Google PMax: Inside The Negative Keyword Limit Increase & What’s Next via @sejournal, @adsliaison

As Google’s Ad Product Liaison, I often share updates and insights with the community of digital advertisers and, best of all, get to hear your feedback first-hand.

We heard quite a lot after our recent announcement that, after a period of beta testing, we’re rolling out negative keywords in Performance Max (PMax) campaigns with a restriction.

We had set a cap of 100 negative keywords per campaign.

While the ability to add negative keywords in PMax directly in Google Ads without having to request them through Support or an account rep has been a long-time ask, we heard very quickly that the cap of 100 negative keywords felt too restrictive for many.

Here’s a look behind the scenes at the reasoning behind the initial cap, what we learned from your feedback, and the subsequent decision to increase the limit to 10,000 negative keywords per campaign.

Why The Cap In The First Place?

AI, by its nature, thrives on flexibility, adapting to real-time data and user behavior.

Performance Max is an AI-powered, goal-based campaign type that’s designed to find conversions based on the goals you set.

The intention of capping negative keywords in PMax at 100 was to give advertisers additional control while still giving PMax the flexibility to achieve your campaign’s stated goal – a limit of 100 negatives felt like a reasonable starting point.

To arrive at that number, we analyzed PMax campaigns in which negative keywords had been added via Support or their account rep.

We found that the 100-keyword limit would cover the vast majority of campaigns using negative keywords.

We also saw that the majority of submitted negative keywords had no actual serving impact – their ads already weren’t triggering for terms advertisers had concerns about.

In many other cases, other targeting exclusions would have been more suitable for blocking unwanted traffic.

We saw this in our beta testing as well. In short, 100 felt like a good compromise between offering enough flexibility without dramatically increasing the risk of accidentally blocking valuable traffic.

Negative keywords are just one way to control where your ads show on Search. Other controls such as brand exclusions, account level negative keywords and keyword prioritization are also available.

The initial cap of 100 negative keywords aimed to:

  • Preserve AI Optimization: Excessive negative keywords can act as rigid constraints, preventing the AI from exploring valuable search paths and hindering its ability to identify emerging trends. Essentially, it can stifle the algorithm’s ability to find the most efficient conversions. Very large negative keyword lists can potentially negatively impact the machine learning systems and hurt performance.
  • Prevent Accidental Traffic Exclusion: We aimed to prevent advertisers from inadvertently excluding valuable traffic through overly broad negative keyword scopes and missing potential high-intent customers.

What Your Feedback Told Us

We heard advertiser feedback loud and clear that while negative keywords are welcomed, the cap of 100 felt too restrictive.

We heard from brands that quickly hit the 100 limit before including the key themes they wanted to negate. In short, it wasn’t a practical solution for many.

After looking at options, the team agreed to align with the limits in Search campaigns and raise the threshold to 10,000 negative keywords per PMax campaign.

That’s obviously a significant jump from 100 and way more than nearly every business will need or should use, but aligning on one common threshold simplifies things and gives advertisers plenty of room to experiment.

Actionable Insights And Considerations For Measuring Impact

Adding negative keywords to a Performance Max campaign can, of course, impact where your ads show on Search and Shopping inventory.

While the increased limit provides greater control, it’s crucial to use negative keywords strategically. Here are several things to keep in mind when applying negative keywords in PMax:

  • Judicious Application: Avoid overly broad exclusions that might hinder the AI’s ability to find valuable conversions. Prioritize high-impact negatives that address specific ROI concerns. Keep in mind that account-level negative keywords you’ve added for brand suitability purposes already apply to your PMax campaigns.
  • Match Type Precision: Understand the nuances of broad, phrase, and exact match negative keywords in PMax. Negative match types work differently than their positive counterparts. For negative broad match keywords, your ad won’t show if the search contains all your negative keyword terms, even if the terms are in a different order. Phrase match negatives exclude queries containing the exact phrase, while exact match excludes only the specific query. Use them strategically to balance precision and reach.
  • Performance Monitoring: Closely monitor key metrics like conversions, conversion value, and conversion rates to ensure negative keywords have a positive rather than negative impact on performance.
  • Conflict Resolution: Be aware that if a user search matches both a positive signal and a negative keyword, the negative keyword will take precedence, and your ad will not be eligible to serve for that query.
  • Beyond Negative Keywords: Remember that PMax offers other control mechanisms to inform when your ads can trigger on Search.
  • Regular Audits: Just as with your Search campaigns, be sure to regularly audit your negative keywords to identify where you might be blocking potential valuable traffic. And Search Term Insights can help you identify query themes and individual search terms you might want to block with negative keywords.

Your Questions Answered

I received several questions about this update from advertisers on LinkedIn and X (Twitter) and want to address some of those here.

“The real challenge is how negative keywords interact with PMax’s black-box decision-making. Will we get more visibility into which search terms PMax is actually serving against? And how will negatives impact machine learning optimization long term?”

While PMax is designed to automate many aspects of campaign management, we recognize the importance of providing advertisers with meaningful insights.

The introduction of negative keywords is one of several recent steps towards providing additional controls.

Search Terms Insights for PMax provides a view of the search term categories as well as specific search terms that triggered your ads in Search. You’ll find performance metrics at the search term level.

Search Terms Insights is designed to make analyzing search term data easier by already grouping similar searches into broader categories, saving you the time to sift through individual search terms.

This data can be downloaded and available via scripts and the Google Ads API.

As for the long-term impact of negative keywords on campaign optimization, it’s important to strike a balance.

While negative keywords provide crucial control, an overly restrictive approach could limit the system’s ability to learn and adapt to new opportunities.

As noted above, our recommendation remains to use negative keywords strategically to exclude truly irrelevant traffic, allowing the AI to continue exploring and finding valuable conversions within the defined boundaries you set.

Reporting and insights are areas the team is actively focused on. Stay tuned for more on this.

“Google never needed <100 negative keywords in order to have>

Our intention was never to encourage spending on irrelevant queries.

Performance Max is a goal-based campaign type which means it’s designed to find more of the conversions that you indicate are valuable to your business.

The initial cap of 100 negative keywords was tested in beta and seemed to provide a reasonable level of control while still allowing the AI the necessary flexibility.

We acknowledge that our initial assessment was not sufficient for many advertisers, and that’s why we listened to your feedback and made the significant increase to 10,000.

“Why can’t negative keywords be limitless at any/every account level? Are there technical/operational issues that would be impacted?”

This is a fair question. There are limits on certain entities in Google Ads accounts to help ensure system and process stability. We have more details on various entity limits here.

“Will Google give us the ability to see the previously applied negative keyword lists we used to do via Support or our reps.”

Yes, you’ll be able to see and edit negative keywords and negative keyword lists that were previously added by Support or a rep.

“Why weren’t negative keywords available from the very start when PMax launched.”

The core principle behind PMax is leveraging AI to discover conversions across Google’s channels.

When PMax launched in 2021, the vision was to give advertisers a streamlined way to tell Google what they want to optimize for and then allow the system to learn and find those desired customers across all of Google’s inventory.

Exclusions were seen as unnecessary and potential impediments to optimization.

Over time, and with advertiser feedback in mind, features within PMax have expanded. And the pace of new insights and controls has been accelerating in recent months.

“What about negative keyword lists?”

Many of you asked about the possibility of using negative keyword lists within Performance Max campaigns, as you can in Search campaigns.

We are actively working on this and expect to have more to share on support for negative keyword lists in PMax later this year.

How PMax Is Evolving

I recently shared the overview below of many of the recent reporting and control updates for PMax at the Paid Search Association Conference.

These features are aimed at giving you more tools and information to steer PMax to find more of the conversions you want to generate for your business.

Features like brand guidelines help ensure your responsive display ads and auto-generated video ads reflect your brand’s visual identity.

Ginny Marvin presented recent PMax controls and insights updates at the Paid Search Association ConferenceRecent controls and insights updates for PMax. Image from author, March 2025

Stay tuned for more on search terms data and analysis capabilities as well as additional insights this year.

This is an area we are actively focused on. And keep the feedback coming.

More Resources:


Featured Image: Gorodenkoff/Shutterstock

[SEO & PPC] How To Unlock Hidden Conversion Sources In Your Sales & Marketing Funnel via @sejournal, @calltrac

 This post was sponsored by CallTrackingMetrics. The opinions expressed in this article are the sponsor’s own.

Did you know 92% of all customer interactions are from phone calls?

And very few know how to track conversions from phone calls.

Brands meticulously track clicks, impressions, and online interactions through SEO, pay-per-click (PPC) ads, and data-driven strategies.

Yet, one critical piece is often missing: offline conversions.

Many high-intent customer interactions, especially in industries like healthcare, legal, home services, and B2B, happen over the phone.

If you’re in an industry that receives any number of calls, you may be struggling to connect these calls to your digital marketing efforts, leading to:

  1. Inefficient marketing strategies.
  2. Wasted ad spend.
  3. Difficulty proving ROI.

How do you fix this? Call tracking.

By leveraging AI-powered tools and advanced attribution technology, marketers can bridge the online-offline gap, ensuring no lead goes unnoticed.

How To Attribute Sales To Phone Calls

TL;DR: Historically, you could not attribute conversions to phone calls; now, you can.

Yes, offline conversions can be tracked.

And despite the high percentage of customer interactions happening over the phone, many brands fail to track which ad or campaign led to those calls.

This could stem from knowledge gaps, tight budgets, or reluctance to integrate more technology into their stack.

Without call attribution, businesses are left guessing about what’s driving revenue.

What Is Offline Conversion Attribution?

Offline conversion attribution is the process of linking your online marketing efforts to offline sales or actions.

It helps you understand which digital marketing channels and campaigns contribute to offline conversions, such as in-store purchases, phone call inquiries, or signed contracts.

How Offline Conversion & Phone Call Attribution Works

By paying attention to phone call conversion data, you can:

1. Connect Online Interactions To A Phone Call: A user clicks on a digital ad, visits a website, fills out a form, or calls a business after seeing an online campaign.
2. Store User Data In One Place: Data from these interactions (such as email, phone number, or a unique tracking ID) is captured and stored.
3. Match Callers With Offline Events: When a purchase or conversion happens in-store, over the phone, or through a sales team, businesses match it back to the initial online touchpoint.
4. Analyze & Optimize Webpages With Content That Converts: You can analyze which digital campaigns, keywords, or ads drive the most offline conversions, optimizing their marketing strategy accordingly.

What You Can Do With Phone Call Conversion Data

When you introduce a tool that acts as Google Analytics for phones, you’ll be able to:

  • Improve ROI Measurement: Helps businesses understand the real impact of digital marketing on offline sales.
    Enhance Ad Targeting: Enables better retargeting of high-intent users.
    Optimize Budget Allocation: Allows marketers to invest more in channels that drive actual sales, not just clicks or website visits.
    Bridge the Online-Offline Gap: Particularly important for industries like retail, automotive, healthcare, and B2B, where many transactions happen offline.

