I often find myself pondering the vital question every marketing leader should consider: How robust are our customer relationships? Not just the campaigns or channels but the genuine connections we forge with our customers.
This question is more challenging than it seems. Over the past two decades, we’ve focused on building around specific channels.
Every channel like email, social media, or ecommerce had its own team, its own metrics, and its own measure of success. From our perspective, it appeared as progress—after all, each team reached its goals.
Yet, customers felt like they were dealing with multiple companies under one logo. Imagine receiving a heartfelt ‘We miss you!’ email the day after a frustrating customer support experience. Sales might not realize a demo had already been seen. In-store purchases could go unnoticed by the ecommerce team. There’s simply no unified memory or relationship there.
On March 11, 2026, top minds in marketing, customer experience, and engagement, including those from BMW, Essity, and Sinch, will converge at Engage with SAP Online. This free, virtual event is essential for leaders ready to shift from isolated channel optimization to holistic customer relationship building.
Who’s Speaking and Why It Matters
The event kicks off with Sara Richter of SAP Engagement Cloud sharing insights from the SAP Engagement Index, a global study. But the real highlight is the presentations that follow.
Mark Ritson, known for his no-nonsense marketing approach, will deliver the keynote on the trends reshaping customer experience. Expect a sharp analysis on the fast-changing customer behaviors and why loyalty needs to transcend marketing.
Following Ritson, Jutta Richter from BMW will discuss modern customer journeys and brand relevance. Daniele Tedesco from Essity and Venky Naravulu from Sinch will share practical lessons on AI and connected systems.
The discussions will focus on what’s effective, what’s not, and actionable steps to enhance engagement.
The Backdrop: Why This Conversation is Urgent
This event is critical as there’s a growing disconnect between customer expectations and organizational delivery capabilities, as highlighted by the SAP Engagement Index.
SAP calls this the Engagement Divide, a widening gap that underscores the urgent need for a new operational model focused more on relationship management rather than isolated channel success.
As businesses navigate this challenging terrain, the speakers at Engage with SAP Online are set to provide the strategies needed to organize around customer relationships effectively.
I recently discovered the transformative power of optimizing my eCommerce brand for AI answer engines. Engaging with platforms like ChatGPT and Google’s AI Overview can significantly enhance my brand’s visibility, trust, and ultimately drive more sales.
Understanding how to tailor my content for these AI platforms ensures that my products appear as helpful, relevant answers to potential customers’ inquiries. It’s about more than just visibility; it’s about building a credible connection with my audience.
By weaving in the best practices of AI Search and AI Optimization, I’ve begun to see a noticeable increase in brand engagement and authority. It’s a journey worth exploring for anyone looking to stay ahead in the competitive eCommerce landscape.
I’ve recently learned that ChatGPT has hit an extraordinary milestone: over 900 million active users every week. OpenAI proudly shared this achievement for the first time, and it’s nothing short of remarkable.
Why It’s Significant. Our online habits are evolving, extending beyond conventional search methods. With so many users turning to ChatGPT weekly, it’s clear that interactions and discoveries are shifting to AI platforms. However, as users, we often still seek reassurance from traditional search engines.
The Facts. OpenAI didn’t just stop at sharing user figures; they also unveiled a substantial $110 billion funding round. Additionally, they’ve gained over 50 million consumer subscribers and more than 9 million businesses are paying clients.
What This Means for Us. ChatGPT isn’t just a chat tool; it’s a competitive landscape where search, intent, and brand visibility meet. Understanding how our content appears in AI-driven results is crucial for boosting conversions, even if these interactions aren’t traditional searches.
OpenAI’s Announcement. For further insights, you can check out OpenAI’s official statement on Scaling AI for everyone.
Let me clarify—this is just a patent document, a flicker of a possibility, not an immediate change in Google Search.
A recently published patent from Google hints at a potential shift in how we experience search results. It suggests that instead of landing on a standard webpage, searchers might be directed to an AI-crafted page tailored to individual queries.
This patent outlines a system using AI to auto-generate personalized landing pages for businesses or organizations. Instead of simply redirecting me to a generic homepage, it aims to deliver a page that’s directly relevant to my search intent and the organization’s offerings.
