Apr 28 2025
Navigating The Complexities Of International PPC Working With Agencies via @sejournal, @brookeosmundson

Running PPC campaigns in one country is challenging enough. Add multiple countries, languages, regulatory quirks, and agency partners into the mix, and things get complicated fast.

If you’re overseeing paid media at large enterprises or multi-location brands, international PPC isn’t just a scale problem. It’s a coordination and consistency problem.

You’re not just launching more campaigns; you’re managing different market expectations, aligning with regional teams, and juggling multiple agencies – each with their own style, processes, and priorities.

So, how do you keep your campaigns on track across borders, without losing your mind (or your brand consistency)?

Let’s break it down.

The Realities Of International PPC Management

In a perfect world, every agency partner would follow your brand guidelines to a ‘T’, campaign messaging would be flawlessly localized, and all markets would operate under the same strategy.

The reality? Not so much.

Some of the most common challenges marketing managers face:

  • Lack of consistency: Creative assets, bidding strategies, or keyword targeting often vary widely between markets. This leads to a disjointed user experience and diluted brand impact.
  • Overlapping or conflicting efforts: Without clear global oversight, multiple agencies may compete in the same auctions or target the same audiences, driving up costs unnecessarily.
  • Limited visibility: Reporting formats differ. Some agencies use custom dashboards; others send PDFs. Comparing performance becomes a spreadsheet nightmare.
  • Varying levels of expertise: Not all agencies are created equal. Some have deep experience in a particular market; others learn as they go.
  • Regulatory hurdles: Different countries have different rules around data collection, targeting, and ad content – and it’s easy to miss a compliance detail if you’re not on top of local policies.

The takeaway? International PPC isn’t just about more campaigns. It’s about more moving parts.

Aligning Global Strategy With Local Execution

It’s tempting to create a single strategy and roll it out globally, but that rarely works.

What resonates in the U.S. may fall flat in Germany or South Korea. Your job as a marketing manager is to set the strategic foundation while giving local teams enough flexibility to adapt.

Here’s how to strike that balance:

  • Create a global playbook: Define your core objectives, brand voice, performance metrics, and non-negotiables. Make it clear which elements must be consistent across markets (e.g., logo usage, value propositions) and which can be localized (e.g., promotions, tone, CTAs).
  • Set up centralized tracking and reporting: Use tools like Looker Studio, Funnel, or Tableau to consolidate data from different platforms and agencies. A unified reporting view helps you spot inconsistencies and optimize faster.
  • Define roles and responsibilities: Who owns budget allocation? Who reviews creative? Who has the final say on the copy? Spell this out. Confusion around ownership slows campaigns down.
  • Use regular syncs to stay aligned: Host monthly or bi-weekly meetings with all agency partners. Even if the agendas are light, the face time builds accountability.

For example, say you’re a global hotel chain that operates in multiple continents. A great place to start is to create a shared creative playbook, but allowing each region to tailor their offers:

  • Ski packages in Switzerland.
  • Beach getaways in Spain.

A shared creative playbook helps keep brand visuals consistent while making local campaigns relevant.

Bottom line: Your global strategy is the blueprint, but you still need local architects to tailor the build.

Choosing And Managing Agency Partners

If you’re working with multiple agencies across regions, things can quickly get siloed.

One agency might crush it in Canada while another underperforms in France. Your role is to manage these relationships without getting stuck in the weeds.

Some tips to keep things streamlined:

  • Standardize onboarding: Whether you’re hiring a new agency in Mexico or expanding a partner’s remit into the UK, start with a structured onboarding checklist: tech stack access, brand guidelines, reporting templates, key contacts, etc.
  • Evaluate based on shared key performance indicators (KPIs): Hold every agency accountable to the same high-level metrics (e.g., return on ad spend, cost per acquisition, conversion volume), even if market-specific tactics differ. This makes it easier to identify outliers.
  • Encourage cross-agency collaboration: Set up a shared Slack channel or quarterly town halls where agency teams can exchange learnings. One partner’s success story could inspire a breakthrough elsewhere.
  • Avoid micromanagement, but stay involved: Agencies need room to operate, but that doesn’t mean you go completely hands-off. Review ad copy regularly. Ask questions about performance drivers. Push for experimentation.
  • Consider a lead regional agency model: Some brands appoint one agency as the lead for a particular continent or cluster. This partner acts as a point of coordination, helping to roll out strategies more efficiently.