Examples of Offline Conversion Attribution

  1. A customer finds your business through organic search.
  2. They see a retargeting ad on Facebook.
  3. Finally, they click a PPC ad and call to book an appointment.

Without call tracking, the PPC ad might receive full credit, even though SEO and social played key roles. Choosing the right attribution model ensures data-driven marketing decisions.

Best Tools for Offline Conversion Tracking

  • Google Ads Offline Conversion Tracking
  • Facebook Offline Conversions API
  • CRMs like HubSpot or Salesforce
  • Call tracking software like CallTrackingMetrics

SEO & Call Tracking: Connecting Organic Efforts To Real-World Conversions

Gain Keyword Attribution Beyond Clicks

Rankings, traffic, and forms typically measure SEO success fills. But what about phone calls? Call tracking technology with dynamic number insertion (DNI) allows businesses to:

  • Identify which organic search queries lead to phone calls
  • Optimize content around real customers’ questions and concerns
  • Understand which landing pages drive the most offline conversions

For example, if multiple callers reference a specific product-related question, that insight can inform new blog topics or FAQ pages to improve SEO efforts, driving even more right-fit traffic into your sales funnel and conversion metrics.

Optimize For True Local SEO

Local search is a major driver of inbound calls. When combined with call tracking, businesses can finally understand:

  • Which local listings (Google Business Profile, Yelp, etc.) generate the most calls?
  • What information do customers search for before calling?
  • How to refine location-based content for higher engagement

How Call Insights Can Strengthen Your SEO Strategy

Phone calls aren’t just conversions—they’re valuable sources of customer insights that your teams can use to refine ad strategies, train teams on sales pitches, and identify areas for growth in your content strategy. Each conversation has the potential to reveal the common questions, pain points, and content gaps that businesses can address to improve their marketing performance.

1. Identify FAQs for Stronger Content

Often, customers call a company’s support phone number when they can’t find information online, either about a product or service they’re considering buying or one they’ve already purchased. By analyzing call transcripts, businesses can spot recurring questions and proactively address them in blog posts, FAQs, or product pages.

For example, if a home services company frequently gets calls asking, “Do you offer emergency repairs on weekends?”, this signals a need to make that information more visible on their website. A dedicated service page or blog post could reduce unnecessary calls while improving customer experience.

2. Refine Your Website Messaging

If callers repeatedly ask about pricing, product differences, or service details, your website messaging probably isn’t clear enough.

For instance, an e-commerce brand selling fitness equipment might notice that callers often ask, “What’s the difference between your basic and premium treadmill?” Adding a simple comparison chart or explainer video can help lessen confusion and improve conversions.

3. Fill Content Gaps To Reduce Sales Friction

Repeated calls about the same topic are a good indicator of missing or unclear content. A B2B SaaS company, for example, might receive frequent inquiries about integrating with a particular CRM or social platform. Instead of solely relying on customer support, the marketing team could identify this pain point and create a step-by-step guide or video tutorial to address it, which would reduce friction and improve self-service for prospects.

PPC & Call Attribution: Maximizing ROI With Better Insights

Tracking clicks alone doesn’t reveal the full ROI of PPC campaigns. Many conversions, especially phone calls, happen offline and go untracked. Without attribution, businesses may waste ad spend and overlook high-intent leads. This section explores how call tracking connects PPC efforts to real conversions, improving marketing efficiency.

Paid Search: Wasted Spend Without the Full Picture

A high cost-per-click (CPC) doesn’t guarantee strong ROI if businesses aren’t tracking offline conversions. Without call tracking, marketers risk:

  • Over-investing in underperforming keywords
  • Missing opportunities to optimize campaigns for call-driven leads
  • Failing to attribute revenue-generating phone calls to PPC efforts

When a business fails to account for ROI in the form of phone calls, they’re losing an opportunity to accurately account for their real CPC and allocate resources accordingly.

Call Tracking + Google Ads = Smarter Bidding

PPC campaigns are only as effective as the data behind them. Without tracking phone calls, businesses risk misallocating budgets to keywords that drive clicks but not conversions. Integrating call tracking with Google Ads provides a clearer picture by linking calls to the specific campaigns, ad groups, and keywords that drive valuable conversions.

With AI-powered call scoring, marketers can identify high-intent leads and adjust bidding strategies based on actual conversion data—not just clicks. This ensures ad spend is focused on quality leads rather than wasted traffic.

Retargeting with First-Party Data

Not every caller converts immediately. Call tracking allows businesses to retarget high-intent leads with personalized follow-ups. By analyzing call topics, marketers can tailor ads or email sequences to address specific customer concerns, increasing the likelihood of conversion.

Additionally, integrating call data with CRM platforms like HubSpot and Salesforce ensures sales teams can nurture prospects effectively, preventing lost opportunities. By combining PPC insights with offline conversions, businesses gain a clearer understanding of customer behavior, leading to smarter ad spend and more targeted outreach.

Back To Basics: Omnichannel Attribution & The Power Of Call Data

As marketing shifts to a mix of online and offline tactics, attribution models must evolve. By integrating call tracking with Google Analytics, CRM systems, and automation tools, businesses can gain a complete view of the customer journey.

A company that integrates CallTrackingMetrics with Google Analytics and its CRM can:

  • See exactly which campaigns drive calls.
  • Automate follow-ups based on conversation insights.
  • Optimize for higher-value interactions.

AI & Conversation Intelligence

Call tracking is no longer just about recordings or basic attribution. AI-driven call analysis provides deep insights, such as:

  • Customer intent and sentiment analysis.
  • Common objections that impact sales.
  • Automated lead qualification based on real conversations.

By leveraging AI, businesses can better understand customer needs, improve sales strategies, and ensure marketing efforts are driving meaningful engagement. Implementing AI-driven call tracking empowers teams to make data-backed decisions that enhance both customer experience and conversion rates.

Proving Marketing’s True Impact

Marketers are often challenged to prove ROI beyond what we might call “vanity metrics”, like impressions and clicks. Though these have a place in any strategy, these metrics don’t necessarily move the needle toward sales goals.

Call tracking, on the other hand, delivers revenue-focused attribution, showing exactly how digital marketing contributes to bottom-line growth. This kind of revenue-focused attribution can help an entire company analyze past efforts and accurately forecast revenue based on real campaigns, real calls, and real results

Case Study: This study from CallTrackingMetrics demonstrated how AI-driven call tracking optimized PPC ROAS and improved lead quality​.

Want to see how conversation intelligence can improve your marketing performance? Check out our guide to building an effective omnichannel communications strategy.

Ready to get to work? Book a demo with our team and see how CallTrackingMetrics’ products can help you.


Image Credits

Featured Image: Image by CallTrackingMetrics. Used with permission.

3 Ways AI Is Changing PPC Reporting (With Examples To Streamline Your Reporting) via @sejournal, @siliconvallaeys

PPC reporting has always been both essential and frustrating. It’s essential to keep clients engaged by informing them of the results you’re driving.

But it’s also frustrating because of data discrepancies, cumbersome analysis, and the time required to share understandable, jargon-free reports with different stakeholders.

Fortunately, AI is turning these obstacles into opportunities by filling in gaps left by privacy-compliant tracking, surfacing insights hidden in overwhelming data sets, and automating reporting so it meets the needs of every stakeholder.

In this article, I’ll walk you through some of the technology used by modern marketers and share examples of how I’ve used AI to streamline my PPC reporting.

1. Collect Complete And High-Quality PPC Data

We need data to guide us before we can optimize accounts and share our wins, so let’s start there.

The Problems With Data Before AI

Inconsistent and missing data plague PPC efforts.

Google, Meta, Microsoft, and Amazon operate in their own silos, each taking credit for all conversions that have any touchpoint with their platforms. This leads to double counting, making it difficult to decide where to allocate budgets for optimal results.

In other words, the data between the various ad platforms is inconsistent. Specifically, the conversion value advertisers see in their business data may be lower than the sum of all conversion values reported by the ad platforms.

Add to this the challenge of missing data. Privacy regulations like GDPR and Apple’s iOS changes limit tracking capabilities, which causes data loss, incomplete conversion paths, and gaps in attribution.

Marketers who rely heavily on pixel-based or third-party cookie tracking, both of which became unreliable due to browser restrictions and user opt-outs, see a continuous decline in the quality of the data they need to operate.

While AI can’t magically give us perfect data, it can fill in gaps and restore insights, so let’s take a look at some of the solutions in this space.

AI-Driven Solutions For Data Hygiene And Compliance

1. Data Clean Rooms And Privacy-First Measurement

Clean rooms like Amazon Marketing Cloud (AMC) and Google Ads Data Hub allow advertisers to securely analyze anonymized cross-channel performance data without violating privacy laws.

These platforms aggregate data from multiple sources, giving marketers a comprehensive view of the customer journey.

Example:

A retail brand can use AMC to evaluate how its Google and Facebook ads influence Amazon purchases. Based on what they find, they can re-allocate budgets between platforms to maximize overall return on investment (ROI).

Clean rooms themselves aren’t an AI innovation; however, they benefit significantly from several AI capabilities.

For example, Meta’s Advantage+ uses clean room insights to build lookalike audiences while staying privacy-compliant.

2. Modeled Conversions

While clean rooms are great for unifying cross-platform data, their usefulness is predicated on data completeness.

When privacy regulations make it impossible to get all the data, clean rooms like Google Ads Data Hub and Amazon Marketing Cloud use AI-powered modeled conversions to estimate user journeys that can’t be fully tracked.

Modeled data is also used by tools like Smart Bidding, which leverages machine learning to predict conversions for users who opted out of tracking.

For users who opt out of tracking, Consent Mode still allows the collection of anonymized signals, which machine learning models can then use to predict conversion likelihood.

Example:

Google’s Smart Bidding leverages machine learning to optimize bids for conversions or conversion value.

In cases where conversion data is incomplete due to user consent choices or other factors, Smart Bidding can use modeled conversions to fill in gaps and make good bidding decisions.

The models do this by identifying patterns and correlations between user attributes, actions, and conversion outcomes.

While modeled conversions offer significant benefits in their ease of use (they’re basically provided without any extra effort by the ad platforms), it’s important to remember that they are only estimates and may not be perfectly accurate in all cases.

Advertisers should consider using modeled conversions in conjunction with other ways to get a more complete picture of campaign performance.

For example, advertisers can use Media Mix Models (MMM), a Marketing Efficiency Ratio (MER), or incrementality lift tests to validate that the data they are using is directionally correct.

3. Server-Side Tagging And First-Party Data Integration

Server-side tagging lets marketers control data collection on their servers, bypassing cookie restrictions.

Platforms like Google Tag Manager now support server-side implementations that improve tracking accuracy while maintaining privacy compliance.

