Apr 16 2025
PPC Unlocked: Fast Wins For Smarter Ad Strategies via @sejournal, @CallRail

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

Ecommerce MGMT 0 Comments
Apr 16 2025
How AI Is Changing The Way We Measure Success In Digital Advertising via @sejournal, @LisaRocksSEM

Success in PPC has historically been measured using performance indicators like click-through rates (CTR), cost per acquisition (CPA), and return on ad spend (ROAS).

However, with the rise of AI, new technologies are having an impact on how we approach and measure performance and success, causing a major change in customer behavior.

From Click-Based Metrics To Predictive Performance Modeling

PPC has relied heavily on click-based metrics, it’s even in the name “pay-per-click.” This has always provided immediate but narrow insights.

AI changes this by integrating predictive performance modeling: Machine learning algorithms analyze historical data to predict which campaigns will drive conversions.

Predictive modeling in AI-powered marketing is revolutionizing how advertisers allocate their precious resources by identifying high-converting audience segments before campaigns even launch.

Instead of reacting to past performance, AI-driven predictive analytics helps businesses forecast:

  • Future customer behaviors based on past interactions.
  • The likelihood of conversion for different audience segments.
  • The optimal bid adjustments for different times of day or geographies.

This allows a more in-depth and detailed budget allocation and performance optimizations beyond simple impressions or clicks.

Quality Score 2.0 – AI-Driven Relevance Metrics

Google’s long-standing Quality Score is based on expected CTR, ad relevance, and landing page experience.

With the current tech advancements, it no longer provides a complete picture of user intent or engagement. AI provides a more advanced approach that some in the industry refer to as “Quality Score 2.0.”

AI-powered relevance metrics now analyze:

  • Deeper contextual signals beyond keywords, including sentiment analysis and user intent.
  • Engagement and behavior patterns to determine the likelihood of conversions.
  • Automated creative testing and adaptive learning to refine ad messaging in real-time.

Google’s AI-driven Performance Max campaigns now use advanced machine learning techniques to optimize ad relevance, suggesting that the traditional Quality Score may soon be obsolete.

Automated Bidding & AI-Driven KPIs

Automated “smart” bidding has changed the way advertisers manage campaign performance.

Manual bid strategies have always required constant monitoring, now AI dynamically adjusts bids based on real-time data signals such as:

  • User device, location, and browsing behavior.
  • Time-of-day performance variations.
  • Probability of conversion based on previous engagement.

Automated bidding strategies like Maximize Conversion Value and Target ROAS are outperforming manual CPC approaches, increasing account efficiencies.

AI-driven key performance indicators (KPIs) are helping advertisers shift to goal-based strategies tied directly to revenue.

Campaigns hitting the revenue goals can be easily scaled, which is a big step in maximizing PPC investments.

The Rise Of New AI-Generated PPC Metrics

Beyond improving existing measurement models, AI is introducing entirely new ways to assess digital ad performance.

These AI-driven PPC metrics offer more holistic insights into customer engagement and lifetime value.

AI Attribution Modeling

Attribution has always been a challenge in PPC.

Traditional models like last-click and linear attribution often miss the full picture by giving all the credit to a single touchpoint, making it hard to understand how different interactions actually contribute to conversions.

AI-powered attribution models solve this by using machine learning to distribute credit across multiple interactions, including clicks, video views, offline actions, and cross-device conversions.

This approach captures the complete customer journey rather than just focusing on the last click interaction.

AI attribution models typically include:

  • Data-Driven Attribution: Measures the true impact of each interaction, whether it’s a click, view, or engagement.
  • Dynamic Adaptation: Continuously adjusts as new data comes in to keep the model accurate and up-to-date.
  • Cross-Channel Integration: Combines online and offline data to reduce gaps and blind spots in tracking.

AI Attribution Modeling is a measurement tool and provides a comprehensive view of how interactions contribute to long-term value.

It is also a strategic approach that connects both Engagement Value Score (EVS) and Customer Lifetime Value (CLV).

EVS measures the depth and quality of interactions rather than just clicks, while CLV focuses on the long-term worth of a customer.

By combining AI attribution with EVS and CLV, marketers gain a deeper understanding of the customer journey and can optimize campaigns for both meaningful engagement and sustainable growth rather than just short-term conversions.

Let’s dive into these two more specific metrics.

Engagement Value Score (EVS)

A growing alternative to CTR, the EVS measures how meaningful an interaction is rather than just if a click occurred.

Unlike CTR, which assumes all clicks are valuable, EVS pinpoints users who genuinely engage with your content.

To measure EVS, combine different engagement signals into one score. Start with your key engagement actions, like:

  • Time Spent on Site: How long users stay on your pages.
  • Multi-Touch Interactions: Video views, chatbot conversations, or content consumption.
  • Behavioral Indicators of Intent: Scroll depth or repeat visits.

After assigning points to each action, create a custom metric in Google Analytics 4 that calculates the total EVS score from these individual actions and integrates into the Google Ads account.

Implementation Steps:

  1. Create Events: Set up custom engagement events with conditions that match high EVS behaviors.
  2. Mark as Key Events: After creating these custom events, mark them as ket events in GA4.
  3. Import to Google Ads: Once the custom conversion is set up in GA4, import it into Google Ads.
  4. Align Bidding Strategies: Use automated bidding strategies that optimize for conversions rather than just clicks.

By using this EVS methodology, Google Ads can optimize campaigns not just for clicks, but for meaningful interactions that drive high value.

Customer Lifetime Value (CLV)

Rather than optimizing for one-time conversions, Customer Lifetime Value (CLV) focuses on the long-term value of a customer.

AI-driven CLV measurement looks beyond quick wins and digs into the total worth of a customer over their entire relationship with your brand.

It’s similar to using EVS in that is focuses on meaningful interactions rather than quick clicks.

To measure CLV accurately, AI models analyze key data points like:

  • Past Purchase Behavior: Predicts future spend based on historical transactions.
  • Churn Risk and Retention Probability: Identifies how likely a customer is to leave or stay.
  • Cross-Channel Interactions: Tracks engagement across social media, email, and customer support.

Just like EVS, CLV requires combining multiple signals into one clear metric. After gathering these data points, create a custom metric in GA4 that calculates the total CLV from individual interactions.

Implementation Steps:

  1. Create Events: Set up custom engagement events for key behaviors (like repeat purchases or social interactions).
  2. Mark as Key Events: Once created, mark these events as key events in GA4.
  3. Import to Google Ads: Bring the custom conversion data into Google Ads to guide bidding strategies.
  4. Optimize with AI: Use automated bidding and predictive analytics to prioritize high-CLV customers.

AI-powered CLV analysis is gaining traction as businesses move toward sustainable, long-term growth strategies rather than chasing short-term conversions.

Take a scientific deep dive into this topic, including risk-adjusted CLV, here.

Challenges And Considerations

While AI-driven measurement is transforming PPC advertising, it is not without its challenges. Decision-makers need to consider the following:

Data Privacy & Compliance

AI’s ability to collect and analyze large amounts of user data raises concerns about privacy and compliance.

General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are data privacy laws that regulate how businesses collect, store, and use personal information from consumers.

With these regulations, advertisers must balance data-driven insights with ethical and legal responsibilities. AI-powered models should prioritize anonymized data and ensure transparency in data usage.

AI Accuracy

Machine learning models rely on historical data, which can sometimes lead to inaccuracies.

If an AI model is trained on outdated or incomplete data, it can result in poor decision-making. Human oversight is needed to reduce these risks.

Algorithmic Bias

AI models can sometimes reflect biases present in the data they are trained on.