Say you’re running a consumer electronics brand’s PPC efforts, and the company is looking to expand into Europe, the Middle East, and Africa. It may be easy to give all that work in-house, but that can essentially double your workload, which can make your existing campaigns’ performance drop since your focus has shifted.

Instead, consider hiring an agency for the EMEA region, where your role may be overseeing their operations across Europe.

This frees up your time to still focus on the core markets, but is still visible in the expansion region to understand what’s working and what’s not.

This leads to reduced duplicated efforts, standardized reporting, and improved speed-to-market.

Your agencies aren’t just vendors; they’re extensions of your team. Treat them like it.

Dealing With Localization Without Losing Brand Consistency

One of the biggest risks in international PPC is watering down your brand, or creating an inconsistent brand. When you allow each market to fully customize messaging, things can veer off course quickly.

However, localization doesn’t mean reinventing your brand. It means adapting the core message to fit cultural norms, search behavior, and language nuances.

Here are a few ways to do that well:

  • Provide flexible brand guidelines: Instead of a rigid rulebook, create a toolkit. Include brand values, tone of voice examples, and dos/don’ts – but leave space for creativity.
  • Use transcreation, not translation: Translating ads word-for-word often leads to awkward or irrelevant messaging. Invest in native-language copywriters who understand local search intent.
  • Vet creative with local experts: Even if your agencies are global, ensure that someone close to the market signs off on copy and visuals. One poorly placed idiom or image can derail an entire campaign.
  • Test and learn by market: What works in France might not work in Spain. Build in budget and time to A/B test creative and offers in each country before scaling.

For example, say you’re running back-to-school ads for an apparel brand across the United States and Japan. You think that everyone has a back-to-school need, right?

You’d be correct, but it’d be incorrect to run them at the same time due to Japan’s school year starting in the spring, whereas the United States typically starts in the fall.

Adjusting campaign timing based on regions can help lead to an uplift in engagement.

Consistency doesn’t mean sameness. It means every ad should feel like your brand, even if it says something slightly different.

Navigating Regulatory And Platform Differences

The compliance side of international PPC often gets overlooked – until it’s a problem. From GDPR in Europe to ad content rules in China, regulatory pitfalls can stall or even shut down campaigns.

Keep these guardrails in place:

  • Work with legal early: Involve your legal or compliance teams in the planning process. Get clarity on what’s allowed in each region before campaigns launch.
  • Stay up to date with platform policies: Google Ads, Meta, and Microsoft all have country-specific ad restrictions. Review them regularly. What flies in the U.S. might get disapproved in Germany.
  • Use regional ad accounts: If you’re running large-scale campaigns, separate ad accounts by region. This makes it easier to manage billing, user access, and compliance settings.
  • Document your approach: Create a shared doc outlining how your team handles regulatory compliance, consent tracking, and ad policy enforcement. It helps new team members and agencies get up to speed quickly.

When in doubt, err on the side of caution. It’s better to delay a campaign than clean up a PR or legal mess later.

When To Consolidate Vs. Decentralize

One of the biggest international strategic decisions you’ll face: Should you centralize all campaigns under one global agency, or let each region work with its own partner?

There’s no perfect answer, but here’s a framework to help you decide:

  • Consolidate if:
    • You need unified reporting and brand control.
    • You operate in fewer countries with similar languages or cultures.
    • Your internal team is small and needs a streamlined workflow.
  • Decentralize if:
    • You’re in highly diverse markets with unique buying behaviors.
    • Local teams have strong relationships with trusted regional agencies.
    • You want to test different approaches and compare outcomes.

Some brands use a hybrid approach – central strategy with local execution. The key is to revisit your setup as you grow. What worked at five markets may not work at 15.

What International PPC Success Looks Like

International PPC management isn’t about perfection. It’s about progress, alignment, and adaptability.

Success doesn’t always mean a flawless launch. It might mean catching a costly bidding overlap between two regions. Or, spotting a creative insight from Japan that you can scale to the UK.

