Personalize your marketing without compromising privacy by Edna Chavira

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As privacy regulations evolve and consumer expectations shift, marketers face a growing challenge: delivering personalized experiences while respecting data privacy. How can you navigate this changing landscape without sacrificing engagement?

Join MarTech.org’s upcoming webinar, Balancing Personalization and Privacy, to explore best practices for responsibly collecting and managing first-party data, building trust with privacy-conscious consumers, and simplifying data integration across large organizations.

Our expert speaker will also address key industry challenges, from handling highly regulated sectors to adapting to opt-out technologies like Apple’s Do Not Track, and discuss the emerging role of generative AI in consent-driven advertising.

Future-proof your data strategy and balance personalization with privacy. Sign up today!

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X launches AI-powered tools that create ads, analyze campaigns

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X launched two new features to help advertisers automate ad creation and analyze real-time ad campaign performance. The new features – Prefill with Grok and Analyze Campaign with Grok – are (as the names imply) powered by Grok, X’s AI assistant.

Prefill with Grok. Enter your website URL and Grok will generate ad copy, imagery, and a call-to-action headline. You can tweak as needed. Here’s what it looks like:

Analyze Campaign with Grok. Grok will analyze campaign data and offer insights and recommendations to optimize targeting and creative strategy.

What’s next. The rollout began Feb. 21. It will continue in phases, expanding to more advertisers.

Why we care. This move aims to streamline the ad creation process and make data-driven optimizations faster, cutting down on manual effort and potentially boosting campaign performance.

The announcement. Grok for Advertisers: Introducing New AI Tools for Brands.

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MTA vs. MMM: Which marketing attribution model is right for you?

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MTA vs. MMM- Which marketing attribution model is right for you?

Measuring marketing effectiveness is essential for any business investing in multiple channels. 

Two popular approaches – multi-touch attribution and marketing mix modeling – help marketers understand which strategies drive results. 

This article tackles the key differences between each attribution method to help you determine which one best fits your business needs.

The growing need for smarter marketing attribution

With Google’s recent update to its open-source marketing mix model, Meridian, interest in marketing mix analysis and channel modeling has surged. 

While enterprise brands have long benefited from these insights, smaller businesses running multi-channel marketing can also gain value. 

Two leading methodologies have emerged to tackle this challenge: 

  • Multi-touch attribution (MTA).
  • Marketing mix modeling (MMM). 

Both aim to measure marketing effectiveness but differ significantly in methodology, scope, and application.

Every business investing in marketing needs to assess whether its efforts are paying off. 

SEO, email campaigns, search ads, and social media all demand time and budget. 

But without the right measurement approach, it’s difficult to know which channels truly drive results.

Many marketers rely on in-platform data, but this only provides a partial view due to differing attribution models and settings. 

Third-party attribution tools attempt to bridge the gap, but they often favor specific marketing channels and impose predefined attribution rules, which may not align with long-term business goals.

For businesses serious about optimizing their marketing, a customized approach is essential – one that fully leverages their own data while integrating additional insights. 

This is where MTA and MMM shine.

Dig deeper: 7 must-know marketing attribution definitions to avoid getting gamed

Understanding the basics

Multi-touch attribution

Multi-touch attribution is a digital-first methodology that tracks individual customer interactions across various touchpoints in their journey to purchase. 

It assigns credit to each marketing touchpoint based on its contribution to the final conversion. 

Operating at a granular, user-level scale, MTA collects data from cookies, device IDs, and other digital identifiers to create a detailed picture of the customer journey.

MTA is commonly supported by marketing channels like Google Ads, which offer different attribution settings – data-driven being the most recommended. 

However, first and last touch models are not considered part of MTA, as they only account for a single touchpoint.

Beyond in-platform attribution, most analytics tools also support multi-touch attribution. 

For SMBs with strong tracking and high data quality, these tools can be sufficient. 

However, taking attribution to the next level requires a customized MTA by:

  • Using a tool that allows customization.
  • Or building custom attribution reports, often in combination with a data warehouse. 

A tailored MTA ensures attribution is aligned with your business and customer journey, leading to more accurate insights.

The need for a customized MTA becomes clear with the following example:

Imagine a user encounters two social touchpoints – an Instagram ad and a TikTok ad – before converting through a Google Search ad. 

A standard MTA might allocate 20% credit to each social channel for awareness and 60% to Google Search, assuming search played the most crucial role due to its intent-driven nature.

  • Instagram ad: 20%
  • TikTok ad: 20%
  • Google Search: 60%

You might conclude that increasing your Google Ads budget and investing more in search is the right move.

While this could work, it could also backfire – without a customized MTA, your decision-making may be flawed.