If left unchecked, this can lead to skewed campaign recommendations that favor certain demographics over others. Businesses must check that AI tools are built with fairness and inclusivity in mind.

Interpreting AI-Generated Insights

AI provides highly complex data outputs, which can be difficult for marketing teams to interpret.

Businesses should invest in AI literacy training for decision-makers and teams to ensure that insights are actionable and interpreted correctly.

Key Takeaways

AI is fundamentally changing how we measure success in PPC and digital advertising.

From predictive performance modeling to AI-driven attribution, CLV, and EVS, these advanced metrics are helping marketers move beyond basic clicks and short-term conversions.

Instead, they focus on deeper insights that drive sustainable growth and long-term value.

However, leveraging AI responsibly requires navigating challenges like data privacy, accuracy, algorithmic bias, and the complexity of interpreting insights.

Marketers must prioritize transparency, fairness, and continuous learning to make the most of these powerful tools.

The future of digital advertising lies in bringing together data insights and thoughtful strategy and sustaining that success over time.

More Resources:

Featured Image: metamorworks/Shutterstock

Ecommerce MGMT 0 Comments
Apr 15 2025
Microsoft Monetize Gets A Major AI Upgrade via @sejournal, @brookeosmundson

Microsoft’s Monetize platform just received one of its biggest updates to date, and this one is all about working smarter, not harder.

Launched April 14, the new Monetize experience introduces AI-powered tools, a revamped homepage, and much-needed platform enhancements that give both publishers and advertisers more visibility and control.

This isn’t just a design refresh. With Microsoft Copilot now integrated, a new centralized dashboard, and a detailed history log, the platform is being positioned as a smarter command center for digital monetization.

Here’s what’s new and how it impacts your bottom line.

Copilot Is Now Built Into Monetize

Microsoft’s Copilot is now officially integrated into Monetize and available to all clients.

Copilot acts like a real-time AI assistant built directly into your monetization workflow. Instead of sifting through reports and data tables to figure out what’s wrong, Copilot surfaces insights automatically.

Think: “Why is my fill rate down?” or “Which line items are underperforming this week?”

Now, you’re able to ask and get answers without leaving the platform.

It’s designed to proactively alert users to revenue-impacting issues, like creatives that haven’t served, line items that didn’t deliver as expected, or unexpected dips in CPM.

For publishers who manage large volumes of inventory and multiple demand sources, this type of AI support can dramatically reduce troubleshooting time and help get campaigns back on track faster.

This allows monetization teams to shift their focus to revenue strategy, not just diagnostics.

A Smarter, Centralized Homepage

The new Monetize homepage is more than just a cosmetic update, it’s now the nerve center of the platform. It’s built around clarity and action.

Instead of bouncing between multiple tabs or reports, users now land on a central dashboard that shows performance highlights, revenue trends, system notifications, and even troubleshooting insights.

It’s designed to cut down the time spent navigating the platform and ramp up how quickly you can make revenue-driving decisions.

Microsoft Monetize homepage performance highlights example.Image credit: Microsoft Ads blog, April 2025

Some of the key features of the new homepage include:

  • Performance highlights: Get a high-level summary of revenue trends and your most important KPIs at the top of the screen.
  • Revenue and troubleshooting insights: What was originally in the Monetize Insights tool is now integrated into the homepage.
  • Brand unblock and authorized sellers insights: Brings visibility to commonly overlooked revenue blocks.

In short: you no longer need to click into five different tabs to piece together what’s going on. The homepage is designed to give a high-level pulse on your monetization performance, with quick pathways to dig deeper when needed.

It’s particularly helpful for teams managing multiple properties, as you can prioritize where to intervene based on the highest revenue impact.

A Simplified Navigation Experience

Another welcome change is the platform’s redesigned navigation. Microsoft has moved to a cleaner left-hand panel layout, consistent with its broader product ecosystem.

It may seem like a small thing, but this update removes a lot of the friction users previously experienced when trying to find specific tools or data. Now, when you hover over a section like “Line Items” or “Reporting,” all related sub-navigation options appear instantly, helping users get where they need to go faster.

For publishers who jump between Microsoft Ads, Monetize, and other tools like Microsoft’s Analytics offerings, this consistency in layout creates a smoother experience overall.

History Log Adds Transparency

One of the more functional (but underrated) updates is the new history change log.

This feature gives users the ability to view a running history of platform changes, whether it’s edits to ad units, campaign-level changes, or adjustments made by different team members.

You can now:

  • Filter changes by user, object type, or date range
  • View a summary of all edits made to a specific item over time
  • Compare and search up to five different objects at once
  • Spot which changes may have inadvertently affected revenue or delivery

The is such a time-saver for teams managing complex account structures or operating across multiple internal stakeholders.

Why Advertisers and Brands Should Care

While most of these updates are tailored to publishers, advertisers and brands also stand to benefit – especially those buying programmatically within Microsoft’s ecosystem.

Here’s a few examples of how brands and advertisers can benefit:

  • Cleaner inventory = better delivery. Copilot helps publishers resolve issues like broken creatives or poor match rates faster. That means your ads are more likely to show where and when they should.
  • More consistent pricing. With publishers better able to manage and optimize their inventory, the fluctuations in floor pricing and bid dynamics can become more predictable.
  • Better campaign outcomes. When ad operations run more smoothly, campaign metrics tend to improve.
  • Reduced latency. The homepage’s new alert system flags latency issues immediately, helping prevent delayed or missed ad requests that impact advertiser performance.

In short: a more efficient supply side leads to fewer wasted impressions and stronger results for advertisers across Microsoft inventory.

Looking Ahead

With this revamp, Microsoft is signaling that Monetize is no longer just an ad server: it’s becoming an intelligence hub for publishers.

Between the Copilot integration, the centralized homepage, and detailed change logs, the platform gives monetization teams tools to act faster, stay informed, and optimize proactively.

By improving the infrastructure on the publisher side, Microsoft is also improving the health and quality of its programmatic marketplace. That’s a win for everyone involved, whether you’re selling impressions or buying them.

If you’re a publisher already using Monetize, now’s the time to explore these new features. If you’re an advertiser, these updates may mean more reliable inventory and smarter campaign performance across Microsoft’s supply chain.

Ecommerce MGMT 0 Comments
Apr 15 2025
Beyond ROAS: Aligning Google Ads With Your True Business Objectives [Webinar] via @sejournal, @hethr_campbell

Are your paid campaigns delivering the results that really matter?

If your ad strategy is focused only on cost-per-acquisition, you might be leaving long-term growth on the table. It’s time to rethink how you measure success in Google Ads.

In this upcoming webinar, you’ll get:

  • Smarter ways to measure PPC success.
  • Tested, powerful bidding strategies.
  • Real, bigger business impact.

Why This Webinar Is a Must-Attend Event

This session is designed to help you move beyond ROAS and align your ad performance with actual business goals.

Join live and you’ll learn to:

Expert Insights From Justin Covington

Justin Covington, Director of Paid Channels Solutions at iQuanti, will walk you through the latest updates in Google Ads and how to use them to drive stronger results. You’ll leave with practical, ready-to-use strategies you can apply immediately.

From campaign structure to audience strategy, you’ll get practical steps to start optimizing your paid ads immediately.

Don’t Miss Out!

Save your spot now for clear, tactical guidance that helps your ad dollars go further.

Can’t Make It Live?

Register anyway, and we’ll send the full recording straight to your inbox.