At the end of the day, your job as a marketing manager is to keep the wheels turning, the messaging on-brand, and the teams aligned.

Global growth isn’t clean or linear, but with the right agency relationships, guardrails, and communication processes in place, it is manageable – and scalable.

Just don’t expect to do it alone.

More Resources:

Featured Image: Ground Picture/Shutterstock

Ecommerce MGMT 0 Comments
Apr 25 2025
The 8 Most Important PPC KPIs You Should Be Tracking via @sejournal, @brookeosmundson

If you’re still measuring your PPC success based on click-through rate and impressions alone, you’re about to be left behind.

The role of paid media has changed – and not just because Google Ads released another round of automation.

It’s changing because people have changed. We live in a multi-device, privacy-first, AI-influenced world where attention spans are shorter, conversion paths are messier, and attribution is murkier than ever.

And yet, many advertisers still optimize like it’s 2015 – staring at dashboards full of click-through-rate, cost-per-click, and average positions like they’re the final word.

Here’s the uncomfortable truth: PPC has never been just about getting someone to click. It’s about driving real, measurable business outcomes – profitable, incremental, sustainable outcomes – even when the platforms don’t make it easy.

This article isn’t another “PPC KPI listicle” telling you to improve your CTR or lower your CPC. We’re going deeper.

The KPIs below are the ones that actually move the needle today, the ones you need in your toolbox if you want to keep your budget, secure executive buy-in, and prove paid media’s value without hiding behind vanity metrics.

1. Profit (Not Just ROAS)

Return on ad spend (ROAS) has long been the default north star in PPC reporting, but frankly, it’s overdue for a demotion.

On its own, ROAS offers a dangerously incomplete picture. It tells you how much revenue was generated for every dollar spent – but revenue isn’t profit.

A campaign might boast a stellar 600% ROAS, but if fulfillment costs, discounts, or shipping fees gobble up 70% of that revenue, what are you really left with?

On the other hand, a modest-looking 300% ROAS campaign could quietly be generating double the profit if it’s driving high-margin sales.

Today’s best-in-class PPC teams know this and build profit measurement directly into their strategy.

They’re calculating contribution margins at the product level and adjusting revenue numbers accordingly before feeding that data back into Google Ads or Microsoft Ads.

This lets algorithms optimize toward profit – not just revenue – giving teams a competitive edge over advertisers still stuck reporting on inflated ROAS figures.

When you can walk into a CMO’s office and confidently show not just “here’s what we sold,” but “here’s what we made,” you earn a different kind of respect.

2. Incrementality (The “Would You Have Gotten This Anyway?” Metric)

This is the key performance indicator (KPI) that separates marketers who report from those who understand.

Incrementality forces you to ask: Did this sale happen because of PPC, or would it have happened anyway?

In the old days, you might have taken every conversion at face value, especially if it showed up as the last click.

Today, with attribution becoming less precise and users bouncing between channels, platforms, and devices, you can’t afford to make that assumption.

Incrementality gets to the heart of what you’re actually contributing to the business. It’s about quantifying the lift your campaigns generate beyond what would have happened without paid media.

Whether through holdout tests, geo-based experiments, or platform-led lift studies, advertisers investing in incrementality measurement consistently find out that some campaigns – often brand and remarketing – are less impactful than they seem.

Sure, measuring incrementality is messy. It doesn’t fit neatly into Google’s default reporting.

However, CMOs don’t want to see PPC taking credit for revenue that would’ve closed regardless. They want to know what’s working because of paid media, not just what’s being tagged by it.

Advertisers who commit to measuring incrementality make better budgeting decisions and protect themselves from over-investing in campaigns that are just skimming the top.

3. Customer Lifetime Value (CLV Or LTV)

There’s no excuse for ignoring Lifetime Value (LTV) today.

Rising acquisition costs and shorter attribution windows have made short-term metrics like first-purchase cost-per-acquisition (CPA) less useful. The most valuable PPC programs today optimize for the long game.

Customer Lifetime Value is about understanding the total value a customer brings to the business, not just their first purchase.

For SaaS, subscription commerce, and many DTC businesses, the initial conversion is merely the opening act. If you’re optimizing toward cheap CPAs but acquiring low-value, one-and-done customers, you’re actively hurting long-term profitability.