Let’s take a closer look at the user journey to see what might be wrong:

  • Instagram ad – Cold awareness: 50%
  • TikTok ad – Remarketing: 40%
  • Google Search – Branded search: 10%

Instead of Google Search being the primary driver, it could be that:

  • Instagram is generating initial awareness.
  • TikTok is handling remarketing.
  • Google is simply capturing conversions from users already familiar with your brand. 

In this case, increasing Google Ads spend wouldn’t necessarily drive more sales. It would just reinforce the final step while neglecting the earlier, more influential touchpoints.

With this in mind, MTA weightings can look completely different. 

Investing more in cold traffic and remarketing while minimizing spend on Google Search might be the smarter approach, as search doesn’t generate demand but rather supports the last step and defends your brand against competitors.

This example highlights why a customized MTA is essential. It allows you to tailor attribution to your specific strategy, funnel, and customer journey. 

However, if data quality is poor or customization is lacking, it can lead to inaccurate insights, poor decisions, and short-term thinking.

Marketing mix modeling

Marketing mix modeling, on the other hand, takes a top-down, aggregate approach. 

It analyzes historical marketing spend across channels along with external factors to assess their impact on business outcomes. 

Using advanced statistical techniques, MMM identifies correlations between marketing investments and results.

An effective marketing mix model incorporates both historical and current data, making it resilient to outliers and short-term fluctuations. 

Depending on the model, it also allows for the inclusion of seasonal trends, industry benchmarks, growth rates, and marketing volume. 

Additionally, MMM can account for brand awareness and loyalty in base sales, as well as measure incremental sales.

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MTA vs. MMM: Key differences

MTA vs. MMM - Key differences

MTA is a valuable tool for digital marketing teams that need immediate insights and real-time tracking to optimize campaigns quickly. 

Its granular data helps marketers refine conversion paths and personalize customer interactions. 

However, increasing privacy restrictions and the phase-out of third-party cookies make MTA more challenging to implement effectively. 

Additionally, its digital-first nature means it struggles to account for offline marketing efforts and may lead businesses to prioritize short-term conversions over long-term brand growth.

MMM, by contrast, provides a broader, privacy-friendly approach that captures both digital and offline marketing performance. 

It is particularly useful for long-term budget planning, helping businesses allocate resources effectively across multiple channels. 

However, its reliance on historical data and aggregate trends makes it less suited for rapid campaign adjustments. 

Companies that operate across both digital and traditional marketing channels may benefit from combining MTA’s real-time insights with MMM’s strategic guidance for a more balanced approach.

Dig deeper: How to evolve your PPC measurement strategy for a privacy-first future

Open-source marketing mix models

Open-source marketing mix models are widely used for several reasons. 

They are free, making them an attractive alternative to expensive enterprise tools. 

Another key advantage is transparency. Since these models can be reviewed, businesses are not reliant on “black box” solutions. 

Some of the most notable open-source models include:

To determine which model best suits your needs, it’s helpful to experiment by uploading test datasets and exploring their functionalities. 

While these models share a common approach, they differ in customization depth and fine-tuning capabilities. 

In my experience, Meridian is the most advanced, offering deep integration with first-party, organic, and third-party data. However, its complexity may require a steeper learning curve. 

For a quicker setup, Robyn from Meta is a solid starting point.

Hybrid approach

As marketing measurement evolves, organizations increasingly adopt hybrid approaches that combine the strengths of both MTA and MMM. This unified framework aims to:

  • Leverage MTA’s granular digital insights for tactical optimization.
  • Use MMM for strategic planning and budget allocation.
  • Cross-validate findings between both methodologies.
  • Provide a more complete view of marketing effectiveness.

For digital-first companies, MTA is often the preferred starting point, offering real-time insights for rapid campaign adjustments.

In contrast, businesses investing heavily in traditional marketing tend to benefit more from MMM, as it:

  • Aligns with privacy regulations.
  • Accounts for external factors.
  • Delivers a holistic view of marketing performance.

A hybrid approach provides the best of both worlds – combining MTA’s agility with MMM’s long-term perspective.

While managing both requires additional resources, businesses implementing this strategy gain precise, channel-specific insights and a broader strategic understanding.

This dual approach is particularly valuable for organizations balancing short-term performance optimization with sustainable, long-term growth.

Boost your marketing performance with the right attribution model

Both MTA and MMM offer valuable insights into marketing effectiveness, but they serve different purposes and have distinct advantages.

As the marketing landscape becomes more complex and privacy-focused, it’s essential to assess your measurement needs and capabilities to determine the best approach – or a combination of both.

The future of marketing measurement likely lies in hybrid solutions that blend MTA’s granular insights with MMM’s strategic perspective while adapting to evolving privacy regulations and technological changes.

By integrating these methodologies, you’ll be better equipped to optimize marketing investments and drive long-term business growth.