Patent Abstract. Here’s an overview from the patent itself:
“Techniques for generating an artificial intelligence (AI)-generated page for a first organization. The system can include a machine-learned model configured to generate the AI-generated page. The system can receive from a user device associated with a user account, the user query. Additionally, the system can generate a search result page for the user query. The search result page can include a first result associated with a first landing page of the first organization. The system can calculate a landing page score for the first landing page. The system can generate an updated search result page based on the landing page score exceeding a threshold value, the updated search result page having a navigation link to an AI-generated page for the first organization. The system can cause a presentation, on a display of the user device, the updated search result page.”
Example Scenario. Picture this: I’m searching for “waterproof hiking boots for wide feet” on a site like REI or Amazon. Normally, I’d end up on a general “Hiking Boots” page and have to sift through countless options. But with AI, Google could direct me to a specially tailored page that zeroes in on exactly what I need.
Why It Matters. This is a mere patent and might never see the light of day. However, it’s intriguing to ponder Google’s potential direction and what it could mean for the future of search.
In any scenario, these insights offer a glimpse into the forward-thinking strategies within Google.
Discover how vibe coding empowers me to create custom PPC tools quickly using intuitive AI prompts instead of traditional coding techniques.
I now find myself able to generate custom PPC tools using plain English, thanks to GPT-5. It’s a game-changer, giving a competitive edge to those who embrace AI-assisted automation.
Frederick Vallaeys, who has built tools in mere minutes instead of months, is leading the way with AI. He has ten years of experience at Google creating invaluable tools like the Google Ads Editor and another decade at Optmyzr as CEO.
Vallaeys has witnessed the evolution of automation firsthand, and vibe coding is the next giant leap. At SMX Next 2025, he shared his personal journey with vibe coding.
If you’re involved in PPC, automation is crucially important. Initially, I relied heavily on Google Ads scripts because there was always more work than could be done in a day.
The problem arises when Vallaeys questions who truly writes their scripts. Only a few people raise their hands, as most often copy and paste due to lack of coding skills.
This results in limitations, confining you to what others have crafted instead of adding your personal flair.
GPT revolutionized scriptwriting for those without coding skills.
The best part lies in large language models being multimodal. Now, a simple photo of my campaign decision flowchart can be deciphered by AI to generate a complete Google Ads script.
Instead of viewing client meetings as additional work, I’ve embraced them as opportunities for prompt-engineering sessions.
Changing my mindset allowed me to treat these meetings as prompt instructions for AI, simplifying task execution.
Instead of delving into code, I merely describe my desired outcome, and AI takes care of the technical side. That’s vibe coding for you!
Imagine needing software to perform functions X, Y, and Z. Detail your needs to a coding tool, and watch as it constructs the software. Vallaeys describes this process as mind-blowing.
Scripts have become outdated; vibe coding is the way forward.
Vallaeys demonstrated the effectiveness of this method by requesting a persona scorer for an ad tailored to various audiences from Lovable. The result was rapid and precise.
Working collaboratively with it, akin to a human developer, you describe needed changes without ever touching code.
The automation framework traditionally targeted tasks ranging from frequent, quick activities to extensive, periodic ones. Vallaeys recommends not limiting automation to what’s already being done, but rather considering what you wish to do more often, making time-consuming tasks manageable.
The old method was slow, taking at least a month to launch anything.
I used to spend days compiling specifications, waiting for engineers to build, finding bugs, organizing meetings, and repeating the cycle.
Traditional code was deterministic, relying purely on if/then logic. While reliable, it struggles with nuanced actions, like identifying competitor terms. Encompassing every variation of competitor keywords was virtually impossible.
Sam Altman’s launch of GPT-5 heralded a new era of on-demand software generation, transitioning beyond software-as-a-service.
Tapping into this new approach takes just minutes, from writing a spec to letting AI build and refine it. Within an hour, you have a functional automation tool.
This code isn’t just deterministic; it’s also flexible. Large language models handle nuanced queries with impressive accuracy.
Vibe coding allows machines to create anything I can articulate clearly, from landing pages adhering to brand guidelines to unique audience tools.
This paradigm shift means even tasks taking 90 minutes by hand are candidates for automation, creating disposable software to save time today, unaffected by future failures.