Advanced teams are feeding LTV data directly into Google Ads through offline conversion imports, enabling smart bidding strategies to optimize for customers likely to return and spend again.

Others are building LTV models internally and using them to guide targeting, creative, and bidding strategies manually.

This shift is more than tactical – it’s strategic. Businesses optimizing for LTV don’t just get more customers; they get better customers. Customers who stay, spend more, and fuel real growth.

4. Cost Per Incremental Acquisition (CPIA)

While CPA still has its place, the real game is CPIA – Cost Per Incremental Acquisition.

CPIA zooms out and asks: What did it cost to acquire net-new, incremental customers – the ones who wouldn’t have converted without this campaign?

This is a much harder question than simply “What did we pay per conversion?”, but it’s the one that matters.

Many PPC accounts are bloated with campaigns that deliver conversions but offer little in the way of incremental lift.

Branded search, retargeting, and display remarketing can often cannibalize organic or direct traffic.

By layering incrementality testing into your cost analysis, you gain a KPI that tells you not just what you paid for a lead or sale, but what you paid for an actual new customer.

It’s where the conversation shifts from “Are we hitting target CPA?” to “Are we paying reasonable amounts for meaningful growth?”

CPIA is where the best PPC teams earn their seat at the strategy table.

5. Conversion Rate (Context Is Everything)

Conversion rate is still important, but not in the way most PPC reports treat it.

Too many teams obsess over maximizing conversion rates without stopping to ask: Conversion rate for whom? Under what circumstances?

A cold prospect clicking a YouTube ad will never convert at the same rate as someone clicking a branded search ad.

And yet, conversion rates are often presented in flat averages that tell you very little about what’s really happening.

The best PPC practitioners contextualize conversion rates:

  • By audience type (new vs. returning).
  • By funnel stage.
  • By device, geography, or time of day.

If your conversion rate drops because you’ve launched an upper-funnel prospecting campaign, it may actually be a sign that you’re reaching new audiences who haven’t been exposed to your brand before, which is a good thing.

Contextualizing conversion rates lets you tell the real story behind your data and prevents knee-jerk optimizations that hurt long-term growth.

6. Lead Quality (For Lead Gen Campaigns)

Lead generation marketers have been plagued for years by one mistake: optimizing for volume, not quality.

It’s easy to pat yourself on the back for delivering leads under the target Cost-Per-Lead (CPL). It’s harder to admit that half of those leads will never close – or worse, never even speak to sales.

True PPC leaders know that leads are just the starting point. What matters is how many of those leads become qualified opportunities and eventually customers.

This means integrating customer relationship management (CRM) data into your PPC strategy and measuring down-funnel impact.

Savvy advertisers have ditched CPL as the sole north star and now track:

  • Marketing qualified lead (MQL) to sales qualified lead (SQL) conversion rates.
  • Pipeline contribution.
  • Closed-won revenue sourced from PPC.

By feeding this data back into ad platforms, either through offline conversion imports or CRM integrations, PPC teams can train algorithms to find leads that not only fill out forms but actually generate revenue.

7. Time To Conversion

This KPI is criminally underutilized. In an age of increasingly complex buying journeys, knowing how long it takes a user to convert after clicking an ad is vital.

For many B2B or considered-purchase brands, conversions don’t happen within Google Ads’ default 7-day or 30-day attribution windows.

Some leads need 45, 60, even 90+ days to convert. Ignoring this means underreporting performance and undervaluing campaigns.

Understanding time to conversion helps you:

  • Build realistic retargeting windows.
  • Set proper expectations with stakeholders.
  • Avoid shutting down high-performing campaigns too soon.

Especially with cookie windows shrinking and attribution getting tougher, knowing your actual conversion lag helps you defend your budget with confidence.

8. Contribution To Pipeline Or Revenue

At the end of the day, this is the KPI that makes or breaks your PPC program. If you can’t tie your campaigns to pipeline or revenue, you’re just spending money and hoping it works.

The best PPC leaders don’t show CTRs and CPCs to the C-Suite. They show:

  • How much qualified pipeline PPC is generated.
  • What portion of closed revenue can be attributed to paid media.