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Google Ads tests new “Advanced Plans” feature for budget optimization

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A new “Advanced Plans” section within Google Ads’ Reach Planner tool was spotted by digital marketing expert Brent Neale.

The big picture. The tool represents Google’s continued push toward automated campaign optimization, offering AI-driven recommendations for budget allocation.

How it works. Advanced Plans suggests a mix of ad types based on advertisers’ goals, creating specific plans for both conversion creation and capture.

Why we care. The feature could help advertisers more effectively allocate their budgets across different ad types based on specific conversion goals.

Between the lines. This appears to be part of Google’s broader strategy to simplify campaign planning while leveraging its machine learning capabilities.

What’s next. The feature appears to be in testing, suggesting Google may be gathering feedback before a wider rollout.

Bottom line. If successful, Advanced Plans could streamline the campaign planning process for advertisers while potentially improving conversion outcomes.

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Google Display & Video 360 API gets major update

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The future of paid search: 3 predictions for Google Ads in 2025

Google announced the public beta of Display & Video 360 API v4 last week, alongside significant updates to v3.

Key changes in v4.

  • Mandatory optimization objective field for new insertion orders
  • Removal of Campaign and InsertionOrder resource targeting management
  • Renaming of FirstAndThirdPartyAudience to FirstPartyAndPartnerAudience

Additional features in v3 and v4.

  • Asset-based creative support
  • Integral Ad Science quality sync integration
  • Expanded geographic region targeting options

Why we care. The beta release of Display & Video 360 API v4 and new v3 features gives advertisers enhanced capabilities for programmatic advertising management.

Between the lines. The mandatory optimization objective requirement suggests Google is pushing for more structured and purposeful campaign setups.

What to watch. Google warns that v4 may undergo breaking changes during the beta period, with updates documented in release notes.

Bottom line. Advertisers need to update their client libraries to access new features and should consider following Google’s migration guide when moving to v4.

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Why you need humans, not just AI, to run great SEO campaigns

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Why you need humans, not just AI, to run great SEO campaigns

“Why can’t we just use AI to do it?”

Whether you’re on the brand or agency side of SEO, I’m guessing you’ve heard some version of this from an exec or a client with little knowledge of AI tools, SEO principles, or both.

I’ve been asked that question multiple times because the other party saw or heard about modest success from LLM-generated content that got some clicks and impressions.

My answer: because thousands of LLM-produced pieces of content do not a successful SEO program make. 

This article dives into the human and AI roles in today’s SEO landscape, including:

  • What people are getting wrong about AI and content.
  • What AI can and can’t do for SEO campaigns.
  • What an expert can tackle with AI tools.
  • The North Star of 2025 SEO (as I see it) and why you need humans to reach it.

(Note: No LLMs were used to write this article.)

What people are getting wrong about AI and content

When people ask, “Can we just have AI write 1,000 blog posts?,” they assume there’s a linear progression. 

For instance, if a blog post gets 100 visits/month, won’t 1,000 blog posts get 100,000 visits? 

  • No, that’s not the way SEO works. It’s not a linear discipline. 
  • More importantly, that approach means you’re just putting crap out there. You’re essentially using AI to build your own content farm of stale, repetitive language. 

There’s no value for the user or positive affinity for the brand.

Now, you could use AI tools and strategic prompts to quickly create a solid base for a piece of content, then apply human editing and a unique POV. 

In most cases, that’s faster than the content process was before AI, and it’ll produce much better content than 1,000 LLM-produced pieces, but it still requires human input.

In short, forget about spamming Google with a ton of poor LLM content. Your users won’t read it, and ultimately, it won’t do anything beyond maybe inflating your vanity metrics. 

And, crucially, Google won’t like it.

Whenever Google deals with an explosion of people doing the same (easy) thing to game the system, you want to zig while others are zagging. 

Don’t be part of the problem that triggers – and gets wiped out by – a huge algo update.

Dig deeper: 3 ways to use AI for SEO wins in 2025

What AI can and can’t do for SEO campaigns

Along with being unable to produce differentiated content, AI is being asked to do things like “come up with keywords” or “do internal links” on its own. 

If you’re just having AI look at your site and update links without careful QA, you’ll just end up with a lot of crappy internal links. 

It’s the same thing with keywords: you might get a huge list, but lots of them will have low volume, be barely relevant, or be straight-up garbage.

Anytime someone says, “Let’s just use AI for [task],” try it once, gauge the output and the time needed to bring it up to anything resembling human baseline, and you’ll have a more nuanced answer.

On the other hand, there are a few proven use cases for AI in SEO – and while they still involve human input, they’re big time-savers that free up the experts to address more strategic initiatives.