Server-side tagging captures anonymous pings even when cookies are declined, feeding better signals into Google’s AI models for more accurate conversion modeling.

This gives AI more complete data when doing things like data-driven attribution (DDA) or automated bidding.

Illustration by author, February 2025

Example:

An ecommerce company transitions to server-side tagging to retain high-quality data even when technologies like Safari’s Intelligent Tracking Prevention (ITP) break JavaScript-based tracking.

As a result, the advertiser sees a complete picture of all the conversions driven by digital marketing and can now justify higher bids, which makes them more competitive in the ad auction and boosts total sales for their brand.

Actionable Tips:

  • Implement GA4 Consent Mode and server-side tagging to maintain accurate performance data.
  • Leverage data clean rooms to analyze cross-platform conversions securely.
  • Use modeled conversions to fill tracking gaps caused by privacy restrictions.

2. Extract Data Insights And Make Smarter Decisions

Now that we’ve covered technologies that can stem the decline in access to data, let’s examine how AI can help make sense of it all.

The Problem With Data Analysis Before AI

Marketers may struggle to extract actionable insights when looking at a mountain of PPC data.

Humans simply aren’t as good as machines at detecting patterns or spotting anomalies in large data sets.

While statistical methods have long been used to find these patterns, many marketing teams lack the expertise to do it themselves or have no access to a qualified analyst to help them.

As a result, teams miss opportunities or spend more time than they can afford looking for signals to guide optimization efforts.

AI Solutions For Data Analysis And Attribution

1. Data-Driven Attribution Models (DDA)

DDA isn’t the newest solution in attribution modeling, but it exists largely because AI has become cheaper and more accessible.

It solves the problem of assigning values to different parts of the consumer journey when users take a multitude of paths from discovery to purchase.

Static attribution models lack the sophistication to account for this and cause advertisers to bid incorrectly.

Google’s data-driven attribution (DDA) uses machine learning to analyze conversion paths and assign credit based on a more complete analysis of a user’s consumer journey.

Unlike static models, DDA dynamically adjusts credit allocation to reflect the many ways consumers behave.

Machine learning, a form of AI, is what enabled Google to make this more advanced attribution model available to all advertisers and what has driven the steady improvement in results from Smart Bidding.

2. Automating Auction Insights Visualization

Generative AI is not only enhancing attribution but also automating repetitive tasks.

Recently, I tested GPT Operator to streamline several PPC reporting workflows.

Operator is OpenAI’s tool that lets the AI use a web browser to achieve tasks. It goes beyond searching on the web; it allows you to follow links, fill in forms, and interact intelligently with websites.

In one task, I asked Operator to download auction insights, visualize the data using Optmyzr’s Auction Insights Visualizer, and email a report.

It handled the data transfer and visualization steps flawlessly, though it struggled with taking a clean screenshot instead of attempting to attach HTML.

Illustration by author, February 2025

This illustrates how AI agents can help when data lives in disparate places. There are no APIs available to move it, as is the case with auction insights data from Google.

While Operator still needs too much hand-holding to be helpful today, it seems likely that we’re less than a year away from when it can do many tedious tasks for us.

3. Advanced Statistical Analysis Available To Anyone

Before AI advancements, conducting a statistical analysis could be a labor-intensive process requiring specialized software or data science expertise.

But today, generative AI enables marketers to explore these areas that were previously firmly outside their realm of expertise.

For example, GPT can explain and execute a process like a seasonality decomposition. AI can quickly write Python code that breaks down campaign data into trend, seasonal, and residual components, helping marketers uncover patterns they can act on.

How AI Automates Seasonal Analysis

In one of my PPC Town Hall podcast episodes, Cory Lindholm demonstrated how GPT can handle complex seasonality analysis in minutes.

Inspired by this, I used GPT’s Advanced Data Analysis feature to upload weekly Google Ads data and run a full decomposition.

GPT efficiently cleaned the data, identified issues like formatting errors, and generated a breakdown of trends, seasonal variations, and residual fluctuations.

In the analysis, GPT flagged recurring trends, allowing me to pinpoint peak demand periods and optimize bid strategies ahead of time. Tasks that previously took hours now take just a few minutes.

On a side note, I have found large language models (LLMs) so helpful with coding that I am now using v0.dev almost weekly to create apps, browser extensions, and scripts on a weekly basis.

3. Communicate Results Effectively Across Teams

With solid data in place and AI-fueled ways to speed up analysis, we should have some great results to share with stakeholders.

But sharing results through reports has traditionally been one of the most time-consuming and least loved tasks that fall on the plate of the typical account manager. And there were other problems, too.

The Problem With Sharing Reports Before AI

Reports were often static, one-size-fits-all documents that failed to meet the needs of different stakeholders.

Executives required high-level summaries focused on ROI, marketing strategists needed cross-channel insights, and PPC specialists required detailed campaign data.

Customizing reports for each audience was time-consuming and prone to error.

AI Solutions For Tailored Reporting

1. LLM Report Summarization

LLMs such as Claude, Gemini, and ChatGPT can quickly generate different explanations of reports from the same underlying data, enabling efficient customization for each audience.

For example, ChatGPT can produce a concise executive summary alongside a more detailed keyword-level report for PPC teams.

But that customization can and should be taken even further. In OpenAI, it’s possible to create custom GPTs, each with its own instructions. This can be used to create a different ChatGPT flavor for every client.

Whereas today, agencies depend on their people to remember how each client likes to get their reports, GPT can be trained to remember these preferences.

Things like how well they know PPC, what jargon they tend to use at their company, and even what the year’s strategic initiatives are.

Then, the LLM can word the summary in a way that resonates with the reader and even explain how the search marketing campaign’s results are key to the company’s strategic objectives for the year.

2. Interactive Dashboards For Real-Time Transparency

AI-driven dashboards provide live, customizable views of campaign performance. Stakeholders can explore data interactively, filtering by date ranges, platforms, or key performance indicators (KPIs), reducing the need for frequent manual report updates.

And while dashboards have been around for a long time, AI can be used to quickly highlight the most salient insights.

For example, AMC lets marketers use AI to generate SQL to explore the data by using natural language.

At my company, Optmyzr, we deployed Sidekick, which can instantly answer questions about data in any account, for example, the biggest optimization opportunities or wins in the last month.

Before AI, these insights might have remained hidden in the data.

Actionable Tips:

  • Set up custom GPTs for every client you work with.
  • Implement reporting tools that use natural language to explore the data.

Conclusion: From Reporting To Strategic Decision-Making With Generative AI

Generative AI has redefined PPC reporting, transforming a once fragmented and time-consuming process into a streamlined, insight-driven workflow.

It doesn’t just automate data collection and report generation; it also surfaces hidden trends, correlations, and anomalies that might otherwise go unnoticed.

This enables marketers to make smarter, faster, and more strategic decisions based on real-time insights.

With AI-driven tools, marketers can see beyond surface-level metrics, discovering patterns and opportunities that traditional reporting might take hours or days to uncover.

This improved understanding of performance empowers teams to refine budget allocation, creative strategy, and campaign targeting more effectively, leading to more substantial outcomes and greater profitability.

The conclusion is simple. With Generative AI, PPC managers have more complete data, leading to better insights and better decisions – all of which can be shared more meaningfully with all involved stakeholders.

More Resources:


Featured Image: Igor Link/Shutterstock

10 Top Converting Landing Pages That Boost Your ROI [With Examples] via @sejournal, @unbounce

This post was sponsored by Unbounce. The opinions expressed in this article are the sponsor’s own.

Want to increase sign-ups, sales, or demo requests from your landing page?

How can you ensure your landing page is optimized for conversions?

Landing pages can make or break your conversions.

A well-designed landing page doesn’t just look good; it also seamlessly guides visitors toward action, such as signing up, purchasing, or booking a demo.

A high-performing landing page should align with your goals:

  • Capturing leads.
  • Driving sales.
  • Promoting an event.

The best landing page templates are designed with conversion in mind, featuring strategic layouts, persuasive copy, and clear calls to action.

So, let’s look at a few top-performing landing page examples to learn about why they work and how you should implement them.

1 & 2. FreshGoods & Radiant Yoga Studio: Great For A Clear & Compelling Unique Selling Point

The secret to beating the competition is positioning your brand so you’re the only one in your specific space.

How? By honing in on your Unique Value Proposition (UVP):

  • What is the one reason to choose you, your products, or services?
  • Where does your competition fall short?
  • How do you make your UVP stand out?

FreshGoods Landing Page

Landing pageImage by Unbounce, 2025

Radiant Yoga Landing Page

yoga landing pageImage by Unbounce, 2025

Why They Work

These conversion-optimized landing page templates effectively highlight a USP throughout the design.

  • A clear and bold headline that immediately communicates the core benefit.
  • The supporting subheadline allows brands to reinforce the core USP message by expanding on the offer in a way that adds clarity without overwhelming visitors.
  • The strategic use of whitespace and strong typography ensures that the USP remains the focal point, making it easy for visitors to grasp the value of the offer at a glance.

How To Recreate These Landing Pages

Step 1: Define Your Unique Selling Proposition

A strong USP makes visitors feel like they’ve found exactly what they need. Instead of blending in with competitors, it positions your brand as the only choice.

  • Ask yourself: What is the one reason customers should choose you over others?
  • Example: FreshGoods & Radiant Yoga Studio’s landing pages showcase a crystal-clear UVP in their messaging and design.

Step 2: Craft a Compelling Headline & Supporting Headline

Your headline is your first impression, so you have to make it count. The supporting headline expands on that core message.

  • Best Practices:
    • Be specific: Instead of “The Best Marketing Tool,” try “Turn Clicks into Customers with AI-Powered Marketing in Minutes.”
    • Reinforce value: “No coding, no guesswork. Just smarter campaigns that drive real revenue.”

Step 3: Address Concerns with Reinforcing & Closing Statements

  • A reinforcing statement builds trust (“Trusted by over 10,000 businesses…”).
  • A closing statement eliminates hesitation (“Every second you wait is a sale you’re losing. Start your free trial now.”)

3 & 4. Vita Health & Orbit SaaS: Great For Hero Images & Visual Storytelling

Before visitors read a single word, visuals will capture their attention and convey meaning.

A strong hero image isn’t just decoration,  it sets the tone, builds trust, and instantly reinforces your message. The right imagery makes your offer feel more tangible, relatable, and desirable.

Vita Health Landing Page

health wearables landing page exampleImage by Unbounce, 2025

Orbit Flow Landing Page

SaaS landing page example and inspirationImage by Unbounce, 2025

Why They Work

A landing page’s imagery is a strategic tool that helps communicate your offer, build trust, and nudge visitors toward conversion. Choose visuals that don’t just look good but work hard to sell.