Whether through CRM integration, manual reconciliation, or marketing automation platforms, you need to bridge the gap between ad clicks and actual business outcomes.

PPC lives and dies by its ability to drive revenue. Every other metric in this article ultimately feeds into this one.

Bonus: Campaign Health Metrics (CTR, CPC, CPM, And Friends)

Before we throw CTR, CPC, and Cost-Per-Mille (CPM) into the vanity metric graveyard, let’s be clear: These metrics still matter, just not the way most people think. They are health metrics, not performance KPIs.

A strong CTR could signal relevant ad copy and healthy engagement. A reasonable CPC might indicate competitive efficiency. CPM can help diagnose shifts in inventory or competition.

However, these numbers are inputs, not outcomes. They provide valuable diagnostics that help you fine-tune campaigns, but they don’t answer the big question: Are you driving profitable, incremental, revenue-generating outcomes?

Good PPC teams know how to use these health metrics to identify friction points or optimization opportunities. Great teams know not to use them as the headline in the quarterly business review (QBR).

Making The Shift: Moving Towards Modern PPC KPIs

So, where do you start if you’re stuck in legacy metrics and looking to level up?

First, realign your strategy. Understand that PPC is no longer just about clicks or even direct conversions. It’s about business growth.

Next, start asking better questions inside your organization or with your clients:

  • What is the average customer’s lifetime value?
  • What is the profit margin by product or service?
  • How does a new lead flow through the sales process?
  • What percentage of current conversions are truly incremental?

For agencies, this can be tricky. Clients might hesitate to share deeper business data, especially if past agencies didn’t ask for it.

However, framing it as necessary for more effective optimization – not just reporting – can help bridge the gap.

Don’t expect to overhaul everything overnight. Start with one or two KPIs, like profit and lead quality, and build from there. The goal isn’t to make reporting harder – it’s to make it matter.

Why This Shift Is Non-Negotiable

The PPC landscape is changing whether we like it or not.

Between privacy regulations, AI-fueled consumer behavior shifts, and increasingly automated ad platforms, surface-level metrics are becoming less trustworthy and less relevant.

Smart marketers are adapting by elevating the KPIs they report on. The teams that master profit, incrementality, LTV, and pipeline contribution will earn bigger budgets, stronger buy-in, and ultimately, better business outcomes.

PPC isn’t just about driving traffic anymore. It’s about driving the business.

More Resources:

Featured Image: Nichcha/Shutterstock

Ecommerce MGMT 0 Comments
Apr 22 2025
Google Ads 2024 Safety Report Unveils AI Protections via @sejournal, @brookeosmundson

Google has released its 2024 Ads Safety Report, and the message is clear: accountability is scaling fast thanks to AI.

With billions of ads removed and millions of accounts suspended, the report paints a picture of an advertising ecosystem under tighter scrutiny than ever.

For marketers, especially those managing significant media budgets, these shifts aren’t just background noise.

They directly impact strategy, spend efficiency, and brand safety. Here’s a closer look at the biggest takeaways and how marketers should respond.

A Record-Setting Year in Ad Removals and Account Suspensions

Google removed 5.1 billion ads in 2024, up slightly from the previous year.

The real eye-opener was the surge in account suspensions. Over 39 million advertiser accounts were shut down, more than triple the number from 2023.

That figure tells us two things:

  • Enforcement is no longer just about the ads themselves.
  • Google is focusing upstream, stopping abuse at the account level before it can scale.

In addition to individual ad removals, 9.1 billion ads were restricted (meaning they were limited in where and how they could serve). Google also took action on over 1.3 billion publisher pages and issued site-level enforcements across 220,000 sites in the ad network.

Whether you’re running Search, Display, or YouTube campaigns, this scale of enforcement can influence delivery, reach, and trust signals in subtle ways.

AI is Doing the Heavy Lifting

The scale of these removals wouldn’t be possible without automation. In 2024, Google leaned heavily on AI, introducing over 50 improvements to its large language models (LLMs) for ad safety.

One notable example: Google is now using AI to detect patterns in illegitimate payment information during account setup. This enables enforcement to occur before an ad even goes live.

And as concerns around deepfakes and impersonation scams continue to grow, Google formed a specialized team to target AI-generated fraud. They focused on content that mimicked public figures, brands, and voices.