For instance, if you have good source data and/or good, well-substantiated original thoughts, AI is great for remixing them into something organized and usable. 

Let’s say you conduct a thorough interview with a solutions engineer. AI can highlight, categorize, and synthesize the most salient parts of the interview, leaving you to QA the output and layer in your own voice. 

Not only does this save you time, it helps surface patterns in big data sets that you might never have spotted on your own – or at least nowhere near as quickly.

Dig deeper: How to optimize your 2025 content strategy for AI-powered SERPs and LLMs

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What an expert can tackle with AI tools

If you approach AI tools with the right expectations, they can be incredibly powerful. 

I often use it for technical content like briefs and concepts – but as part of the drafting process. Draft 0.5 (we’re not talking 1.0) is a ChatGPT remix for me. 

That said, non-technical people using LLMs to help establish a base for technical content is fine, but even after you make it sound good, you still need an expert in the field to review the end product for fact and substance.

As mentioned, AI tools can be great for synthesizing large data sets and producing trend and sentiment analyses. 

If you’ve got a list of keywords, it’s a good practice to ask AI to come up with additional keywords. 

I also like using it for title tag and headline options. 

I’ll write one good headline with a character limit and a target persona and ask an LLM to riff on that version.

Instead of painstakingly writing five, I’ll write one really good one, use an LLM to produce a few more, and let the client choose.

So, sometimes AI is a great starting point, and sometimes it’s a great second step.

It depends on the scenario, and it takes practice to understand where its power is most effectively leveraged. 

But the answer is rarely to let AI run wild and consider the output final.

Dig deeper: 15 AI tools you should use for SEO

Why you need humans to reach the SEO pinnacle in 2025

If we can agree that SEO’s ultimate goal should be to drive down-funnel results like pipeline and sales, I’d like to offer what I see as the best way to get there in 2025: become the primary source for Google and LLMs to cite. 

Use proprietary data and establish a unique POV for your brand, and own the topic by understanding everything the user needs to learn related to the primary keyword (or conversational question).

Becoming a primary reference is fundamentally incompatible with LLMs and AI, which are by nature derivative. (In other words, you can’t be the source by pulling from the source.) 

LLMs and AI, at this point, don’t produce anything new or unique, which is what users crave – hence the rise of TikTok and Reddit search juxtaposed with the emergence of LLM search. 

That means you need human input to truly stand out and engage users by being a trusted reference on Google or LLMs.

Smart SEO uses AI – but still needs people to win

The other day, a colleague asked me what kind of AI tool I wish someone would build for SEO. 

My answer, which is completely wishful thinking, was a tool that would show me a network of connected ideas that haven’t been written about. A content gap analyzer of sorts that identifies what people aren’t saying. 

Given the nature of AI and the way it sources material, though, I think that’s inherently impossible (how can you source a negative?) – at least for now. 

At the rate AI tools are being developed, it’s worth monitoring. 

We’ll be surprised at the use cases that get addressed in the next year alone. 

I’m also guessing that no matter how good the tool, humans will always be needed to operate it. 

Dig deeper: AI can’t write this: 10 ways to AI-proof your content for years to come

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How to find your next PPC agency: 12 top tips

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How to evaluate your next PPC agency

With so many PPC agencies claiming to be experts, how do you separate true performers from the ones who just talk a good game? 

This guide walks you through a no-nonsense evaluation process to find an agency that delivers real results.

1. Define your goals first

Before reaching out to agencies, have a clear understanding of what you want to achieve with PPC. 

Are you looking for lead generation, ecommerce sales, local service inquiries, or brand awareness? 

Knowing your objectives will help you ask the right questions and assess whether an agency is a good fit.

Also, factor in your budget constraints and expected ROI. 

A good agency should work within your financial limits while setting realistic performance expectations.

Dig deeper: How to set and manage PPC expectations for teams and stakeholders

2. Assess their industry experience

Not all PPC strategies work across every industry. 

Look for agencies that have experience managing campaigns in your specific vertical. 

Ask for case studies or examples of past success in your industry, especially in:

  • Ecommerce.
  • Local services.
  • B2B lead generation.
  • SaaS.
  • Healthcare.
  • Finance.

Agencies with industry expertise will understand common challenges and effective strategies unique to your business type. 

They should also demonstrate an ability to adapt to changes in industry regulations and trends.

3. Understand their approach to strategy and optimization

A good PPC agency should have a structured approach to campaign strategy, including:

  • Account structure: How do they build and organize campaigns?
  • Keyword strategy: Do they effectively use broad, phrase, and exact match?
  • Bid management: Are they using automated bidding, manual strategies, or a hybrid approach?
  • Ad copy and creative: How do they optimize messaging and testing?
  • Landing page optimization: Do they provide insights or recommendations?
  • Conversion tracking and attribution: Can they track conversions accurately and integrate with your CRM?