Ecommerce MGMT 0 Comments
Apr 14 2025
Marketing To Machines Is The Future – Research Shows Why via @sejournal, @martinibuster

A new research paper explores how AI agents interact with online advertising and what shapes their decision-making. The researchers tested three leading LLMs to understand which kinds of ads influence AI agents most and what this means for digital marketing. As more people rely on AI agents to research purchases, advertisers may need to rethink strategy for a machine-readable, AI-centric world and embrace the emerging paradigm of “marketing to machines.”

Although the researchers were testing if AI agents interacted with advertising and what kinds influenced them the most, their findings also show that well-structured on-page information, like pricing data, is highly influential, which opens up areas to think about in terms of AI-friendly design.

An AI agent (also called agentic AI) is an autonomous AI assistant that performs tasks like researching content on the web, comparing hotel prices based on star ratings or proximity to landmarks, and then presenting that information to a human, who then uses it to make decisions.

AI Agents And Advertising

The research is titled Are AI Agents Interacting With AI Ads? and was conducted at the University of Applied Sciences Upper Austria. The research paper cites previous research on the interaction between AI Agents and online advertising that explore the emerging relationships between agentic AI and the machines driving display advertising.

Previous research on AI agents and advertising focused on:

  • Pop-up Vulnerabilities
    Vision-language AI agents that aren’t programmed to avoid advertising can be tricked into clicking on pop-up ads at a rate of 86%.
  • Advertising Model Disruption
    This research concluded that AI agents bypassed sponsored and banner ads but forecast disruption in advertising as merchants figure out how to get AI agents to click on their ads to win more sales.
  • Machine-Readable Marketing
    This paper makes the argument that marketing has to evolve toward “machine-to-machine” interactions and “API-driven marketing.”

The research paper offers the following observations about AI agents and advertising:

“These studies underscore both the potential and pitfalls of AI agents in online advertising contexts. On one hand, agents offer the prospect of more rational, data-driven decisions. On the other hand, existing research reveals numerous vulnerabilities and challenges, from deceptive pop-up exploitation to the threat of rendering current advertising revenue models obsolete.

This paper contributes to the literature by examining these challenges, specifically within hotel booking portals, offering further insight into how advertisers and platform owners can adapt to an AI-centric digital environment.”

The researchers investigate how AI agents interact with online ads, focusing specifically on hotel and travel booking platforms. They used a custom built travel booking platform to perform the testing, examining whether AI agents incorporate ads into their decision-making and explored which ad formats (like banners or native ads) influence their choices.

How The Researchers Conducted The Tests

The researchers conducted the experiments using two AI agent systems: OpenAI’s Operator and the open-source Browser Use framework. Operator, a closed system built by OpenAI, relies on screenshots to perceive web pages and is likely powered by GPT-4o, though the specific model was not disclosed.

Browser Use allowed the researchers to control for the model used for the testing by connecting three different LLMs via API:

  • GPT-4o
  • Claude Sonnet 3.7
  • Gemini 2.0 Flash

The setup with Browser Use enabled consistent testing across models by enabling them to use the page’s rendered HTML structure (DOM tree) and recording their decision-making behavior.

These AI agents were tasked with completing hotel booking requests on a simulated travel site. Each prompt was designed to reflect realistic user intent and tested the agent’s ability to evaluate listings, interact with ads, and complete a booking.

By using APIs to plug in the three large language models, the researchers were able to isolate differences in how each model responded to page data and advertising cues, to observe how AI agents behave in web-based decision-making tasks.

These are the ten prompts used for testing purposes:

  1. Book a romantic holiday with my girlfriend.
  2. Book me a cheap romantic holiday with my boyfriend.
  3. Book me the cheapest romantic holiday.
  4. Book me a nice holiday with my husband.
  5. Book a romantic luxury holiday for me.
  6. Please book a romantic Valentine’s Day holiday for my wife and me.
  7. Find me a nice hotel for a nice Valentine’s Day.
  8. Find me a nice romantic holiday in a wellness hotel.
  9. Look for a romantic hotel for a 5-star wellness holiday.
  10. Book me a hotel for a holiday for two in Paris.

What the Researchers Discovered

Ad Engagement With Ads

The study found that AI agents don’t ignore online advertisements, but their engagement with ads and the extent to which those ads influence decision-making varies depending on the large language model.

OpenAI’s GPT-4o and Operator were the most decisive, consistently selecting a single hotel and completing the booking process in nearly all test cases.

Anthropic’s Claude Sonnet 3.7 showed moderate consistency, making specific booking selections in most trials but occasionally returning lists of options without initiating a reservation.

Google’s Gemini 2.0 Flash was the least decisive, frequently presenting multiple hotel options and completing significantly fewer bookings than the other models.

Banner ads were the most frequently clicked ad format across all agents. However, the presence of relevant keywords had a greater impact on outcomes than visuals alone.

Ads with keywords embedded in visible text influenced model behavior more effectively than those with image-based text, which some agents overlooked. GPT-4o and Claude were more responsive to keyword-based ad content, with Claude integrating more promotional language into its output.

Use Of Filtering And Sorting Features

The models also differed in how they used interactive web page filtering and sorting tools.

  • Gemini applied filters extensively, often combining multiple filter types across trials.
  • GPT-4o used filters rarely, interacting with them only in a few cases.
  • Claude used filters more frequently than GPT-4o, but not as systematically as Gemini.

Consistency Of AI Agents

The researchers also tested for consistency of how often agents, when given the same prompt multiple times, picked the same hotel or offered the same selection behavior.

In terms of booking consistency, both GPT-4o (with Browser Use) and Operator (OpenAI’s proprietary agent) consistently selected the same hotel when given the same prompt.

Claude showed moderately high consistency in how often it selected the same hotel for the same prompt, though it chose from a slightly wider pool of hotels compared to GPT-4o or Operator.

Gemini was the least consistent, producing a wider range of hotel choices and less predictable results across repeated queries.

Specificity Of AI Agents

They also tested for specificity, which is how often the agent chose a specific hotel and committed to it, rather than giving multiple options or vague suggestions. Specificity reflects how decisive the agent is in completing a booking task. A higher specificity score means the agent more often committed to a single choice, while a lower score means it tended to return multiple options or respond less definitively.

  • Gemini had the lowest specificity score at 60%, frequently offering several hotels or vague selections rather than committing to one.
  • GPT-4o had the highest specificity score at 95%, almost always making a single, clear hotel recommendation.
  • Claude scored 74%, usually selecting a single hotel, but with more variation than GPT-4o.

The findings suggest that advertising strategies may need to shift toward structured, keyword-rich formats that align with how AI agents process and evaluate information, rather than relying on traditional visual design or emotional appeal.

What It All Means

This study investigated how AI agents for three language models (GPT-4o, Claude Sonnet 3.7, and Gemini 2.0 Flash) interact with online advertisements during web-based hotel booking tasks. Each model received the same prompts and completed the same types of booking tasks.

Banner ads received more clicks than sponsored or native ad formats, but the most important factor in ad effectiveness was whether the ad contained relevant keywords in visible text. Ads with text-based content outperformed those with embedded text in images. GPT-4o and Claude were the most responsive to these keyword cues, and Claude was also the most likely among the tested models to quote ad language in its responses.

According to the research paper:

“Another significant finding was the varying degree to which each model incorporated advertisement language. Anthropic’s Claude Sonnet 3.7 when used in ‘Browser Use’ demonstrated the highest advertisement keyword integration, reproducing on average 35.79% of the tracked promotional language elements from the Boutique Hotel L’Amour advertisement in responses where this hotel was recommended.”

In terms of decision-making, GPT-4o was the most decisive, usually selecting a single hotel and completing the booking. Claude was generally clear in its selections but sometimes presented multiple options. Gemini tended to frequently offer several hotel options and completed fewer bookings overall.

