How Sprout Social Became Its Own Best Case Study as Customer Zero

Social media demands rapid creativity, responsiveness, and data-driven insights. Sprout Social confronted the same challenge many organizations face: efficiently managing large content volumes, analytics, and customer interactions without compromising quality.

To address this, Sprout Social implemented its own AI-powered tools internally, becoming "Customer Zero" to enhance productivity while maintaining high audience engagement standards.

The Solution: AI-Powered Social Media Management

1. Efficient and Creative Content Generation

Overcoming creative blocks and creating accessible content can consume significant resources. Sprout's AI tools help by drafting social posts aligned with brand voice, offering stylistic suggestions and automating alt text generation.

This approach reduces manual tasks, strengthens creative capabilities and ensures content accessibility. Also, new team members adapt more quickly to the brand's voice, maintaining consistency across communications.

2. Faster Decision-Making with AI Insights

Manual social data analysis is time-consuming and can delay strategic responses. Sprout's AI analytics quickly summarize social data, revealing key trends, sentiment patterns, and engagement metrics. This enables faster, more confident decision-making, allowing the team to respond promptly to emerging opportunities or challenges. These tools effectively function as an "AI analyst," transforming large volumes of social data into actionable insights.

3. Enhanced Customer Support

Sprout's AI-driven customer support capabilities automatically identify sentiment and intent behind social messages, helping prioritize responses effectively. AI-powered chatbots handle common inquiries, while automation routes complex issues promptly. The AI tools also assist in crafting personalized, brand-consistent replies, improving both customer satisfaction and response times.

4. Streamlined Governance and Operations

Scaling social media operations efficiently presents significant challenges. Sprout's AI governance tools include personally identifiable information (PII) masking and stop-word functions, helping maintain customer trust and protect brand integrity. Automated compliance processes simplify oversight across channels, reducing risk and ensuring consistent management.

Learn more about Sprout Social AI and automation capabilities.

Results: Efficiency and Community Engagement

By implementing their own AI-driven tools, Sprout Social achieved:

• 72 hours quarterly saved on reporting

• $16,000 annual savings in reporting costs

• $132,000 annual savings in customer care

It allowed the team to focus more on strategic thinking, enhancing creativity and connection with their audience.

Continuous Evolution

Sprout Social's internal experience validated the impact of AI in real-world social media management. As social media continues to evolve, Sprout remains focused on refining AI capabilities to drive efficiency, enhance creativity, and deliver exceptional audience experiences.

AI Overload? Three Principles to Keep You Balanced and Focused

AI moves fast—too fast, in fact. With new tools, features, and breakthroughs announced almost daily, it's easy for business and marketing leaders to feel overwhelmed or fall prey to "shiny object syndrome." I understand how challenging it is—I am facing these same issues myself. Staying current doesn't mean chasing every update. Completely ignoring these developments is equally dangerous, as falling too far behind can pose significant strategic risks. You just need the right guiding principles to navigate AI wisely without burning out.

1. Choose and Master Your Core Tools and Use Cases

Choose your core AI tools based on your strategic needs and master them thoroughly. New capabilities often surface first on emerging platforms, but proven features typically appear shortly afterward on your trusted tools. Rather than jumping from one trend to the next, trust your core toolset, refine existing workflows, and only consider new tools when they clearly enhance your specific use cases.

2. Stay Informed and Selectively Test New Capabilities

Testing new AI capabilities doesn't require immediate, widespread adoption. Instead, take a strategic pilot approach—maybe once a quarter—to identify a small, low-risk project where you can explore and measure actual impact and understand potential challenges. If the pilot succeeds, expand thoughtfully. If it doesn't, you've gained valuable insights without large-scale commitments.

3. Resist FOMO and Prioritize Long-Term Impact

The Fear of Missing Out (FOMO) is real, especially with AI's breakneck pace. But remember, no company can adopt every new advancement at once. Trust your strategy and roadmap. If a new AI feature doesn’t immediately align with your business objectives and use cases, don’t force it. Saying "no" or "not yet" is perfectly acceptable, as long as you keep a running list and revisit it regularly. The tools worth adopting will mature over time and align naturally with your long-term plans.

Putting It All Together

Managing AI advancements doesn't have to be overwhelming. By sticking to your core tools, selectively piloting innovations, staying informed without chasing every update, and prioritizing lasting impact, you can stay in control without burning out. AI’s rapid evolution can feel chaotic, but these principles offer clarity, confidence, and a calm path forward. I would love to hear your thoughts.

Hi, marketers in the loop!

AI is moving fast, and the fear of missing out (FOMO) is real—I feel it too. Here are my guiding principles to help you navigate AI strategically.

Also, I’ve been accepted into Perplexity’s AI Business Fellowship starting March 21st. I'm excited to gain insights into Perplexity's overall strategy and hear from leaders like Jensen Huang (NVIDIA) and Amjad Masad (Replit). I'll share key learnings soon.

Onward,
- Rei

• Feeling AI Overload? 3 Principles to Navigate AI Stress-Free

• How Sprout Social Became Its Own Best Case Study

• Google Receives 373X More Searches than ChatGPT

• And much more

Reading time: 5 minutes

Must Read

• Google Isn’t Dead: Google Search Grew 20%+ in 2024 and Receives ~373X More Searches Than ChatGPT (SparkToro)

• Making Reinvention Real With Gen AI - Lessons From 2,000 projects (Accenture)

• The State of AI in Marketing 2025 (Jasper AI)

Events & Webinars

• Top AI Marketing Tools Demos From Demo Fest 2025 (On Demand)

  → Apologies for the broken link last week. It's now fixed.

• The AI x Marketing Meetup #8 (March 26, San Francisco)

• Lovable and Anthropic’s Virtual “Vibe-Coding” Hackathon (March 28, Online)

Industry News

• Google is Adding More AI Overviews and a New ‘AI Mode’ to Search (The Verge)

• What Coca-Cola’s Marketing Team Has Learned on Its Generative AI Journey So Far (Marketing Dive)

• AI Will Soon Dominate Ad Buying, Whether Marketers Like It or Not (WSJ)

Industry Reports

• The Top 100 Gen AI Consumer Apps - Spoiler Alert: DeepSeek is #2 (a16z)

• Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential (McKinsey)

• AI & The Agency of the Future: Redefining Roles, Relationships, and Results (NinjaCat)

AI Agents

• AI Agents in 2025: Expectations vs. Reality (IBM)

• Framework for Assessing Use Cases of AI Agents (Crew AI)

• Signal President Calls Out Agentic AI as Having ‘Profound’ Security and Privacy Issues (TechCrunch)

Practical Tips

• The Official Prompt Engineering Guide by OpenAI (OpenAI)

• Prompt Engineering Tips, Including Prompt Generator tool, for Claude Models (Anthropic)

• How to Create Kickass AI Writing Prompts for Any Marketing Task (Writer)

Thought Leadership

• The AI Talent Gap Isn't What Most Companies Think It Is (Kieran Flanagan)

• AI Is Killing Web Traffic For Some Companies, Yet Others Are Thriving (Elena Verna)

• The Impact of AI on Marketing Agency Pricing (Marcomm News)

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Ready to embrace a new era of task delegation?

HubSpot’s highly anticipated AI Task Delegation Playbook is your key to supercharging your productivity and saving precious time.

Learn how to integrate AI into your own processes, allowing you to optimize your time and resources, while maximizing your output with ease.

Get the free guide here.

AI Overload? Three Principles to Keep You Balanced and Focused

AI moves fast—too fast, in fact. With new tools, features, and breakthroughs announced almost daily, it's easy for business and marketing leaders to feel overwhelmed or fall prey to "shiny object syndrome." I understand how challenging it is—I am facing these same issues myself. Staying current doesn't mean chasing every update. Completely ignoring these developments is equally dangerous, as falling too far behind can pose significant strategic risks. You just need the right guiding principles to navigate AI wisely without burning out.

1. Choose and Master Your Core Tools and Use Cases

Choose your core AI tools based on your strategic needs and master them thoroughly. New capabilities often surface first on emerging platforms, but proven features typically appear shortly afterward on your trusted tools. Rather than jumping from one trend to the next, trust your core toolset, refine existing workflows, and only consider new tools when they clearly enhance your specific use cases.

2. Stay Informed and Selectively Test New Capabilities

Testing new AI capabilities doesn't require immediate, widespread adoption. Instead, take a strategic pilot approach—maybe once a quarter—to identify a small, low-risk project where you can explore and measure actual impact and understand potential challenges. If the pilot succeeds, expand thoughtfully. If it doesn't, you've gained valuable insights without large-scale commitments.

3. Resist FOMO and Prioritize Long-Term Impact

The Fear of Missing Out (FOMO) is real, especially with AI's breakneck pace. But remember, no company can adopt every new advancement at once. Trust your strategy and roadmap. If a new AI feature doesn’t immediately align with your business objectives and use cases, don’t force it. Saying "no" or "not yet" is perfectly acceptable, as long as you keep a running list and revisit it regularly. The tools worth adopting will mature over time and align naturally with your long-term plans.

Putting It All Together

Managing AI advancements doesn't have to be overwhelming. By sticking to your core tools, selectively piloting innovations, staying informed without chasing every update, and prioritizing lasting impact, you can stay in control without burning out. AI’s rapid evolution can feel chaotic, but these principles offer clarity, confidence, and a calm path forward. I would love to hear your thoughts.

How Sprout Social Became Its Own Best Case Study as Customer Zero

Social media demands rapid creativity, responsiveness, and data-driven insights. Sprout Social confronted the same challenge many organizations face: efficiently managing large content volumes, analytics, and customer interactions without compromising quality.

To address this, Sprout Social implemented its own AI-powered tools internally, becoming "Customer Zero" to enhance productivity while maintaining high audience engagement standards.

The Solution: AI-Powered Social Media Management

1. Efficient and Creative Content Generation

Overcoming creative blocks and creating accessible content can consume significant resources. Sprout's AI tools help by drafting social posts aligned with brand voice, offering stylistic suggestions and automating alt text generation.

This approach reduces manual tasks, strengthens creative capabilities and ensures content accessibility. Also, new team members adapt more quickly to the brand's voice, maintaining consistency across communications.

2. Faster Decision-Making with AI Insights

Manual social data analysis is time-consuming and can delay strategic responses. Sprout's AI analytics quickly summarize social data, revealing key trends, sentiment patterns, and engagement metrics. This enables faster, more confident decision-making, allowing the team to respond promptly to emerging opportunities or challenges. These tools effectively function as an "AI analyst," transforming large volumes of social data into actionable insights.

3. Enhanced Customer Support

Sprout's AI-driven customer support capabilities automatically identify sentiment and intent behind social messages, helping prioritize responses effectively. AI-powered chatbots handle common inquiries, while automation routes complex issues promptly. The AI tools also assist in crafting personalized, brand-consistent replies, improving both customer satisfaction and response times.

4. Streamlined Governance and Operations

Scaling social media operations efficiently presents significant challenges. Sprout's AI governance tools include personally identifiable information (PII) masking and stop-word functions, helping maintain customer trust and protect brand integrity. Automated compliance processes simplify oversight across channels, reducing risk and ensuring consistent management.

Learn more about Sprout Social AI and automation capabilities.

Results: Efficiency and Community Engagement

By implementing their own AI-driven tools, Sprout Social achieved:

• 72 hours quarterly saved on reporting

• $16,000 annual savings in reporting costs

• $132,000 annual savings in customer care

It allowed the team to focus more on strategic thinking, enhancing creativity and connection with their audience.

Continuous Evolution

Sprout Social's internal experience validated the impact of AI in real-world social media management. As social media continues to evolve, Sprout remains focused on refining AI capabilities to drive efficiency, enhance creativity, and deliver exceptional audience experiences.

• DeepSeek Effect on Enterprise is Bigger than you Think

• AI Agents Will Make Your AI More Useful

• “What Are the Best (and Easiest) Platforms to Build AI Agents?”

• How to Choose the Right AI Tool for Your Marketing Needs

• Why Leaders and Employees Don’t See Eye to Eye  

• Agency (Or Other Partners) Using AI? Here's What You Need to Know

• How to Unlock Marketing Analytics with AI

One of my regular “must-reads” ⭐⭐⭐⭐⭐

"Marketer in the Loop has become one of my main sources of inspiration with regard to AI developments. Relevant, easy-to-read, and with a touch of humour that makes it one of my regular "must-reads"."

AI and Copyright Updates – A 2025 Guide for Marketers

Yes, AI is rewriting the rules of content creation – but what about copyright? If your marketing team is using generative AI for blogs, social posts, ads, or any design, it’s crucial to understand how recent copyright guidelines affect you. Below is a detailed guide to the latest insights (as of 2025) on AI-generated content and copyright, with practical takeaways for marketers.

1. Can You Copyright AI-Generated Content? Human Authorship Still Reigns

The short answer: No, not if it’s purely AI-generated. The U.S. Copyright Office’s 2025 report reaffirms that only works with substantial human creative input are eligible for copyright. Simply providing a text prompt or making minor edits doesn’t make you the author.

However, using AI as a tool is fine if a human determines the expressive elements. If an AI drafts a paragraph and you rewrite, reorganize, and add original ideas, the parts you wrote are protected. But a fully AI-generated blog post or image has no copyright protection - meaning competitors could reuse it, and you’d have no legal recourse.

Some examples:

• A blog post lightly edited from AI text is not protected.

• A marketer who rewrites, restructures, and infuses brand voice gets protection for the human-created portions.

• AI-generated images used in ads are likely unprotected unless creatively altered by a human designer.

2. The AI Training Data Dilemma – Copyright Infringement Risks

AI models are trained on vast amounts of data, which could eventually be copyrighted content. While AI tools aim to avoid direct copying, outputs can unintentionally resemble existing works. If an AI-generated ad copy or image closely mirrors copyrighted material, you could face legal trouble.

How to stay safe:

• Run plagiarism checks on AI-written content.

• Reverse image search AI-generated visuals.

• Be cautious with niche prompts that may regurgitate existing work.

• If AI-generated content is critical to a campaign, have legal review its originality before publication.

3. Who Owns AI Outputs? Ownership ≠ Copyright

Many AI tools claim that you “own” the output. However, this does not mean you have copyright protection. If AI generates an ad tagline and a competitor uses the same phrase, you might not be able to stop them.