A truly data-driven agency should also be A/B testing different elements, using insights from past campaigns to improve performance, and continuously optimizing for better results.

4. Ask about their reporting and transparency

A top-tier PPC agency should provide clear and actionable reporting. Look for:

  • Regular reports: Weekly, bi-weekly, or monthly reporting with key performance indicators (KPIs).
  • Transparency: Do they provide full access to the ad accounts, or do they keep you in the dark?
  • Actionable insights: Reports should not just be data dumps but should include insights and recommendations.
  • Real-time dashboard access: Can you see your ad performance whenever you like?

Additionally, ensure they use third-party analytics tools like Google Analytics 4 or other attribution models to verify data accuracy and avoid misrepresenting results.

Dig deeper: How to approach weekly, monthly, quarterly and annual PPC reporting

5. Understand their pricing model

PPC agencies use different pricing structures, and understanding them is key to making a cost-effective decision. 

Common models include:

  • Percentage of ad spend: Typically 10-20% of your monthly budget. Good for scaling but can lead to overspending if not managed properly.
  • Flat monthly fee: A predictable expense, but ensure they have clear deliverables.
  • Performance-based: Payment is based on lead volume or ROAS. This can align incentives but may not work for all businesses.
  • Hybrid model: A combination of the above.

Ask about additional costs for services like ad creative development, landing page optimization, or advanced analytics to avoid unexpected fees.

6. Check for red flags

Be cautious of agencies that exhibit the following warning signs:

  • Guaranteed results: No agency can guarantee specific PPC results.
  • Lack of transparency: You should have access to your ad accounts and full visibility into performance.
  • Cookie-cutter strategies: Every business is unique. Beware of agencies that use the same approach for all clients.
  • No focus on tracking: They aren’t serious about results if they don’t emphasize accurate tracking and reporting.

Additionally, be wary of agencies that avoid discussing long-term strategies or only focus on short-term wins without considering sustainable growth.

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7. Evaluate their client communication and support

Great PPC management requires ongoing communication. Ask:

  • How often will we have meetings?
  • Who will be our main point of contact?
  • How quickly do they respond to emails or support requests?
  • Will we receive proactive recommendations, or need to ask for updates?

Also, evaluate their level of customer support. 

An agency that prioritizes proactive communication and offers dedicated account managers can be more effective in optimizing your campaigns.

Dig deeper: 8 tips to craft clear and impactful client communication

8. Understand their onboarding process

A smooth onboarding process sets the foundation for a successful agency partnership. Ask:

  • What does the onboarding process look like?
  • What information and assets will they need from you?
  • How long does onboarding usually take?
  • What key milestones should you expect in the first 30, 60, and 90 days?

A well-structured onboarding should include an initial strategy session, access setup (Google Ads, analytics, CRM), and alignment on key metrics and reporting expectations.

Dig deeper: Client onboarding and offboarding: The PPC agency’s guide

9. Assess their team structure and stability

Understanding who will manage your account is critical for a long-term, successful relationship. Ask:

  • Who will be directly managing your PPC campaigns?
  • How is their PPC team staffed?
  • What level of experience do their account managers have?
  • What is their turnover rate? How often do they replace account managers?

A high staff turnover can lead to inconsistencies in account management, so it’s important to partner with an agency that retains experienced professionals.

10. Request case studies and references

A reputable agency should have a portfolio of successful campaigns. Ask for:

  • Case studies: Examples of past campaigns, including challenges and results.
  • References: Client testimonials and contacts for past or current clients.

Look for verifiable success stories that align with your industry and goals. 

If possible, reach out to their past clients to gain insight into their experience with the agency.

11. Test with a trial or audit

If you’re unsure about committing, consider starting with a:

  • Short-term contract: A three-month trial period to assess performance.
  • PPC audit: Have them audit your existing campaigns and provide recommendations.

An audit should provide a comprehensive analysis of campaign structure, keyword effectiveness, ad performance, and tracking setup. 

The agency’s recommendations should be data-driven and actionable.

12. Ensure cultural and goal alignment

Choose an agency that aligns with your company’s values, communication style, and growth objectives. A strong partnership is key to long-term PPC success.

Consider factors like:

  • Do they understand your brand’s mission and voice?
  • Are they flexible and open to collaboration?
  • Do they have a track record of long-term client relationships?

An agency that shares your vision and integrates well with your team will be more effective in achieving your marketing goals.

Dig deeper: 4 tips to build a data-centric culture in your agency

Final thoughts

Evaluating a PPC agency takes time, but choosing the right partner will maximize your advertising investment. 

By focusing on experience, strategy, transparency, and results, you’ll be well-equipped to make an informed decision and drive meaningful business growth through paid search and social campaigns.

