Automation and AI are revolutionizing the PPC landscape. Now, PPC teams are transforming into data teams, mastering data infrastructure, measurement, analysis, and experimentation.
Like many people, I worry about AI taking over jobs. Where do my ‘old school’ PPC skills fit in an AI-dominated landscape?
Relax. It’s not a binary situation. The shift is towards data and strategy. Media buying might look automated from the outside, but don’t be misled. The role is simply evolving once more.
Having been in PPC for over 15 years, I’ve learned that there’s nothing to fear. The real question is: am I riding the wave or getting left behind?
Let’s explore what the current PPC landscape looks like with ad network automation, and more importantly, where today’s PPC teams truly add value.
The Return of the Technical PPC Team
A decade ago, technical PPC agencies distinguished themselves by developing scripts, managing data on a large scale, and overseeing complex structures. As automation matured, many teams pivoted towards strategy and creativity.
Now, with AI’s help, producing quality creatives or analyzing massive datasets to create strategies is easier than ever. However, these outputs aren’t flawless.
From a client’s perspective, the typical creative-centric or strategy-focused agency might be out of the game. Therefore, rejoice, PPC folks: the technical edge is back, albeit in a different form. It’s time to bring back the spreadsheet enthusiasts from the 2010s who can now drive the PPC industry forward.
Still skeptical? Let’s rewind and get a clearer view of the necessary skill sets.
The PPC Edge: From Spreadsheet Skills to Data Nerds
Today, successful PPC agencies sell something vastly different than a decade ago, though the core mindset remains the same.
Why? Let’s consider the key performance drivers nowadays:
- Integrating down-funnel data into strategy.
- Building a data infrastructure to support strategy.
- Providing accurate signals to ad algorithms.
- Building systems to scale operations, including creative tasks.
See the pattern? A broken data model can’t be solved just by prompts. This is your advantage, what clients value most. Automation enhances the value of technical literacy rather than diminishing it.
Who do you turn to for technical literacy? The seasoned PPC marketers who thrived on manipulating paid search ads using custom Excel macros or managing extensive product feed items. They have the mindset: a love for automation, data, and math.
1. Data Engineer
The data engineer builds and maintains the infrastructure. Although they might come after the tracking specialist in the data chain, they are central, which is why we mention them first.
In today’s multi-platform world, think of CRM integration with Google Ads or blending online and offline data sets to strategize effectively.
Without a comprehensive data model, strategies become vague gut feelings needing constant reality checks. The data engineer’s role is to set a strong foundation to prevent such situations.
Without this role, you face repetitive manual exports and inconsistent numbers across teams, leading to sluggish decision-making.
What is the Data Engineer’s Scope?
Building a data infrastructure follows an ETL process: extract data, manipulate it, and make it usable in tools like Looker Studio, Power BI, or Tableau.
- Build data pipelines from ad platforms, analytics, or CRM tools into the warehouse for data like spend and revenue.
- Structure tables for these sources and merge them for specific use cases.
- Maintain datasets and perform automated QA, including refresh schedules.
What Skill Sets and Tools Does the Data Engineer Use?
In a Google-centric world, we often hear about BigQuery, but there are alternatives like Microsoft Azure. The essential skills are coding, particularly SQL and Python.
These languages are used to structure tables within the data warehouse (using SQL) and to create data pipelines (using Python).
2. Tracking and Measurement Architect
Some might think this role overlaps with data engineers, but I strongly disagree. This person focuses solely on maintaining signal quality within tight deadlines when issues arise.
Tracking failures mean lost conversion data, impacting ad platforms’ performance because they’re built on conversion data insights.
Notice this when CPAs fluctuate unexpectedly or in-platform data varies drastically from your ‘source of truth’ (GA, CRM, others). These architects help stabilize bidding and improve event match quality for better data in Google Ads.
What is the Tracking Architect’s Scope?
They design comprehensive, regulation-compliant data collection mechanisms, making sure everything is aligned with privacy compliance.
- Align tracking with privacy regulations.
- Design client- and server-side tracking.
- Implement GTM and server containers.
- Co-manage Conversions API integrations with the data engineer.
- Co-ensure deduplication logic with the media buyer.
What Skill Sets and Tools Does the Tracking Architect Use?
While many PPCs have used Google Tag Manager, few have set up server-side tagging. This role needs a deep understanding of Consent Mode frameworks, CAPI, among other tools.
3. Data Analyst
If data engineers build the pipes and tracking architects secure the signals, data analysts interpret what the data implies. It’s a role quite affected by AI, yet crucial due to the risk of misinterpretation.
Wrong interpretations can lead to costly errors. Fully relying on AI over data analysts could be a grave mistake, as misinterpreted metrics like ROAS versus actual contribution margins or CPA disparities can derail strategies.
What is the Data Analyst’s Scope?
While outsiders might think they only build dashboards, data analysts handle much more, like designing models aligned with KPIs and rigorous analysis, all while questioning platform narratives.
- Align data models with business KPIs.
- Analyze performance cohorts, churn rates, and profitability.
- Challenge existing platform narratives critically.
What Skill Sets and Tools Does the Data Analyst Use?
Think of data analysts as translators; understanding numbers doesn’t mean you’re ready to interpret them correctly. They need SQL for warehouse queries and modeling skills for strategic planning, along with strong statistical reasoning.
4. CRO and Experimentation Lead
Once data is cleaned and analyzed, CROs leverage insights to enhance visitor economics. A low conversion rate can mean higher CPA, which no one wants. Their expertise helps scale operations efficiently rather than throwing money at inefficient processes.
What is the CRO’s Scope?
CRO roles are not just about landing pages but full-funnel optimizations, identifying friction points, structuring tests, and working with creative teams to position offers effectively.
- Navigate from impression to revenue.
- Utilize heat maps to locate friction points.
- Use proper methodologies instead of random experiments.
- Coordinate with creative and product teams for best offer placements.
What Skill Sets and Tools Does the CRO Lead Use?
Core tools include GA4 and heat mapping software, with options to scale based on needs. Critical skills involve a firm grasp of statistical reasoning and translation of business metrics into actionable insights.
From Media Buyers to Data Teams
Today’s PPC teams resemble hybrids of marketing, data, and product roles rather than mere media buyers. Successful teams deliberately build capabilities around understanding algorithms, data dynamics, and economics, enabling AI to become a strategic asset rather than a threat.
Inspired by this post on Search Engine Land.
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