A well-chosen visual:

  • Supports the UVP.
  • Evokes an emotion that drives action
  • Showcases the product, service, or outcome in action
  • Makes the page feel polished, professional, and credible

In addition to the visual, the full landing page benefits from:

  • Strong hero image placement
  • An opportunity to reinforce the messaging conveyed with the hero image throughout the page
  • White space highlights supporting visuals
  • Visual hierarchy guides site visitors down the page to the parts that matter.

How To Recreate These Landing Pages

Step 1: Choose the Right Hero Image

Before visitors read a word, visuals capture attention. A great hero image should:

  • Support the USP
  • Evoke emotion & drive action
  • Showcase the product, service, or outcome

Step 2: Guide the Visitor’s Eye

Strategic use of visuals can nudge visitors toward your CTA:

  • Eye gaze: People follow where others are looking in an image.
  • Angles & positioning: Lines or arrows subtly direct attention to the CTA.
  • Contrast & color: Key elements should stand out.

Step 3: Reinforce Messaging with Supporting Imagery

Don’t rely on just one image. Use:

  • Icons & illustrations
  • Graphs & charts
  • Customer photos & testimonials
  • Short videos or GIFs

Bonus Tip:

Use A/B testing to find the ingredients for maximum impact.

The right image can make or break conversions, so test different options. Some images resonate better with your audience, drive more engagement, or feel more aligned with your brand.

Some elements to test include:

  • People vs. product-focused visuals.
  • Static images vs. motion (GIFs or videos).
  • Close-ups vs. wider perspective shots.
  • Different background colors or lighting.

5 & 6. Serene Vista & Digital Foundry: Great For Clearly Conveying Benefits

Visitors specifically care about what it does for them.

That’s why benefits should take center stage on a conversion-optimized landing page, not just a list of features.

Serene Vista

Travel website landing page inspirationImage by Unbounce, 2025

The Digital Foundry Landing Page

Marketing agency landing page inspirationImage by Unbounce, 2025

Why They Work

  • The benefits are concise and audience-focused
  • Each feature section is well-spaced to garner attention
  • Benefits are integrated well into the page structure with the subheadings and images to help visitors scan

How To Recreate These Landing Pages

Step 1: Translate Features into Benefits

  • Feature: “AI-powered keyword research tool”
  • Benefit: “Find high-converting keywords in seconds—no guesswork needed.”

Step 2: Address Pressing Concerns

  • What pain points does your audience face?
  • How does your product solve them better than competitors?

Step 3: Qualify Your Audience

  • Use benefit-driven copy that attracts the right people:
  • Example: “Perfect for fast-growing teams who need to scale without the chaos.”

7 & 8. Revive Aesthetics & Smile Dental: Great For Social Proof That Builds Trust

Not all social proof is created equal.

The best reinforces your UVP, addresses concerns, and speaks directly to your audience.

See what we mean here.

Revive Landing Page

Health and spa landing page inspirationImage by Unbounce, 2025

Smile Kids Landing Page

Dentist landing page inspirationImage by Unbounce, 2025

Why These Landing Page Templates Work

  • The headshots paired with the social proof enhance trustworthiness and make a connection with site visitors because they can see themselves in the experiences being described.
  • The rounded shape and contrasting colors make the social proof stand out.
  • Located near the point of conversion.

How To Create This Landing Page

Step 1: Choose the Right Type of Social Proof

  • Customer testimonials & reviews
  • Case studies & success stories
  • Logos of recognizable brands
  • Ratings & review scores
  • Media mentions & awards

Step 2: Strategically Place Social Proof

  • Near the CTA: Reinforces trust before action.
  • Midway down the page: Nudges hesitant visitors.
  • In the hero section: Puts endorsements front and center.

9 & 10. Livewell Lifestyle & Inner Handyman: Great For Turning Interest Into Conversions With Calls To Action

A landing page without a strong CTA is like a roadmap without a destination.

Your CTA is the single most important element that tells visitors what to do next.

And if it’s unclear, compelling, and easy to find, you’ll lose conversions.

A compelling CTA is a combination of copy, design, and placement that removes hesitation and drives action.

Livewell Landing Page

Healthy living landing page exampleImage by Unbounce, 2025

Inner Handyman Landing Page

Local business landing page and website inspirationImage by Unbounce, 2025

Why They Work

  • CTAs can be customized to stand out and get attention
  • CTA sizing and positioning make them clear focal points despite having multiple elements on the page. It ensures you get the most conversion power in every pixel
  • The CTA buttons are placed where it matters throughout the page, making sure the page attempts the conversion when and where it matters most

How To Recreate These Landing Pages

Step 1: Craft a Clear, Compelling CTA

A high-converting CTA should be:

  • Action-oriented: “Start Growing Today” vs. “Submit”
  • Benefit-driven: “Unlock Exclusive Access” vs. “Sign Up”
  • Urgent (if appropriate): “Claim Your Spot Today”

Step 2: CTA Placement for Maximum Impact

  • Above the fold: First CTA visible immediately.
  • After key information: CTA follows value explanation.
  • Near social proof or benefits: Reinforces trust.
  • At the end of the page: Captures hesitant visitors.

Step 3: CTA Design That Stands Out

  • Color contrast: The CTA should pop from the background.
  • Size & positioning: Large enough to be noticeable but not overwhelming.
  • Whitespace & directional cues: Ensures the CTA is the focal point.

Bonus Tip:

A/B test your CTAs for better conversions.

CTAs aren’t one-size-fits-all. Even small tweaks can make a huge impact on conversions, so A/B testing different variations is essential:

  • Wording – Try “Get Started” vs. “Try It Free”
  • Color – A bold button color vs. a softer, branded one
  • Placement – Above the fold vs. midway down the page
  • Size and shape – Larger buttons vs. compact ones
  • Personalization – “Start My Free Trial” vs. “Start Your Free Trial”

Build High-Converting Landing Pages Faster

A great landing page isn’t just about design.

It’s about strategy.

Every element, from your USP and hero images to your social proof and CTAs, is critical in guiding visitors toward conversion. When these elements work together, your landing page drives action.

But building a high-converting landing page from scratch can be time-consuming and complex. That’s why using proven, conversion-optimized templates can give you a head start.

With Unbounce, you get access to 100+ professionally designed landing page templates built for maximum conversions. Whether capturing leads, promoting a product, or running a campaign, these templates help you launch faster, test smarter, and convert better—without needing a developer.

Ready to build an optimized landing page that converts?

Explore Unbounce’s best-performing templates and start optimizing today!


Image Credits

Featured Image: Image by Shutterstock. Used with permission.

Google’s VP of Ads and Commerce Outlines 2025 Priorities via @sejournal, @brookeosmundson

Google is making big moves in 2025, and unsurprisingly, AI is at the heart of it all.

In a recent update, Vidhya Srinivasan, Google’s VP and GM of Ads and Commerce, outlined the company’s top priorities for the coming year.

From AI-powered ad experiences to deeper integrations with YouTube and Google Shopping, these changes signal a clear direction: more automation, more personalization, and a stronger push for immersive ad formats.

Here’s a breakdown of what’s coming and how brands can prepare.

Google’s 2025 Ad Priorities

In Srinivasan’s letter to the industry, she summed up Google’s main priorities into these categories:

  • AI and personalization
  • YouTube’s engaged audiences
  • New ways to search

AI-Driven Personalization and Shopping Experiences

AI isn’t just a buzzword for Google—it’s the backbone of its advertising strategy. Srinivasan emphasized that AI will play a larger role in shaping ad creatives, optimizing bidding strategies, and curating shopping experiences tailored to individual users.

With over a billion shopping activities happening daily on Google, the company is investing heavily in AI-powered product discovery.

Expect to see enhanced AI-generated visuals, automated ad variations, and an improved ability to match users with products based on intent rather than just search keywords.

The revamped Google Shopping experience will feature AI-powered recommendations, immersive 3D product spins, and new ad placements that seamlessly blend into organic search experiences.

YouTube and Search: A Shift to More Visual, Interactive Ads

YouTube is becoming even more central to Google’s ad strategy, especially as younger audiences rely on creators for product recommendations.

Srinivasan noted that Google is working to make ads more interactive and non-disruptive, allowing users to explore products without leaving their video experience.

On the search side, Google is expanding AI-powered search capabilities with tools like AI Overviews and Circle to Search. These innovations will change how users find and engage with ads.

Advertisers will need to rethink their strategies beyond just bidding on keywords—visual and interactive ad formats will become key to capturing attention.

How Advertisers Should Prepare

Staying ahead in PPC in 2025 means adapting to AI-driven changes now.

Google’s changing ad landscape will reward those who embrace automation, optimize creative strategies, and rethink audience targeted.

If you’re not sure where to start, these three components would be a great foundation to shift your PPC strategy.

#1: Shift Toward AI-Optimized Creative

With AI taking a bigger role in ad creation, advertisers need to start testing AI-generated assets now.

Google’s AI tools will allow for automatic variations of images, headlines, and ad copy, making creative testing more efficient.

Brands should focus on providing high-quality inputs—strong branding, clear messaging, and compelling visuals—to ensure AI-generated outputs align with their goals.

#2: Rethink Shopping and Video Strategies

E-commerce brands should lean into AI-powered shopping experiences, ensuring their product feeds are optimized with detailed descriptions, high-resolution images, and accurate inventory data.

With YouTube becoming an even bigger shopping destination, brands should explore shoppable video ads and creator partnerships to drive engagement.

#3: Prepare for a Post-Keyword Ad Landscape

As search evolves, traditional keyword-based targeting will matter less. Instead, audience intent and AI-driven placements will take center stage.

Advertisers should start leveraging first-party data, testing Performance Max campaigns, and using Google’s audience insights to reach the right customers in a more predictive, automated way.

Final Thoughts

Google’s 2025 ad strategy is all about AI, personalization, and more immersive ad experiences.

Advertisers who rely solely on manual optimizations or traditional search strategies may find themselves falling behind.

Now is the time to experiment with AI-powered creative, embrace new ad formats, and rethink how to engage audiences in a world where discovery is just as important as search.

10 Google Shopping Product Feed Optimization Tips & Tricks via @sejournal, @brookeosmundson

Google Shopping isn’t just about bidding and budget management – it’s about feeding Google the best possible data.

Unlike traditional search ads, where keywords dictate targeting, Shopping campaigns rely on your product feed. The quality, accuracy, and completeness of your product data determine how often and where your ads appear.

A well-optimized feed improves impressions, click-through rates (CTR), and return on ad spend (ROAS).

On the other hand, a neglected feed leads to wasted ad spend, disapproved listings, and poor performance.

Let’s dive into 10 proven ways to optimize your Google Shopping product feed for maximum performance.

1. Perfect Your Product Titles To Improve Rankings And CTR

Your product title is arguably the most critical field in your feed. It directly influences where and how your ad appears in search results.

A well-structured title increases visibility, while a vague or poorly formatted one can bury your product in a sea of competitors.