The right agency does more than manage your PPC campaigns.

They act as an extension of your team, providing expert insights and continuously optimizing for long-term success.

Dig deeper: 5 essential PPC skills every agency pro must have

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How to prevent PPC from cannibalizing your SEO efforts

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How to prevent PPC from cannibalizing your SEO efforts

If you manage both SEO and PPC, striking the right balance is key to maximizing efficiency and ROI. 

When paid search campaigns compete with high-performing organic listings, brands end up spending more while gaining little additional traffic. 

Keyword cannibalization dilutes search performance, inflates costs, and reduces overall marketing effectiveness.

This guide will help you recognize the warning signs of PPC cannibalization, test its impact, and implement strategies to ensure both channels work together for optimal results.

Signs your PPC campaigns are cannibalizing your SEO rankings

Declining organic click-through rates

If your organic rankings remain stable but CTRs are dropping, your paid ads might be stealing traffic from your organic listings. 

This is usually the result of branded or high-ranking keywords being simultaneously targeted in PPC campaigns.

It’s also important to note that additional SERP features, ad placements, and AI-driven search results have contributed to a general decline in organic CTRs across the board.

Increased PPC clicks with no overall traffic growth

If PPC campaigns drive more paid traffic, but total website visits remain unchanged, your ads may be diverting clicks that would have otherwise come from organic search.

Google Analytics 4 (GA4)’s Traffic Acquisition Report makes identifying this issue easier. You can compare period-over-period traffic changes by channel side by side.

GA4 Traffic Acquisition report

Organic conversions declining while paid conversions increase

If paid search conversions are rising but overall conversions remain flat or decline, PPC may be cannibalizing organic conversions rather than expanding your reach.

This is especially common with Performance Max (PMax) campaigns, which often prioritize branded terms for their higher ROI. More on that later.

Dig deeper: How to maximize PPC and SEO data with co-optimization audits

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3 steps to prevent PPC from cannibalizing your SEO

1. Audit PPC and SEO keyword overlap

Not all overlapping PPC and SEO keywords cause cannibalization. 

However, to safeguard your top-ranking keywords, exclude them from your PPC campaigns.

To speed up your analysis, filter organic search terms where your website ranks position 4 or below – since most clicks go to pages ranking in positions 1-3.

Additionally, sort search terms by click volume to identify phrases most susceptible to cannibalization. 

Then, cross-reference your organic search terms with your Google Ads Search Terms report to pinpoint where you’re paying for traffic you’d otherwise get for free.

2. Use negative keywords to exclude strong SEO performers

If certain terms already perform well organically, you can use negative keywords to prevent them from triggering paid ads. 

By applying exact-match negative keywords, you avoid cannibalization while still targeting related peripheral phrases in your ads.

Google Ads Negative Keyword tool

Dig deeper: How to use negative keywords in PPC to maximize targeting and optimize ad spend

3. Refine brand bidding strategies and implement brand exclusion lists

Bidding on branded terms is often unnecessary since users searching for a brand already intend to visit its website.

Paying for traffic that would otherwise be free is rarely a good investment.

However, PPC brand bidding becomes essential when competitors target your brand.

In such cases, recapturing your brand space is a necessary expense – but fortunately, it’s much cheaper than bidding on a competitor’s brand.

The importance of brand exclusion lists

Brand exclusion lists help prevent wasteful spending on branded queries where organic listings already dominate. 

This ensures PPC budgets are focused on non-branded, high-intent searches rather than duplicating organic traffic. 

This is especially critical for PMax campaigns, which aim to drive positive ROI, often through low-cost branded visibility with high conversion potential.

One example of branded cannibalization my team identified involved a branded PMax campaign that inadvertently paid for an estimated $500,000 in organic revenue. 

Since PMax campaigns receive premium visibility – even in areas where results may not be highly relevant – this campaign bid on nearly every branded term, running unchecked.

A major issue arose when a shopping carousel for the company’s two most-searched branded phrases appeared above all other SERP features. 

This pushed the usual search ad lower on the page and forced the organic homepage listing completely out of view without scrolling. 

As a result, impressions dropped by 12%, and organic clicks fell by 33%.

If you haven’t yet taken steps to prevent your campaigns from bidding on your brand, make sure to check Google’s guide to brand exclusions. 

Benchmark your SEO performance on branded terms before launching PMax campaigns to make identifying cannibalization easier.

Dig deeper: Google brings negative keyword exclusions to Performance Max

Special considerations for Performance Max campaigns and targeting options

PMax campaigns use AI-driven automation to serve ads across Google’s entire inventory, including Search, Display, YouTube, Discover, Gmail, and Maps. 

Unlike traditional PPC campaigns, PMax lacks detailed keyword-level control, making it difficult to prevent overlap with organic rankings.