The result? Over 700,000 advertiser accounts were permanently disabled under updated misrepresentation rules, and reports of impersonation scams dropped by 90%.

AI isn’t just a marketing tool anymore. It’s a core part of how ad platforms decide what gets to run.

A Shift in Ad Policy That Marketer’s Shouldn’t Overlook

One of the more under-the-radar updates was a policy change made in April 2025 to Google’s long-standing Unfair Advantage rules.

Previously, the policy limited a single advertiser from having more than one ad appear in a given results page auction. But the update now allows the same brand to serve multiple ads on the same search page, as long as they appear in different placements.

This creates both opportunity and risk. Larger brands with multiple Google Ads accounts or aggressive agency strategies can now gain more real estate.

For smaller brands or advertisers with limited budgets, this may lead to increased competition for top spots and inflated CPCs.

Even though this change is meant to address transparency and competition, it could cause performance swings in high-intent keyword auctions.

It’s the kind of change that may not be immediately obvious in your dashboard but can quietly reshape performance over time.

What Advertisers Should Keep in Mind Moving Forward

Staying compliant isn’t just about avoiding policy violations.

It’s now about being proactive with AI and understanding how enforcement impacts delivery.

Here are a few ways to stay ahead:

1. Know your ad strength tools, but don’t rely on them blindly

AI is behind many of Google’s enforcement and performance scoring systems, including Ad Strength and Asset Diagnostics. These are helpful tools, but they’re not guarantees of policy compliance.

Always cross-check new ad formats or copy variants against the most recent policy updates.

2. Double-check account structures if you’re running multiple brands or regions.

With the rise in multi-account suspensions, it’s more important than ever to document relationships between brands, resellers, and advertisers.

Google’s systems are increasingly adept at pattern recognition, and even unintentional overlap could flag your account.

3. Be careful with impersonation-style creative or influencer tie-ins

If you’re featuring people in ads (especially public figures), ensure that the usage rights are clear.

AI-generated content that resembles celebrities or influencers, even if satirical, could trip enforcement filters.

When in doubt, opt for original or clearly branded creative.

4. Review how recent policy changes could affect your real estate in search results

Marketers should test how often their brand appears on a single search page now that the Unfair Advantage update allows more flexibility.

Use tools like Ad Preview and multi-account diagnostics to understand if your visibility is shifting.

Wrapping It Up

Google’s latest Ads Safety Report is a reminder that digital advertising is becoming more regulated, more automated, and more tied to platform-defined trust.

Google’s tolerance for risk is dropping fast. And enforcement isn’t just about bad actors anymore. It’s about building an ecosystem where consumers trust what they see.

Marketers who pay attention to these shifts, stay flexible, and put transparency front and center will be in a stronger position. Those who assume “business as usual” are more at risk to be caught off guard.

Don’t wait for a suspension notice to rethink your ads strategy.

Have you noticed any account changes as a result of Google’s ad safety updates?

Ecommerce MGMT 0 Comments
Apr 21 2025
SEO Vs. PPC: What’s The Best Strategy For Your Business? via @sejournal, @brookeosmundson

Both SEO and PPC are essential components of digital marketing, yet they operate in entirely different ways.

One delivers instant visibility and quick results, while the other builds long-term authority and organic traffic.

But, when budgets are tight, and results need to be justified to the board, which channel deserves more attention?

The answer isn’t one-size-fits-all. It depends on factors like business goals, industry competition, timeline, and available resources.

In this article, we’ll break down the key differences, advantages, and trade-offs between SEO and PPC, helping you make an informed decision about where to allocate your marketing dollars.

SEO: The Long Game For Sustainable Growth

SEO is the process of increasing organic visibility in search engines through high-quality content, technical optimization, and authoritative backlinks.

Unlike PPC, SEO doesn’t provide instant gratification, but the payoff is well worth the effort.

Once a page ranks well, it can drive continuous, high-intent traffic at little to no cost per click.

Key Benefits Of SEO

SEO is often seen as the foundation of a long-term digital marketing strategy.

While it requires patience and investment upfront, the ability to generate consistent, high-quality traffic without paying for each click makes it a compelling choice for many businesses.