Best Practices For Writing Effective Product Titles

  • Front-load the most important details. Google prioritizes the first 70 characters, so key attributes should come first.
  • Follow a structured format based on your industry. A few examples may include:
    • Apparel: Brand + Product Name + Product Type + Color + Size.
    • Electronics: Brand + Product Type + Size + Color + Carrier.
    • Home & Garden: Brand + Product Type + Feature + {Other Attractive Feature}.
  • Use descriptive but concise language. Don’t add fluff like “Best Price” or “High-Quality.”
  • Avoid excessive keyword stuffing. Google may view it as spammy and hurt performance.

Why does this matter? A well-optimized title ensures your product appears in the right searches, increasing relevance and CTR.

2. Write Product Descriptions That Inform And Convert

While product descriptions don’t have as much direct impact on rankings as titles, they still play a crucial role in providing context to Google – and persuading shoppers to convert.

Think of your description as a sales pitch. It should highlight key features, answer common questions, and differentiate your product from competitors.

What To Include In Your Product Description

  • Essential product details: Size, color, material, features, and compatibility.
  • Unique selling points (USPs): Why should someone buy from you instead of a competitor?
  • Use cases: Help shoppers visualize how the product fits into their lives.
  • Avoid manufacturer descriptions: Rewrite in your own words to add value.

Here’s an example of what not to do:

  • “This is a high-quality vacuum with advanced suction power.”

Using the tips above, a proper description for a vacuum could read like this:

  • “The Dyson V15 Detect uses laser dust detection and HEPA filtration, capturing 99.99% of particles for a deep clean. With a 60-minute runtime, it’s ideal for large homes.”

So, why do descriptions matter? It’s the little details that make the biggest differences.

A compelling description not only helps Google categorize your product better, but also increases conversions.

3. Use High-Quality, Compliant Product Images

Images are often the first thing shoppers notice, and low-quality visuals can hurt engagement.

Google also has strict guidelines, and violating them can lead to product disapproval.

Image Optimization Tips

  • Use high-resolution images (at least 800 x 800 pixels) for clarity and professionalism.
  • Ensure images accurately depict the product – no misleading visuals.
  • Avoid promotional overlays, text, or watermarks: Google may reject these.
  • Use multiple images if possible: Include lifestyle shots to showcase real-world use.

For example, if you sell furniture, provide close-up images of textures and finishes. For fashion items, include front, back, and close-up shots to give shoppers a better view.

Better images can help improve CTR, reduce bounce rates on product detail pages, and ultimately drive more conversions.

4. Assign The Most Specific Google Product Category

Google assigns predefined categories to products, and selecting the most accurate one improves your ad’s relevance.

Many advertisers default to broad categories, potentially missing out on better placements.

Here are a few tips on how to choose the right category:

  • Avoid generic selections. Instead of “Clothing & Accessories,” choose “Clothing > Dresses > Maxi Dresses.
  • Review the full Google Product Taxonomy regularly. You can find it updated regularly here.
  • Regularly update your category selections. Because Google’s taxonomy evolves, refining your choices can improve campaign performance over time.

The Google Product Category is an underestimated part of your Google Shopping product feed. The correct category ensures your product appears in relevant searches and prevents misplacements.

5. Utilize The Product Type Attribute For Better Segmentation

Unlike Google’s predefined Product Category, the Product Type attribute is completely customizable.

It’s an opportunity to refine targeting further and structure your campaigns more effectively.

How To Use Product Type Effectively

  • Use detailed, hierarchical labels whenever possible. For example: “Electronics > Laptops > Gaming Laptops.”
  • Segment by product performance. For example, separating high vs. low-margin items.
  • Use it for bidding strategies! You can adjust bids by product type for more control. Just remember that bidding strategies are set at the campaign level, so this would make more sense if your feed has very differently priced or wider margins for certain product categories.

Remember, a well-structured product type attribute can help improve reporting, targeting, and even bid management when done right.

6. Maintain Real-Time Pricing & Availability Accuracy

A common reason for disapproval is mismatched pricing between your website and Google Shopping feed.

If shoppers see one price on an ad and another at checkout, you risk losing trust – and conversions along the way.

Below are a few ways you can ensure pricing and availability are (almost) always correct:

  • Enable automated feed updates via Google’s Content API or scheduled fetches.
  • Check Google Merchant Center’s Diagnostics regularly for mismatches.
  • If you run flash sales or limited-time discounts, ensure your feed updates accordingly.

7. Leverage GTINs And MPNs For Stronger Product Matching

If you’re selling branded products, make sure to include Global Trade Item Numbers (GTINs) and Manufacturer Part Numbers (MPNs). Including these helps Google match your product more accurately.

Some key benefits of providing these attributes to your Google Shopping feed include:

  • Improved ad placement in Google Shopping and free product listings.
  • Greater visibility in comparison shopping results.
  • Increased likelihood of appearing in Google’s Buy on Google listings.

Again, you may think these product feed attributes may not be necessary, but better product matching means more impressions and, ultimately, more conversions.

8. Use Custom Labels To Refine Bidding Strategies

Custom labels help segment products based on a number of items, like performance, price, or promotions.

Here are a few examples of how you can use custom labels:

  • Profitability Segmentation: Separating high-margin vs. low-margin items makes segmenting your campaign and ad group structure easier.
  • Seasonal Promotions: “Winter Collection” vs. “Summer Deals.”
  • Stock Levels: Best-sellers vs. clearance items.

Why do custom labels matter in Google Shopping? Better segmentation can lead to more cost-efficient results without lacking conversion volume.

9. Optimize Your Feed For Query-Level Performance

Once you’ve nailed the fundamentals, the next step is optimizing your feed based on actual search queries and performance data.

Instead of treating your feed as a static dataset, you can dynamically adjust product attributes to improve alignment with high-converting queries.

How To Use Query-Level Optimization:

First, start by analyzing your high-performing search terms. Navigate to the search terms report to identify which queries drive the most conversions.

Now, compare those queries with your current product titles and descriptions. Do they match?

If a top-converting term isn’t in your title, update your feed to include it for better alignment.

If you want to take this optimization to the next level, try creating feed rules for automation.

To do this, navigate to “Feed Rules” in Google Merchant Center to set up a logic to append high-performing keywords to titles dynamically.

For example, if a query like “wireless noise-canceling headphones” converts well but your product title only says “Sony WH-1000XM5 Headphones,” a rule can automatically update the title to something like “Sony WH-1000XM5 Wireless Noise-Canceling Headphones.”

This technique ensures your product titles stay relevant without manual updates.

10. Use First-Party Data To Enhance Your Product Feed For Better Personalization

Many advertisers focus solely on optimizing their product feed for Google’s algorithm, but what if you optimized your feed based on your own customer data?

For advertisers managing large Shopping campaigns, leveraging first-party data (like customer purchase behavior, loyalty data, and audience segmentation) can significantly improve feed relevance and drive higher conversion rates.

How To Use First-Party Data To Improve Your Google Shopping Feed

One way to do this is to segment your product feed by buyer intent.

If you have access to customer behavior data from your website, customer relationship management (CRM), or analytics, you can refine your feed to better match different types of shoppers.

  • Returning Customers: Highlight products frequently purchased by loyal customers by assigning a custom label like “best_seller_loyalty.”
  • First-Time Shoppers: Adjust product descriptions or titles to emphasize best-sellers or high-converting entry-level products. Try adding a custom label like “high_first_time_purchase_rate.”
  • High-Value Customers: If certain products have higher purchase frequency among repeat buyers, ensure these have optimized titles, more detailed descriptions, and premium images in your feed.

Secondly, you can set up exclusive offers in the feed if you use loyalty programs or subscriber discounts.

For example, a cosmetics brand sees that loyal customers frequently buy three packs of foundation instead of single bottles.

Instead of just relying on campaign bidding, they optimize the feed by ensuring these multi-packs are included and promoted with proper product titles, descriptions, and subscriber pricing.

Currently, the loyalty feature for Google Shopping is available in the United States and Australia.

Your Product Feed Is The Competitive Edge In Google Shopping

Going beyond traditional feed optimization is key to staying ahead in Google Shopping.

Strategies like query-based feed enhancements and audience-driven bidding can elevate Shopping campaigns from just good to highly profitable and efficient.

By continuously refining how Google understands and matches your products to real shoppers, you gain an edge over competitors still relying on static feeds and generic bidding strategies.

If you’re running high-budget Google Shopping campaigns, it’s worth testing these advanced tactics and letting Google’s automation work smarter, not harder.

More Resources:


Featured Image: ST.art/Shutterstock

How To Navigate Performance Fluctuations In Google Shopping Campaigns via @sejournal, @brookeosmundson

Managing Google Shopping campaigns is both an art and a science.

Even with the most refined strategies and detailed data, performance fluctuations can happen – and when they do, they often leave marketers scrambling for answers.

Understanding why these fluctuations occur, knowing how to respond, and effectively communicating with clients are essential skills for anyone managing these campaigns.

This article will explore:

  • Factors behind expected and unexpected performance changes.
  • How to create actionable strategies for troubleshooting.
  • Advice on communicating effectively with clients when things don’t go as planned.

Expected Fluctuations In Google Shopping Campaigns

Expected fluctuations are those that follow predictable patterns, often driven by external factors like time of year or consumer behavior trends

While they can still be challenging to manage, they’re usually easier to anticipate and explain.

Seasonality Fluctuations

Seasonality is one of the most common drivers of performance fluctuations in Google Shopping campaigns.

Consumers behave differently depending on the time of year, and these patterns often align with major holidays or specific shopping periods.

For instance, campaigns tend to see increased traffic and conversions during Black Friday and Cyber Monday, as well as in the lead-up to Christmas. Conversely, industries like outdoor recreation may see a downturn in the winter months.

If your campaigns cater to niche markets, other seasonal trends might also come into play – such as back-to-school shopping in August or summer sales for outdoor equipment.

Leveraging historical data can help identify and pinpoint these trends.

Proper preparation is key to managing these seasonal shifts. This can include:

  • Increasing budgets and bids ahead of high-traffic periods.
  • Aligning your creative assets with seasonal themes.
  • Leveraging historical data to predict performance patterns.

By staying proactive, you can turn expected fluctuations into opportunities for growth.

Market Trends Fluctuations

Broader market trends also play a role in campaign performance.

For example, rising interest in eco-friendly products or the emergence of new tech gadgets can influence consumer buying behavior. These trends are often gradual, making them easier to spot and account for in your campaigns.

Monitoring industry reports and using tools like Google Trends can help you stay ahead of market shifts. Adjusting your product feeds to emphasize trending items or updating your bidding strategy can ensure your campaigns remain competitive.

Competitor Activity

Competitor behavior can lead to sudden Google Shopping performance changes.

For example, a new competitor entering the market may bid aggressively on your top-performing products, driving up cost-per-click (CPC).

Alternatively, an established competitor might launch a promotional campaign, temporarily capturing a larger share of clicks.

