The role of the PPC manager in 2025 will be markedly different from when I first stepped into it back in 2006. At that time, nearly everything was manual. Conversion tracking, as we know it today, was almost non-existent. While some rudimentary forms of tracking were available in-app, they lacked the integration and precision modern systems offer. Automation, which now drives campaign success, was unheard of.
Over the years, I’ve trained hundreds of PPC professionals across agencies of all sizes and directly with clients.
One constant theme has been change—change at a pace that has accelerated dramatically in recent years due to automation. These transformations are not just about technology but about a shift in strategic thinking.
From Keyword Focus to Full-Funnel Strategies
When I started, the focus was on strict keyword targeting. Campaigns prioritised bottom-of-the-funnel actions like immediate purchases, and success was often achieved by executing the basics well.
Google’s introduction of concepts like micro-moments and intent-based targeting shifted the paradigm.
Today’s PPC managers must adopt full-funnel strategies, extending efforts to prospecting and nurturing new audiences. This approach mirrors the complexity of modern consumer journeys, where decision-making spans multiple touchpoints.
The job is undeniably more complex now, requiring PPC managers to prioritise overarching strategies rather than isolated tactics.
Much like the “gold rush” of PPC’s early days, where outstanding performance was achievable with simple actions, the landscape today demands a nuanced, holistic approach.
The Shift to Exclusions and Refined Automation
An interesting evolution is the move from targeting to exclusions. With changes in match types and increased automation,
PPC managers now need to focus heavily on refining campaigns by excluding negative keywords, irrelevant placements, or underperforming audiences. This proactive exclusion strategy ensures campaigns remain efficient and focused.
Moreover, the modern PPC manager must understand broader marketing metrics beyond traditional KPIs like ROI or sales.
Metrics such as Marketing Efficiency Ratio (MER) provide a more comprehensive view of how campaigns contribute to overall business success, aligning PPC efforts with wider marketing objectives.
The Evolution of Automation in PPC Over the Past Decade
Over the last ten years or so, PPC automation has undergone significant advancements, driven by major platform changes and innovations.
These changes have fundamentally reshaped how advertisers manage campaigns, emphasising intent and automation over manual processes.
The growing reliance on AI and machine learning for campaign optimisation stems from a shift in focus—from targeting specific keywords to prioritising signals and user intent.
This change reflects how modern PPC systems emphasise broader targeting strategies and automation to maximise efficiency and effectiveness.
Keyword Targeting and the Shift to Intent-Based Advertising
The transformation began with Google Ads’ shift from strict keyword targeting to targeting based on ‘intent’.
A notable milestone was the introduction of close variants in 2014, allowing ads to trigger for variations of exact-match keywords.
By 2017-2018, “exact match” keywords became less restrictive, prioritising user intent rather than strict keyword matching.
Automated Bidding: From eCPC to Smart Bidding
Automated bidding strategies began with Enhanced CPC (eCPC) in 2010, which allowed the system to adjust bids automatically based on the likelihood of conversions.
This evolution accelerated in 2016 with the introduction of Smart Bidding, making possible new kinds of strategies such as Target ROAS and Maximise Conversions.
These approaches leveraged machine learning to optimise bids in real-time, moving beyond manual bidding adjustments.
The Rise of Micro-Moments
Also in 2016, Google introduced the concept of micro-moments—intent-rich instances in which users turn to devices to satisfy immediate needs.
Examples include “I-want-to-buy” moments, where users demonstrate a high intent to purchase.
This framework highlighted the importance of catering to specific user needs in real-time.
Expanding Automation: Auto-Apply Recommendations and Performance Max
Further advancements in automation arrived with the launch of Auto-Apply Recommendations in 2019. These were rolled out more broadly by 2021.
This feature uses machine learning to automatically implement recommendations, streamlining campaign management for advertisers.
The launch of Performance Max campaigns in 2021 marked another milestone.
Performance Max enables advertisers to consolidate various campaign types—such as Search, Display, Video, and Gmail—into a single campaign, maximising visibility and efficiency across multiple channels.