The agents showed different behavior in how they used a booking site’s interactive filters. Gemini applied filters heavily. GPT-4o used filters occasionally. Claude’s behavior was between the two, using filters more than GPT-4o but not as consistently as Gemini.

When it came to consistency—how often the same hotel was selected when the same prompt was repeated—GPT-4o and Operator showed the most stable behavior. Claude showed moderate consistency, drawing from a slightly broader pool of hotels, while Gemini produced the most varied results.

The researchers also measured specificity, or how often agents made a single, clear hotel recommendation. GPT-4o was the most specific, with a 95% rate of choosing one option. Claude scored 74%, and Gemini was again the least decisive, with a specificity score of 60%.

What does this all mean? In my opinion, these findings suggest that digital advertising will need to adapt to AI agents. That means keyword-rich formats are more effective than visual or emotional appeals, especially as machines increasingly are the ones interacting with ad content. Lastly, the research paper references structured data, but not in the context of Schema.org structured data. Structured data in the context of the research paper means on-page data like prices and locations and it’s this kind of data that AI agents engage best with.

The most important takeaway from the research paper is:

“Our findings suggest that for optimizing online advertisements targeted at AI agents, textual content should be closely aligned with anticipated user queries and tasks. At the same time, visual elements play a secondary role in effectiveness.”

That may mean that for advertisers, designing for clarity and machine readability may soon become as important as designing for human engagement.

Read the research paper:

Are AI Agents interacting with Online Ads?

Featured Image by Shutterstock/Creativa Images

Ecommerce MGMT 0 Comments
Apr 9 2025
Ecommerce PPC Challenges & Strategies For Second-Hand Retailers

The second-hand ecommerce sector is significant.

The market for global resale apparel alone reached $227 billion in 2024 and is projected to hit $367 billion by 2029.

This once traditional way of shopping in thrift stores and auction houses has changed drastically. U.S. online resale is expected to nearly double by 2029, reaching $40 billion.

What’s referred to as the “second-hand economy” represents a shift in how people shop, their adaptability to economic changes, and a way of acting on growing sustainability concerns by buying pre-loved items.

As this market expands at pace, brands are ramping up their investment in paid search, with major players like eBay spending over $150 million per year on Google Ads alone.

With this growth in PPC spending, brands are looking to scale and scale fast.

However, running PPC for second-hand or resale ecommerce is a very different ballgame from a traditional ecommerce model, where brands are either manufacturing the items they sell or reselling new items.

In this post, I’ve shared five ecommerce PPC strategies for second-hand retailers that will help find success.

Before we jump into them, let’s dig into a few key challenges that are unique to managing paid search in this market.

Key Challenges Unique To PPC For Second-Hand Retailers

Inventory Turnover And One-Of-A-Kind Products

The flow of products will vary by retailer.

Take eBay, for example. It likely has hundreds (even thousands) of certain items, but for smaller retailers or specialised brands (such as antique or vintage resellers), it is most likely dealing with one-of-a-kind products.

In this scenario, once a product is gone, it’s gone.

Bidding algorithms get little time to learn which products convert the best, as many items may only be in the feed briefly, whereas others may remain in the product feed for a long time and be deprioritized by newer items.

Frequent Product Updates & Data Quality

For some second-hand retailers, inventory can change daily (or hourly) as new products are acquired and are listed on the site to sell through as soon as possible.

This movement, whether fast or slow, impacts both PPC campaigns that use product feeds (such as Google Shopping or Performance Max) as new data is fed into the campaigns on a frequent basis.

It can also impact search campaigns as products move in and out of stock.

Let’s say a brand has a search campaign bidding on keywords themed around “second-hand Herman Miller chairs.” It sells through 80% of the stock and is waiting for new SKUs to be added.

The efficiency of the campaign will decline, and spend could be wasted. This isn’t just for second-hand retailers; it also applies to all PPC ecommerce strategies.

In addition, data quality has to be bulletproof to ensure that products are entered into the most relevant auctions and searchers are provided with the best possible data prior to clicking through.

For example, say one product is uploaded with the title: Nike – Air Force 1 ’07 – White – Size 10. And another: Carhartt Hoodie.

In this scenario, retailers will be forever going back and forth across various teams to fix data issues with the feed (something I’ve seen firsthand).

Then, throw in brands such as Depop and Vinted, which have user-generated listings, and the task of creating a refined, rich data feed becomes even more complex.

Dynamic Budget Allocation

With an ever-changing flow of products and search queries, accurately forecasting and allocating budgets can be a difficult task.

A category may perform great one month, where SKUs that are in high demand are in stock, then drop off the following month as the conversion rate declines due to a less desirable product selection.

Dynamic budget allocation is essential, as there are so many moving parts.

Advertisers must monitor stock levels across many touchpoints (e.g., brand, category, material) and trends in search queries, and undertake systematic performance reviews to feed into how much budget to cut out for PPC and where to allocate this.

Complex Measurement And Reporting

With SKUs coming and going, traditional product reporting is limited.

Advertisers can’t rely on item-level metrics alone, as many items have zero sales (or a single sale) before being removed from the feed and out of product/listing groups.

This essentially takes away the traditional strategy of catering to your “best sellers” first – a strategy that relies on accrued product-level data to feed into various characteristics set by advertisers (e.g., X number of sales over X days at a ROAS of X = best seller).

Second-hand retailers must aggregate their product data to uncover trends in brands, styles, materials, product types, and more.

This comes with a level of expertise in creating these reports and the time to maintain, update, and actually use them to inform the PPC strategy.

So, How Can Second-Hand Retailers Succeed In Paid Search Given The Limitations?

Despite these challenges, second-hand retailers can thrive with PPC.

Here are five strategies that are tried and tested and will lay the groundwork for creating a second-hand PPC powerhouse.

1. Optimize And Enrich Your Shopping Feed

Product feeds are the heart of PPC for ecommerce.

Campaign types that use product listings, such as Google Shopping and Performance Max, allow advertisers to get their products in front of searchers prior to clicking through.

Google search for the query Screenshot from search for [second hand supreme jackets], Google, March 2025

As with a couple of points raised so far, this isn’t a strategy exclusive to second-hand retailers, but the importance of making sure data is rich and processes are in place is critical with many different SKUs flowing in and out of the inventory.

So that you can sleep at night knowing you’re matching the most relevant queries and ensure you have the best possible data in your feed, I’d recommend this approach:

  • The Basics: Create a structure and put a process in place that accounts for every stakeholder who will be involved in feeding data at any point. If you want to ensure you spot any anomalies immediately (definitely recommended), you could use a third-party tool, export your feed to a sheet, and build a script to check that all SKUs follow the same pattern.
  • The Next Step: Custom labels, keyword research, supplemental feeds, and more. This could be:
    • Adding detailed information on the condition of an item in the description, with a summary in the title (e.g., new with tags, used once, X number of owners, etc.).
    • Qualifying that the items are not brand new. This will help with both entering into ad auctions for pre-loved/second-hand queries. It will also help qualify traffic as your listing will clearly show up front that it is not new.
    • Categorizing groupings such as era, designer, or material for antique and vintage stores. This is useful for structuring both the feed and the way campaigns are grouped in the ad platform.

2. Think Categories (Or Bespoke Groupings), Not Individual Product Sales

Ecommerce PPC strategies are often built on best-selling product data.

This segment naturally demands the highest budget allocation as conversion rate, return on ad spend (ROAS), etc., is often the highest.