Key takeaways:

• AI-generated content is often non-exclusive - others may get similar results.

• If exclusivity matters (e.g., logos, slogans), ensure significant human involvement.

• Consider trademarking important branding elements that involve AI-assisted creativity.

4. Global Copyright Trends for AI Content

Most countries are aligning with the U.S. stance that human authorship is required for copyright protection:

• EU: No protection for fully AI-generated works. New regulations focus on AI training data transparency and data mining restrictions.

• UK: Reviewing laws that previously allowed computer-generated works to have copyright, with potential changes ahead.

• Canada: Likely moving toward requiring human authorship following a government consultation.

• Elsewhere: Many countries are following similar trends, reinforcing human originality as a core principle.

5. Protecting Your Own IP

Marketers should also safeguard their proprietary data when using AI. In general, avoid entering confidential or strategic information into free tiers or non-approved AI tools, as some platforms store prompts for training. For example, if you feed a draft press release about a confidential product into an AI for editing, that text might inadvertently become part of the AI’s training data and emerge elsewhere.

How to protect yourself: Establish clear internal policies on AI use. If your company hasn’t yet, set guidelines on what kind of data can or cannot be put into AI systems​. Many AI tools now offer privacy settings – use them. Some let you opt out of having your prompts saved or used to train models. Educate your team: a quick training on the do’s and don’ts of AI can prevent an inadvertent leak of client data or upcoming campaign details.

Marketer’s Checklist:

✅ Emphasize Human Creativity – Use AI for inspiration but ensure meaningful human edits.

✅ Vet AI Outputs – Check for accidental plagiarism before publishing.

✅ Understand AI Tool Terms – Read the fine print on ownership rights.

✅ Protect Sensitive Information – Avoid inputting confidential data into AI systems.

✅ Stay Informed – Copyright laws are evolving—monitor updates from the U.S. Copyright Office and other regulators.

✅ Develop Internal AI Use Policies – Define guidelines for AI use in marketing to ensure compliance and originality.

Final Thoughts

AI is a powerful tool, but copyright protection still hinges on human creativity. To maximize legal safeguards, marketers should lead AI, not just use it - ensuring their unique contributions remain protected.

Disclaimer: This article is for educational purposes and does not constitute legal advice. Please reach out to your legal partner for formal advice.

Implementing Tofu for Scalable Personalization

Vic.ai, an AI accounts payable company based in New York, faced challenges in creating personalized content efficiently for their campaigns. With a small team responsible for numerous campaigns, manual personalization was time-consuming and led to underperforming email metrics, including low open and click-through rates. Even with tools like ChatGPT, the process remained cumbersome and lacked scalability.

The Solution: Implementing Tofu for Scalable Personalization

To address these challenges, Vic.ai adopted Tofu's AI-powered platform to automate and enhance their content personalization efforts. Key implementations included:

• Personalized Email Campaigns: Utilizing Tofu, Vic.ai generated highly personalized emails and dynamic landing pages for approximately 2,000 companies. The AI incorporated specific details, such as a company's retail locations or unique strategies, which were previously difficult to extract manually.

• Automated Content for Stalled Opportunities: Vic.ai's marketing team created personalized press releases and FAQs for stalled sales opportunities. Tofu automated this process by integrating with HubSpot, generating long-form content tailored to each opportunity, and reintegrating it back into their CRM.

The Impact: Improved Engagement and Efficiency

Implementing Tofu's AI-driven personalization led to significant improvements:

• Increased Click-Through Rates: Personalized email campaigns saw click-through rates rise from 6% to 19%, with read rates increasing from 50% to 65%.

• Accelerated Sales Engagements: In campaigns targeting stalled opportunities, Vic.ai booked 3 times more meetings, a notable increase from previous efforts.

• Operational Efficiency: Automating the creation of press releases and FAQs saved approximately 150 hours over 50 opportunities, allowing the team to focus more on strategy and less on manual tasks.

Future Outlook: Expanding AI Applications in Marketing

The success with Tofu has encouraged Vic.ai to explore additional AI applications in their marketing strategies. The team continues to identify new use cases, leveraging Tofu's capabilities to enhance various aspects of their outreach and engagement efforts.

Hey, marketers in the loop!

It's been a minute—but we're back with a fresh look! We kicked off this redesign late last year to deliver a cleaner, more enjoyable reading experience. Huge thanks to Ricky and the Pixels team for bringing it to life. I'd love your feedback—hit reply, and I'll send you a small treat as a thank you.

Now, two quick productivity resources:

1. Inbox overload? Meet Cora, an email management tool I've been testing (and loving) for months. They're currently at 10,000+ on the waitlist, but I secured 100 invites for you to skip the line. Grab yours here before they are gone.

2. Want to see top AI marketing tools in action? Catch up on demos from Jasper AI, HeyGen, ElevenLabs, 6sense, Clay, and others. Discover how these tools are transforming creative content, analytics, and customer engagement. Watch Demo Fest 2025 on-demand.

Onward.

-Rei

• AI and Copyright Updates

• Tofu’s AI-Powered Personalization Case Study

• 3 Change Management Actions to Support AI Adoption

• And Much More

Reading time: 7 minutes

Must Read

• 3 Change Management Actions to Support GenAI Adoption in Marketing (Gartner)

• For Marketers, Generative AI Moves from Novelty to Necessity (Bain)

• Old But Gold: 100 AI Transformation Stories from Google and Microsoft

Events & Webinars

• What’s new in ChatGPT for Work (OpenAI)

• AI and Technology for Marketers Conference (ANA)

• The AI x Marketing Summit 2025

  Use code "EARLYREI" for a 10% discount.

Industry News

• Reckitt’s Global Director of Data-Driven Marketing and Media Says Generative AI Must Be Used to “Replace Tasks Not Jobs” (Marketing Week)

• What the Future Holds for Marketing: Hyper-Personalization, Industrialization & the Battle for Brand Relevance (The Drum)

• Turns Out AI Is More Empathetic Than Allstate’s Insurance Reps (The Wall Street Journal)

Industry Reports

• LinkedIn's Global Marketing Jobs Outlook - Marketers Cite AI as the No. 1 Change They Are Experiencing at Work (LinkedIn)

• Gartner Marketing Predictions for 2025 and Beyond (Gartner)

• Four Futures of Generative AI in the Enterprise: Scenario Planning for Strategic Resilience and Adaptability (Deloitte)

AI Agents

• Forrester's Debate on AI Agents: Why The “AI Agent As Coworker” Narrative Is The Future vs. The “AI Agent As Coworker” Narrative Is Nonsense (Forrester)

• Crawl, Then Walk, Before You Run With AI Agents (ZDNET)

• Tech Vendors Are Banking on Business Readiness to Use AI Agents, But Companies Aren’t So Sure (The Wall Street Journal)

Practical Tips

• Gemini for Google Workspace Free Courses - How to Use Gemini to Improve Productivity and Efficiency (Google)

• LinkedIn Highlights the Importance of Human Connection in AI Content (Social Media Today)

• Everyone Wants to Teach "AI Skills" But Nobody Knows What "AI Skills" Are as of Today, Let Alone For the Future (Ethan Mollick on LinkedIn)

Thought Leadership

• How To Drive AI Adoption Among Marketers (Forbes’ CMO Newsletter)

• 2025: The Year of AI-Ready Data (MarketingOps)

• The Rise Of AI Resentment At Work: Why Employees Are Pushing Back (Forbes)

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You can learn more at AdQuick.com

AI and Copyright Updates – A 2025 Guide for Marketers

Yes, AI is rewriting the rules of content creation – but what about copyright? If your marketing team is using generative AI for blogs, social posts, ads, or any design, it’s crucial to understand how recent copyright guidelines affect you. Below is a detailed guide to the latest insights (as of 2025) on AI-generated content and copyright, with practical takeaways for marketers.

1. Can You Copyright AI-Generated Content? Human Authorship Still Reigns

The short answer: No, not if it’s purely AI-generated. The U.S. Copyright Office’s 2025 report reaffirms that only works with substantial human creative input are eligible for copyright. Simply providing a text prompt or making minor edits doesn’t make you the author.

However, using AI as a tool is fine if a human determines the expressive elements. If an AI drafts a paragraph and you rewrite, reorganize, and add original ideas, the parts you wrote are protected. But a fully AI-generated blog post or image has no copyright protection - meaning competitors could reuse it, and you’d have no legal recourse.

Some examples:

• A blog post lightly edited from AI text is not protected.

• A marketer who rewrites, restructures, and infuses brand voice gets protection for the human-created portions.

• AI-generated images used in ads are likely unprotected unless creatively altered by a human designer.

2. The AI Training Data Dilemma – Copyright Infringement Risks

AI models are trained on vast amounts of data, which could eventually be copyrighted content. While AI tools aim to avoid direct copying, outputs can unintentionally resemble existing works. If an AI-generated ad copy or image closely mirrors copyrighted material, you could face legal trouble.

How to stay safe:

• Run plagiarism checks on AI-written content.

• Reverse image search AI-generated visuals.

• Be cautious with niche prompts that may regurgitate existing work.

• If AI-generated content is critical to a campaign, have legal review its originality before publication.

3. Who Owns AI Outputs? Ownership ≠ Copyright

Many AI tools claim that you “own” the output. However, this does not mean you have copyright protection. If AI generates an ad tagline and a competitor uses the same phrase, you might not be able to stop them.

Key takeaways:

• AI-generated content is often non-exclusive - others may get similar results.

• If exclusivity matters (e.g., logos, slogans), ensure significant human involvement.

• Consider trademarking important branding elements that involve AI-assisted creativity.

4. Global Copyright Trends for AI Content

Most countries are aligning with the U.S. stance that human authorship is required for copyright protection:

• EU: No protection for fully AI-generated works. New regulations focus on AI training data transparency and data mining restrictions.

• UK: Reviewing laws that previously allowed computer-generated works to have copyright, with potential changes ahead.

• Canada: Likely moving toward requiring human authorship following a government consultation.

• Elsewhere: Many countries are following similar trends, reinforcing human originality as a core principle.

5. Protecting Your Own IP

Marketers should also safeguard their proprietary data when using AI. In general, avoid entering confidential or strategic information into free tiers or non-approved AI tools, as some platforms store prompts for training. For example, if you feed a draft press release about a confidential product into an AI for editing, that text might inadvertently become part of the AI’s training data and emerge elsewhere.

How to protect yourself: Establish clear internal policies on AI use. If your company hasn’t yet, set guidelines on what kind of data can or cannot be put into AI systems​. Many AI tools now offer privacy settings – use them. Some let you opt out of having your prompts saved or used to train models. Educate your team: a quick training on the do’s and don’ts of AI can prevent an inadvertent leak of client data or upcoming campaign details.

Marketer’s Checklist:

✅ Emphasize Human Creativity – Use AI for inspiration but ensure meaningful human edits.

✅ Vet AI Outputs – Check for accidental plagiarism before publishing.

✅ Understand AI Tool Terms – Read the fine print on ownership rights.

✅ Protect Sensitive Information – Avoid inputting confidential data into AI systems.

✅ Stay Informed – Copyright laws are evolving—monitor updates from the U.S. Copyright Office and other regulators.

✅ Develop Internal AI Use Policies – Define guidelines for AI use in marketing to ensure compliance and originality.

Final Thoughts

AI is a powerful tool, but copyright protection still hinges on human creativity. To maximize legal safeguards, marketers should lead AI, not just use it - ensuring their unique contributions remain protected.

Disclaimer: This article is for educational purposes and does not constitute legal advice. Please reach out to your legal partner for formal advice.

Vic.ai Enhances Marketing Outreach with Tofu's AI-Powered Personalization

Vic.ai, an AI accounts payable company based in New York, faced challenges in creating personalized content efficiently for their campaigns. With a small team responsible for numerous campaigns, manual personalization was time-consuming and led to underperforming email metrics, including low open and click-through rates. Even with tools like ChatGPT, the process remained cumbersome and lacked scalability.

The Solution: Implementing Tofu for Scalable Personalization

To address these challenges, Vic.ai adopted Tofu's AI-powered platform to automate and enhance their content personalization efforts. Key implementations included:

• Personalized Email Campaigns: Utilizing Tofu, Vic.ai generated highly personalized emails and dynamic landing pages for approximately 2,000 companies. The AI incorporated specific details, such as a company's retail locations or unique strategies, which were previously difficult to extract manually.

• Automated Content for Stalled Opportunities: Vic.ai's marketing team created personalized press releases and FAQs for stalled sales opportunities. Tofu automated this process by integrating with HubSpot, generating long-form content tailored to each opportunity, and reintegrating it back into their CRM.

The Impact: Improved Engagement and Efficiency

Implementing Tofu's AI-driven personalization led to significant improvements:

• Increased Click-Through Rates: Personalized email campaigns saw click-through rates rise from 6% to 19%, with read rates increasing from 50% to 65%.

• Accelerated Sales Engagements: In campaigns targeting stalled opportunities, Vic.ai booked 3 times more meetings, a notable increase from previous efforts.

• Operational Efficiency: Automating the creation of press releases and FAQs saved approximately 150 hours over 50 opportunities, allowing the team to focus more on strategy and less on manual tasks.

Future Outlook: Expanding AI Applications in Marketing

The success with Tofu has encouraged Vic.ai to explore additional AI applications in their marketing strategies. The team continues to identify new use cases, leveraging Tofu's capabilities to enhance various aspects of their outreach and engagement efforts.

• DeepSeek Effect on Enterprise is Bigger than you Think

• AI Agents Will Make Your AI More Useful

• “What Are the Best (and Easiest) Platforms to Build AI Agents?”

• How to Choose the Right AI Tool for Your Marketing Needs

• Why Leaders and Employees Don’t See Eye to Eye  

• Agency (Or Other Partners) Using AI? Here's What You Need to Know

• How to Unlock Marketing Analytics with AI

Such a great way to start the day! ⭐⭐⭐⭐⭐

“I just wanted to say I loved today's Marketing in the Loop! I too was experimenting with a couple of AI agents and found them fascinating which led me to want to create one myself. Anyways, thank you so much for the newsletter!”