Vibe coding enables building a range of online solutions — from landing pages to browser extensions — all through simple directives.
Begin with tools you may already use, such as Claude or ChatGPT, for data analysis or visualization tasks.
For more complex applications with databases or user logins, tools like Lovable, V0.dev, Replit, or Bolt simplify the process.
If you have technical skills, Codex, Bolt.new, or Cursor offer robust capabilities, but simpler tools are often sufficient.
I challenged someone in my team with no coding background to create a seasonality analysis tool using Claude.
The process involved gathering materials, crafting a prompt, and testing via a browser without requiring installation.
The team quickly iterated, enhancing features. The AI efficiently added helpful guidance and streamlined interfaces, leveraging its extensive training.
I envisioned a tool sequence for expert review of blog posts, synthesizing feedback through a consolidated summary. This was easily vibe-coded in V0.dev.
A Chrome extension for demos needing to blur sensitive numbers was swiftly constructed via simple prompts, addressing specific visibility needs.
Effective prompting involves specifying the exact use case, allowing AI to generate relevant options and suggest innovative methods.
Engage with questions to uncover insights such as process approaches or data storage solutions, furthering learning opportunities.
Utilizing chat mode for alternative exploration is advantageous, allowing detailed direction without altering code initially.
You can experiment with my team’s audience analyzer, adapting it with ease to suit specific needs like logo integration or functional adjustments.
It’s clear from Vallaeys: the competition isn’t against AI but against individuals harnessing its capabilities more effectively.
Dive into vibe coding today. Select a tool, issue a simple prompt, and witness the remarkable outcomes firsthand. My first attempt left me in awe.
By integrating this newfound knowledge, improving AI skills becomes attainable, ensuring a competitive edge.
I’ve been asked numerous times about how to track prompts effectively, especially by those using tools like Profound, Athena, and Peec. The big question on everyone’s mind is, “Which prompts are worth tracking?” In this ever-evolving landscape, it’s challenging to determine what buyers are querying about my company when they use LLMs.
Currently, there isn’t a reliable data source that puts my mind at ease. Unlike traditional search with publicly available Keyword Planner data, it’s unlikely that OpenAI or Google will fully release this kind of data for analysis. Though there have been recent proposals by the UK CMA about Google and data transparency, I’m not holding my breath for significant change.
Long story short, LLM tracking feels like navigating a black box. So, are there any alternative data sources we can use to track which prompts? Perhaps.
Back in November, Jason Packer published an interesting report highlighting how ChatGPT searches accidentally leaked into Google Search Console reports, featuring PII. When this was confirmed by Ars Technica, OpenAI stated the problem affected only a small number of queries.
This confirmed, for me, that ChatGPT queries do appear in some Search Console profiles. While privacy implications are significant and beyond this article’s scope, it shows that LLM queries are not impossible to capture.
Additionally, Barry Schwartz has reported that AI Mode data is available in Search Console. This supports the idea that Search Console can track how users interact with LLMs.
Based on my analysis, it seems that AI data appears to come from this area. By applying specific filters, I’ve noted steady increases in impressions over recent months, coinciding with Google’s roll-out of AI Mode features.
So, how can I access user prompt data in Search Console? The key is focusing on longer queries. Using regex, we can filter queries with 10 or more words, unveiling prompt-like behavior:
1. Navigate to Search Console Performance > Search Queries
2. Select Add Filter > Query
3. Choose Custom Regex
4. Input: ^(?:S+s+){9,}S+$
This method revealed understandable, prompt-styled queries when applied to various properties. Though the actual data cannot be shared, examples such as “Map out a full day in Glacier National Park…” highlight the trend.
Mind you, there’s no direct evidence these queries originate from ChatGPT or similar AI platforms. It’s possible they reflect new user behavior patterns within Google.
Regardless, analyzing these conversational query patterns provides invaluable insight into how customers search using longer strings.
Will Critchlow wisely said, “we’re doing business, not science.” In our shift toward less attributed, zero-click data collection, the choice to leverage this available data is up to us.
Currently, my preferred tool for prompt analysis is Claude. Its results are reliably robust, and its visualizations are effective. Integrating Claude into existing frameworks streamlines the process.