To address competitor-driven fluctuations, conduct a competitive analysis using tools like Auction Insights.

If you notice increased competition, consider differentiating your offerings by highlighting unique selling points or adjusting bids to focus on less competitive segments.

Unexpected Fluctuations And Their Challenges

While expected fluctuations can often be forecasted, unexpected changes in performance are trickier to diagnose.

These shifts might not have an obvious external cause, leaving PPC managers to dig into the depths of the Google Shopping campaigns to uncover underlying issues.

Below are some common unexpected fluctuations and what to investigate.

1. Seeing A Sharp Decline In Impressions

When impressions suddenly drop, it’s a red flag that your ads are no longer reaching as many people as possible. Several factors could be at play:

  • Budget Constraints: A limited daily budget can throttle impressions, especially if you’re running out of budget early in the day. Review your budget pacing to ensure you’re not capping performance.
  • Changes In Search Demand: While seasonality can explain some shifts, there are instances where search demand for specific products dips unexpectedly. Use the “Search Terms” report to spot if a few users are searching for your targeted keywords.
  • Bid Strategy Changes: If bid changes were recently made, they might have inadvertently lowered your competitive edge. Analyze auction insights to determine whether competitors have increased their bids, pushing your ads lower in the rankings.
  • Policy Violations: Account suspensions or disapprovals due to policy changes or errors in the product feed can lead to a sudden halt in ad delivery. Check the “Diagnostics” tab in the Merchant Center for any alerts.

2. A Sudden Decline In Conversions

A sudden drop in conversions is unsettling, especially when impressions and clicks remain steady. Here’s a quick look at where to investigate:

  • Landing Page Issues: A broken link, slow page load times, or changes to the landing page experience can derail conversions. Use tools like Google’s Page Speed Insights to test performance.
  • Inventory Problems: Out-of-stock or incorrect availability data in the product feed can negatively impact conversion rates. Make sure the Merchant Center feed is syncing properly.
  • Pricing Discrepancies: If competitors undercut your pricing, customers may click but not convert. Monitor competitor pricing to ensure your client remains competitive.
  • Shifts In Audience Behavior: Use the “Audience Insights” report to check if your targeting still aligns with customer intent.

It’s important to note that your product data feed is the backbone of your Google Shopping campaigns, and even minor errors can lead to unexpected drops in performance.

Regularly auditing your data feed is crucial to avoiding these issues. Ensuring your feed is accurate, up-to-date, and optimized can help prevent performance dips caused by feed-related problems.

3. Other Unexpected Shifts

Sometimes the fluctuations in Google Shopping campaigns are more subtle, but still indicative of deeper issues:

  • Click-Through Rate (CTR) Drops: A sudden decline in CTR might indicate that your ad creatives are losing relevance. Test new product images, titles, or promotional messaging. Additionally, review what products are being triggered by search terms to determine if a more granular product structure is needed to maintain relevance.
  • ROAS Changes: If your return on ad spend suddenly dips, assess whether you’re overbidding on low-value clicks or if your campaign bid strategies need adjustment.

4. Algorithm Updates

Now you’re probably thinking – don’t algorithm updates only affect SEO rankings?

Think again.

Google’s algorithm changes can be one of the most common culprits of unexpected fluctuations. These updates can impact how products are displayed, how ads are served, and even which search queries trigger your Shopping ads.

Unfortunately, Google doesn’t always announce these changes right away, which means marketers often find out the hard way – through dips in performance.

When faced with algorithm-related fluctuations, your best course of action is to monitor key metrics closely and investigate any significant changes.

Look for shifts in impression share, CTR, or CPC that might signal an update.

Do some search and discovery testing “in the wild” to trigger your products, and identify if the user experience has changed, and adapt your strategy based on the outcomes.

How To Communicate Performance Fluctuations To Clients

Handling performance fluctuations isn’t just about solving the problem; it’s also about maintaining client confidence.

Clients may not understand the nuances of Google Shopping campaigns, so it’s your job to explain the situation in a way that builds trust and sets realistic expectations.

Be Proactive

Don’t wait for clients to notice a performance dip before addressing it. As soon as you identify a fluctuation, reach out with an explanation of what’s happening, why it’s happening, and what steps you’re taking to resolve it.

For example, if a seasonal lull is causing lower conversion rates, provide historical data to show that this pattern is normal and temporary.

Use Data To Support Your Points

Data is your best friend when communicating with clients.

Use visualizations like graphs or charts to illustrate trends, compare performance to previous periods, and highlight your optimization efforts.

This helps clients see the bigger picture and understand that fluctuations are part of a broader strategy.

Offer A Plan Of Action & Manage Expectations

End every client conversation with clear next steps.

Rather than focusing solely on the issue, highlight the steps you’re taking to address the problem(s). For example:

  • Short-Term Solutions: “We’re adjusting the bid strategy and budgets to stabilize performance while we investigate further.”
  • Long-Term Strategies: “We’re monitoring search demand weekly to ensure we’re not missing out on new opportunities.”

This reassures them that their campaigns are in capable hands.

Set realistic timelines for recovery and provide regular updates.

Avoid overpromising quick fixes. Instead, frame your efforts as part of a comprehensive strategy.

Turning Fluctuations Into Opportunities

Performance fluctuations in Google Shopping campaigns are inevitable, but they don’t have to derail your strategy.

By understanding the difference between expected and unexpected fluctuations, preparing for seasonal changes, staying vigilant about potential issues, and communicating effectively with clients, you can navigate these challenges with confidence.

Remember, fluctuations are not failures – they’re opportunities to refine your approach and drive even better results for your campaigns.

More Resources:


Featured Image: CrizzyStudio/Shutterstock

Beyond Tools: A Google Ads Guide To Detecting And Preventing Click Fraud In Lead Generation

Click fraud in lead generation can drain your marketing budget and corrupt your data, leading to misguided strategic decisions.

While automated detection tools serve as a first line of defense, relying solely on them is not enough.

This guide presents practical, hands-on approaches to identify and combat click fraud in your lead generation campaigns in Google Ads.

Understanding Modern Click Fraud Patterns

Click fraud isn’t just about basic bots anymore. The people running these scams have gotten much smarter, and they’re using tricks that your regular fraud tools might miss.

It’s a big business, and if you think you are not affected, you are wrong.

Here’s what’s really happening to your ad budget: Real people in click farms are getting paid to click on ads all day long.

They use VPNs to hide where they’re really coming from, making them look just like normal customers. And they’re good at it.

The bots have gotten better, too. They now copy exactly how real people use websites: They move the mouse naturally, fill out forms like humans, and even make typing mistakes on purpose.

When these smart bots team up with real people, they become really hard to spot.

The scammers are also messing with your tracking in clever ways. They can trick your website into thinking they’re new visitors every time.

They can make their phones seem like they’re in your target city when they’re actually on the other side of the world.

If you’re counting on basic click fraud protection to catch all this, you’re in trouble. These aren’t the obvious fake clicks from years ago – they’re smart attacks that need smart solutions.

That being said, the good old competitor trying to click 50 times on your ad is also still existent and not going away anytime soon.

Luckily, it is safe to say that Google can spot and detect those obvious fraud clicks in many cases.

Google’s Click Fraud Dilemma: Walking The Revenue Tightrope

Google faces a tricky problem with click fraud.

Every fake click puts money in Google’s pocket right now, but too many fake clicks will drive advertisers away. This creates a conflict of interest.

Google needs to show that it’s fighting click fraud to keep advertisers happy and the ad platform and all of its networks healthy, but it can’t afford to catch every single fake click.

If it did, its ad revenue would drop sharply in the short term because it also runs the risk of blocking valid clicks if it goes in too aggressively.

But if it doesn’t catch enough fraud, advertisers will lose trust and move their budgets elsewhere.

Some advertisers say this explains why Google’s fraud detection isn’t as strict as it could be.

They argue Google has found a sweet spot where it catches just enough fraud to keep advertisers from leaving, but not so much that it seriously hurts its revenue.

This balance gets even harder as fraudsters get better at making fake clicks look real.

This is also why many advertisers don’t fully trust Google’s own click fraud detection and prefer to use third-party tools.

These tools tend to flag more clicks as fraudulent than Google does, suggesting Google might be more conservative in what it considers fraud.

The Over-Blocking Problem Of Third-Party Tools

Third-party click fraud tools have their own business problem: They need to prove they’re worth paying for every month.

This creates pressure to show lots of “blocked fraud” to justify their subscription costs. The result? Many of these tools are too aggressive and often block real customers by mistake.

Other tactics are to show lots of suspicious traffic or activities.

Think about it. If a click fraud tool shows zero fraud for a few weeks, clients might think they don’t need it anymore and cancel.

So, these tools tend to set their detection rules very strict, marking anything slightly suspicious as fraud. This means they might block a real person who:

  • Uses a VPN for privacy.
  • Shares an IP address with others (like in an office).
  • Browses with privacy tools.
  • Has unusual but legitimate clicking patterns.

This over-blocking can actually hurt businesses more than the fraud these tools claim to stop.

It’s like a store security guard who’s so worried about shoplifters that they start turning away honest customers, too.

Why Click Fraud Tools Are Still Valuable

Despite these issues, click fraud tools are still really useful as a first line of defense.

They’re like security cameras for your ad traffic. They might not catch everything perfectly, but they give you a good picture of what’s happening.

Here’s what makes them worth using:

  • They quickly show you patterns in your traffic that humans would take weeks to spot.
  • Even if they’re sometimes wrong about individual clicks, they’re good at finding unusual patterns, like lots of clicks from the same place or at odd hours.
  • They give you data you can use to make your own decisions – you don’t have to block everything they flag as suspicious.

The key is to use these tools as a starting point, not a final answer. Look at their reports, but think about them carefully.

Are the “suspicious” clicks actually hurting your business? Do blocked users fit your customer profile?

Use the tool’s data along with your own knowledge about your customers to make smarter decisions about what’s really fraud and what’s not.

In terms of functionality, most third-party click fraud detection tools are somewhat similar to each other.

A simple Google search on “click fraud tool” shows the market leaders; the only bigger difference is usually pricing and contract duration.

Tackling Click Fraud With Custom Solutions

After getting a first impression with third-party click fraud tools, it’s best to build a collection of custom solutions to tackle your individual scenario.

Every business has a different situation with different software environments, website systems, and monitoring.

For custom solutions, it’s recommended to work closely with your IT department or developer, as many solutions require some modification on your website.

The Basics: Selecting An Identifier

There are a handful of solutions to cover 80% of the basics.

The first way to do something against click fraud is to find a unique identifier to work with.

In most cases, this will be the IP address since you can exclude certain IP addresses from Google Ads, thus making it a good identifier to work with.

Other identifiers like Fingerprints are also possible options. Once an identifier is found, you need to make sure your server logs or internal tracking can monitor users and their identifiers for further analysis.