DEEP DIVE

I Asked ChatGPT to Predict the Best SuperBowl Ads

What started as a far-fetched idea turned into a full-blown project. After running various prediction exercises with ChatGPT, the ad that scored the highest for the 2025 Super Bowl was:

Lay’s, “The Little Farmer” (Note: I have a 10-year-old daughter and loved this ad)

And here’s the rest of our top 10 lineup:

1. Lay's, "The Little Farmer" - 6.30

2. Stella Artois, "David and Dave" - 6.27

3. Hims & Hers, "Sick of the System" - 6.26

4. Cirkul, "You Got Cirkul" - 6.24

5. Booking.com, "Get Your Stay Ridiculously Right" - 6.21

6. Uber Eats, "Century of Cravings" - 6.21

7. Pfizer, "Knock Out" - 6.19

8. NFL, "Somebody" - 6.19

9. Google, "Dream Job" - 6.18

10. Haagen-Dazs, "Not So Fast, Not So Furious" - 6.18

How We Got Here

I’m no professional data scientist, but I do consider myself pretty data-driven. Since my skills alone couldn’t handle every layer of this project, I enlisted Lex (my ChatGPT “colleague”) for help. We experimented with different AI solutions, exploring multiple linear models, decision trees, and even neural networks. They did the heavy lifting; I was just the human in the loop, offering moral support.

That said, it’s important to remember that this is an experiment, not necessarily a definitive prediction of the actual Super Bowl Ad Meter results. The goal was to push AI’s capabilities, test different methodologies, and see how well an algorithm could anticipate audience reactions.

Why USA Today Ad Meter?

Each year, the Ad Meter serves as a widely recognized benchmark for Super Bowl ads. I replicated its general approach, covering the same ads their panelists review - though I could only get my hands on 38 out of 55 ads by my cutoff time (1 p.m. PST). Once USA Today announces the official scores, I’ll remove any ads I didn’t analyze so we have a fair comparison. 🤞🏼

ChatGPT - Deep Research

My Process: The Journey and Lessons

• Understanding USA Today’s Methodology
  I first read up on how the Ad Meter works. Then I collaborated with ChatGPT Deep Research to build a project plan that could mimic the diversity of the voting panel. We crafted various “personas” to reflect different audience segments.

• Figuring Out How AI Could ‘Watch’ Videos
  Current AI models aren’t great at directly analyzing video content, especially when dialogue is minimal - so I had to get creative. Instead of relying on AI to "watch" the ads, I gathered text-based data from multiple sources and used ChatGPT Deep Research to generate detailed, scene-by-scene descriptions. Each summary ranged from 30,000 to 50,000 characters and took about five minutes to compile, pulling insights from 10–20 sources.

  Each ad summary covered the following key aspects:

  • Narrative Overview: Plot summary and key themes

  • Scene-by-Scene Breakdown: Step-by-step breakdown of visuals and actions

  • Emotional Tone and Atmosphere: How the ad makes viewers feel and its comedic, dramatic, or uplifting elements

  • Visual and Audio Elements: Cinematography, special effects, music, and dialogue analysis

  • Brand Message and Intended Impact: What the ad is trying to communicate and how it resonates with the audience

  • Audience Reception and Industry Reactions: How the public and marketing experts are responding

• Building a Scoring Model
  Next came the real challenge: constructing a reliable system for scoring ads. We started with the o1 pro model, which crashed frequently. The o3-mini models also struggled, so I ended up creating a custom GPT instance that worked surprisingly well. It was the most consistent and reliable model amongst all tested. After several hours of back-and-forth tweaks - using 2024 ad results as a training set - the model settled on a Gradient Boosting approach, scoring ads on:

  • Emotional Impact (EI) – Does it evoke joy, nostalgia, excitement, or other strong emotions?

  • Entertainment Value (EV) – Is it fun, engaging, and watchable?

  • Brand Integration (BI) – Does the brand fit naturally into the storyline?

  • Storytelling (ST) – Is there a compelling arc or narrative?

  • Originality (OR) – Does it feel fresh and stand out?

  • Cultural Relevance (CR) – Does it tap into timely trends or social moments?

  • Virality/Social Impact (VF) – Will people talk about and share it?

Each factor was weighted based on the model’s training data. We used around 20 older ads for calibration - far from perfect, but still a neat experiment.

For the data enthusiasts out there, the predicted ad score is calculated using a weighted sum of factors, optimized via gradient boosting:

Predicted Score=w1(EI)+w2(EV)+w3(BI)+w4(ST)+w5(OR)+w6(CR)+w7(VF)+ϵ\text{Predicted Score} = w_1(EI) + w_2(EV) + w_3(BI) + w_4(ST) + w_5(OR) + w_6(CR) + w_7(VF) + \epsilonPredicted Score=w1​(EI)+w2​(EV)+w3​(BI)+w4​(ST)+w5​(OR)+w6​(CR)+w7​(VF)+ϵ

Where w1,w2,...,w7w_1, w_2, ..., w_7w1​,w2​,...,w7​ are the optimized weight coefficients for each factor and ϵ\epsilonϵ represents model error (residual).

ChatGPT - GPT

Key Insights: What Works (and What Doesn’t)

Top Drivers of a Great Super Bowl Ad

• Humor

  • Always a strong crowd-pleaser.

  • Especially effective when combined with celebrity cameos or relatable scenarios.

  • Playful, absurd, or nostalgic humor scores highest.

• Emotional Appeal

  • Sentimental or inspirational tones resonate deeply.

  • Overly serious ads can backfire if they become heavy-handed.

• Brand Familiarity

  • Established brands often outperform newcomers.

  • For a lesser-known brand, a big creative hook is essential.

• Celebrity Endorsements

  • Works if the celeb is a natural fit and not just a gimmick.

  • Authenticity matters - forced cameos rarely pay off.

• Surprise Factor

  • Plot twists or unexpected turns keep viewers engaged.

  • Shock value for its own sake isn’t enough; it must tie back to the brand.

• Strong Storytelling

  • Clear beginnings, middles, and endings perform best.

  • Character-driven “mini-movies” stand out in viewer recall.

Common Pitfalls

• Overly Sales-Driven Spots
  Hard-sell commercials feel like standard ads, which nobody wants during the Big Game.

• Confusing or Overly Abstract Storylines
  If the message is unclear, viewers will lose interest.

• Pure Nostalgia Without a Twist
  Throwbacks can work but need to add something new to keep modern audiences engaged.

• Being Too Polarizing or Edgy
  Viewers generally want fun content; heavy politics or controversial issues risk backlash.

ChatGPT - GPT

Wrapping Up

These scores are just predictions, of course. Real results will vary, especially since we had limited data and used a modest training set. But even if we only get a few ads right, this has been an invaluable test of how far AI-based experimentation can go.

Big Takeaways:

1. Expect Roadblocks and Stay Resourceful
   Innovation involves creative problem-solving. If one avenue fails, try another.

2. Model Selection Matters
   Picking the right AI model isn’t always straightforward. Research, test, and pivot until you find something that works.

3. New Frontiers Are Accessible
   The tools are out there for anyone curious enough to explore and iterate, even if you’re not a coding guru.

ChatGPT - GPt

Ultimately, this was a testament to the opportunity we have ahead - to push the boundaries of data-driven marketing, creativity, and audience engagement. While the approach isn’t perfect, it’s already proving to be a powerful way to uncover insights, test hypotheses, and enhance decision-making in ways that were nearly impossible just a few years ago.

As marketers, we’re just scratching the surface of what’s possible. Whether it’s predicting ad performance, optimizing creative strategies, or personalizing content at scale, new technologies are evolving from futuristic concepts into practical tools that, when used thoughtfully, can drive real results.

Now, I’m curious - what do you think? Could data and analytics one day predict Super Bowl ad winners with near-perfect accuracy? Or do human instincts and emotions make this an unpredictable art form? Reply and let me know your thoughts!

If you’re interested in a deeper discussion, I’m hosting a live community session this Friday to explore how we can apply this approach to test marketing campaigns, strategies, and creative effectiveness. Click below to let me know if you’d like to join, and I’ll send an invite soon. Hope to see you there!

Hi, marketers in the loop,

I had a wild idea while wrapping up my 2024 AI recap: what if a “standard” AI tool could predict the best Super Bowl ad? Some of you agreed in that edition’s poll that it sounded like a plausible challenge.

So this week, I put that theory to the test using ChatGPT - after trying practically every AI under the sun. My experiment included solutions built for video descriptions, real-time screen-sharing analysis, and advanced reasoning. It was a blast to push boundaries, but also more time-consuming than my usual newsletter writing process. This time, I easily spent twice the usual 8–12 hours.

The good news is that I had an experienced “data analyst” working alongside me - Lex, my ChatGPT-based helper. We had fun collaborating on this special project: predicting this year’s top Super Bowl ads. 😁

Enjoy the read!

- Rei

P.S. I would love to hear from you. Please reply and let me know your thoughts on this week’s newsletter. I read and respond to every message.

In this issue:

• What’s the Best Super Bowl Ad According to ChatGPT?

• Too Busy for AI? Think again

• Why Employees Smuggle AI Into Work

• And much more

Reading time: 7 minutes

MY FAVORITE FINDS THIS WEEK

Must Read

▪️Too Busy for AI? Think Again. (Masthead)

▪️The Stochastic Mindset - AI tools will change the way we work by changing the way we think. (Sequoia Capital)

Events

▪️The AI x Marketing Summit 2025 (March 12-13, San Francisco)

Use code "EARLYREI" for a 10% discount.

▪️The AI Value Creation Playbook (Section)

Industry News

▪️Marketers Say They Are Ready To Move Beyond Google Search (Forbes)

▪️Google AI Overviews Now with Detailed Shopping Comparisons (Search Engine Land)

Industry Reports

▪️Why Employees Smuggle AI Into Work (BBC)

▪️The State of Generative AI in the Enterprise (Deloitte)

AI Agents

▪️OpenAI Just Quietly Demoed a New Agent - a Sales Agent. (Kieran Flanagan on Linkedin)

▪️You've Heard of B2B and B2C, so What's B2A (Business to AI-Agent)? (Jeremiah Owyang)

Practical Tips

▪️5 Ways to Use NotebookLM Plus for your Business (Google)

▪️AI Execs at Big Consulting Firms Share their Favorite Prompts and How They Use the Technology (Business Insider)

Thought Leadership

▪️What Marketers Can Expect as CTV and Retail Media Converge in 2025 (Marketing Dive)

▪️Bring Your Own AI: How to Balance Risks and Innovation (MIT Review)

DEEP DIVE

I Asked ChatGPT to Predict the Best SuperBowl Ads

What started as a far-fetched idea turned into a full-blown project. After running various prediction exercises with ChatGPT, the ad that scored the highest for the 2025 Super Bowl was:

Lay’s, “The Little Farmer” (Note: I have a 10-year-old daughter and loved this ad)

And here’s the rest of our top 10 lineup:

1. Lay's, "The Little Farmer" - 6.30

2. Stella Artois, "David and Dave" - 6.27

3. Hims & Hers, "Sick of the System" - 6.26

4. Cirkul, "You Got Cirkul" - 6.24

5. Booking.com, "Get Your Stay Ridiculously Right" - 6.21

6. Uber Eats, "Century of Cravings" - 6.21

7. Pfizer, "Knock Out" - 6.19

8. NFL, "Somebody" - 6.19

9. Google, "Dream Job" - 6.18

10. Haagen-Dazs, "Not So Fast, Not So Furious" - 6.18

How We Got Here

I’m no professional data scientist, but I do consider myself pretty data-driven. Since my skills alone couldn’t handle every layer of this project, I enlisted Lex (my ChatGPT “colleague”) for help. We experimented with different AI solutions, exploring multiple linear models, decision trees, and even neural networks. They did the heavy lifting; I was just the human in the loop, offering moral support.

That said, it’s important to remember that this is an experiment, not necessarily a definitive prediction of the actual Super Bowl Ad Meter results. The goal was to push AI’s capabilities, test different methodologies, and see how well an algorithm could anticipate audience reactions.

Why USA Today Ad Meter?

Each year, the Ad Meter serves as a widely recognized benchmark for Super Bowl ads. I replicated its general approach, covering the same ads their panelists review - though I could only get my hands on 38 out of 55 ads by my cutoff time (1 p.m. PST). Once USA Today announces the official scores, I’ll remove any ads I didn’t analyze so we have a fair comparison. 🤞🏼

ChatGPT - Deep Research

My Process: The Journey and Lessons

• Understanding USA Today’s Methodology
  I first read up on how the Ad Meter works. Then I collaborated with ChatGPT Deep Research to build a project plan that could mimic the diversity of the voting panel. We crafted various “personas” to reflect different audience segments.

• Figuring Out How AI Could ‘Watch’ Videos
  Current AI models aren’t great at directly analyzing video content, especially when dialogue is minimal - so I had to get creative. Instead of relying on AI to "watch" the ads, I gathered text-based data from multiple sources and used ChatGPT Deep Research to generate detailed, scene-by-scene descriptions. Each summary ranged from 30,000 to 50,000 characters and took about five minutes to compile, pulling insights from 10–20 sources.

  Each ad summary covered the following key aspects:

  • Narrative Overview: Plot summary and key themes

  • Scene-by-Scene Breakdown: Step-by-step breakdown of visuals and actions

  • Emotional Tone and Atmosphere: How the ad makes viewers feel and its comedic, dramatic, or uplifting elements

  • Visual and Audio Elements: Cinematography, special effects, music, and dialogue analysis

  • Brand Message and Intended Impact: What the ad is trying to communicate and how it resonates with the audience

  • Audience Reception and Industry Reactions: How the public and marketing experts are responding

• Building a Scoring Model
  Next came the real challenge: constructing a reliable system for scoring ads. We started with the o1 pro model, which crashed frequently. The o3-mini models also struggled, so I ended up creating a custom GPT instance that worked surprisingly well. It was the most consistent and reliable model amongst all tested. After several hours of back-and-forth tweaks - using 2024 ad results as a training set - the model settled on a Gradient Boosting approach, scoring ads on:

  • Emotional Impact (EI) – Does it evoke joy, nostalgia, excitement, or other strong emotions?

  • Entertainment Value (EV) – Is it fun, engaging, and watchable?

  • Brand Integration (BI) – Does the brand fit naturally into the storyline?

  • Storytelling (ST) – Is there a compelling arc or narrative?