After export, uploading prompt lists to Claude lets it perform behavioral analysis, identifying data themes and trends for better prompt tracking.
Posing specific questions to Claude about customer behavior opens a treasure trove of insights. Analyzing this data reveals learning opportunities I would not have anticipated.
For instance, I discovered searches probing a PR issue from over three years ago are still frequent and that searches often use one company as a benchmark against its competitors.
Finally, leveraging Claude to suggest new prompt-tracking methods, based on this data, offers an informed way to continually hone tracking efforts.
While there’s no definitive system for selecting which prompts to track, incorporating Search Console data provides a clearer direction. The insights derived can help unearth unique user prompts and discern scalable themes for ongoing data tracking.
I’m thrilled to share the exciting news about Google’s latest innovation, Nano Banana 2. This powerhouse merges pro-level image quality with lightning-fast speed, enabling me to create stunning, production-ready images faster than ever.
Google DeepMind has introduced Nano Banana 2, officially known as Gemini 3.1 Flash Image. This new model seamlessly blends the intelligence of Nano Banana Pro with the swift performance of Gemini Flash.
What’s new. Here are some standout features of Nano Banana 2:
Advanced world knowledge: It elevates how I render subjects by integrating Gemini’s real-time web grounding, making it easier to create infographics and data visualizations.
Precision text rendering and translation: The model delivers cleaner, more readable text in images, even providing localization options if needed.
Stronger instruction adherence: It’s great to finally have a tool that handles complex, multi-layered prompts with ease.
Subject consistency: I can maintain up to five characters and 14 objects within a single workflow, enhancing my creative projects.
Production-ready outputs: With support for resolutions from 512px to 4K, I can generate content suitable for any project specification.
Enhanced visual fidelity: Enjoy sharper details, richer textures, and more dynamic lighting — all at incredible speeds.
Why I care. Nano Banana 2 revolutionizes how I generate high-quality images, slashing the time and cost usually associated with creative development. This innovation means that I can quickly produce campaign assets and localized variations, saving me days of work.
Fully integrated into Google Ads and Gemini, it streamlines the creative production process by accelerating testing and iteration cycles, allowing me to focus more on creativity and less on logistics.
The rollout. Nano Banana 2 is now available within Google’s ecosystem, including Google Ads, Gemini app, Search AI Mode, Lens, and more — making it more accessible than ever.
Between the lines. Google is raising the bar by making high-end image generation a standard feature. This shift suggests that premium creative control is now the norm, not an expensive upgrade.
The bottom line. With Nano Banana 2, Google is predicting that creators like me desire fewer compromises — offering fast generation, robust reasoning, and production-ready visuals all within a single, streamlined model.
I recently discovered that the world of ChatGPT ads is rapidly evolving, with major brands tapping into high-intent prompts like “best” and “new.”
After hearing about this trend, I delved into the findings from AI ad intelligence firm Adthena, which has been monitoring the acceleration of ChatGPT’s ad ecosystem. It’s fascinating to see more brands joining in, along with clearer patterns for ad placements.
What’s happening? Adthena first spotted advertisers within ChatGPT just last week, and they’re already reporting a marked increase in both advertiser activity and ad delivery tactics.
Advertisers spotted so far:
Best Buy
AT&T
Pottery Barn
Enterprise
Qualcomm
Expedia
How ads are triggering: Analyzing over 1,500 prompts in the past week has revealed that most ads show up on the first prompt, while others activate on the third or fourth reiteration of the same query. High-intent words like “best” and “new” play a significant role.
“I am going to buy a new phone. What is the best phone?”
“I need a new phone.”
“I need to buy a new desk, what’s best?”
Between the lines: The keyword triggers are simple, focusing on commercial intent rather than emotional nuance. For instance, Best Buy managed to secure two ad slots in responses to iPhone-related prompts, indicating their early moves to capture this evolving market.
Why this matters: As the ChatGPT advertising space grows, understanding these trigger behaviors — even at a basic keyword level — can be crucial for brands exploring this new avenue.
The bottom line: ChatGPT ads are steadily transitioning from experimental phases to established patterns. While signals remain simple, competitive tensions are already brewing.
Spotted. Insights into the competitive ChatGPT ad landscape were shared by Adthena’s CMO, Ashley Fletcher, who uploaded screenshots on LinkedIn.