The Basics: CAPTCHAs

Another basic tool, which is often forgotten, is CAPTCHAs.

CAPTCHAs can detect bots or fraudulent traffic. Google offers a free and simple-to-implement solution with reCAPTCHA.

CAPTCHAs might seem like an easy answer to bot traffic, but they come with serious downsides.

Every time you add a CAPTCHA, you’re basically telling your real users, “Prove you’re human before I trust you.” This creates friction, and friction kills conversions.

Most websites see a drop in form completions after adding CAPTCHAs if they are set too aggressively.

Smart CAPTCHAs can limit the frequency, but not all CAPTCHA providers allow that option, so choose your provider or solution wisely.

The Basics: Honeypot Fields

Honeypot fields are hidden form fields that act as traps for bots.

The trick is simple but effective: Add extra fields to your form that real people can’t see, but bots will try to fill out.

Only bots reading the raw HTML will find these fields; regular users won’t even know they’re there. The key is to make these fields look real to bots.

Use names that bots love to fill in, like “url,” “website,” or “email2.” If any of these hidden fields get filled out, you know it’s probably a bot. Real people won’t see them, so they can’t fill them out.

Pro tip: Don’t just add “honeypot” or “trap” to your field names. Bots are getting smarter and often check for obvious trap names. Instead, use names that look like regular-form fields.

Advanced Validation Methods

Smart Form Validation: Email

Most businesses only check if an email address has an “@” symbol and looks roughly correct.

This basic approach leaves the door wide open for fake leads and spam submissions.

Modern email validation needs to go much deeper. Start by examining the email’s basic structure, but don’t stop there.

Look at the domain itself: Is it real? How long has it existed? Does it have proper mail server records?

These checks can happen in real time while your user fills out the form. It should be noted, however, that smart form validation usually requires some sort of third-party provider to check the details, which means you need to rely on external services.

A common mistake is blocking all free email providers like Gmail or Yahoo. This might seem logical, but it’s a costly error.

Many legitimate business users rely on Gmail for their day-to-day operations, especially small business owners.

Instead of blanket blocks, look for unusual patterns within these email addresses. A Gmail address with a normal name pattern is probably fine; one with a random string of characters should raise red flags.

For enterprise B2B sales, you expect bigger companies to sign up with their company domain email address, so blocking free mail providers might work.

Smart Form Validation: Phone

Phone validation goes far beyond just counting digits. Think about the logic of location first.

When someone enters a phone number with a New York area code but lists their address in California, that’s worth investigating.

But be careful with this approach – people move, they travel, and they keep their old numbers. The key is to use these mismatches as flags for further verification, not as automatic rejections.

The Art Of Smart Data Formatting

Data formatting isn’t just about making your database look neat. It’s about catching mistakes and fraud while making the form easy to complete for legitimate users.

Name fields are a perfect example.

While you want to catch obviously fake names like “asdfgh” or repeated characters, remember that real names come in an incredible variety of formats and styles.

Some cultures use single names, others have very long names, and some include characters that might look unusual to your system.

Modify Your Google Ads Campaign Settings To Tackle Click Fraud

Google offers multiple campaign options to increase reach, on the downside most of those options come along with an increase of click fraud activities.

App Placements

Performance Max campaigns can place your ads across Google’s entire network, including in apps. While this broad reach can be powerful, it also opens the door to potential fraud.

The challenge is that you have limited control over where your ads appear, and some of these automatic placements can lead to wasted ad spend.

Kids’ games are often a major source of accidental and fraudulent clicks. These apps frequently have buttons placed near ad spaces, and children playing games can accidentally tap ads while trying to play.

What looks like engagement in your analytics is actually just frustrated kids trying to hit the “play” button.

Another issue comes from apps that use deceptive design to generate clicks. They might place clickable elements right where ads appear, or design their interface so users naturally tap where ads are located.

This isn’t always intentional fraud. Sometimes, it’s just poor app design, but it costs you money either way.

Unlike traditional campaigns, where you can easily exclude specific placements, Performance Max’s automation makes this more challenging.

The system optimizes for conversions, but it might not recognize that clicks from certain apps never lead to quality leads. By the time you spot the pattern, you’ve already spent money on these low-quality clicks.

Excluding app placements is for almost all advertisers a must have. Very few advertisers benefit from app placements at all.

Partner And Display Network

Lead generation businesses face a unique challenge with Performance Max campaigns that ecommerce stores can largely avoid.

While ecommerce businesses can simply run Shopping-only campaigns and tap into high-intent product searches, lead gen businesses are stuck dealing with the full Performance Max package, including the often problematic Display Network.

The Display Network opens up your ads to a mass of websites, many of which might not be the quality placements you’d want for your business.

While Google tries to filter out bad actors, the display network still includes sites that exist primarily to generate ad clicks.

These sites might look legitimate at first glance, but they’re designed to encourage accidental clicks or attract bot traffic.

Some are specifically designed for server bot farms, as they run on expired domains and have no content besides ads.

Lead generation businesses don’t have this luxury. Their Performance Max campaigns typically run on all networks except shopping. This creates several problems:

  • The quality of clicks varies wildly. Someone might click your medical practice ad while trying to close a pop-up on a gaming site. They’ll never become a patient, but you still pay for that click.
  • Display placements can appear on sites that don’t match your brand’s professional image. Imagine a law firm’s ad showing up on a site full of questionable content – not ideal for building trust with potential clients.
  • Bot traffic and click farms often target display ads because they’re easier to interact with than shopping ads. You might see high click-through rates that look great until you realize none of these clicks are turning into leads.

All those are reasons to question PMax campaigns for lead gen, but that’s a decision every marketer has to make.

Advanced Google Ads Settings To Tackle Click Fraud

If the basics are implemented but there is still a higher amount of suspected click fraud, advanced solutions need to be implemented.

Besides excluding suspicious IP addresses, you can also build negative audiences.

The idea is to have a second success page for your lead generation form and only forward potential bots or fake sign-ups to this page.

To achieve that, your website needs to evaluate potential bots live during the sign-up process.

You can then setup a dedicated “bot pixel” on the second success page in order to send data of this audience to Google.

Once enough data is retrieved, you can exclude this audience from your campaigns. This approach is a little trickier to implement but is worth the effort as those audience signals are of high quality if enough data is supplied.

Make sure to only fire the “bot pixel” on the special success page and only there, otherwise you run the risk of mixing your audiences which would render the system useless.

Filtering Fake Leads With Conditional Triggers

Another tracking-based strategy is to set up condition-based conversion tracking. Combined with hidden form fields, you can modify the conversion trigger not to send data if the hidden field was filled.

In that scenario, you would filter out bots from conversion tracking, sending back only real conversion to your campaign, and therefore, also training the Google algorithm and bidding strategy only on real data.

You eliminate a majority of fake leads and traffic with this setup.

Making Sign-Ups More Challenging To Improve Lead Quality

Another advanced strategy is to make the sign-up process a lot harder.

Tests have shown that much longer forms are not finished by bots because they are usually trained on simpler and shorter forms, which require only mail, name, phone, and address.

Asking specific questions and working with dropdowns can dramatically increase the lead quality. It should be noted, however, that longer forms can also hurt the valid signup rate, which is a risk you want to take if you have to deal with bot and fraud traffic.

A fitting case was a car dealer I worked with. They had a form where people could offer their cars for sale and retrieve a price estimate.

A short form had almost three times the signup rate than before, but it turned out later that a lot of them were spam signups or even very low-qualified leads.

A shorter form leads to more spam because it’s easy to sign up. After switching to a longer form, the signups dropped, but quality increased drastically.

Almost 20 fields long, and potential clients had to upload pictures of their car.

It took a few minutes to finish the signup, but those who did were committed to doing business and open to discussing the sale, which also made it easier for the salespeople to follow up properly.

A Hard Truth About Lead Fraud

Let’s be honest: You can’t completely stop lead fraud. It’s like shoplifting in retail – you can reduce it, you can catch it faster, but you can’t eliminate it entirely.

The fraudsters are always getting smarter, and for every security measure we create, they’ll eventually find a way around it.

But here’s the good news: You don’t need perfect protection. What you need is a balanced approach that catches most of the bad leads while letting good ones through easily.

Think of it like running a store: You want security, but not so much that it scares away real customers.

The key is to layer your defenses. Use click fraud tools as your first line of defense, add smart form validation as your second, and keep a human eye on patterns as your final check.

Will some fake leads still get through? Yes. But if you can stop 90% of the fraud, you’re winning the battle.

Remember: Perfect is the enemy of good. Focus on making fraud expensive and difficult for the bad actors, while keeping your lead generation process smooth and simple for real prospects. That’s how you win in the long run.

More Resources:


Featured Image: BestForBest/Shutterstock

How To Drive Google Shopping Growth With Only One Of Each Product

Google Shopping is a Google Ads product that allows advertisers to serve feed-based ads on the search engine results page (SERPs).

The auction for Shopping Ads works in a similar way to Google Text Ads, in the sense that the auction is query-based.

However, Google Shopping does not target keywords and uses the feed (and a few other factors) to determine when and where to serve ads.

Here’s an example of the Google Shopping results on a SERP:

Screenshot of the Google Shopping results when a search is made for 'Tiago Lemos 1010 New Balance' Screenshot from search for [tiago lemos 1010 new balance], Google, January 2025

Advertisers are set to ramp up their spending on U.S. retail media search ads, with a projected 23.4% year-over-year growth in 2028, pushing the total spend to $76.83 billion.

Google Shopping offers advertisers the freedom to serve:

  • Product images.
  • Clear product titles.
  • Content-rich descriptions.
  • Upfront pricing.
  • Promotions.
  • Shipping costs.

Google Shopping allows advertisers to inform searchers about their products prior to clicking through – and when compared to standard text ads – has the potential to drive better-qualified traffic.

From multinational retailers to local bakeries, hundreds of thousands of brands use Google Shopping to get their products in front of searchers every day.

How To Find Success With Google Shopping Ads?

Many factors determine how online advertising performs, from key performance indicators (KPIs) to pricing, payment options, imagery, site speed, the social responsibility of a company, and more.

However, looking solely from an ad platform perspective at Google Shopping, the one factor that will determine success is data.

  • Product Feed: The data within your feed should be high quality, accurate, and well-planned. This is the heart of Google Shopping and is a huge factor in determining the search queries your shopping ads will enter the auction for. Where possible, ingest additional data that will help feed bidding strategies, reports, and more with valuable insights about your products.
  • Segmentation: There are many ways to segment Google Shopping campaigns: by margin, product categories, search query length, best sellers, and more. Segmentation and structure are important because this is where advertisers can control their budgets, set targets, and lay the foundations for scaling spend.
  • Budgets and Bidding: If your structure and segmentation lend themselves to your KPIs, you’ll be able to set budgets with confidence and build a portfolio of bidding targets that will work towards the correct goal.
  • Refinement: There aren’t any keywords, but there are negative keywords. Use these to refine your campaigns and ad groups to enter auctions for search queries that align with your KPIs. It may be that for upper funnel generic queries, you want to serve a certain category but not another; this is a perfect use case for negating queries and funneling traffic.
  • Performance Max: I couldn’t talk about shopping without mentioning PMax. All of the above applies; the only difference is that segmentation works slightly differently with asset groups and one single target, which is set at the campaign level vs. ad group level for Google Shopping.