  • Originality (OR) – Does it feel fresh and stand out?

  • Cultural Relevance (CR) – Does it tap into timely trends or social moments?

  • Virality/Social Impact (VF) – Will people talk about and share it?

Each factor was weighted based on the model’s training data. We used around 20 older ads for calibration - far from perfect, but still a neat experiment.

For the data enthusiasts out there, the predicted ad score is calculated using a weighted sum of factors, optimized via gradient boosting:

Predicted Score=w1(EI)+w2(EV)+w3(BI)+w4(ST)+w5(OR)+w6(CR)+w7(VF)+ϵ\text{Predicted Score} = w_1(EI) + w_2(EV) + w_3(BI) + w_4(ST) + w_5(OR) + w_6(CR) + w_7(VF) + \epsilonPredicted Score=w1​(EI)+w2​(EV)+w3​(BI)+w4​(ST)+w5​(OR)+w6​(CR)+w7​(VF)+ϵ

Where w1,w2,...,w7w_1, w_2, ..., w_7w1​,w2​,...,w7​ are the optimized weight coefficients for each factor and ϵ\epsilonϵ represents model error (residual).

ChatGPT - GPT

Key Insights: What Works (and What Doesn’t)

Top Drivers of a Great Super Bowl Ad

• Humor

  • Always a strong crowd-pleaser.

  • Especially effective when combined with celebrity cameos or relatable scenarios.

  • Playful, absurd, or nostalgic humor scores highest.

• Emotional Appeal

  • Sentimental or inspirational tones resonate deeply.

  • Overly serious ads can backfire if they become heavy-handed.

• Brand Familiarity

  • Established brands often outperform newcomers.

  • For a lesser-known brand, a big creative hook is essential.

• Celebrity Endorsements

  • Works if the celeb is a natural fit and not just a gimmick.

  • Authenticity matters - forced cameos rarely pay off.

• Surprise Factor

  • Plot twists or unexpected turns keep viewers engaged.

  • Shock value for its own sake isn’t enough; it must tie back to the brand.

• Strong Storytelling

  • Clear beginnings, middles, and endings perform best.

  • Character-driven “mini-movies” stand out in viewer recall.

Common Pitfalls

• Overly Sales-Driven Spots
  Hard-sell commercials feel like standard ads, which nobody wants during the Big Game.

• Confusing or Overly Abstract Storylines
  If the message is unclear, viewers will lose interest.

• Pure Nostalgia Without a Twist
  Throwbacks can work but need to add something new to keep modern audiences engaged.

• Being Too Polarizing or Edgy
  Viewers generally want fun content; heavy politics or controversial issues risk backlash.

ChatGPT - GPT

Wrapping Up

These scores are just predictions, of course. Real results will vary, especially since we had limited data and used a modest training set. But even if we only get a few ads right, this has been an invaluable test of how far AI-based experimentation can go.

Big Takeaways:

1. Expect Roadblocks and Stay Resourceful
   Innovation involves creative problem-solving. If one avenue fails, try another.

2. Model Selection Matters
   Picking the right AI model isn’t always straightforward. Research, test, and pivot until you find something that works.

3. New Frontiers Are Accessible
   The tools are out there for anyone curious enough to explore and iterate, even if you’re not a coding guru.

ChatGPT - GPt

Ultimately, this was a testament to the opportunity we have ahead - to push the boundaries of data-driven marketing, creativity, and audience engagement. While the approach isn’t perfect, it’s already proving to be a powerful way to uncover insights, test hypotheses, and enhance decision-making in ways that were nearly impossible just a few years ago.

As marketers, we’re just scratching the surface of what’s possible. Whether it’s predicting ad performance, optimizing creative strategies, or personalizing content at scale, new technologies are evolving from futuristic concepts into practical tools that, when used thoughtfully, can drive real results.

Now, I’m curious - what do you think? Could data and analytics one day predict Super Bowl ad winners with near-perfect accuracy? Or do human instincts and emotions make this an unpredictable art form? Reply and let me know your thoughts!

If you’re interested in a deeper discussion, I’m hosting a live community session this Friday to explore how we can apply this approach to test marketing campaigns, strategies, and creative effectiveness. Click below to let me know if you’d like to join, and I’ll send an invite soon. Hope to see you there!

SUPER BOWL AD POLL

ICYMI

▪️DeepSeek Effect on Enterprise is Bigger than you Think

▪️AI Agents Will Make Your AI More Useful

▪️“What Are the Best (and Easiest) Platforms to Build AI Agents?”

▪️How to Choose the Right AI Tool for Your Marketing Needs

▪️Why Leaders and Employees Don’t See Eye to Eye  

▪️Agency (Or Other Partners) Using AI? Here's What You Need to Know

▪️AI And Copyright: A Quick Marketing Guide

▪️How to Unlock Marketing Analytics with AI

Did you like this email? Share this link with a friend for a virtual high five!

CASE STUDY

How Yum! Brands is Using AI to Personalize Marketing and Drive Sales

Yum! Brands, the parent company of Taco Bell, KFC, and Pizza Hut, is leveraging AI to enhance its marketing effectiveness, leading to increased consumer engagement and sales. By deploying AI-driven personalization, the company has made strides in optimizing its customer interactions and improving the return on its marketing investments.

The Challenge

With a vast global customer base, Yum! Brands needed a more precise way to engage its consumers. Traditional marketing methods often relied on broad segmentation rather than personalized messaging, which meant that some campaigns failed to fully resonate with individual customers. The company sought a scalable AI-driven solution to improve personalization and marketing efficiency.

The AI-Driven Solution

Yum! Brands has launched 40 AI-driven marketing projects across its three largest U.S. brands, optimizing digital campaigns through data-driven personalization. AI analyzes purchase history, time of day, and channel preferences to tailor messaging for upselling, retention, and customer re-engagement.

For example, Pizza Hut segments customers based on ordering behaviors - frequent buyers receive menu recommendations, while occasional buyers get event-specific promotions. Instead of generic offers, AI delivers better-timed, personalized interactions, making marketing more relevant and engaging. By shifting from a one-size-fits-all approach, Yum! Brands enhances customer experience and long-term loyalty.

New AI-Driven Opportunities on the Horizon

Yum! Brands has emphasized that its AI initiatives are designed to be scalable across multiple brands and regions. The AI systems in use allow for rapid learning and adaptation, meaning campaigns can be optimized dynamically without relying solely on traditional A/B testing.

At Taco Bell, for example, AI-powered voice ordering technology is being expanded to hundreds of U.S. drive-thru locations. This technology is designed to streamline ordering processes and improve customer experience, showcasing another dimension of how AI is transforming Yum! Brands’ customer interactions.

Real-Time Gains

Compared to traditional campaigns, AI-driven personalization has led to double-digit increases in customer engagement and conversions. The ability to optimize marketing in real-time - rather than waiting weeks for test results - has allowed Yum! Brands to capture more impulse purchases and increase overall basket sizes.

Future Directions

Encouraged by its initial successes, Yum! Brands plans to further expand its AI capabilities. This includes refining AI-driven messaging across digital marketing channels and integrating AI-powered customer interaction points, such as its drive-thru technology at Taco Bell.

The company is committed to scaling these AI initiatives across its portfolio, ensuring that automation and personalization continue to drive better consumer experiences and business performance.

Click below to learn more:

▪️Yum Brands says AI-powered marketing is helping drive transactions (Marketing Dive)

▪️Taco Bell and KFC’s owner says AI-driven marketing is boosting purchases (Wall Street Journey)

YOU ASK, I ANSWER

“What Are the Best (and Easiest) Platforms to Build AI Agents?”

AI agents have the potential to transform how marketing teams operate - automating repetitive tasks, analyzing customer data in real time, and ensuring consistent brand interactions. The good news? You don’t need advanced programming skills to get started. No-code and low-code platforms now make it easier than ever to explore AI agents quickly and cost-effectively.

But… What Is an AI Agent, Really?

One of the biggest challenges in discussing AI agents is the lack of standardized industry terminology. Depending on the context, an AI agent could refer to anything from a simple automated chatbot to a sophisticated system capable of multi-step reasoning, task execution, and even web browsing. Given this fluid definition, we take a broader perspective in this article - focusing on AI-powered tools that can execute tasks independently, enhance marketing workflows, and improve customer experiences. If you're looking for a deeper dive into AI agent fundamentals, check out my 'AI Agents 101' article.

Why You Should Start Now

AI agents are evolving rapidly, but waiting for a “perfect” solution means falling behind. Early adoption gives marketing teams an edge by allowing them to:

• Build foundational AI capabilities before technology becomes mainstream.

• Optimize workflows incrementally, rather than undergoing a massive transformation later.

• Understand AI’s limitations firsthand and adjust strategies accordingly.

• Stay ahead of competitors who delay implementation.

When (and When Not) to Use AI Agents

While AI agents offer significant benefits - automation at scale, data-driven insights, and reduced manual workload - they aren’t a universal solution.

When AI Agents Make Sense:
✅ Repetitive, time-consuming tasks (e.g., email categorization, social media responses)
✅ High-volume, data-driven workflows (e.g., personalization, predictive analytics)
✅ Multi-step, automated processes (e.g., lead nurturing sequences)

When to Hold Off:
❌ Tasks requiring deep domain expertise and nuanced decision-making (e.g., legal compliance, medical decisions)
❌ High-stakes creative or emotional work (e.g., brand storytelling, therapy)
❌ Environments with strict regulations or privacy concerns (e.g., finance, healthcare)

10 Key Questions to Ask Before Implementing an AI Agent

Before deploying an AI agent, assess its impact using these questions (plus this guide):

1. What is the complexity of the task?

2. How frequently does the task occur?

3. What is the expected volume of data or queries?

4. How would be the impact on the human workforce?

5. What level of accuracy is required?

6. Is human expertise or emotional intelligence essential?

7. What are the privacy and security implications?

8. What are the regulatory and compliance requirements?

9. What is the cost-benefit analysis?

10. What does “hiring" and “onboarding” look like? (See example below)

Solutions to Consider

1. What You Already Have in Your Tech Stack

If you're using OpenAI, you already have access to Custom GPTs and, if you're a Pro user, Operator - with Deep Research expected to roll out soon. Microsoft users can tap into Copilot and Copilot Studio, which integrate seamlessly across their ecosystem. Google users, however, may need to wait for broader access to Project Mariner or Agentspace - or apply for early access through experimental programs here and here.

These are just a few options. The best starting point? Look at the AI tools you already have access to and explore their agent-like capabilities - you might be surprised by what they can do.

2. Custom GPTs and Operator (OpenAI)

Custom GPTs make it easy to tailor AI to your specific needs, whether that’s refining your content creation process, incorporating proprietary knowledge, or optimizing workflows. Operator, OpenAI’s more advanced AI agent, can autonomously complete multi-step tasks.

3. Copilot (Microsoft)

Microsoft’s Copilot and Copilot Studio are already embedded in its ecosystem, helping businesses automate internal processes, analyze data, and manage marketing content. For organizations already using Microsoft products, this is a seamless way to integrate AI agents into existing workflows.

4. Agent.ai

Agent.ai describes itself as a "marketplace and professional network for AI agents and the people who love them." Users can create, share, and discover AI agents designed for various tasks - ranging from company research to optimizing landing pages for better conversion rates.

5. Taskade

Originally a productivity and collaboration tool, Taskade now supports AI-driven automation. Users can build AI-powered workflows for task management, team communication, and custom automation needs - all in one streamlined platform.

6. Zapier

Zapier has rebranded Zapier Central to Agents, introducing AI teammates that can be equipped with company knowledge and integrated across 7,000+ apps. If you're already familiar with Zapier’s automation tools, this could be a natural next step.

7. Relevance AI

Relevance AI helps users build an "AI workforce" without coding. It offers semi-ready templates and customization tools for creating AI agents that automate tasks, analyze data, and streamline workflows.

Final Thoughts

No-code and low-code AI platforms make adoption more accessible, but implementation should be strategic. By evaluating your existing tech stack, identifying key workflows, human workforce impact, and weighing costs against benefits, you can ensure that AI agents enhance your operations - without introducing unnecessary complexity.

DEEP DIVE

The Hidden Risks of DeepSeek and Shadow AI in the Enterprise

The rise of “shadow AI” or “BYO AI”, where employees use unapproved AI tools for work, presents major risks. DeepSeek, a high-performing Chinese-developed AI model, has gained rapid adoption (it’s one of the most downloaded apps worldwide), but it raises unique concerns for enterprises.

In this article, we’ll explore the key risks of DeepSeek and similar unvetted AI tools, compare them to established AI platforms like ChatGPT, Gemini, Copilot, and Claude, and provide strategies for business and marketing leaders to mitigate risks while fostering innovation.

A Balanced Perspective on AI Governance

It’s important to note I am not against DeepSeek or AI innovation in general. My goal is to propose a balanced and thoughtful approach that weighs both risks and rewards, while highlighting why AI governance matters.

This is a rapidly evolving space. I encourage you to do your own research. Many new articles are emerging on this topic such as DeepSeek hit with ‘large-scale’ cyber-attack after AI chatbot tops app store from The Guardian.

7 Key Risks of DeepSeek and Shadow AI

1. AI Training Risks: Is Your Data Being Used?

AI providers improve their solutions using user interactions. Unless an AI tool explicitly states otherwise, it’s recommended to assume that any data input is used for training. As an example, OpenAI’s ChatGPT originally trained on user data but later provided opt-outs for business users. DeepSeek has not publicly disclosed its data retention and training policies to the same extent as OpenAI, Google and others, which raises concerns for enterprise users.

2. Data Security and Confidentiality Risks

AI tools process and store user inputs, meaning employees might unknowingly expose sensitive business data. For example, Samsung engineers accidentally uploaded proprietary source code to ChatGPT, leading to concerns about unintended leaks. Many major banks and enterprises have banned generative AI over similar fears."

DeepSeek’s security risks stem not only from where it stores data but also from the lack of clear enterprise safeguards against accidental data sharing. Without strict policies, employees may input confidential information into AI tools, which could expose proprietary data.

3. Security Vulnerabilities and Cyber Risks

All AI platforms can be exploited by prompt injections (tricking the AI into revealing restricted info) or malicious outputs (e.g., insecure code, phishing attempts). However, DeepSeek faces additional security scrutiny because it has already been targeted by large-scale cyberattacks.