How PMax can cannibalize SEO traffic

  • Broad matching across multiple channels: PMax may automatically target keywords where your brand already ranks well organically, leading to unnecessary ad spend.
  • Limited transparency on search terms: Without keyword-level reports, identifying overlap with organic rankings is challenging.
  • Competing with organic listings: PMax can push organic results further down by occupying both paid search and shopping ad placements.

Dig deeper: Performance Max vs. Search campaigns: New data reveals substantial search term overlap

Mitigating SEO cannibalization in Performance Max

  • Use account-level negative keywords: Google now allows negative keywords for PMax – exclude high-performing organic keywords to reduce redundancy.
  • Optimize asset groups and search themes: If certain categories already perform well organically, ensure PMax focuses on different product lines or services. Since PMax is designed for maximum reach, precise targeting is essential.

Tests to confirm PPC is cannibalizing SEO

  • Run a PPC pause test: Temporarily pause PPC ad groups or use exact-match negative keywords for strong organic terms. If organic traffic, CTR, and conversions improve, PPC may be cannibalizing SEO.
  • Compare pre- and post-bid adjustments: Lower PPC bids on high-ranking organic keywords and track shifts in paid and organic performance.
  • Analyze assisted conversions in Google Analytics: Determine whether PPC ads drive conversions that organic search alone wouldn’t achieve. If not, adjustments may be needed.
  • Monitor organic CTR changes: Use Google Search Console to analyze CTR fluctuations for top organic keywords before and after PPC campaigns launch.

Aligning PPC and SEO requires careful keyword management and strategic bidding

Reduce ad spend where possible and avoid paying for traffic that would otherwise be free.

For Performance Max campaigns, mitigating SEO cannibalization through negative keywords and refined targeting ensures a balanced approach. 

A well-coordinated PPC-SEO strategy improves efficiency and maximizes the value of digital marketing investments.

Read more at Read More

From search to AI agents: The future of digital experiences

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From search to AI agents- The future of digital experiences

We rely on search engines to find information every day, but what if there was a better way? 

Instead of manually gathering details from multiple sources, AI agents can do the heavy lifting for you. 

They don’t just retrieve information. They analyze, organize, and personalize it in real time.

This article explores:

  • How AI agents help businesses create more personalized customer experiences.
  • The key components and frameworks behind AI-powered agents.
  • How multi-agent systems can collaborate to solve complex tasks.

From information retrieval to intelligent problem-solving

AI agents represent a fundamental shift in how we interact with AI. 

As brands, we are moving beyond passive information retrieval – a slow process of manually collecting data from various websites – to active problem-solving, where multimodal data seamlessly adapts to a preferred interface in real time.

Imagine a world where multiple independent AI agents collaborate to complete complex workflows. 

Industry experts anticipate significant transformation due to AI agents. Here’s what they have to say:

  • Satya Nadella: AI agents will proactively anticipate user needs and assist seamlessly.
  • Bill Gates: AI agents are driving the most significant software transformation since graphical user interfaces.
  • Jensen Huang: IT departments are managing AI agents the way human resources manage employees.
  • Jeff Bezos: AI agents act as digital copilots, enhancing daily interactions.
  • Gartner: Search engine volume will decline by 25% by 2026 as AI chatbots and virtual agents revolutionize customer interactions.

Today, brands have a significant opportunity to leverage AI agents as intelligent virtual teammates, enabling businesses to deliver hyper-personalized experiences.

As AI agents and technology evolve, we are moving away from the time-consuming effort of manually gathering information. 

In the future, AI agents will interact with one another, collect relevant data, organize it to match user preferences, and deliver it seamlessly – creating a faster and more efficient experience.

ai-agents-impact-on-humans.

Dig deeper: Mastering AI and marketing: A beginner’s guide

To understand how AI agents deliver these intelligent, real-time experiences, we need to break down their core components. 

Let’s explore the anatomy of AI agents and how each layer contributes to their functionality.

Anatomy of AI agents 

AI agents are designed to enhance the capabilities of LLMs by incorporating additional functionalities. 

Agents have four layers:

  • Foundation layer.
  • Application layer.
  • Management layer.
  • Data layer. 
anatomy-of-an-agent

An AI agent typically consists of the following components:

  • Memory: Stores past interactions and feedback to provide contextually relevant responses. Memory resides in the data layer.
  • Tools/Platform: Retrieves real-time data and interacts with internal databases. The chosen tools and platforms are part of the application layer.
  • Planning: Uses reasoning techniques to break down complex tasks into simpler steps.
  • Actions: Executes tasks based on insights from LLMs and other sources.
  • Critique: Provides a feedback loop for actions based on different use cases to ensure accuracy.
  • Persona: Adapts to different roles, such as research assistant, content writer, or customer support agent.