With these basics in place, from the moment you activate your campaigns, you’ll be gathering data and learning.

This learning is the backbone of shopping campaigns, providing Google (and the bidding algorithm) crucial data all the way down to an SKU level.

Over time, you’ll start to uncover a wealth of insights, such as:

  • Which products have the highest conversion rate?
  • How does engagement look for category A when served for upper funnel search queries?
  • What happens to the conversion rate when products A, B, and C drop out of stock?

This data feeds machine learning as Google understands how your products perform across hundreds of thousands of touchpoints.

This model fits most ecommerce brands with multiple stocks of each item to gather learnings overtime on what works and what doesn’t.

But if you’ve only got one of every product, how can you drive success on Google Shopping when once a product’s gone, it’s gone?

What Business Models Have One Of Each Product?

  • Auctions, e.g., eBay.
  • Marketplaces, e.g., Etsy.
  • Second-hand/pre-loved, e.g., Vinted.
  • A mix of the above. Typical retailers who have adopted a marketplace feature or a pre-loved arm of their business, such as Farfetch.

The scale of the business, vertical, market, etc., all play a role in determining the stock of each SKU.

Take a brand like eBay, a global online marketplace with both auction and “buy it now” functionality. They have thousands of items where the stock level is above one, and thousands where it is one of one.

There are thousands of auction houses, second-hand retailers, marketplaces, and more that have a similar setup, but on a smaller scale.

But for this post, we are focussing solely on one of one product.

How Does This Business Model Impact Google Shopping?

This campaign type thrives on data, and this flows through every layer, from the bidding strategy down to individual SKU performance.

The feed is the heart of Google Shopping, and with the SKUs changing frequently (depending on the business), accruing data on which SKU performs the best/worst works differently as SKUs sell through and may not be in the feed again for weeks, months, or in some cases, ever again.

There are a number of considerations that need to be taken into account:

  • Learning: With only one of each SKU, items may sell out quickly, whereas some items may be in the feed for longer. Bidding algorithms will struggle to gather data to optimize toward your KPIs, and a lack of historical data will be limiting for machine learning, especially at a product level.
  • Feed: The data within your product feed should be rich, up-to-date, and aligned with your paid media goals. This is even more important when SKUs are being added/removed frequently, as this will cause instability with learning, crawls, and more.
  • Reporting: With one-of-a-kind SKUs, the interpretation of the data within the ad platform is critical; it’s not like you can filter by sales > 0 over a date range and decide how to structure your campaigns, as many SKUs will have been and gone.
  • Automation: Bid strategies can certainly be used, but unlike traditional retailers who may have in-platform ROAS/CPA targets that remain fairly stable, the intricacies of category performance and knowing exactly what products have sold is critical as this is ever-changing and will impact how you feed data into machine learning.
  • Budget Allocation: When building for the long term, fluctuations in performance make it difficult to set budgets to get the most out of your media spend. Watertight reporting is essential, and communication between teams is key to helping spot trends, plan inventory ahead of time, and stay as efficient as possible.
  • Dynamic Retargeting (and PMax): Dynamic retargeting uses the feed to serve product ads to audiences (e.g., website visitors who have added an item to a cart and not purchased) and can be run in isolation or as part of PMax. Having one of every product creates a disconnect as multiple users could be interested in one item, and when it’s sold, it’s gone from the feed.

These are just a snapshot of the limitations, and there are more.

But that certainly doesn’t mean it’s a non-starter.

A different approach is needed, compared to Google Shopping, for a traditional e-commerce model. Above all, communication and planning will be the backbone for success as these campaigns most certainly don’t fall into “set and forget” paid search management.

Can You Scale Google Shopping For This Business Model?

Absolutely.

This will require a fresh perspective on how you report, optimize, and plan your media budget, but it’s certainly achievable.

Look at eBay. It spends >$150 million each year on Google Ads, with the majority being through Product Listing Ads (PLAs).

Here are a few approaches that are tried and tested:

Reporting

Product-level reports are going to be useful for any ecommerce business. However, with products dropping in and out of stock frequently, a focus on categories (or bespoke groupings) is essential.

Say you’re a home furniture auction house with a large inventory. In the mass of data, you’ll need to find trends, and these trends sit within various categories, which are formed from aggregated product data over time.

This could be:

  • Top-searched designers or brands.
  • Most purchased colors of category A.
  • Share of search by category across AOV brackets.

This data will feed into almost all strategies and tactics adopted in the account, from structuring to forecasting and setting bidding targets.

This reporting can be automated and then queried to provide each stakeholder with a different view of performance that all leads back to driving growth through Google Shopping:

  • Buyers may want to see which categories or designers are indexing highly by search volume to feed into planning, which, in turn, helps Google Shopping as the products/categories that are performing the best are then stocked moving forward.
  • Paid search teams will want a view of how ROAS/CAC has trended over time by category to know how to set realistic targets at the campaign, ad group, and product group levels.
  • Analytics teams need a view of the time lag between the first session date by campaign and the purchase date to provide feedback to marketing teams on how to accurately report on Google Ads performance.

Optimization

Google is going to struggle to gain enough data to optimize at a product level.

Mirroring your reporting, you will need a view of performance at the category (or another grouping) level, as individual product performance isn’t going to feed into your campaigns as it would for a typical ecommerce store sat on the stock.

You’ll need to do the work analysing performance across multiple segments to build a picture of how each category performs to then set budgets and bidding targets and maintain the day-to-day tasks required to manage Google Shopping campaigns.

Product Feed

It is critical that your feed is optimized and you are ingesting as much supplemental data as possible (within reason).

This data will feed into your Google Shopping campaigns, and the time invested will pay for itself down the line.

Take the furniture store example. It can supplement its data with era, designer, etc. When new items are added, this additional data can help group products into segments with realistic targets and budgets vs. being dropped into a top-level category and leaning on product performance to determine what SKUs to serve.

Above all, there has to be ownership and a process for adding SKUs to the feed.

Although products will be moving in and out of your feed frequently, there will likely be cohorts of SKUs that will remain in the feed for a while, which you should keep an eye on as these may need removing/scaling back in line with efficiency.

Summary: Advertisers Will Need To Think On Their Feet

A great deal of the work involved in navigating this business model and scaling Google Shopping happens outside of the ad accounts.

Advertisers need to interpret and share data across the wider business, and this process works both ways.

What are buyers in the company looking at bringing in and where would this sit with the Google Shopping strategy? Are there categories trending upwards that can be shared with the wider team to capitalize on?

Without stable product data, advertisers will need to think on their feet and get fully ingrained within the business, which in 2025 is essential – whatever the business model.

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Featured Image: BestForBest/Shutterstock

Google Responsive Search Ads Just Got More Flexible via @sejournal, @brookeosmundson

Google Ads just rolled out an update to Responsive Search Ads (RSAs), and while it may not seem groundbreaking at first glance, it could have a noticeable impact on how advertisers optimize their campaigns.

This update focuses on how Google assembles ad assets, giving marketers more control over messaging while still leveraging AI-driven automation. If you’ve ever been frustrated with how Google randomly mixes and matches your headlines and descriptions, this change is worth paying attention to.

Here’s what’s changing, why it matters, and how it could impact PPC performance.

What’s Changing With Responsive Search Ads?

In the announcement from Google, there’s one main component of how RSAs are changing.

Before diving into the update, it’s important to note the change that Google implemented last year. In February 2024, Google updated Responsive Search Ads to be able to show only one headline if it was predicted to improve performance.

Now, they’re building off that update with these key aspects.

New Ways To Use Headline Assets 

Previously, Google’s approach to Responsive Search Ads was all about maximum automation—headlines and descriptions were combined dynamically, sometimes in ways that didn’t make complete sense.

With this update, Google is adjusting its system to create more cohesive and logical ad combinations.

Starting now, up to two (2) headlines are eligible to serve in previously reserved spaces for sitelinks – if they’re predicted to improve performance.

Below is an example Google provided on what this change could look like:

Image credit: Google, February 2025

If a user clicks on any of those allocated headlines, they’ll be directed to the ad’s final URL.

While the specifics of Google’s algorithm tweaks aren’t spelled out, the general goal is clear: ads should make more sense contextually.

Advertisers won’t have to worry as much about disjointed messaging or assets being strung together in ways that feel unnatural to users.

Asset Pinning and Reporting Expectations

Google Ads Liaison Ginny Marvin took to LinkedIn to provide a clear, thought-out update regarding how asset pinning and combination reporting would be affected.

Per Marvin’s post, existing asset pinning will be respected. If headlines are pinned in positions 1 or 2, and if descriptions are pinned in position 1, those will still serve in those dedicated positions.

As for combination reporting, advertisers will still be able to see the most commonly served combination of headlines and descriptions. In this update, it will also show which headlines served as a sitelink.

The stats will be reported at the headline and not the sitelink level, per user feedback in initial testing.

How Does This Impact Advertisers?

This update isn’t just a behind-the-scenes tweak—it has real implications for how advertisers structure their ads and optimize ad performance. Here’s why:

  • More Consistent Messaging = Better Engagement. Disjointed or awkward ad combinations have long been an issue with RSAs. By improving how assets are paired, Google is helping advertisers deliver messages that feel more natural and cohesive, which could lead to higher click-through rates (CTR).

  • Stronger Brand Control. While RSAs are still dynamic, this update reduces the likelihood of brand messaging getting lost in automation. Advertisers can have more confidence that key value propositions and calls to action will appear in logical combinations.

  • Improved Performance Insights. With better visibility into how Google structures ad assets, advertisers can make more informed decisions about which headlines and descriptions to test, adjust, or remove. This leads to more efficient A/B testing and better data-driven optimizations over time.

  • Potential for Higher Quality Scores. If Google’s adjustments result in more relevant ad combinations, it could improve expected CTR, which is a major factor in Quality Score. Higher Quality Scores can lead to lower cost-per-click (CPC) and better ad placements.

Wrapping Up

Google’s update to Responsive Search Ads is a step toward more intelligent automation, helping advertisers maintain better messaging consistency while still benefiting from AI-driven optimizations.

While this won’t eliminate the need for careful asset planning, it does make RSAs a more reliable tool for brands that want to scale their search campaigns efficiently.

If RSAs have frustrated you in the past, now might be the time to revisit them.

With better asset pairing and improved visibility into ad assembly, this update could give advertisers a bit more control—without taking away the automation that makes RSAs so powerful.