Governments are taking note: Italy and Taiwan have raised security concerns about DeepSeek, while U.S. agencies, including the National Security Council, are reviewing the risks of foreign AI models. Some organizations have restricted unvetted AI tools due to security risks.

4. Data Residency and Global Compliance Risks

Many industries like finance, healthcare, and legal must comply with strict data residency laws (e.g., GDPR in the EU, HIPAA in the U.S.). DeepSeek currently does not provide an EU-based data hosting option, which may raise GDPR compliance concerns for businesses handling regulated data.

Comparison with other AI tools: Unlike ChatGPT, Gemini, and Claude, which offer enterprise users some degree of regional data controls, DeepSeek stores all data in China. Under China’s cybersecurity laws, data stored in the country is subject to government access, posing additional compliance risks for enterprises operating in the EU and U.S..

5. Hallucination and Misinformation Risks

All AI models hallucinate, meaning they generate false information that sounds plausible. Some benchmarks report that DeepSeek’s R1 model has a hallucination rate of 14.3%, though rates vary depending on tasks and datasets. Without verification, employees risk spreading misleading data in reports, marketing materials, or customer communications.

6. Ethical Bias and Censorship Risks

AI tools inherit biases from their training data, potentially leading to unethical or misleading outputs. DeepSeek adheres to Chinese content regulations, restricting responses on topics like Tiananmen Square and China’s political system. While other AI models also have content restrictions, DeepSeek’s censorship aligns with government policies, raising concerns about bias and completeness.

Business impact: similar to other AI tools, if employees rely on DeepSeek for any work related activities they may inadvertently produce biased or incomplete content, harming brand reputation.

7. Regulatory and Compliance Risks

Using unapproved AI tools may expose businesses to compliance violations. In regulated industries, using an AI like DeepSeek without governance controls can lead to breach of customer privacy, violations of data-sharing agreements, and regulatory fines and penalties.

Italy, France, and South Korea have expressed concerns about AI data privacy risks, and U.S. regulators (FTC, SEC) are warning businesses about improper AI use. While DeepSeek has not been directly named in major enforcement actions, regulatory scrutiny around AI data handling is increasing.

Additionally, DeepSeek’s terms of service pose legal uncertainties: Dispute resolution is required in China, meaning international businesses have little recourse if legal issues arise.

Basic Recommendations for Business and Marketing Leaders

• Establish an AI Usage Policy: Build a cross-functional team to provide guidelines and educate employees on approved AI tools, data privacy risks, and compliance rules.

• Use Enterprise-Grade AI Tools: Provide vetted enterprise-grade solutions like ChatGPT Enterprise, Google Gemini, or Microsoft Copilot.

• Protect Proprietary Data from AI Training: Use AI tools that allow opt-outs from training and ensure employees don’t paste confidential data into AI tools.

• Partner with IT, Legal and HR: Proactively discuss overall guidance and actions related to new AI tools.

Balancing Innovation and Risk in AI Adoption

The rise of new AI tools like DeepSeek brings both opportunities and risks. While AI can enhance efficiency, creativity, and automation, business and marketing leaders must ensure that AI adoption is secure, ethical, and compliant.

By proactively managing AI risks, companies can innovate responsibly without exposing themselves to data breaches, compliance violations, or reputational harm.

Hi, marketers in the loop,

A big thank you to those who joined our live community session on Friday! One of the key takeaways? We need more time for peer-to-peer discussions and networking.

It got me thinking about a paradox of our time: AI is becoming so good at generating and optimizing content that what’s truly scarce - real human connection - is more valuable than ever. I came across an insightful piece on Big Think that explores this idea: as the digital world grows louder and more automated, we’ll increasingly crave what’s rare, authentic, and deeply human.

With that in mind, stay tuned as we work on new ways to foster meaningful conversations and deeper connections in our community.

Onward,
Rei

P.S.: OpenAI launched a new AI agent on Sunday called Deep Research, and I used it to write the piece about DeepSeek below. The research quality is mind-blowing - it will save me at least 4–6 hours per project. You should give it a try, it will likely be available to Enterprise and Plus users soon.

In this issue:

• What you should know before using DeepSeek at work

• Easy to use agentic platforms for marketing workflows

• Taco Bell, KFC, and Pizza Hut case study

• And much more

Reading time: 10 minutes

MY FAVORITE FINDS THIS WEEK

Must Read

▪️How Accenture is building an agentic marketing workforce (Accenture)

▪️From potential to profit: Closing the AI impact gap (BCG)

▪️Which AI to use now: An updated opinionated guide (Ethan Mollick)

Event

▪️The AI x Marketing Summit 2025 (March 12-13, San Francisco)

Use code "EARLYREI" for a 10% discount.

Industry News

▪️DeepSeek’s safety guardrails failed every test researchers threw at its AI chatbot (Wired)

▪️U.S. Copyright Office issues guidance on copyrightability of AI content (U.S. Copyright Office)

▪️Zapier Agents: AI agents that automate work across apps (Zapier)

Industry Reports

▪️Creative industry workers feel job worth and security under threat from AI (Queen Mary University of London)

▪️Is AI making you suffer from FOBO (Fear of Becoming Obsolete)? Here's what can help (World Economic Forum)

▪️Digital and wealth gaps have no place in the Intelligent Age: How everyone can benefit from AI (World Economic Forum)

AI Agents

▪️The future of marketing: How AI agents can enhance customer journeys in retail (Nvidia)

▪️AI workers for future marketing jobs (Moonshot)

▪️The great debate: Will agentic AI kill SaaS? (Bain)

Practical Tips

▪️Google's prompting guide 101 - A quick-start handbook for effective prompts (Google)

▪️The RAPPEL AI prompt framework will surprise you (Trust Insights)

▪️Nvidia CEO Jensen Huang says everyone should get an AI tutor right away (Fortune)

Thought Leadership

▪️The new AI role every content team needs (Content Marketing Institute)

▪️How to maintain humanity in agencies’ creative work in the AI era (Forbes)

▪️Soft skills are more critical than ever for post-AI professionals (HR Dive)

DEEP DIVE

The Hidden Risks of DeepSeek and Shadow AI in the Enterprise

The rise of “shadow AI” or “BYO AI”, where employees use unapproved AI tools for work, presents major risks. DeepSeek, a high-performing Chinese-developed AI model, has gained rapid adoption (it’s one of the most downloaded apps worldwide), but it raises unique concerns for enterprises.

In this article, we’ll explore the key risks of DeepSeek and similar unvetted AI tools, compare them to established AI platforms like ChatGPT, Gemini, Copilot, and Claude, and provide strategies for business and marketing leaders to mitigate risks while fostering innovation.

A Balanced Perspective on AI Governance

It’s important to note I am not against DeepSeek or AI innovation in general. My goal is to propose a balanced and thoughtful approach that weighs both risks and rewards, while highlighting why AI governance matters.

This is a rapidly evolving space. I encourage you to do your own research. Many new articles are emerging on this topic such as DeepSeek hit with ‘large-scale’ cyber-attack after AI chatbot tops app store from The Guardian.

7 Key Risks of DeepSeek and Shadow AI

1. AI Training Risks: Is Your Data Being Used?

AI providers improve their solutions using user interactions. Unless an AI tool explicitly states otherwise, it’s recommended to assume that any data input is used for training. As an example, OpenAI’s ChatGPT originally trained on user data but later provided opt-outs for business users. DeepSeek has not publicly disclosed its data retention and training policies to the same extent as OpenAI, Google and others, which raises concerns for enterprise users.

2. Data Security and Confidentiality Risks

AI tools process and store user inputs, meaning employees might unknowingly expose sensitive business data. For example, Samsung engineers accidentally uploaded proprietary source code to ChatGPT, leading to concerns about unintended leaks. Many major banks and enterprises have banned generative AI over similar fears."

DeepSeek’s security risks stem not only from where it stores data but also from the lack of clear enterprise safeguards against accidental data sharing. Without strict policies, employees may input confidential information into AI tools, which could expose proprietary data.

3. Security Vulnerabilities and Cyber Risks

All AI platforms can be exploited by prompt injections (tricking the AI into revealing restricted info) or malicious outputs (e.g., insecure code, phishing attempts). However, DeepSeek faces additional security scrutiny because it has already been targeted by large-scale cyberattacks.

Governments are taking note: Italy and Taiwan have raised security concerns about DeepSeek, while U.S. agencies, including the National Security Council, are reviewing the risks of foreign AI models. Some organizations have restricted unvetted AI tools due to security risks.

4. Data Residency and Global Compliance Risks

Many industries like finance, healthcare, and legal must comply with strict data residency laws (e.g., GDPR in the EU, HIPAA in the U.S.). DeepSeek currently does not provide an EU-based data hosting option, which may raise GDPR compliance concerns for businesses handling regulated data.

Comparison with other AI tools: Unlike ChatGPT, Gemini, and Claude, which offer enterprise users some degree of regional data controls, DeepSeek stores all data in China. Under China’s cybersecurity laws, data stored in the country is subject to government access, posing additional compliance risks for enterprises operating in the EU and U.S..

5. Hallucination and Misinformation Risks

All AI models hallucinate, meaning they generate false information that sounds plausible. Some benchmarks report that DeepSeek’s R1 model has a hallucination rate of 14.3%, though rates vary depending on tasks and datasets. Without verification, employees risk spreading misleading data in reports, marketing materials, or customer communications.

6. Ethical Bias and Censorship Risks

AI tools inherit biases from their training data, potentially leading to unethical or misleading outputs. DeepSeek adheres to Chinese content regulations, restricting responses on topics like Tiananmen Square and China’s political system. While other AI models also have content restrictions, DeepSeek’s censorship aligns with government policies, raising concerns about bias and completeness.

Business impact: similar to other AI tools, if employees rely on DeepSeek for any work related activities they may inadvertently produce biased or incomplete content, harming brand reputation.

7. Regulatory and Compliance Risks

Using unapproved AI tools may expose businesses to compliance violations. In regulated industries, using an AI like DeepSeek without governance controls can lead to breach of customer privacy, violations of data-sharing agreements, and regulatory fines and penalties.

Italy, France, and South Korea have expressed concerns about AI data privacy risks, and U.S. regulators (FTC, SEC) are warning businesses about improper AI use. While DeepSeek has not been directly named in major enforcement actions, regulatory scrutiny around AI data handling is increasing.

Additionally, DeepSeek’s terms of service pose legal uncertainties: Dispute resolution is required in China, meaning international businesses have little recourse if legal issues arise.

Basic Recommendations for Business and Marketing Leaders

• Establish an AI Usage Policy: Build a cross-functional team to provide guidelines and educate employees on approved AI tools, data privacy risks, and compliance rules.

• Use Enterprise-Grade AI Tools: Provide vetted enterprise-grade solutions like ChatGPT Enterprise, Google Gemini, or Microsoft Copilot.

• Protect Proprietary Data from AI Training: Use AI tools that allow opt-outs from training and ensure employees don’t paste confidential data into AI tools.

• Partner with IT, Legal and HR: Proactively discuss overall guidance and actions related to new AI tools.

Balancing Innovation and Risk in AI Adoption

The rise of new AI tools like DeepSeek brings both opportunities and risks. While AI can enhance efficiency, creativity, and automation, business and marketing leaders must ensure that AI adoption is secure, ethical, and compliant.

By proactively managing AI risks, companies can innovate responsibly without exposing themselves to data breaches, compliance violations, or reputational harm.

SHADOW AI POLL

YOU ASK, I ANSWER

“What Are the Best (and Easiest) Platforms to Build AI Agents?”

AI agents have the potential to transform how marketing teams operate - automating repetitive tasks, analyzing customer data in real time, and ensuring consistent brand interactions. The good news? You don’t need advanced programming skills to get started. No-code and low-code platforms now make it easier than ever to explore AI agents quickly and cost-effectively.

But… What Is an AI Agent, Really?

One of the biggest challenges in discussing AI agents is the lack of standardized industry terminology. Depending on the context, an AI agent could refer to anything from a simple automated chatbot to a sophisticated system capable of multi-step reasoning, task execution, and even web browsing. Given this fluid definition, we take a broader perspective in this article - focusing on AI-powered tools that can execute tasks independently, enhance marketing workflows, and improve customer experiences. If you're looking for a deeper dive into AI agent fundamentals, check out my 'AI Agents 101' article.

Why You Should Start Now

AI agents are evolving rapidly, but waiting for a “perfect” solution means falling behind. Early adoption gives marketing teams an edge by allowing them to:

• Build foundational AI capabilities before technology becomes mainstream.

• Optimize workflows incrementally, rather than undergoing a massive transformation later.

• Understand AI’s limitations firsthand and adjust strategies accordingly.

• Stay ahead of competitors who delay implementation.

When (and When Not) to Use AI Agents

While AI agents offer significant benefits - automation at scale, data-driven insights, and reduced manual workload - they aren’t a universal solution.

When AI Agents Make Sense:
✅ Repetitive, time-consuming tasks (e.g., email categorization, social media responses)
✅ High-volume, data-driven workflows (e.g., personalization, predictive analytics)
✅ Multi-step, automated processes (e.g., lead nurturing sequences)

When to Hold Off:
❌ Tasks requiring deep domain expertise and nuanced decision-making (e.g., legal compliance, medical decisions)
❌ High-stakes creative or emotional work (e.g., brand storytelling, therapy)
❌ Environments with strict regulations or privacy concerns (e.g., finance, healthcare)

10 Key Questions to Ask Before Implementing an AI Agent

Before deploying an AI agent, assess its impact using these questions (plus this guide):

1. What is the complexity of the task?

2. How frequently does the task occur?

3. What is the expected volume of data or queries?

4. How would be the impact on the human workforce?

5. What level of accuracy is required?

6. Is human expertise or emotional intelligence essential?

7. What are the privacy and security implications?

8. What are the regulatory and compliance requirements?

9. What is the cost-benefit analysis?

10. What does “hiring" and “onboarding” look like? (See example below)

Solutions to Consider

1. What You Already Have in Your Tech Stack

If you're using OpenAI, you already have access to Custom GPTs and, if you're a Pro user, Operator - with Deep Research expected to roll out soon. Microsoft users can tap into Copilot and Copilot Studio, which integrate seamlessly across their ecosystem. Google users, however, may need to wait for broader access to Project Mariner or Agentspace - or apply for early access through experimental programs here and here.