  • Long-Term Traffic Without Per-Click Costs. While SEO requires upfront investment in content and optimization, its long-term benefits far outweigh PPC in terms of cost-efficiency. Once a page ranks, it can drive organic traffic for years without requiring a constant budget.
  • Higher Trust And Credibility. Consumers tend to trust organic search results more than ads. Studies show that organic listings receive significantly higher click-through rates (CTR) than paid ads, making SEO a valuable channel for establishing brand credibility.
  • Compounds Over Time. Unlike PPC, where you pay for every visitor, SEO works like a snowball effect. The more high-quality content and backlinks you accumulate, the stronger your site’s domain authority becomes, making it easier to rank for new keywords in the future.

Disadvantages Of SEO

Although SEO can be incredibly rewarding for any business, it’s not without its challenges.

Businesses need to understand the trade-offs that come with relying on organic search, particularly when it comes to the time and resources required to see meaningful results.

  • Results Take Time. SEO is not an overnight success story. Depending on your industry and competition, it can take months (or even years) to rank competitively. This makes SEO a long-term play rather than a quick win.
  • Algorithm Uncertainty. Google frequently updates its ranking algorithms, meaning that even well-ranked pages can see fluctuations. If your SEO strategy isn’t built on a strong foundation of best practices, you could be at risk of losing visibility overnight.

When SEO Makes The Most Sense

SEO is best suited for businesses looking to:

  • Establish long-term brand authority and recognition.
  • Generate consistent, cost-effective leads or sales over time.
  • Compete in industries where paid advertising costs are prohibitive.
  • Build a sustainable content marketing strategy that drives traffic and engagement.

PPC: The Power Of Instant Results

PPC advertising offers immediate visibility on search engines and social platforms. It’s the equivalent of flipping a switch – your brand appears in front of potential customers right away.

This visibility comes at a price, literally. Once you stop spending, the traffic stops. However, when executed correctly, PPC can drive high-quality leads and sales faster than any other marketing channel.

Key Benefits Of PPC

PPC advertising has several compelling advantages that make it a powerful tool for businesses looking to gain immediate traction.

While it requires an ongoing budget, the ability to reach high-intent users and measure performance in real-time makes it an essential component of a well-rounded marketing strategy.

  • Immediate Traffic And Quick Wins. With PPC, there’s no waiting game. Unlike SEO, where ranking takes time, PPC can get your business to the top of search results instantly. Whether it’s Google Ads, Microsoft Ads, or paid social campaigns, your ads are live essentially the moment your campaign is approved.
  • Granular Targeting. PPC allows you to target potential customers with laser precision. You can define your audience based on search intent, location, device, demographics, behavior, and even specific interests. This ensures that your budget is spent reaching only the most relevant users, which increases efficiency and conversions.
  • Measurable And Scalable. Every click, impression, and conversion is trackable in PPC, making it one of the most measurable digital marketing strategies. You can quickly assess performance, make data-driven decisions, and scale up or down depending on return on investment (ROI). This level of control is unmatched in SEO.

Disadvantages Of PPC

Despite its advantages, PPC isn’t a perfect solution.

Businesses need to be aware of the potential challenges that come with running paid campaigns, particularly when it comes to costs, competition, and ad performance over time.

  • Costs Can Escalate Quickly. Unlike organic traffic, PPC is a pay-to-play model. The moment you stop funding campaigns, the traffic disappears. If your cost-per-click (CPC) is high, profitability can be challenging without a well-optimized campaign and conversion funnel.
  • Ad Fatigue And Diminishing Returns. Users can become blind to repetitive ads, leading to declining performance over time. This means ongoing creative refreshes, audience testing, and bid adjustments are necessary to maintain strong results.

When PPC Makes The Most Sense

PPC is ideal when you need immediate results, such as:

  • Launching a new product or service that needs instant visibility.
  • Running seasonal promotions or limited-time offers.
  • Competing in a saturated market where organic ranking is difficult.
  • Driving leads or sales in industries with high transaction values.

SEO Vs. PPC: Side-By-Side Comparison

SEO vs PPC Comparison Chart

Choosing The Right Strategy For Your Business

The best marketing strategies align with your business goals, industry dynamics, and available resources.