These campaigns heavily rely on machine learning to optimise performance in alignment with business objectives.
The Shift to Responsive Ads
By 2022, Google Ads retired Expanded Text Ads (ETAs), making Responsive Search Ads (RSAs) the default format.
RSAs leverage automation and machine learning to dynamically create ads based on provided headlines and descriptions, optimising ad performance based on real-time data.
The Evolution in Keyword Usage and Campaign Structure
The evolution in keyword targeting has brought major changes to account and campaign structures.
With automation, broader keyword match types like broad match are now central to campaign strategies. This shift requires advertisers to rethink their approach to structuring campaigns and accounts.
Previously, strategies like SKAGs (Single Keyword Ad Groups) were widely used to isolate one keyword per ad group.
This approach maximised relevancy between the keyword, ad copy, and landing page, enhancing Quality Score and reducing CPCs.
However, as automation and machine learning prioritise signals and user intent, SKAGs have become less effective.
Advertisers are now moving toward consolidated campaigns and ad groups that focus on broader themes and leverage machine learning to optimise based on intent-driven signals.
Why Manual Processes Are Increasingly Becoming Obsolete
The reliance on machine learning is driven by the growing complexity of modern advertising.
While manual bidding allows for adjustments based on limited inputs like location or time, it cannot match the scalability and precision of AI-driven strategies.
Machine learning enables advertisers to tap into a wider array of signals, optimise campaigns in real-time, and achieve better results through data-driven decision-making.
The Role of Signals in Optimisation
Modern automation relies heavily on signals such as user behaviour, location, device type, time of day, and other data points.
While manual bidding allows advertisers to adjust based on some visible signals, AI and machine learning can process and act on hundreds of hidden signals that are inaccessible to humans.
This allows for greater precision in targeting and bid adjustments.
For instance, Smart Bidding strategies like Target ROAS and Maximise Conversions use these signals to automatically optimise bids, outperforming manual bidding by handling the vast permutations of signal combinations that would be impossible to manage manually.
Google’s Push Toward Automation
Google continues to push for automation and streamlined account structures. Earlier this year, for example, they announced that starting in June keywords with no impressions for the past 13 months will be automatically paused, reinforcing the need for advertisers to adopt a more streamlined and intent-driven approach.
The Benefits of Automation
1. Time Efficiency: Automating Repetitive Tasks such as Bidding and Reporting
The increasing use of AI and machine learning in campaign optimisation has brought numerous benefits, particularly in automating repetitive tasks like bidding and reporting.
Tools like Smart Bidding and other automated solutions are replacing manual processes, delivering superior performance by leveraging the ability to analyse and optimise based on hundreds of signals that may not be visible to campaign managers.
2. Enhanced Efficiency Through Automation
Algorithms powered by machine learning can analyse vast amounts of data in real-time, optimising campaigns for conversions and aligning with business objectives.
This capability surpasses human capacity to process large datasets effectively and efficiently.
Automation eliminates the need for manual bid adjustments, significantly reducing time spent on routine tasks.
Additionally, it minimises human error, removing the need for constant cross-checking and validation.
This allows campaign managers to focus on higher-value activities that require strategic thinking and creativity.
3. Redeploying Time to Strategic Tasks
The time saved through automation can be redirected toward strategic efforts, such as:
- Developing campaign strategies: Creating and refining campaign plans based on performance insights.
- Analysing data-driven insights: Automated reporting tools can surface meaningful insights, uncovering trends and opportunities that would be difficult for humans to identify manually.
- Optimising creative assets: With more time available, campaign managers can enhance ad copy, visuals, and messaging to drive better engagement.
- Improving landing pages: Insights provided by automation can help refine landing page performance, ensuring better alignment with campaign goals.
4. Automation in Reporting
Automated reporting eliminates the need for manual data compilation, providing real-time insights that can be used to make informed decisions.
AI-driven reporting tools can analyse large datasets, extracting valuable trends and actionable recommendations that enable campaign managers to focus on strategic opportunities instead of data processing.