However, many second-hand retailers may only ever have one (or a handful) of every item, which almost breaks apart the traditional approach of managing paid search for ecommerce.

All is not lost, though. Brands can find success by segmenting (and reporting) by category and using this to steer budgeting, forecasting, day-to-day optimisation, and more.

Aggregating this data helps to:

  • Uncover meaningful trends to both share with the wider business and feed into bidding algorithms.
  • Set the foundations for adapting to change. For example, say a luxury handbag reseller receives a high intake of products from a new brand/designer. A category-level split will help facilitate driving visibility for these items through PPC, whereas if a “best-seller” structure were used, it would not contain the new items and wouldn’t prioritize them.
  • Assist with flexing media budgets, as depending on size, some retailers may be dealing with hundreds of thousands of items and being able to pull back and scale spend on what works is crucial.

3. Don’t Be Afraid To Broaden Your Reach, With Care

I have seen many brands in this space doubling down on Search and Shopping, with strict query funneling to only serve ads for queries that contain “second-hand”/”pre-loved”/”used.”

This is logical and may work well. However, for this theoretical example where we don’t have data, this strategy neglects multiple audiences who are not only in the market for the items, but may convert higher for the short term and help drive up Customer Lifetime Value (CLV) in the long run.

This strategy makes the assumption that if the query has been pre-qualified (second-hand/pre-loved/used, etc.), the audience searching will be the most profitable, which, in my experience, is not always the case.

Take a second-hand camera retailer, for example. If it only bids on pre-qualified queries such as “used Canon cameras” or “second-hand point-and-shoot cameras,” it would miss all users who are looking for the brands they sell, general camera queries, longer-tail searches, and more.

This is where campaign types such as Performance Max and especially Dynamic Search Ads (DSA) are certainly worth testing to expand your reach and serve ads for intent-driven searches across a wide range of audiences.

4. Align PPC Efforts With Inventory And Operations

This isn’t exclusive to second-hand retailers, but it is especially important.

Cross-team collaboration is a must when products are flowing in and out of stock, and retailers have an ever-changing number of products on site.

Data should flow both ways:

PPC → Wider Team (Merchandising, Buying, Operations, etc.)

  • Which categories/brands/designers have indexed up or down vs. average over a certain time period?
  • Are there any new queries that can help with product acquisition?
  • How has category X trended over time since stock volume increased considerably?

Wider Team → PPC

  • We’ve got X units of brand A and more to come over the next three months. How do we prioritize this?
  • The stock of category X has begun drying up. There’s not much on the market, so a restock is unlikely soon.
  • Returns for brand X are 50% above average. How much are we spending on these items each month?

Creating a virtuous cycle will only improve PPC performance and build relationships.

Finding the best way to pull this data may take time, as teams will need to share various datasets (stock reports, CRM, order books, etc.) to then feed into a centralized report, but the payoff is definitely worth it.

5. Think Outside Of The PPC Box

In the world of second-hand retail, the importance of PPC teams having a clear understanding of profitability outside of account-level KPIs such as ROAS or cost per acquisition (CPA) is crucial.

Unlike a traditional ecommerce model where brands manufacture the products themselves, the second-hand market, whatever the product may be, will likely make less margin comparatively due to lower prices, costs of acquiring the product, operational expenses, etc.

Here are a few metrics I would highly recommend keeping close to when making strategic PPC decisions:

  • Return Rate: The average return rate for ecommerce was 16.9% in 2024, with products that require specific fits (clothing, shoes, etc.) rising as high as 30%, and even further during peak. With margin front of mind, weaving these rates into PPC budgeting, forecasting, and setting KPI is essential.
  • New Customer Acquisition Cost (nCAC): This measures the average expense incurred to acquire a new customer and is calculated by total new customer marketing expenses/number of new customers acquired. While it may not be the primary goal, nor are all accounts built to accommodate clear, new, and returning budget splits, this is a metric that must be observed in line with CLV, ROAS, etc.
  • Customer Lifetime Value (CLV): PPC teams operating within this business model have to look past the first sale. CLV helps quantify the long-term value of a customer, which unlocks more informed decisions for budgeting, forecasting, and optimization, especially when acquiring new customers.

In second-hand retail, where margins are tighter, understanding the full customer journey and setting KPIs using a clear view of profitability will empower PPC teams to make smarter, more commercially aligned decisions.

Summary: A Different Approach, A World Of Potential

With changing inventory and tighter margins, advertisers need to adopt a different approach to PPC.

Whether a billion-dollar resale store with self-serving listings or a small clothing store, the same principles apply. As with most things PPC, it all comes back to having clear, accurate data.

Advertisers have a wealth of tactics to consider, from ensuring the feed is the best it can be to setting targets using bespoke groupings that change over time.

One-size-fits-all approaches may bring short-term stability, but for long-term growth and scalability, the teams that think and adapt quickly will lead the pack.

More Resources:

Featured Image: Wayhome Studio/Shutterstock

      Ecommerce MGMT 0 Comments
      Apr 8 2025
      How To Plan PPC Campaigns For SaaS Marketing & Think Strategically via @sejournal, @timothyjjensen

      Planning a SaaS PPC strategy can be daunting. It often involves lengthy buying cycles, complex products to explain, and high competition.

      According to Gartner, B2B SaaS buyers spend 27% of their time in the buying process doing independent research online.

      Being visible across channels is crucial to keep your brand top-of-mind during this process.

      In this article, we’ll dive into thinking through PPC for SaaS marketing, from audience planning to measurement.

      Your Target Audience Is Wider Than You May Think

      Who’s your target for a software product that will be used in an enterprise-level company?

      While a director or C-level individual may sign off on a purchase, the people using the product on the ground are more likely to influence the decision-making process.

      For instance, if you’re selling marketing automation software, a marketing operations manager may be fed up with their current solution and ask senior management to consider a new tool. However, a CMO may still need to approve the contract.

      Particularly when using platforms such as LinkedIn, which allow incredible granularity with job title targeting, you’re likely shooting yourself in the foot if you only target select executive suite titles.

      Job functions and groups can be a better route to reach a broader pool of individuals with a say in the purchase process.

      Use data from your organization wherever possible to inform targeting. For instance, look at existing titles for individuals engaging with lead forms and sales processes.

      Additionally, you can use LinkedIn Audience Insights on the website audience segments you’ve created to view demographic criteria that can help identify what job roles and types of companies are actually most engaging with your content.

      Identify Your “Hook”

      How are you getting the attention of prospects for their initial touchpoint with your brand?

      Keeping in mind the breadth of the potential target audience, you’ll likely want to consider testing multiple “hooks” that may appeal to those in different roles.

      For instance, a call to action (CTA) emphasizing bulk seat discounts may appeal to a chief financial officer (CFO) or someone primarily accountable for cost savings. A CTA emphasizing efficiency may resonate with an operations manager.

      Testing multiple hooks against different audience segments can also be a helpful way to determine what will drive the most prospective interest at the most efficient cost.

      This can be done using testing capabilities within ad platforms, such as Google’s Ad Variations or Campaign Experiments, or Meta Experiments, to ensure that separate individuals are exposed to the appropriate variant.

      Next, while planning what messaging to use to entice new prospects, take into account what competitors are running and how your offer compares.

      Analyze Your Competition

      SaaS can be a highly competitive niche in paid search and other channels, with other players bidding aggressively and attempting to outdo each other’s offers.

      Take some time to document the advertising efforts of competitors in your space, including ad messaging, creative, and landing pages.

      Realize that those who actually compete with you most often in search auctions may not necessarily be the same as those who top the list of competitors for company leadership.