These are just a few options. The best starting point? Look at the AI tools you already have access to and explore their agent-like capabilities - you might be surprised by what they can do.

2. Custom GPTs and Operator (OpenAI)

Custom GPTs make it easy to tailor AI to your specific needs, whether that’s refining your content creation process, incorporating proprietary knowledge, or optimizing workflows. Operator, OpenAI’s more advanced AI agent, can autonomously complete multi-step tasks.

3. Copilot (Microsoft)

Microsoft’s Copilot and Copilot Studio are already embedded in its ecosystem, helping businesses automate internal processes, analyze data, and manage marketing content. For organizations already using Microsoft products, this is a seamless way to integrate AI agents into existing workflows.

4. Agent.ai

Agent.ai describes itself as a "marketplace and professional network for AI agents and the people who love them." Users can create, share, and discover AI agents designed for various tasks - ranging from company research to optimizing landing pages for better conversion rates.

5. Taskade

Originally a productivity and collaboration tool, Taskade now supports AI-driven automation. Users can build AI-powered workflows for task management, team communication, and custom automation needs - all in one streamlined platform.

6. Zapier

Zapier has rebranded Zapier Central to Agents, introducing AI teammates that can be equipped with company knowledge and integrated across 7,000+ apps. If you're already familiar with Zapier’s automation tools, this could be a natural next step.

7. Relevance AI

Relevance AI helps users build an "AI workforce" without coding. It offers semi-ready templates and customization tools for creating AI agents that automate tasks, analyze data, and streamline workflows.

Final Thoughts

No-code and low-code AI platforms make adoption more accessible, but implementation should be strategic. By evaluating your existing tech stack, identifying key workflows, human workforce impact, and weighing costs against benefits, you can ensure that AI agents enhance your operations - without introducing unnecessary complexity.

CASE STUDY

How Yum! Brands is Using AI to Personalize Marketing and Drive Sales

Yum! Brands, the parent company of Taco Bell, KFC, and Pizza Hut, is leveraging AI to enhance its marketing effectiveness, leading to increased consumer engagement and sales. By deploying AI-driven personalization, the company has made strides in optimizing its customer interactions and improving the return on its marketing investments.

The Challenge

With a vast global customer base, Yum! Brands needed a more precise way to engage its consumers. Traditional marketing methods often relied on broad segmentation rather than personalized messaging, which meant that some campaigns failed to fully resonate with individual customers. The company sought a scalable AI-driven solution to improve personalization and marketing efficiency.

The AI-Driven Solution

Yum! Brands has launched 40 AI-driven marketing projects across its three largest U.S. brands, optimizing digital campaigns through data-driven personalization. AI analyzes purchase history, time of day, and channel preferences to tailor messaging for upselling, retention, and customer re-engagement.

For example, Pizza Hut segments customers based on ordering behaviors - frequent buyers receive menu recommendations, while occasional buyers get event-specific promotions. Instead of generic offers, AI delivers better-timed, personalized interactions, making marketing more relevant and engaging. By shifting from a one-size-fits-all approach, Yum! Brands enhances customer experience and long-term loyalty.

New AI-Driven Opportunities on the Horizon

Yum! Brands has emphasized that its AI initiatives are designed to be scalable across multiple brands and regions. The AI systems in use allow for rapid learning and adaptation, meaning campaigns can be optimized dynamically without relying solely on traditional A/B testing.

At Taco Bell, for example, AI-powered voice ordering technology is being expanded to hundreds of U.S. drive-thru locations. This technology is designed to streamline ordering processes and improve customer experience, showcasing another dimension of how AI is transforming Yum! Brands’ customer interactions.

Real-Time Gains

Compared to traditional campaigns, AI-driven personalization has led to double-digit increases in customer engagement and conversions. The ability to optimize marketing in real-time - rather than waiting weeks for test results - has allowed Yum! Brands to capture more impulse purchases and increase overall basket sizes.

Future Directions

Encouraged by its initial successes, Yum! Brands plans to further expand its AI capabilities. This includes refining AI-driven messaging across digital marketing channels and integrating AI-powered customer interaction points, such as its drive-thru technology at Taco Bell.

The company is committed to scaling these AI initiatives across its portfolio, ensuring that automation and personalization continue to drive better consumer experiences and business performance.

Click below to learn more:

▪️Yum Brands says AI-powered marketing is helping drive transactions (Marketing Dive)

▪️Taco Bell and KFC’s owner says AI-driven marketing is boosting purchases (Wall Street Journey)

ICYMI

▪️AI Agents Will Make Your AI More Useful

▪️How to Choose the Right AI Tool for Your Marketing Needs

▪️Why Leaders and Employees Don’t See Eye to Eye  

▪️Agency (Or Other Partners) Using AI? Here's What You Need to Know

▪️AI And Copyright: A Quick Marketing Guide

▪️How to Unlock Marketing Analytics with AI

Did you like this email? Share this link with a friend for a virtual high five!

CASE STUDY

How HexClad Enhanced Marketing Performance with Predictive Analytics

HexClad, known for its innovative cookware, faced a familiar challenge in today’s e-commerce world: achieving scalable growth while maintaining efficient ad spend. To address this, they implemented marketing mix modeling and predictive technology to supercharge their marketing efforts. This case study highlights how HexClad boosted revenue while cutting costs, offering a roadmap for other brands looking to scale efficiently.

The Challenge: Balancing Growth and Efficiency

HexClad experienced significant growth in recent years, driven by its unique hybrid cookware design. However, like many brands scaling rapidly, they encountered diminishing returns on ad spend and inefficiencies in campaign management. Their team needed a solution to optimize ad investments, predict revenue outcomes, and reduce wasted spend - all while making data-driven decisions faster.

The Solution: Predictive Analytics in Action

HexClad utilized an AI-driven analytics solution called Prescient AI to optimize marketing spend. Unlike traditional tools that rely on historical data, the platform’s forward-looking models predicted the revenue impact of marketing decisions in real time. This allowed HexClad’s team to:

• Optimize Marketing Spend: Prescient’s platform helped HexClad build a more cost-efficient marketing mix by analyzing the potential impact of different budget allocations and channels.

• Long-Term Projections: By forecasting ROAS several months out, HexClad made informed decisions that aligned with their growth goals.

• Real-Time Testing: The team performed on-demand gut checks of ad spend, allowing them to adjust campaigns dynamically for better performance.

The Results: Revenue Growth and Cost Efficiency

In just a few months, HexClad saw impressive results:

• Improved ROAS: Their return on ad spend (ROAS) improved by 20%, proving the efficiency of predictive decision-making.

• Cost efficiencies: HexClad achieved a 40% more cost-efficient marketing mix, delivering better results and proving the value of predictive decision-making.

• Time Savings: Automated insights eliminated over 20 hours per month of manual analysis, allowing the team to focus on higher-value strategic tasks.

These results demonstrate how marketing mix modeling and predictive analytics can help brands scale effectively without sacrificing efficiency - a critical advantage in today’s competitive e-commerce landscape.

Lessons for Marketers

HexClad’s success offers valuable lessons for marketers:

1. Invest in Marketing Mix Modeling and Predictive Analytics: Forward-looking tools enable marketers to anticipate outcomes, not just react to past performance.

2. Focus on ROI: Prioritize campaigns that drive measurable business results, and don’t hesitate to cut underperformers.

3. Leverage Automation: Save time by using tools that handle complex data analysis, allowing teams to focus on creative and strategic tasks.

HexClad’s journey highlights the potential of AI-powered analytics solutions in modern marketing. By optimizing ad spend and leveraging data-driven insights, they achieved scalable growth while maintaining efficiency. For marketers looking to replicate this success, the key takeaway is clear: predictive analytics is not just a tool for analysis - it’s a strategic advantage in building high-performing campaigns.

YOU ASK, I ANSWER

How Marketers Can Use AI for Competitor Analysis

Over the holidays, I worked with a brand to run some competitor analysis to inform their 2025 plans. One key focus was web presence, where we leveraged tools like ChatGPT 4o, Semrush, and SimilarWeb to provide a comprehensive report in hours and save us days of manual work. For more in-depth design analysis, you can experiment with ChatGPT 4.0 or 4.1 and Gemini Advanced by uploading screenshots, or use Google AI Studio to navigate sites, discuss findings, and gather real-time feedback on the user experience.

AI tools will save you time and help to analyze competitor initiatives, identify best practices, and create actionable recommendations to improve your own.

Here’s a streamlined approach for websites analysis:

1. Goal Setting and Benchmarking: Define objectives and identify KPIs like engagement or search rankings. Use tools like Similarweb to compare competitors’ traffic and performance metrics.

2. User Experience and Design Audit: Assess site navigation, mobile responsiveness, and design aesthetics. If prompted correctly ChatGPT can provide a comprehensive report, and specialized AI tools like Crayon can help monitor broader competitors’ updates.

3. Content Analysis: Besides ChatGPT, evaluate content relevance and quality with platforms like BuzzSumo to identify top-performing topics and gaps in your strategy.

4. Technical SEO and Performance Evaluation: Analyze competitors’ technical SEO and performance using tools like Ahrefs or SEMrush.

5. Reporting and Recommendations: Summarize findings into an actionable report with visual insights. Use ChatGPT to help you craft the narrative and slides.

Ready-to-use prompt that you can customize for your needs:

Objective: Evaluate and compare multiple websites to identify best practices, strengths, and areas for improvement. The goal is to inform the update or creation of a similar website that delivers an exceptional user experience for [insert target audience].

Websites to Compare:

• Website 1: [Insert Website URL]

• Website 2: [Insert Website URL]

• Website 3: [Insert Website URL]

• Website 4: [Insert Website URL]

Evaluation Criteria:

1. Core Content and Functional Pages

Placeholder: Replace with the specific pages or sections relevant to your industry/project.
Examples:

• Test Directory or Product/Service Catalog

• Resource Library or Knowledge Base

• Industry-Specific Tools or Calculators

• FAQ or Support Pages

For each page:

• How well is the content structured and organized?

• Is the information comprehensive and clear for the target audience?

• Are there supporting visuals or interactive elements (e.g., diagrams, videos)?

2. Navigation and User Experience (UX)

• How intuitive is the website’s navigation?

• Are menus well-organized, with clear categories and subcategories?

• Does the website provide quick links or search functionality for commonly accessed sections?

• How consistent is the branding and design across pages?

3. Personalization and Target Audience Segmentation

• Does the website offer tailored experiences for different user groups (e.g., [Insert Specific Roles or Expertise])?

• Are there features that dynamically adjust recommendations based on user preferences or behavior?

• How does the website address the specific needs of its target audience?

4. Accessibility and Compliance

• Does the website adhere to [Insert Relevant Standards (e.g., WCAG 2.1, GDPR)]?

• Are accessibility features implemented (e.g., alt text, high contrast, screen reader compatibility)?

• Is the website responsive and usable across devices (desktop, tablet, mobile)?

5. Design and Aesthetics

• Is the overall design modern, professional, and visually appealing?

• Are the layout and typography user-friendly?

• Does the website maintain consistent branding (e.g., logos, colors, style)?

6. Updates, News, or Notifications

Placeholder: Adjust this section to focus on industry-specific updates.
Examples:

• How are recent updates (e.g., new tests, product launches) communicated?

• Is there a dedicated section for announcements, or are updates embedded within relevant pages?

• Is the update format user-friendly (e.g., filters, search, or downloadable summaries)?

7. Engagement and Interactivity

• Does the website include interactive tools (e.g., calculators, live chat, or dynamic filters)?

• Are there resources that encourage user engagement, such as webinars, white papers, or videos?

• How effective are the contact or support features?

8. AI Optimization

• Voice Search Readiness:

  • Is the content written in a conversational, question-and-answer format to match voice search queries?

  • Does it target long-tail, intent-driven keywords (e.g., "How do I use [product capability]?")?

  • Are local and "near me" phrases included where relevant?

• Intent-Based Content:

  • Does the content address user intent clearly, focusing on informational (e.g., FAQs), navigational (finding resources), and transactional (e.g., bookings, purchases) needs?

  • Are headings and subheadings optimized to align with likely user search phrases?

• Technical AI Optimization:

  • Is structured data (schema.org) used for rich results like featured snippets?

  • Are page speed, mobile optimization, and clean URL structures prioritized for better rankings?

9. Localization and Regional Customization

• Does the website support multiple languages or regions?

• Are there region-specific resources, guidelines, or contact options?

Output:

For each website:

1. Provide a summary of strengths and weaknesses for each evaluation category.

2. Identify unique features or best practices that can inspire the new website design.

3. Suggest specific recommendations for improving the [Insert Target Website/Project Name].

DEEP DIVE

OpenAI's New Agent: A Glimpse into the Future of Work

After much anticipation, OpenAI has launched Operator, an AI agent built to handle repetitive tasks.

Picture an assistant capable of booking your flights, filling out forms, purchasing gifts, and even navigating complex web workflows - all while you supervise or step in as needed. This isn't a far-off idea. Operator offers this functionality today, leveraging its own browser to mimic human-like actions in real time.

In addition to Operator’s announcement, Perplexity also launched their agent this week: Perplexity’s Assistant for Android devices. It’s free and allows users to set reminders, send messages, play media, or book rides through app integrations.

All major players are working on their versions of AI agents, and given the popularity of ChatGPT, I will focus on Operator today.

Why Operator Matters

Operator shifts the paradigm from simply answering questions to completing tasks. It has access to tools and can act autonomously. For example, tasks like researching flights or booking a reservation - each often requiring 15–20 minutes of clicking and navigating - can be completed simultaneously in just minutes.

Looking back at the rapid adoption and impact of ChatGPT since its launch two years ago, it’s clear how quickly AI technology evolves. As Operator develops, its potential utility will only grow, making this an exciting development for businesses and individuals alike.

What It Can Do

Here’s a quick overview of Operator’s key features:

• Autonomous Web Navigation: Operator can browse websites, fill out forms, and complete transactions. Use cases highlighted by OpenAI include booking flights, reserving tables at restaurants, or automating repetitive online tasks.

• Workflow Automation: Users can save workflows and run them as needed to handle recurring tasks, such as updating a sales spreadsheet or checking inventory.