5. Identifying Trends and Opportunities
Automation doesn’t just save time—it adds value by uncovering patterns and areas for improvement. Machine learning algorithms can identify:
- Emerging trends in user behaviour or campaign performance.
- Opportunities for better targeting or audience segmentation.
- Areas where optimisation is needed, such as ad performance or landing page effectiveness.
6. Improved Performance: Machine Learning’s Role in Smarter Targeting and Decision-Making
Automation, powered by machine learning, plays a crucial role in enhancing campaign performance through smarter targeting and informed decision-making.
Smart Bidding, when combined with streamlined and consolidated account structures, flexible keyword targeting (e.g., broad match), and audience layering, delivers superior results.
This is because Smart Bidding can analyse vast datasets in real-time and optimise for the most valuable auction opportunities based on advertiser-defined criteria.
In 2024, Optmyzr’s analysis of 14,584 Google Ads accounts revealed that approximately 48% of advertisers employ multiple bidding strategies within a single account.
Furthermore, a Google case study highlighted that Citibanamex experienced a 27% increase in booked credit cards after implementing value-based bidding strategies. .
7. Real-Time Optimisation and Smarter Decisions
Smart Bidding algorithms evaluate every auction in real-time, determining which opportunities align best with the advertiser’s goals.
This capability leads to stronger, data-driven decision-making and better campaign outcomes.
However, the quality of these decisions is significantly influenced by the actions and inputs of the PPC manager.
These examples underscore the growing adoption and effectiveness of automated bidding strategies in enhancing campaign performance.
Feeding the Algorithm: The Role of Data Quality
To ensure the system makes smarter decisions, it is essential to provide:
- First-Party Data: Leveraging first-party data enhances the system’s ability to target users effectively, as it provides unique insights into audience behaviour and preferences. This data empowers the machine learning model to make better-informed decisions.
- High-Quality Data: The more accurate and comprehensive the data fed into the system, the better its ability to optimise. For campaigns with lead generation objectives, it’s critical not only to optimise for the number of leads but also to provide feedback on lead quality. By distinguishing high-quality leads from lower-quality ones, the system can refine its decision-making and prioritise valuable outcomes.
- Negative Keywords and Boundaries: Inputting negative keywords helps define boundaries for the system, ensuring that broad match keyword targeting does not trigger ads for irrelevant or non-performing searches. This helps prevent wasted spend and focuses the campaign on productive opportunities.
The Evolving Role of the PPC Manager
The success of machine learning in improving performance is not entirely autonomous; it requires thoughtful management. PPC managers play a vital role in:
- Providing accurate data inputs, including performance feedback.
- Refining campaign parameters, such as adding exclusions or criteria to guide the algorithm.
- Regularly reviewing and adjusting campaign goals to ensure alignment with broader business objectives.
By collaborating with automation systems, PPC managers ensure that machine learning algorithms have the right tools and context to optimise effectively.
Potential (and Actual) Challenges of Automation
Loss of Control: Overcoming Over-Reliance on Automated Tools
While automation has streamlined many aspects of PPC management, it remains far from a “set-and-forget” process.
Unlike platforms like Meta, managing campaigns in Google Ads and Microsoft Ads often requires greater involvement, particularly in optimisation and strategic adjustments.
Regularly feeding accurate information and providing contextual data to the algorithm is essential to ensure effective performance.
The Balance Between Automation and Control
Automation introduces a challenge: loss of control over certain aspects of campaign management.
PPC managers must rely on automated tools for tasks like bidding, targeting, and ad delivery, but they also need to actively oversee and fine-tune campaigns to guide machine learning systems effectively.
To overcome the loss of control, PPC managers need a deep understanding of how machine learning and AI work. This expertise allows them to:
- Leverage advanced features to counterbalance automation’s limitations.
- Identify when and where to intervene.
- Adjust campaign settings to align with broader goals.