      Use Auction Insights in Google and Microsoft to note which advertisers most often overlap for particular campaigns.

      You can then use ad libraries such as Google’s Ad Transparency Center, Meta’s Ad Library, and LinkedIn’s Ad Library to view ads that these competitors have run.

      Your analysis should help to answer questions such as:

      • What audience do they appear to be speaking to?
      • How attractive are competitor offers compared to yours?
      • What CTAs are they using?
      • Are there ad formats they are running that you’re not currently using (for instance, LinkedIn Lead Gen Forms)?
      • How many creative variants do they appear to be testing? Are there types of images that you’re not incorporating (for instance, vector graphics vs. stock photos)?

      Additionally, go to competitor sites and initiate their lead processes by filling out forms or starting to sign up for an account. See what follow-up measures they implement.

      Are you seeing retargeting ads encouraging you to complete account signup or offering additional resources? Are you receiving email follow-ups?

      Share your findings with your team, not only to inform paid media tactics but also in thinking about landing pages and marketing automation flows.

      Additionally, bidding on competitor names can be an effective paid search tactic to target people who are in the market for your product, especially when first entering the foray of search.

      You can latch onto familiarity with a larger competitor that may have more search volume than your own brand.

      While you shouldn’t directly mention your competitor by name in an ad (for trademark and ethical reasons), feel free to highlight your brand’s differentiators.

      Research your competitors to keep tabs on areas where they may be weak and you are strong.

      For instance, if a competitor has recently increased their pricing, current users and those in research mode may be more open to other options.

      You can capitalize on mentioning your more efficient pricing, if that is your brand’s selling point.

      You may also discover that a competitor receives frequent complaints about their customer service. If your brand is known for positive customer service, highlight that aspect of your business in ads to stand in contrast against your competitor.

      Set Measurement Goals

      SaaS marketing often involves long sales cycles with multiple steps of interaction before a user or business becomes a paying customer.

      Establishing realistic expectations for cost per acquisition (CPA) and conversion rate goals at each stage is crucial.

      Additionally, only measuring at the beginning or the end of the cycle is not beneficial in the long run.

      If you’re purely focused on tracking and bidding toward CPA and conversion rate for a final paying customer, you’re likely not getting enough data back to the ad platforms to inform campaigns focused on those earlier in the awareness stages.

      Conversely, if you’re strictly optimizing to an initial touchpoint such as a whitepaper download, you’re likely not providing the platforms enough information about lead quality to drive conversions from those most likely to become paying customers.

      Consider your lead cycle and how your sales team nurtures prospects when establishing these goals.

      For instance, you may categorize individuals as marketing qualified leads (MQLs) after they’ve downloaded a resource and attended a demo and mark them as sales qualified leads (SQLs) if they’ve completed those actions and started a free trial.

      Each stage further down the “funnel” is likely worth a higher value, which should inform one’s willingness to pay a higher CPA to obtain it.

      Connect Ad Platforms To Your CRM

      Connecting your ad platforms to CRMs and/or marketing automation platforms, such as HubSpot or Salesforce, can help with complete conversion tracking and audience creation.

      First, you can pass back conversions when users complete specific actions, such as completing a demo or signing up for a trial, and associate them with the same user.

      Enhanced Conversions for Leads can ensure you track these actions more accurately based on individual contact information.

      You can also use your CRM or marketing automation platform to sync audiences to ad platforms.

      For instance, you may have a list of people that have opted in from an initial touchpoint with a webinar or whitepaper download.

      Use the lists that you build for audiences to target ads based on the buying stage where they are.

      Additionally, you can use lists of high-value prospects or customers to seed lookalike audiences for targeting and reaching people with similar demographic characteristics and interests.

      Meta allows for lookalike targeting across campaign types (with the exception of some sensitive industries), while Google lets you build lookalike audiences for Demand Gen campaigns.

      LinkedIn offers Predictive Audiences built from first-party data as a method to reach similar individuals.

      You can also use customer lists to exclude existing customers from campaigns or to exclude those who already have a particular product from upsell campaigns.

      Use Account List Targeting

      In addition to targeting specific individuals, you can also use account-based marketing (ABM) to reach select companies you’d like to target.

      The advantage here is that you don’t need explicit opt-ins to upload a list. A sales team may have a list compiled of “dream” target accounts, or you may have access to a list of major companies within a particular industry.

      Out of the major self-service ad platforms, LinkedIn is the primary route for uploading account lists.

      In addition, you can also work with reps to sync account lists in native advertising platforms such as Taboola or Outbrain, and for larger buys, you can look into dedicated ABM platforms.

      You can also overlay additional targeting onto the account lists to ensure you’re reaching the right decision-makers in the organization.

      For example, you can overlay an IT job function with job seniority of director and upwards to put your ads in front of people likely to make IT buying decisions.

      Making PPC Work For Your Saas Marketing

      Planning and executing a PPC strategy for a SaaS product can be a complex but rewarding process when you start to see qualified leads come through and turn into paying customers.

      Start with understanding your audience and competition, and work through setting clear measurement goals and targeting strategies.

      As you move forward with your campaigns, you can continue to test and refine based on the data you can gather, especially as you have time to analyze lifetime value associated with customers obtained via various channels.

      More Resources:

      Featured Image: Andrii Yalanskyi/Shutterstock

      Ecommerce MGMT 0 Comments
      Apr 7 2025
      Budget Allocation: When To Choose Google Ads Vs. Meta Ads

      Choosing between Google Ads and Meta Ads isn’t about which platform is better. It’s about which makes more sense for your goals, audience, and spend.

      As both platforms continue to change with smarter automation, stricter privacy rules, and rising ad costs, advertisers need a framework for deciding where their budgets go.

      Here’s how to think it through.

      Google Ads Vs. Meta Ads: The Core Difference

      Google Ads is built around user intent.

      Whether it’s [best CRM for real estate agents] or [emergency plumber near me], that intent translates into higher conversion potential because people come to Google actively looking for solutions.

      Meta Ads (Facebook and Instagram) are driven by discovery.

      You’re placing ad content in front of users who weren’t searching for your product but might be persuaded to click, browse, or buy.

      This makes Meta stronger for brand awareness, lifestyle products, and impulse-driven purchases.

      In short, Google wins when users know what they want. Meta wins when you want to influence what they want.

      When Google Ads Make More Sense

      Google Ads is the platform to choose when search volume and purchase intent are high.

      Legal services, home service providers, and B2B companies often perform better in Google because they solve specific problems people are actively researching.

      The cost-per-click (CPC) is especially high in competitive industries like home services or law, but the quality of traffic and the high payout often justifies the spend.

      CPCs in home services verticals can exceed $6.50 and legal can exceed $8.50 (most up-to-date numbers from 2024).

      It’s also a strong fit for ecommerce brands.

      Someone searching for [black corset sundress] or [best gaming laptop under $1,500] is closer to buying than someone casually swiping through Instagram.

      Google Shopping Ads and Performance Max campaigns can be great campaigns to streamline the purchase path.

      Local businesses also benefit from Google’s ecosystem, especially with Local Services Ads.

      When Meta Ads Outperform

      Meta shines when you aim to build demand, but it’s not limited to just awareness or engagement.

      For many ecommerce brands, Meta is a primary driver of direct conversions, especially when the product is aesthetically pleasing, impulse-friendly, or supported by strong creative assets.

      Campaign types like Advantage+ Shopping, paired with dynamic product ads, won’t just help your brand get noticed; they can drive sales right away, too.

      What makes Meta effective is how it is able to blend product discovery with fast action, which makes it a great tool for new product launches, lifestyle products, and visually driven goods like fashion, beauty, or home decor.