• Multi-Workflow Management: Several workflows can be run simultaneously, streamlining time-intensive processes (at least until you reach your daily limit, which happened with me on Saturday ☺️).

• Real-Time Oversight: Operator’s actions can be monitored in real time, allowing users to step in when necessary. This provides a layer of control, particularly for sensitive or complex tasks.

• Session Sharing: Every task generates a session video, enabling users to review or share workflows with colleagues, supporting collaboration and transparency.

Operator is truly autonomous, but prompting still matters. Based on early results, more comprehensive prompts tend to yield better outcomes.

Challenges to Overcome

Despite its potential, Operator isn’t perfect. During testing, the agent occasionally misclicked, struggled with unfamiliar websites, or deleted spreadsheet content, requiring human intervention to correct errors. It also avoids handling sensitive data, like payment details, which means users must step in for certain actions.

These limitations highlight that Operator isn’t seamless yet. It remains in research preview mode, offering a window into the possibilities of task automation rather than a fully polished solution.

Why Should We Care

The arrival of Operator and similar tools signals a shift in how businesses and marketers will work. Here are three reasons to pay attention:

• Workforce Transformation: Autonomous agents like Operator are set to transform work dynamics by automating repetitive tasks. This shift may create new roles, such as AI workflow designers, to optimize collaboration between humans and AI. If you haven’t yet, read the World Economic Forum report on the future of work. 🤔

• Marketing Automation: Operator demonstrates the potential to streamline workflows like lead qualification, competitor analysis, campaign management and others without requiring specialized tools - I’ve been hearing a lot that agents will kill SaaS, and I suspect that’s true. Starting to experiment now can help teams prepare for wider adoption.

• Future-Proofing Digital Strategies: Marketers must adapt their digital presence for AI agents. Data, AI-friendly content, human-generated social proof, and seamless user experiences will be key. Is your website ready for AI agents to consume content and use? What about ads being integrated or skipped by these tools? What happens when most web users are agents, not humans? These questions will shape the future of digital marketing.

A Small Experiment - and a Resource for You

To explore Operator’s potential, I tasked it with compiling a list of 20 reports on AI agents for business and marketing leaders. After some back and forth and adjustments, it generated insights from reputable sources like McKinsey, Deloitte, Accenture, BCG, and others. This list will be updated weekly and is available for anyone interested.

Final Thoughts

Operator may not yet be perfect, but it represents a significant step forward in AI’s ability to handle real-world tasks. Its current limitations reflect the early stages of a technology that could save countless hours, redefine workflows, and transform how we work.

Whether you’re curious or cautious, Operator is a glimpse into the not-so-distant future. Businesses that start exploring the potential of AI agents now will be better prepared to thrive in the age of automation.

Hi, marketers in the loop,

Last week, I attended the AI Agents in B2B event in San Francisco, featuring Jeremiah Owyang (General Partner at Blitzscaling Ventures) and Akash Saraf (CEO of TheAgentic), along with 10 startup pitches. With ~70% of the audience being AI agent founders, it was packed with bold ideas and insights into the future of autonomous AI.

A key takeaway? AI agents are set to transform the economy and social norms, autonomously trading, buying, selling, and evolving with minimal human input. Imagine a world where agents represent most web users, closing ads, shopping for books, groceries or concert tickets, and selling your unused items. That future? It’s already starting.

I tested OpenAI’s new agent, Operator, this weekend. While it blocked ads effortlessly, it struggled with poorly designed websites (even humans would). What can marketers do to address these challenges? I’ve shared some key insights below.

Last but not least, I’m hosting a live session on Friday to explore agents’ current capabilities and their impact. Invites are coming soon. Stay tuned.

Onward,
Rei

In this issue:

• Autonomous agents are here

• AI can help with competitive analysis

• How HexClad boosted ROAS by 20%

• And much more

Reading time: 10 minutes

MY FAVORITE FINDS THIS WEEK

Must Read

▪️CMO's Guide to Responsible & Results-Driven AI in 2025 (Jasper)

▪️ICYMI: The Future of Jobs Report 2025 (World Economic Forum)
Notable quote: "On average, workers can expect that two-fifths (39%) of their existing skill sets will be transformed or become outdated over the 2025-2030 period. Extrapolating from the predictions shared by Future of Jobs Survey respondents, on current trends over the 2025 to 2030 period job creation and destruction due to structural labour-market transformation will amount to 22% of today’s total jobs."

Industry News

▪️AI’s Impact on Marketing Strategies (Marketing Drive)

▪️AI Tools for Influencer Vetting POV (Digiday)

▪️Brands See Traffic Surge from ChatGPT and Google Gemini (Modern Retail)

Industry Reports

▪️2025 AI Business Predictions (PwC)

▪️Marketers Increasingly Prioritize Automation (Marketing Drive)

▪️Every AI Copyright Lawsuit in the US (Wired)

Events & Webinars

▪️Assessing AI’s Role in Marketing for 2025 (Marketing Drive)

▪️WHOOP’s Journey to Hyper-Personalized Customer Experiences (Snowflake)

▪️Predictions 2025 Webinar: B2B Marketing and Sales (Forrester)

AI Agents

▪️AI Agents: 2025 Predictions (MarTech)

▪️Managing AI Agents as Employees is the Challenge of 2025 (Goldman Sachs)

▪️“Have your agent talk to my agent, and they’ll figure it out.” (Reid Hoffman on LinkedIn)

Practical Tips

▪️A Guide to Intent Analysis, the Most Important SEO Skill this Year (The Drum)

▪️Mastering Voice Search: Optimizations & SEO Strategies (Neil Patel)

▪️She tried 13 AI SEO Tools (Paid & Free) - Here is What Works (Self Made Millenials)

Thought Leadership

▪️If You Want Your Team to Use Gen AI, Focus on Trust (Harvard Business Review)

▪️An Early-Adopter Of AI Marketing Offers Advice for Small Businesses (Forbes)

▪️Generative AI for SMBs: Myths, Uses, and Risk Mitigation (ANA)

DEEP DIVE

OpenAI's New Agent: A Glimpse into the Future of Work

After much anticipation, OpenAI has launched Operator, an AI agent built to handle repetitive tasks.

Picture an assistant capable of booking your flights, filling out forms, purchasing gifts, and even navigating complex web workflows - all while you supervise or step in as needed. This isn't a far-off idea. Operator offers this functionality today, leveraging its own browser to mimic human-like actions in real time.

In addition to Operator’s announcement, Perplexity also launched their agent this week: Perplexity’s Assistant for Android devices. It’s free and allows users to set reminders, send messages, play media, or book rides through app integrations.

All major players are working on their versions of AI agents, and given the popularity of ChatGPT, I will focus on Operator today.

Why Operator Matters

Operator shifts the paradigm from simply answering questions to completing tasks. It has access to tools and can act autonomously. For example, tasks like researching flights or booking a reservation - each often requiring 15–20 minutes of clicking and navigating - can be completed simultaneously in just minutes.

Looking back at the rapid adoption and impact of ChatGPT since its launch two years ago, it’s clear how quickly AI technology evolves. As Operator develops, its potential utility will only grow, making this an exciting development for businesses and individuals alike.

What It Can Do

Here’s a quick overview of Operator’s key features:

• Autonomous Web Navigation: Operator can browse websites, fill out forms, and complete transactions. Use cases highlighted by OpenAI include booking flights, reserving tables at restaurants, or automating repetitive online tasks.

• Workflow Automation: Users can save workflows and run them as needed to handle recurring tasks, such as updating a sales spreadsheet or checking inventory.

• Multi-Workflow Management: Several workflows can be run simultaneously, streamlining time-intensive processes (at least until you reach your daily limit, which happened with me on Saturday ☺️).

• Real-Time Oversight: Operator’s actions can be monitored in real time, allowing users to step in when necessary. This provides a layer of control, particularly for sensitive or complex tasks.

• Session Sharing: Every task generates a session video, enabling users to review or share workflows with colleagues, supporting collaboration and transparency.

Operator is truly autonomous, but prompting still matters. Based on early results, more comprehensive prompts tend to yield better outcomes.

Challenges to Overcome

Despite its potential, Operator isn’t perfect. During testing, the agent occasionally misclicked, struggled with unfamiliar websites, or deleted spreadsheet content, requiring human intervention to correct errors. It also avoids handling sensitive data, like payment details, which means users must step in for certain actions.

These limitations highlight that Operator isn’t seamless yet. It remains in research preview mode, offering a window into the possibilities of task automation rather than a fully polished solution.

Why Should We Care

The arrival of Operator and similar tools signals a shift in how businesses and marketers will work. Here are three reasons to pay attention:

• Workforce Transformation: Autonomous agents like Operator are set to transform work dynamics by automating repetitive tasks. This shift may create new roles, such as AI workflow designers, to optimize collaboration between humans and AI. If you haven’t yet, read the World Economic Forum report on the future of work. 🤔

• Marketing Automation: Operator demonstrates the potential to streamline workflows like lead qualification, competitor analysis, campaign management and others without requiring specialized tools - I’ve been hearing a lot that agents will kill SaaS, and I suspect that’s true. Starting to experiment now can help teams prepare for wider adoption.

• Future-Proofing Digital Strategies: Marketers must adapt their digital presence for AI agents. Data, AI-friendly content, human-generated social proof, and seamless user experiences will be key. Is your website ready for AI agents to consume content and use? What about ads being integrated or skipped by these tools? What happens when most web users are agents, not humans? These questions will shape the future of digital marketing.

A Small Experiment - and a Resource for You

To explore Operator’s potential, I tasked it with compiling a list of 20 reports on AI agents for business and marketing leaders. After some back and forth and adjustments, it generated insights from reputable sources like McKinsey, Deloitte, Accenture, BCG, and others. This list will be updated weekly and is available for anyone interested.

Final Thoughts

Operator may not yet be perfect, but it represents a significant step forward in AI’s ability to handle real-world tasks. Its current limitations reflect the early stages of a technology that could save countless hours, redefine workflows, and transform how we work.

Whether you’re curious or cautious, Operator is a glimpse into the not-so-distant future. Businesses that start exploring the potential of AI agents now will be better prepared to thrive in the age of automation.

SUPERAGENT POLL

YOU ASK, I ANSWER

How Marketers Can Use AI for Competitor Analysis

Over the holidays, I worked with a brand to run some competitor analysis to inform their 2025 plans. One key focus was web presence, where we leveraged tools like ChatGPT 4o, Semrush, and SimilarWeb to provide a comprehensive report in hours and save us days of manual work. For more in-depth design analysis, you can experiment with ChatGPT 4.0 or 4.1 and Gemini Advanced by uploading screenshots, or use Google AI Studio to navigate sites, discuss findings, and gather real-time feedback on the user experience.

AI tools will save you time and help to analyze competitor initiatives, identify best practices, and create actionable recommendations to improve your own.

Here’s a streamlined approach for websites analysis:

1. Goal Setting and Benchmarking: Define objectives and identify KPIs like engagement or search rankings. Use tools like Similarweb to compare competitors’ traffic and performance metrics.

2. User Experience and Design Audit: Assess site navigation, mobile responsiveness, and design aesthetics. If prompted correctly ChatGPT can provide a comprehensive report, and specialized AI tools like Crayon can help monitor broader competitors’ updates.

3. Content Analysis: Besides ChatGPT, evaluate content relevance and quality with platforms like BuzzSumo to identify top-performing topics and gaps in your strategy.

4. Technical SEO and Performance Evaluation: Analyze competitors’ technical SEO and performance using tools like Ahrefs or SEMrush.

5. Reporting and Recommendations: Summarize findings into an actionable report with visual insights. Use ChatGPT to help you craft the narrative and slides.

Ready-to-use prompt that you can customize for your needs:

Objective: Evaluate and compare multiple websites to identify best practices, strengths, and areas for improvement. The goal is to inform the update or creation of a similar website that delivers an exceptional user experience for [insert target audience].

Websites to Compare:

• Website 1: [Insert Website URL]

• Website 2: [Insert Website URL]

• Website 3: [Insert Website URL]

• Website 4: [Insert Website URL]

Evaluation Criteria:

1. Core Content and Functional Pages

Placeholder: Replace with the specific pages or sections relevant to your industry/project.
Examples:

• Test Directory or Product/Service Catalog

• Resource Library or Knowledge Base

• Industry-Specific Tools or Calculators

• FAQ or Support Pages

For each page:

• How well is the content structured and organized?

• Is the information comprehensive and clear for the target audience?

• Are there supporting visuals or interactive elements (e.g., diagrams, videos)?

2. Navigation and User Experience (UX)

• How intuitive is the website’s navigation?

• Are menus well-organized, with clear categories and subcategories?

• Does the website provide quick links or search functionality for commonly accessed sections?

• How consistent is the branding and design across pages?

3. Personalization and Target Audience Segmentation

• Does the website offer tailored experiences for different user groups (e.g., [Insert Specific Roles or Expertise])?

• Are there features that dynamically adjust recommendations based on user preferences or behavior?

• How does the website address the specific needs of its target audience?

4. Accessibility and Compliance

• Does the website adhere to [Insert Relevant Standards (e.g., WCAG 2.1, GDPR)]?

• Are accessibility features implemented (e.g., alt text, high contrast, screen reader compatibility)?

• Is the website responsive and usable across devices (desktop, tablet, mobile)?

5. Design and Aesthetics

• Is the overall design modern, professional, and visually appealing?

• Are the layout and typography user-friendly?

• Does the website maintain consistent branding (e.g., logos, colors, style)?

6. Updates, News, or Notifications

Placeholder: Adjust this section to focus on industry-specific updates.
Examples:

• How are recent updates (e.g., new tests, product launches) communicated?

• Is there a dedicated section for announcements, or are updates embedded within relevant pages?

• Is the update format user-friendly (e.g., filters, search, or downloadable summaries)?

7. Engagement and Interactivity

• Does the website include interactive tools (e.g., calculators, live chat, or dynamic filters)?

• Are there resources that encourage user engagement, such as webinars, white papers, or videos?

• How effective are the contact or support features?

8. AI Optimization

• Voice Search Readiness:

  • Is the content written in a conversational, question-and-answer format to match voice search queries?

  • Does it target long-tail, intent-driven keywords (e.g., "How do I use [product capability]?")?