While some businesses can afford to take a long-term approach with SEO, others may need the immediacy of PPC.

The key is to evaluate your needs carefully and choose the right mix of paid and organic efforts.

If You Need Instant Wins: Focus On PPC

If your business needs immediate traffic, leads, or sales, PPC is the way to go. This is especially true for:

  • Startups and new businesses: When brand awareness is low, PPC can help put your company in front of potential customers quickly.
  • High-margin industries: Businesses that generate high profits per conversion (e.g., legal services, SaaS, finance) can justify PPC spend more easily.
  • Seasonal promotions: If your business thrives on specific times of the year (e.g., holiday sales, back-to-school shopping), PPC ensures you capture demand at the right moment.
  • Local businesses: Companies with a local presence can use PPC to dominate searches for high-intent queries like “best plumber near me.”

If You Want Long-Term Growth: SEO Is The Way To Go

If you’re focused on building a sustainable marketing funnel that pays dividends in the future, SEO is the smarter play.

Prioritize SEO if:

  • Your business relies on organic search traffic: Industries like blogging, ecommerce, and B2B SaaS benefit from strong SEO foundations.
  • You’re in a highly competitive PPC market: If CPCs are prohibitively expensive, investing in organic search can provide a more cost-effective alternative.
  • You’re willing to invest in content marketing: High-quality, evergreen content fuels SEO and positions your business as an authority in your space.
  • Your audience conducts research before purchasing: If customers compare multiple options before making a decision, strong SEO can help you stay top-of-mind.

Invest In SEO And PPC For The Best Of Both Worlds

For most businesses, the real answer isn’t SEO or PPC – it’s SEO and PPC. A blended approach allows you to capture immediate opportunities while building long-term organic growth.

Businesses that view these two strategies as complementary, rather than competing, often see the best results.

Here’s why integrating both SEO and PPC is a smart move:

SEO Supports Long-Term Sustainability, While PPC Fills Gaps

Even with the best SEO strategy, organic rankings fluctuate due to algorithm updates and competition.

PPC acts as a safety net, ensuring your brand remains visible even when organic rankings dip. It also allows you to target high-intent keywords that may be too competitive to rank organically in the short term.

PPC Provides Data To Strengthen SEO Efforts

One of the most effective ways to refine your SEO strategy is by using PPC data.

Paid search campaigns provide insights into which keywords convert best, which messaging resonates, and which audience segments drive the most revenue.

This data can be leveraged to optimize SEO efforts, helping to prioritize content creation and organic keyword targeting.

SEO Reduces Long-Term Costs, While PPC Provides Immediate ROI

A well-executed SEO strategy reduces reliance on paid ads over time. Once your site ranks well for high-value keywords, you receive continuous traffic without ongoing ad spend.

PPC, on the other hand, delivers instant results, making it ideal for new product launches, promotions, or when entering new markets.

A combined strategy ensures you’re not putting all your eggs in one basket.

Using PPC To Boost SEO Content

Even the best content needs exposure to gain traction. PPC can be used to drive initial traffic to newly published blog posts, product pages, or other high-value content.

The added engagement signals from paid visitors, such as time on page, shares, and backlinks, can indirectly improve organic rankings.

Retargeting SEO Visitors With PPC

Not all organic visitors convert on their first visit. Using PPC remarketing campaigns, you can re-engage visitors who found you through SEO but didn’t take action.

This keeps your brand top-of-mind and helps improve overall conversion rates.

By investing in both SEO and PPC, you build a balanced marketing strategy that delivers short-term wins while positioning your business for long-term success.

Rather than choosing one over the other, leveraging their combined strengths leads to more sustainable and profitable growth.

Final Thoughts

SEO and PPC each have distinct advantages, and the right choice depends on your business objectives. If you need fast results, PPC is the clear winner. If you’re playing the long game, SEO is invaluable.

But in reality, the most effective digital marketing strategies don’t rely on just one – they integrate both.

The best approach? Evaluate your budget, resources, and competitive landscape. Align your strategy with short-term and long-term goals.

And if you have the ability, combine SEO and PPC for a well-rounded marketing engine that delivers both immediate and sustained results.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments
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.

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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.

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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

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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

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