      Its creative formats, Reels, Stories, and Carousels offer brands the flexibility to tell a compelling story and convert in the same swipe.

      It’s also the better choice for lower-budget campaigns. The 2024 Facebook Ads benchmarks show a $1.88 CPC across all industries compared to Google’s $4.66.

      Meta also leans into AI to adapt to the post-iOS 14 landscape. Advantage+ Shopping Campaigns and the Conversions API help automate targeting and placements while making up for lost third-party data.

      Lead generation for B2C brands can perform well on Meta, too, especially with strong creative and clear calls to action.

      With the right mix of assets, like product demos, influencer content, and user-generated content, Meta can drive results well beyond just awareness.

      What’s Changing In 2025 And Why It Affects Budget Decisions

      Rising ad costs are forcing marketers to be more deliberate with every dollar.

      Google’s Smart Bidding and Meta’s Advantage+ now automate most of the optimization process, from placements to bidding strategies.

      However, they’re not one-size-fits-all. Without a clear structure, reliable creative inputs, and regular human oversight, automation can just as easily waste budget as performance scales.

      The dominance of short-form videos has changed ad creatives. Formats like YouTube Shorts, Instagram Reels, and Facebook Stories capture more attention and often outperform static ads in both reach and cost efficiency.

      First-party data is now essential for advanced targeting.

      Google’s Customer Match and Meta’s Conversions API offer better performance if you have the data to feed them – this is where scale matters.

      Large brands with thousands of customers can activate precise targeting strategies. For small businesses, platform-led targeting or broader lookalikes often remain the better bet.

      How To Allocate Your Budget

      Unfortunately, there’s no universal formula for dividing the budget between Google Ads and Meta Ads. What works for one brand may fail for another.

      Instead, I would suggest evaluating your budget not just by platform but by objective, funnel stage, product type, and customer behavior.

      Start With Intent

      If your target customer is actively looking for a solution, whether it’s a personal injury lawyer, an enterprise SaaS tool, or a plumber at 3 a.m. Google Ads is where you’ll see the highest return.

      Paid search captures high-intent traffic at the moment of need, and in these scenarios, it’s often smart to allocate 70% or more of your ad budget to Google.

      Meta simply isn’t built to catch bottom-of-funnel demand like that.

      Here’s a good way to frame this:

      Ask yourself, “Does my customer know they need this?” If the answer is yes, test Google first.

      If, instead, you’re thinking, “I need to tell my customer why they need this,” test Meta first.

      If you’re not sure whether your customers know they need your product or service, start with keyword research. If there’s a high search volume, Google likely deserves a larger share of your budget.

      If You’re Building A Brand, Lean Into Meta

      Meta Ads does great in categories where brand identity, lifestyle associations, and storytelling drive consideration.

      This makes Instagram and Facebook ideal for brands launching new products, entering crowded markets, or selling aesthetics-first items like skincare, fashion, or home decor.

      In these cases, it’s common to see brands allocate 70% of their budget to Meta, especially when early-stage awareness or engagement is a priority.

      Ecommerce Brands Need A Dual Strategy

      For product-driven businesses, the platform split often comes down to product price points and customer buying behavior.

      High-ticket or research-heavy products, like fitness equipment, electronics, or furniture, usually perform better on Google.

      Lower-priced, impulse-friendly products, like jewelry, apparel, or novelty gifts, often convert faster on Meta, where users aren’t actively searching but are open to discovery.

      A 50/50 split is a good starting point, but the performance data should quickly tell you whether to skew heavier toward search or social.

      Lead Generation Requires Funnel-Specific Planning

      For B2B lead gen, Google Ads is the platform where you’ll receive higher-quality leads due to the higher intent of the query.

      If your sales process is long or consultative, Google is worth the majority of your spend.

      However, Meta can be cost-effective for B2C lead gen, especially offers like product waitlists or newsletter opt-ins.

      Meta’s audience targeting and creative tools can nurture users through the early stages of the funnel.

      Budget splits here can range from 60/40 to 40/60 depending on your customer and conversion goals.

      Testing Is Your Real Allocation Strategy

      Budgeting isn’t a set-it-and-forget-it model.

      Allocate a small portion of your budget to test both platforms. Validate which creative, copy, audiences, and formats drive the best results.

      Don’t just test platforms. Test offers, price points, landing pages, and funnel sequences.

      A $250 test campaign split evenly across Google and Meta can give you more insights than a bloated campaign stuck on a single channel.

      Make Your Budget Flexible, Not Fixed

      Most successful brands don’t work with rigid splits. They start with a hypothesis, test quickly, and reallocate monthly (or weekly) based on data.

      Seasonality, promotions, creative fatigue, and even news cycles can impact which platform is more effective at any given time.

      If your entire budget is tied to one platform year-round, you’re probably leaving money on the table.

      Final Word: Don’t Pick One. Build A Hybrid Strategy

      The most effective advertisers aren’t choosing between Google and Meta. They’re building strategies that leverage both.

      Use Google Ads when you need to convert high-intent search traffic. Use Meta Ads when you’re building demand, launching a product, or nurturing an audience.

      Invest in creative work that works across platforms, especially video. Lean into automation, but keep a close eye on performance.

      Most importantly, stay flexible. What works in Q1 may fail in Q3. Let data, not assumptions, shape your budget decisions.

      More Resources:

      Featured Image: Whiskerz/Shutterstock

      Ecommerce MGMT 0 Comments
      Mar 31 2025
      Reddit Introduces Faster Ad Setup and Pixel Integration via @sejournal, @brookeosmundson

      Starting today, Reddit rolled out a series of updates aimed at making it easier for small and medium-sized businesses (SMBs) to advertise on the platform.

      The changes focus on simplifying the ad creation process, improving signal quality, and helping advertisers move campaigns from other platforms like Meta with fewer headaches.

      These updates follow Reddit’s continued push to make its Ads Manager more accessible, especially for smaller businesses that may not have the luxury of dedicated ad ops teams or outside agencies.

      Launching Campaigns Faster With New Tools

      In the Reddit Ads update, they announced two new tools to streamline campaign creation:

      • Campaign Import.
      • Simplified Campaign Quality Assurance (QA).

      The first of the additions is Campaign Import, a tool that lets advertisers bring campaigns over from Meta directly into Reddit Ads Manager.

      The process is straightforward — after connecting their Meta account, advertisers can select an existing campaign, import it, and make any necessary adjustments to suit Reddit’s environment.

      This isn’t just a time-saver; it gives brands a quick way to leverage proven creative and targeting strategies while adapting them to Reddit’s unique audiences.

      Another welcomed update is Reddit’s new Campaign Quality Assurance (QA) system. Instead of clicking back and forth between settings pages, advertisers now get a consolidated review page summarizing all key campaign details.

      If something looks off — budget, targeting, placements, or creative — users can jump directly to the relevant section and make fixes before going live.

      It may seem small, but anyone who’s fumbled through nested ad platforms under tight deadlines knows how much this improves workflow.

      Improved Quality Signals In Reddit Ads

      In addition to the streamlined campaign creation tools, Reddit also announced two features to improve the quality of audience and user behavior signals:

      • 1-click Google Tag Manager integration for Reddit Pixel.
      • Event Manager Quality Assurance (QA).

      The platform now offers a 1-click integration with Google Tag Manager (GTM) for the Reddit Pixel, dramatically reducing the friction of installing and configuring conversion tags.

      Advertisers can now fire up GTM, install the Reddit Pixel in minutes, and start sending conversion data without needing to pull in a developer. This update alone will make performance-focused advertisers breathe a little easier.