Practical Strategies to Regain Control
Even when relying on automated tools like Smart Bidding, PPC managers can regain a degree of control by:
- Setting Bid Constraints: Using features like floor and maximum CPCs at the bid strategy level helps guide the system within predefined boundaries, preventing over-optimisation that could result in inefficient spending.
- Providing High-Quality Inputs: Accurate data, well-defined goals, and clear audience segmentation help the algorithm make better decisions.
- Layering Manual Adjustments: For example, combining automated bidding with manual controls like negative keyword lists or ad scheduling can fine-tune campaign performance.
- Monitoring and Feedback: Regular performance reviews and feedback loops ensure the machine learning system evolves in alignment with the campaign’s objectives.
Why Active Campaign Management Remains Essential
Automation does not replace the need for active campaign management. PPC managers play a critical role in:
- Leveraging tools and insights to refine strategy and drive better results.
- Adjusting campaign settings to respond to market trends.
- Feeding contextual information, such as performance feedback or exclusions, that algorithms might miss.
The Skills Gap: Navigating the Need for Technical Expertise
As automation and machine learning play an increasingly central role in PPC advertising, a deep understanding of these technologies has become essential.
Marketers must learn to work with these systems effectively, leveraging their potential while ensuring that automation aligns with strategic goals.
The increasing sophistication of PPC platforms like Google Ads and Microsoft Ads, with their diverse automation features, presents challenges for marketers.
It’s essential for marketers to have a deep understanding of how these automation features, from Smart Bidding to audience targeting, work and how to configure them effectively.
Furthermore, the large amounts of data generated by these tools require strong analytical skills to interpret, identify trends, and gain actionable insights.
Staying ahead of the curve is crucial, as automation in marketing is constantly evolving.
Marketers need to remain informed about the latest technological developments and continuously update their technical and analytical skills.
A solid understanding of machine learning and algorithms is also becoming increasingly important. While marketers don’t need to be data scientists, grasping the basics of these algorithms is vital for optimising campaign performance, troubleshooting issues, understanding automation behaviors, and making informed decisions about campaign configurations.
Although automation excels at handling repetitive tasks and optimising based on predefined criteria, it cannot replace strategic thinking and human oversight.
Marketers must use critical thinking to adapt strategies based on automation results, provide human oversight to identify areas for improvement, refine campaign objectives, and maintain the adaptability to respond to changes in performance or external factors.
Investing in automation expertise has its own set of challenges.
Teams require ongoing training and upskilling to stay effective with evolving tools. Hiring specialists with automation expertise can also strain resources.
Finally, finding the right balance between automation and human input adds another layer of complexity. While automation reduces manual workload, ensuring effective human oversight is essential for success.
Redefining the PPC Manager’s Role
Focus Areas for PPC Managers: Strategy, Creative, and Interpreting Data
With automation taking over many of the manual tasks in PPC management, the role of the PPC manager is evolving. The focus is shifting towards:
Strategy: Developing comprehensive campaign strategies that align with business goals and leverage automation effectively. This includes defining clear objectives, identifying target audiences, and setting appropriate KPIs.
Creative: Creating compelling ad copy and visuals that resonate with the target audience and drive engagement. This involves A/B testing different creative elements and continuously optimising for better performance.
Interpreting data: Analysing campaign data to identify trends, opportunities, and areas for improvement. This requires strong analytical skills and the ability to extract actionable insights from large datasets.
The Importance of Collaboration Between Automation Tools and Human Oversight
The future of PPC management lies in a collaborative approach where automation tools and human expertise work together.
Automation handles the heavy lifting of data processing and optimisation, while human oversight provides strategic direction, creative input, and critical analysis.
This partnership ensures that campaigns are both efficient and effective.
Conclusion: The Future of PPC Management as a Partnership Between Automation and Human Expertise
As automation continues to evolve, the PPC landscape will undoubtedly undergo further transformation.
However, the human element will remain crucial.
The future of PPC management lies in a symbiotic relationship between automation and human expertise, where each complements the other to achieve optimal results.