Planning, actions, critique, and persona identification occur in the management layer.

Frameworks for building AI agents

There are many frameworks available for building AI agents and multi-agent systems, each catering to a different need:

  • AutoGen (Microsoft): Focuses on conversational AI and automation.
  • CrewAI: Designed for role-playing agents that collaborate effectively.
  • LangGraph: Structures agent interactions in a graph-based model.
  • Swarm (OpenAI): Primarily for educational purposes.
  • LangChain: A popular framework enabling AI agents to work with LLMs and other tools.

Each platform offers unique advantages based on the task’s use case, scalability, and complexity.

Multi-agent AI systems and their importance

multi-agent-application-examples

A multi-agent system consists of multiple AI agents working seamlessly, each performing a distinct function to collaboratively solve problems.

These systems are particularly useful for handling complex scenarios where a single AI agent might struggle. 

Below is a simple example of a multi-agent system:

  • Query processing agent: Breaks the question into multiple parts.
  • Retrieval agent: Fetches relevant data from internal sources.
  • Validation agent: Verifies the response against various parameters such as brand voice and query intent.
  • Formatting agent: Structures the response appropriately.

This structured approach to distributing responsibilities among agents ensures more accurate and intelligent responses while reducing errors.

Before exploring how AI agents deliver real-time personalization, let’s look at why traditional methods are no longer enough.

Dig deeper: AI optimization: How to optimize your content for AI search and agents

Why AI-powered personalization is essential

As data availability declines and user expectations rise, businesses can no longer rely on traditional methods to understand customer intent. 

The shift away from third-party cookies, the rise of zero-click content, and the demand for real-time, tailored experiences have made AI-driven personalization a necessity.

AI enables businesses to analyze behavior, predict intent, and deliver dynamic, personalized experiences at scale – from search and social to email and on-site interactions. 

Unlike static personalization, AI adapts in real time, ensuring relevance across every customer touchpoint.

With traditional strategies losing effectiveness, AI agents offer a smarter, more scalable way to engage and convert audiences.

Dig deeper: How to boost your marketing revenue with personalization, connectivity and data

Delivering personalized experiences with search and chat agents

Modern websites are no longer one-size-fits-all. They provide immersive experiences tailored to each visitor’s intent. 

AI agents enable this through two key approaches:

Search agents 

Traditional site searches relied on keywords and filters, which have limitations with multimodal searches (like voice or visual) and long-tail queries. 

They also require more user clicks, increasing the likelihood of search abandonment. 

AI-powered search agents overcome these challenges by delivering a more intuitive and efficient on-site search experience.

Chat agents

Early AI chatbots responded using pre-programmed scripts or existing website content. 

Today, advanced chat agents offer personalized experiences using audience data. They can:

  • Build detailed user profiles.
  • Understand user intent by analyzing historical interactions and purchase data.
  • Learn from similar interactions to ask relevant follow-up questions.
  • Adapt on-site experiences in real time based on user behavior.
  • Inform cross-channel marketing strategies – such as email, social, paid, and retargeting – using insights gathered from user interactions.

AI agents also offer industry-specific personalization. Brands can implement:

  • Digital marketing automation agents.
  • Customer support chat agents.
  • Specialized solutions, like:
    • Financial risk assessment agents.
    • Automotive inventory management agents.

Personalize or perish

Many businesses still view personalization as optional. 

In reality, without personalized experiences, traffic and conversions will decline, leading to higher marketing costs and lower ROI as more spending is needed to attract, engage, and convert visitors. 

To improve efficiency, AI-powered personalization offers a scalable, intelligent, and adaptive solution.

Dig deeper: Hyper-personalization in PPC: Using data to deliver tailored ad experiences

Read more at Read More

How to Leverage Snowflake and OneTrust for Consent Management at Scale by Snowflake

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Join experts from OneTrust and Snowflake for an exclusive look into how modern organizations are integrating privacy and consent management into their data ecosystem. In this session, Snowflake and OneTrust will share real-world use cases and insights into how organizations are activating consent for marketing purposes, all while streamlining compliance at scale.

Tune in on March 4 to learn about:

  • The intersections between consent, privacy, and data governance
  • How enterprise brands integrate privacy and consent management with Snowflake
  • OneTrust’s new Native App for accelerating compliance workflows within Snowflake

This session is perfect for marketers, data governance professionals, and anyone looking to improve their data privacy practices with real-world examples. Here is the link to learn more and register >>

The Details

Webinar:
How Privacy-First Marketers Leverage OneTrust and Snowflake for Consent Management at Scale

Date: March 4, 2025
Time: 10 am PT / 1 pm ET

Link to register: Here!

Read more at Read More