ChatGPT has significantly impacted e-commerce site conversions, with traffic from ChatGPT converting 31% better than non-branded organic search across 94 sites in 2025. Despite this impressive performance in conversion rates, it still contributes only a small fraction of the overall revenue. This insight comes from a detailed year-long analysis by Visibility Labs, covering from January to December 2025.
Why I’m Interested. This data is crucial because it highlights how AI referral traffic, while not yet dominant, is showcasing higher conversion potential compared to traditional non-branded search traffic. It indicates a growing value in AI-driven referrals, supplementing rather than replacing existing channels.
Higher Conversion Rate. The analysis found that ChatGPT traffic converted at 1.81% compared to 1.39% for non-branded organic traffic, translating to a 31% higher conversion rate. This trend was consistent for 10 out of the 12 months analyzed.
Visibility Labs points to intent compression as the key reason behind this high conversion rate. Users often use ChatGPT to refine their product preferences, arriving at product pages with a clearer purchase intent compared to visitors from typical search channels.
Key Observations. While ChatGPT shows a conversion advantage, the overall growth has decelerated, and the traffic volume remains modest.
Significant Traffic Growth: There was an astonishing growth of 1,079% in ChatGPT visits, escalating from 1,544 in January to 18,202 in December. In comparison, non-branded organic traffic increased by 17% during the same timeframe.
Lower AOV: The average order value (AOV) for ChatGPT was $204, compared to $238 for organic traffic, marking a 14.3% difference.
Increased Revenue Per Session: Despite the lower AOV, ChatGPT generated $3.65 in revenue per session versus $3.30 from organic, yielding a 10.3% higher earning per session.
Minor Revenue Share: ChatGPT accounted for $474,000 in revenue against $32.1 million from non-branded organic traffic, amounting to 1.48%, which rose to 2.2% in the latter half of 2025.
Growth Correlated with Updates: The increase in traffic during the first half is linked to the introduction of shopping carousel features in April 2025. However, growth rates began to stabilize around August.
Overshadowed by Organic Traffic: Overall, non-branded organic traffic was 70 times larger than ChatGPT, narrowing to 47 times in Q4. Early 2025 saw variability, with conversions ranging from 15 to 37 per month, which limited confidence levels until the middle of the year.
The Attribution Challenge. GA4’s referral data may not fully capture ChatGPT’s impact. According to Visibility Labs, many users receive recommendations through ChatGPT, then search for brands via Google before making a purchase, which are typically tracked as branded organic conversions.
To better capture AI-influenced sales, it’s advised to implement post-purchase surveys.
Data Insights. Visibility Labs’ analysis included GA4 data span over 12 months (January to December 2025), gathered from 94 e-commerce brands with seven- and eight-figure turnovers, comparing 9.46 million non-branded organic sessions to 135,000 ChatGPT referral sessions. The study focused exclusively on visits with commercial intent, excluding homepage and blog traffic.
The Complete Report. Find the detailed report here.
I’ve got some exciting news to share: Google is rolling out its AI Max text guidelines across the globe! This means that as advertisers, we gain more autonomy over the creative processes of AI-generated ad copy by implementing custom text rules to maintain on-brand messaging.
Here’s What’s Happening: Now, AI Max provides worldwide access to text guidelines for Search and Performance Max campaigns. These guidelines come with comprehensive language and vertical support.
We can now use natural language instructions to shape AI-generated creatives. This includes the power to exclude certain terms or phrases, ensuring that what we publish stays true to our brand.
Why This Matters to Us: In an era where AI-powered creative content is central to performance marketing, keeping a tight rein on brand safety and tone is crucial. By customizing text, we can ensure that ads align with user intent and our brand’s unique positioning. This way, we establish guardrails ensuring consistency, like guiding AI to avoid language that misrepresents our brand. Early adopters, such as BYD, have witnessed increased lead generation at reduced costs—proving that human-guided AI can significantly enhance campaign outcomes.
The Bottom Line: Maintaining your brand voice in AI-generated ads is probably a top priority, just like it is for me. With Google’s expanded text guidelines, we now have practical and easy-to-use tools to keep control while scaling AI capabilities.