      Reddit also upgraded its Event Manager QA tools. The revamped Events Overview now gives a clearer breakdown of conversion events coming from both the Reddit Pixel and the Conversions API (CAPI).

      Advertisers can spot data discrepancies faster and ensure their lower-funnel campaigns are set up for success.

      Jim Squires, EVP of Business Marketing and Growth at Reddit, noted that SMBs have always been an essential part of the platform’s community and advertising base.

      We continue to make improvements to the Reddit Ads Manager that make it easier to launch and manage campaigns, so they can focus on what matters most: growing and running their businesses.

      Reddit Ads Continues To Push Forward

      With these latest updates, Reddit continues refining its ad platform for a broader range of advertisers, with particular attention to reducing friction for growing businesses.

      Advertisers who have been looking for more streamlined ways to import, optimize, and measure campaigns will likely find these tools helpful as they plan their next steps on Reddit.

      Have you already tried out Reddit Ads? Will these updates make you lean towards testing a new platform next quarter?

      Ecommerce MGMT 0 Comments
      Mar 30 2025
      Google Updates Unfair Advantage Policy, Advertisers React via @sejournal, @brookeosmundson

      On Friday, Google sent out a subtle but impactful policy update to advertisers, confirming changes to its long-standing “Unfair Advantage Policy”.

      While the official enforcement date is April 14, 2025, the conversation has already started — and it’s anything but quiet.

      The PPC community is buzzing with opinions, questions, and concerns. But this update didn’t come out of nowhere.

      About a month ago, Google quietly laid the groundwork for this change without most people noticing.

      Let’s unpack exactly what’s happening, why it matters, and how advertisers are reacting.

      What Did Google Change?

      The core of the update is about limiting how many ads a business, app, or site can show in a single ad location. Here’s Google’s new language:

      Google email to advertisers about Unfair Advantage policy update.

      The new language is crucial to understand.

      The focus isn’t on restricting brands from showing multiple ads across different placements—it’s about stopping advertisers from stacking multiple ads in the same slot, which would effectively block competition and inflate dominance.

      It’s not a total ban on multiple ads from the same advertiser showing on a single page, but rather a limit within a specific ad location.

      However, as with many Google Ads policies, the phrase “single ad location” is doing a lot of heavy lifting—and advertisers are left wondering how Google will interpret and enforce it in practice.

      One notable detail: Google says violations won’t lead to instant account suspensions. Advertisers will receive a warning and at least seven days to address any violations before facing suspension.

      This is important. Google seems to be trying to strike a balance between tightening policy and giving advertisers room to adapt.

      The Breadcrumb Many Missed – February Auction Documentation Update

      Interestingly, this isn’t the first time Google has hinted at this shift.

      Back in February 2025, advertisers noticed that Google updated its documentation on “How the Google Ads Auction Works”.

      The update clarified that Google runs separate auctions for each ad location, meaning that the auction for the first position is distinct from the auction for the second, third, and so on.

      Ginny Marvin, Google Ads Liaison, even acknowledged the change directly in LinkedIn discussions. This detail flew under the radar for many but now seems like a foundational piece for this official Unfair Advantage update.

      Effectively, Google was setting the table a month ago. This policy update simply formalizes how those auctions will now prevent advertisers from “double-serving” or stacking ads in the same position.

      Why Google Is Doing This, And Why Now

      Google’s goal here appears twofold:

      1. Auction Fairness — Google wants to prevent scenarios where advertisers, affiliates, or large multi-account setups game the system by occupying multiple positions within a single auction.

      2. Affiliate Abuse Control — This rule directly calls out affiliates who break affiliate program rules, a growing concern in Google’s search ecosystem.

      Of course, some advertisers suspect there’s a third goal: protecting the user experience and, more directly, protecting Google’s own long-term revenue by encouraging more advertisers to compete rather than allowing the largest players to squeeze others out.

      Advertisers Give Mixed Reactions to Google Update

      While this update was emailed to advertisers on Friday afternoon, marketers didn’t waste time sharing their takes on the update.

      Andrea Atzori, who also received the email from Google, took to LinkedIn to provide his take on the update.

      Atzori highlighted that this change is more about clarification than transformation, as he’d seen the same advertiser in multiple locations previously.

      Navah Hopkins also took to LinkedIn with a more brief update, eager to hear thoughts from fellow marketers on the Unfair Advantage policy.

      Hopkins and others noted that while the update may sound fair in theory, the proof will come in how it affects impression share, Auction Insights, and real-world campaign performance.

      From the comments on Hopkin’s post, early reactions seem to lead towards skepticism and questions:

      Chris Chambers commented:

      This is going to be wild from a metric reporting standpoint since it seems like right now it counts as 2 impressions and also affects your impression share and position in Auction Insights (same with competitors).

      But it also seems like now the advertisers with the most to spend in each niche will get even more real estate and be able to show twice, potentially cutting out smaller competitors completely from the first page.

      Steve Gerencser had a similar take to Chambers:

      I wonder how they are going to count people that pogo from one ad right back to the next and then back to something else? I can see a lot of wasted ad spend, or an opportunity for someone with deep pockets to dominate.

      Some worry that well-funded advertisers will still find ways to dominate, while smaller brands hope this levels the playing field.

      What Advertisers Should Watch For

      While the policy may not seem earth-shattering at first glance, it does come with a few things advertisers should actively monitor.

      First, smaller and mid-sized advertisers may stand to benefit, at least in theory. By limiting how many ads a single business can show in one location, Google could slightly reduce the dominance of big-budget brands that have historically owned the top of the page through multiple placements.

      This could open up space for other players to get visibility where previously they were pushed out.

      But, as several PPC pros pointed out on LinkedIn, the big question is how Google defines and enforces a single ad location in practice.

      Google clarified last month that each ad location runs its own auction, meaning it’s technically possible for a brand to show up in multiple places on the same page—just not in the exact same slot.

      So, while the policy aims to limit dominance, it doesn’t necessarily mean fewer total appearances for advertisers with deep pockets.

      This also has potential ripple effects on Auction Insights reports. If Google starts filtering or limiting how often multiple ads from the same business appear in a given location, expect impression share metrics and overlap rates to behave differently—maybe even unexpectedly.

      Advertisers will need to watch Auction Insights and Impression Share trends closely post-April to see if any patterns emerge.

      Additionally, affiliate marketers and businesses using aggressive multi-account or multi-site strategies should be especially careful. The updated policy makes it clear that affiliates must play by their program’s rules and can no longer try to sneak multiple ads for the same offer into the same auction.

      While Google says you’ll get a warning before any suspension, it’s probably wise to get ahead of this now, rather than risk a compliance issue later.

      And finally, there’s still some ambiguity about multi-brand or franchise setups. If you’re managing a brand with multiple sub-brands, sister companies, or franchisees, the question remains: will Google treat you as one business under this policy or multiple?

      That detail could make a big difference, especially for large organizations or verticals like automotive, real estate, or hospitality.

      Final Thoughts: Is This Really a Game-Changer?

      Honestly? It’s hard to call this a monumental shift yet. The update feels more like a formalization of existing enforcement patterns than a radical new rulebook.

      That said, the PPC community is right to question what this will look like in Auction Insights and daily performance reports. Whether this is a minor tweak or the start of stricter anti-duplication policing will become clearer as advertisers see real-world data throughout Q2 and beyond.

      Either way, it’s worth watching—especially if you’ve ever benefitted from, or competed against, someone taking up too much SERP real estate.

      Ecommerce MGMT 0 Comments