  • Are local and "near me" phrases included where relevant?

• Intent-Based Content:

  • Does the content address user intent clearly, focusing on informational (e.g., FAQs), navigational (finding resources), and transactional (e.g., bookings, purchases) needs?

  • Are headings and subheadings optimized to align with likely user search phrases?

• Technical AI Optimization:

  • Is structured data (schema.org) used for rich results like featured snippets?

  • Are page speed, mobile optimization, and clean URL structures prioritized for better rankings?

9. Localization and Regional Customization

• Does the website support multiple languages or regions?

• Are there region-specific resources, guidelines, or contact options?

Output:

For each website:

1. Provide a summary of strengths and weaknesses for each evaluation category.

2. Identify unique features or best practices that can inspire the new website design.

3. Suggest specific recommendations for improving the [Insert Target Website/Project Name].

CASE STUDY

How HexClad Enhanced Marketing Performance with Predictive Analytics

HexClad, known for its innovative cookware, faced a familiar challenge in today’s e-commerce world: achieving scalable growth while maintaining efficient ad spend. To address this, they implemented marketing mix modeling and predictive technology to supercharge their marketing efforts. This case study highlights how HexClad boosted revenue while cutting costs, offering a roadmap for other brands looking to scale efficiently.

The Challenge: Balancing Growth and Efficiency

HexClad experienced significant growth in recent years, driven by its unique hybrid cookware design. However, like many brands scaling rapidly, they encountered diminishing returns on ad spend and inefficiencies in campaign management. Their team needed a solution to optimize ad investments, predict revenue outcomes, and reduce wasted spend - all while making data-driven decisions faster.

The Solution: Predictive Analytics in Action

HexClad utilized an AI-driven analytics solution called Prescient AI to optimize marketing spend. Unlike traditional tools that rely on historical data, the platform’s forward-looking models predicted the revenue impact of marketing decisions in real time. This allowed HexClad’s team to:

• Optimize Marketing Spend: Prescient’s platform helped HexClad build a more cost-efficient marketing mix by analyzing the potential impact of different budget allocations and channels.

• Long-Term Projections: By forecasting ROAS several months out, HexClad made informed decisions that aligned with their growth goals.

• Real-Time Testing: The team performed on-demand gut checks of ad spend, allowing them to adjust campaigns dynamically for better performance.

The Results: Revenue Growth and Cost Efficiency

In just a few months, HexClad saw impressive results:

• Improved ROAS: Their return on ad spend (ROAS) improved by 20%, proving the efficiency of predictive decision-making.

• Cost efficiencies: HexClad achieved a 40% more cost-efficient marketing mix, delivering better results and proving the value of predictive decision-making.

• Time Savings: Automated insights eliminated over 20 hours per month of manual analysis, allowing the team to focus on higher-value strategic tasks.

These results demonstrate how marketing mix modeling and predictive analytics can help brands scale effectively without sacrificing efficiency - a critical advantage in today’s competitive e-commerce landscape.

Lessons for Marketers

HexClad’s success offers valuable lessons for marketers:

1. Invest in Marketing Mix Modeling and Predictive Analytics: Forward-looking tools enable marketers to anticipate outcomes, not just react to past performance.

2. Focus on ROI: Prioritize campaigns that drive measurable business results, and don’t hesitate to cut underperformers.

3. Leverage Automation: Save time by using tools that handle complex data analysis, allowing teams to focus on creative and strategic tasks.

HexClad’s journey highlights the potential of AI-powered analytics solutions in modern marketing. By optimizing ad spend and leveraging data-driven insights, they achieved scalable growth while maintaining efficiency. For marketers looking to replicate this success, the key takeaway is clear: predictive analytics is not just a tool for analysis - it’s a strategic advantage in building high-performing campaigns.

ICYMI

▪️AI Agents Will Make Your AI More Useful

▪️How to Choose the Right AI Tool for Your Marketing Needs

▪️Why Leaders and Employees Don’t See Eye to Eye  

▪️Agency (Or Other Partners) Using AI? Here's What You Need to Know

▪️AI And Copyright: A Quick Marketing Guide

▪️How to Unlock Marketing Analytics with AI

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YOU ASK, I ANSWER

Unlocking Marketing Analytics with AI

As we step into 2025, marketing leaders are sitting on a goldmine of insights buried within their 2024 data. The real question is: Are we tapping into this potential effectively? Many teams use AI for content creation and campaign optimization. Yet, there’s an overlooked opportunity—using AI as a strategic partner for data analysis. By doing so, you can unlock insights that might otherwise remain hidden, especially when evaluating year-end performance or planning for the year ahead.

Your AI Data Analyst: A Strategic Partner at Your Fingertips

AI platforms like ChatGPT, Claude, Gemini, and Copilot are no longer just tools for generating content. They’ve become powerful allies for data analysis. Here’s how these platforms can support your marketing efforts:

• Spot trends and patterns that may go unnoticed by human observation.

• Generate compelling visualizations that simplify complex data stories.

• Translate raw numbers into actionable insights, tailored to your business needs.

• Brainstorm innovative strategies by exploring historical and real-time data from fresh angles.

Best of all? These tools are intuitive, accessible, and ready to help you unlock the hidden value in your 2024 data.

Your Roadmap to AI-Powered Marketing Analytics

Follow these practical steps to leverage AI and extract actionable insights from your 2024 marketing data. This is the process I used to analyze my newsletter data—feel free to adapt it to uncover valuable insights for your business. And let me know if it helped you find any gold.

1. Define Your Objective

Before diving into the data, start by identifying a specific goal or KPI that matters to your business. AI can help refine your focus and suggest new areas to explore. Unsure where to start? Ask your AI platform to suggest potential areas of focus or refine your initial ideas until you fully understand the core problem or opportunity to address.

Common areas to explore:

• Customer behavior changes throughout the year

• Campaign performance trends across platforms

• ROI shifts by marketing channel

2. Prepare and Clean Your Data

Don’t aim for perfection—data doesn’t have to be flawless. AI can assist in organizing and cleaning both structured and unstructured data, flagging duplicates, filling gaps, and ensuring consistency across datasets. If gaps remain, add complementary data such as metadata to enrich your insights.

Steps to get started:

• Export your data: Begin with a CSV or other structured format. If your data is unstructured, that’s fine—AI can help sort and format it.

• Clean your data: Use AI to identify and remove duplicates, fill in gaps, and ensure consistency across datasets. It can even highlight missing data that may be critical to achieving your goals. For example, I used AI to extract metadata directly from my newsletter content, saving significant time and effort.

• Aggregate metrics: AI can reconcile and aggregate metrics, ensuring alignment across datasets. It can also identify discrepancies and suggest ways to harmonize your data.

3. Ask Insightful Questions

AI’s value is rooted in the quality of the questions you ask. Start by going wide—explore a broad range of possibilities—before narrowing your focus on specific problems. This ensures you uncover unexpected patterns or opportunities.

Example questions to ask AI:

• What unexpected patterns emerged in our Q4 campaigns?

• Which marketing channels delivered the highest ROI growth?

• What were the key drivers influencing customer conversions?

4. Validate and Iterate

Treat AI as a collaborative partner. It thrives on feedback and iteration to improve its outputs. Here’s how to maximize its value:

• Spot-check results: Look for errors, inconsistencies, or unusual patterns.

• Test prompts across tools: Experiment with different questions or tools to see which provides the most useful insights.

• Refine your prompts: Adjust your questions based on initial results to uncover more depth or clarity.

5. Communicate Your Findings

Once AI delivers insights, focus on making them actionable and shareable.

• Visualize your data: Use AI to create charts, graphs, and other visuals for presentations.

• Translate findings into action: Develop clear recommendations based on the insights.

• Pressure-test your analysis: Align findings with broader business goals to ensure relevance and accuracy.

Pro Tips for Success

• Collaborate with AI: Use it to brainstorm, test ideas, and refine your prompts.

• Protect sensitive data: Use enterprise-approved platforms to avoid exposing proprietary information.

• Understand platform limits: Each AI tool has data size and complexity thresholds (e.g., free vs. paid versions).

• Prioritize accuracy: Always validate AI outputs before acting on them.

Looking Ahead: Building a Data-Driven 2025

Your 2024 marketing data isn’t just a record of the past—it’s a strategic asset that can shape your 2025 strategy. By incorporating AI-powered analysis into monthly or quarterly reviews, you can proactively identify trends, adapt to changing market conditions, and make data-driven decisions with confidence.

The ultimate goal isn’t just to understand what happened in 2024 but to turn those insights into smarter strategies for 2025. With AI as your strategic partner, your data has stories to tell. Are you ready to discover them?

Ready-to-use prompt that you can customize for your business needs:

"I’m a [insert your role, e.g., senior marketer] seeking to analyze my 2024 [insert data type, e.g., campaign, newsletter, or sales data] to uncover actionable insights and optimize strategies for 2025. Below is a detailed outline of my needs:

1. Data Context

• Type of Data: [Briefly describe your dataset, e.g., campaign performance metrics, engagement rates, or sales figures].

• Time Frame: The data spans [insert time frame, e.g., January to December 2024].

• Scope: Includes [insert scope, e.g., email campaigns, regional sales data, or customer feedback].

• Format and Size: Data is in [format, e.g., CSV, Excel].

Additional Info:

• [Describe any known issues or challenges with the dataset, e.g., missing values, inconsistent formats, or duplicates].

2. Objectives

I aim to achieve the following:

• Identify key trends, anomalies, and outliers.

• Assess the performance of [specific initiatives, e.g., a marketing campaign, product launch, or newsletter series].

• Pinpoint underperforming areas and high-growth opportunities.

Additional Info:

• [List any specific objectives, KPIs, or focus areas you’d like to add].

3. Workflow for Analysis

Step 1: Understand the Data

• Review the dataset’s structure, key columns, and any potential issues (e.g., missing or inconsistent data).

• Confirm priority metrics, segments, or questions to address.

Additional Info:

• [Highlight any priority areas or specific segments to focus on first].

Step 2: Identify Insights

• Summarize trends and performance metrics (e.g., engagement rates, ROI, sales growth).

• Highlight high- and low-performing categories or segments.

• Surface unexpected patterns, correlations, or opportunities.

Additional Info:

• [Provide any questions or hypotheses you’d like the analysis to explore].

Step 3: Create Visualizations

• Recommend visuals (e.g., bar charts, line graphs, heatmaps) to clarify trends.

• Confirm preferences for visual style and format.

• Generate visuals for review and refinement.

Additional Info:

• [Specify any preferences for visualization types, styles, or formats].

Step 4: Provide Recommendations

• Suggest actionable strategies based on findings (e.g., resource reallocation, A/B testing, optimizing underperforming areas).

• Confirm if additional analysis is needed to support recommendations.

Additional Info:

• [Indicate whether you’d like recommendations to focus on specific areas, campaigns, or strategies].

4. Customization Options

• Scope: Refine specific metrics, segments, or categories of interest.

• Data Types: Clarify if multiple datasets (e.g., social vs. email data) are included.

• Visuals: Specify preferences for visualizations (e.g., heatmaps, dashboards).

• Collaboration: Maintain a collaborative workflow, confirming direction and priorities throughout.

Additional Info:

• [Describe any customization preferences or unique requirements for analysis and deliverables].

5. Expected Deliverables

By the end of the analysis, I expect:

• A summary of key insights and trends across relevant dimensions (e.g., regions, product types).

• Clear, professional visualizations to illustrate findings.

• Actionable recommendations tailored to my objectives.

Additional Info:

• [List any specific deliverables or additional outputs you’d like to include]."

CASE STUDY

How Zapier Leveraged AI to Streamline Go-To-Market and Drive Revenue Growth

In today’s dynamic marketing landscape, organizations are under immense pressure to align sales and marketing for maximum impact. Zapier, a leader in automation tools, has risen to the challenge by embracing AI to refine its go-to-market strategy and generate measurable outcomes. Through a partnership with Common Room, Zapier unified disparate customer data, unlocked actionable insights, and saw a tangible lift in meeting bookings and revenue.

Here’s how they did it—and what we can learn from their success.

The Problem: Disconnected Data and Missed Opportunities

Zapier faced a common issue: their data was scattered across platforms, making it difficult to see the full picture of customer behavior and intent. Without a unified view, identifying high-intent prospects and tailoring outreach at scale was a constant struggle.

The stakes were high: With thousands of businesses relying on their platform for automation, missing buying signals meant lost opportunities. Zapier needed a solution to consolidate their data and enable smarter decision-making.

The Solution: A Unified View with Common Room’s AI-Powered Insights

Zapier turned to Common Room, a go-to-market intelligence platform, to unify and enrich its data. Common Room aggregated customer signals from website, social media, support tickets, and CRMs into a single view. With AI, the platform identified high-intent prospects based on engagement patterns, product usage, and sentiment analysis.

For instance, when Zapier noticed a surge in questions around specific integrations within their community, they cross-referenced this activity with product usage data. This allowed the sales team to identify prospects primed for conversion and offer timely solutions.

The Results: More Meetings, Greater Revenue

Zapier’s investment in AI and data unification delivered measurable results:

• Higher Meeting Volume: By focusing on high-intent leads, Zapier saw a 31% boost in meetings booked.

• Faster Sales Cycles: With detailed customer insights at their fingertips, sales teams had more meaningful conversations, reducing the time it took to close deals.

• Revenue Impact: These smarter, data-driven engagements translated into substantial revenue growth, directly tied to the AI-driven improvements in lead prioritization.

Lessons for GTM Leaders

Zapier’s experience offers a blueprint for go-to-market professionals aiming to align their teams and accelerate growth. Here are three key takeaways:

1. Break Down Data Silos: Centralizing customer data enables a more complete understanding of your audience and their needs.

2. Use AI to Surface Priorities: Let AI do the heavy lifting by identifying patterns, uncovering opportunities, and highlighting the prospects most likely to convert.

3. Align Sales and Marketing: Unified data fosters collaboration between teams, enabling them to engage with prospects more effectively.

A New Standard for AI-Driven Growth

Zapier’s shows how AI can elevate go-to-market strategies from reactive to proactive. By leveraging insights hidden in customer signals, they turned data into decisions and decisions into results.

For businesses looking to scale smarter, Zapier’s approach is a testament to the power of integrating AI into your sales and marketing toolkit.