR or VR spaces—creating continuous, personalized interactions.
Inna predicts that ads will become a continuous conversation across devices and AI touchpoints, making integrated, multi-platform strategies essential for reaching users across their digital lives.
From search to answers: How LLMs are changing discovery
At the same time, LLMs are transforming how people search and consume information.
Queries are becoming conversational, driven by context and intent rather than specific keywords. Users increasingly expect direct, AI-generated answers instead of scrolling through results pages. This means shifting from keyword-centric tactics to optimizing for context, conversation, and semantic relevance—aligning content with how AI understands and responds to intent.
This evolution demands a new discipline for marketers: answer engine optimization (AEO). Rather than competing for rankings, it’s about structuring content to be factual, clear, and context-rich so that AI systems recognize it as a trusted answer source.
As Inna puts it, “You need to think about how AI interprets your message, not just how humans read it.”
Continuous monitoring and refinement are crucial to safeguard brand integrity as AI increasingly shapes how information reaches audiences.
Ads aren’t disappearing—they are evolving
Despite these shifts, Inna remains confident in advertising’s resilience.
Even if dominant platforms change, Inna believes monetization through ads will persist. “If Google ever loses its position to ChatGPT, I guarantee there will be ads inside ChatGPT. They’re already starting to appear.”
The same applies to Google’s AI-generated answers, which already include ads in the US and are expanding globally.
As every major AI model develops its monetization layer, marketers will face new frontiers—contextual, LLM-based advertising tailored to how users express intent inside conversational systems.
AI Max: The next evolution of search campaigns
Building on PMAX campaigns, the emerging AI Max model taps into advanced AI capabilities—multi-channel orchestration, real-time learning, and deeper signal processing.
“The goal is that you still give it signals, like keywords, but it adapts to different types of searches—especially this new LLM-style behavior,” Inna explains. “Google promises it can adjust to changing search patterns.”
Advertisers can’t control exactly where their ads appear, but AI Max interprets the signals to optimize search, display, video, and shopping channels. Some of Inna’s peers are already testing it and seeing results that outperform traditional search campaigns. These early adopters hint at the potential for AI Max to reward advertisers who experiment early and learn fast.
Building a sustainable solo practice
Why small can be strategic
For Inna, running a solo practice is a deliberate choice. She doesn’t see it as a limitation.
“I still aim to stay small, so there will be no classical agency,” she explains. “That would be the next logical step, but working solo with trusted contractors—for tracking, creatives, and data analysis—lets me focus on the actual work, not on overhead.”
This model allows her to deliver premium, personalized services and nurture strong client relationships without the complexity of managing a large team. Inna treats side projects as low-risk testing grounds for new tools, strategies, and ideas. Lessons learned from these “guinea pig” experiments often feed back into her main practice, keeping campaigns innovative and strategies fresh.
Continuous experimentation is valuable when structured in a way that doesn’t jeopardize client performance.
Finding satisfaction in direct impact
For Inna, one of the biggest rewards of running a solo practice is the clarity of cause and effect. Seeing that direct link between strategy and results keeps her focused on e-commerce performance.
“I really enjoy seeing products sell and revenue grow—that motivates me,” Inna says. “There’s that dopamine hit when something you’ve calculated and set up actually works—it’s the same satisfaction people get from ticking off tasks on a to-do list.”
The immediate feedback loop fuels constant improvement and sharper decisions. It builds ownership and purpose—proof that focus and accountability can rival sheer scale.
Thriving at the intersection of strategy and automation
Inna’s journey shows that mastery in performance marketing comes from combining strategy, technical expertise, and continuous experimentation. Success comes when you focus on what you can control, using AI and automation wisely and keeping privacy at the forefront.
From resolving complex Merchant Center issues to optimizing product feeds and running AI-driven campaigns like PMAX, Inna’s experience offers a practical blueprint for marketers seeking sustainable results. Her lean, agile setup—working solo while collaborating with trusted contractors—delivers high-quality execution without the overhead, while side projects fuel ongoing learning and innovation.
Even as AI chat interfaces and conversational platforms evolve, ads remain a core channel for reaching audiences at scale, with monetization opportunities following wherever users engage.
Inna’s experience shows that lasting marketing success comes from mastering the fundamentals, adapting to change, and using tools and automation to amplify human strategy.
If you want to adopt similar approaches, tools like Bïrch can help transform those disciplined insights into actionable performance—bridging human strategy with automation precision.
To explore more of Inna’s work, you can connect with her on LinkedIn or explore more of her thinking on her website: innaleitner.com.
FAQs
PMAX and AI Max require high-quality data, strong feeds, and creative assets to perform. Automation amplifies results, but human strategy and oversight remain critical.
Misconfigured tracking can lead to lost conversions, poor campaign performance, and legal issues—especially in regulated markets like Germany. Certified tracking specialists are essential.
Working with trusted contractors allows solo practitioners to access specialized skills when needed, maintain quality, and adapt quickly to new client needs without overhead.
Ads remain a core monetization model. As Inna notes, ads will follow users across AI systems like LLMs, chatbots, and AR/VR environments, even if platforms change.
Shift focus from exact keywords to context and conversational intent. Answer engine optimization ensures AI systems recognize your content as a trusted response.
Think strategically, not linearly. Resist knee-jerk reactions to daily fluctuations, focus on inputs over outputs, and plan for seasonality and algorithmic learning cycles.
