For years, I’ve seen Salesforce Marketing Cloud become the go-to choice for marketers.
It’s powerful, reliable, and trusted by enterprises globally.
However, recently, I’ve been hearing a different story:
“Our data is too tangled to activate.”
“We’re locked into contracts.”
“We’re stuck sending the same emails on repeat.”
“Everything is Band-Aids and duct tape — I don’t know how we can move without breaking everything.”
“We feel stuck.”
Does this resonate with you? If so, let me invite you to a fireside chat tailored for you.
We’ve successfully guided numerous brands away from Salesforce, transitioning into flexible, modern engagement systems tailored for optimal CRM performance. Not solely because it’s trendy, but because we need speed, adaptability, and innovation more than ever.
In our upcoming session on April 14, I look forward to discussing:
Why so many brands are feeling stuck (it’s more common than you might think).
What’s occurring within the Salesforce landscape.
The biggest myths surrounding migration.
A comprehensive view of the current martech environment.
What life truly looks like after switching to a platform like Braze.
How CMOs and martech leaders should approach platform decisions in the next 3 to 5 years.
Ways to get your entire organization on board with these changes.
The steps you can take now to ensure a smooth migration.
To clarify, this isn’t about criticizing Salesforce.
It’s about having a transparent discussion regarding innovation, marketing autonomy, and what embracing the next era of marketing truly necessitates.
Disclaimer: To ensure an open and candid exchange, the live session is exclusively open to brand-side marketing leaders. Unverified participants will not be allowed in the live event, but all registrants will receive access to the recorded session post-event.
I can’t help but feel intrigued as I ponder the evolving world of SEO in 2026. With AI’s growing influence and an ever-shifting digital landscape, navigating these changes is both a challenge and an opportunity.
In 2025, I witnessed a fascinating trend: SEO standards continued to rise, which is encouraging. The data from the Web Almanac sheds light on these advancements, showcasing a more secure and user-friendly web. But there’s still more work to be done to keep up with these higher standards.
Let’s dive into the specifics. The adoption rate of HTTPS stands impressively high at over 91%, and the use of title tags has skyrocketed to nearly 99%. These figures are boosting our confidence in SEO’s direction, yet challenges remain, ensuring these advancements are consistently applied across all sites.
Reflecting on my experiences, I’ve realized that content management systems (CMSs) and SEO plugins are pivotal in setting industry-standard practices. It’s remarkable to see how deeply SEO tools are embedded in our daily workflows, underpinning many defaults we now consider standard.
However, not all implementations are ideal; default settings sometimes need our intervention to be truly effective. Engaging with major platforms and tools becomes essential to shaping SEO’s future.
Even as we embrace new trends, remnants of the past linger. Deprecated standards, though not forgotten, still exist. It’s critical to balance the old and the new, ensuring every part of SEO continues to improve incrementally.
The developments around AI in SEO are particularly captivating. Whether it’s the evolving role of robots.txt as more of a policy document or the cautious uptake of llms.txt, SEOs must strategically navigate these new waters.
Finally, I can’t ignore the intriguing rise of the FAQPage schema. Despite Google’s limitations on FAQ snippets, their implementation has not waned. This indicates a strategic shift toward structured data for reasons beyond just search engine visibility, potentially influencing AI strategies.
In conclusion, while 2026 may not revolutionize SEO, it will certainly refine and redefine our approaches, integrating AI layers without demolishing the foundation laid by years of SEO evolution.
Backlinks are still important, but today, authority also thrives on mentions and citations. I’m here to guide you on crafting content that garners both, significantly boosting your presence in AI search results.
In the past, links were the main authority signal in search. Creating backlinks was my go-to strategy for visibility, and earning placements was key for credibility. This still holds relevance, but it’s no longer the sole method.
In the realm of AI-driven search, my authority is now shaped by how frequently my brand is mentioned, cited, and associated with specific topics. Visibility is achieved through references in AI-generated answers.
With this in mind, my aim is to craft content that consistently earns brand mentions and citations, which are the new driving forces for AEO visibility.
The Philosophy Driving Content that Fuels AEO Growth
In 2026, organic discovery is driven by authority incorporating entity recognition. On platforms like Google and AI models such as ChatGPT, authority is strengthened through:
High-quality backlinks.
Brand mentions (linked or unlinked).
Consistent citations across trusted publications.
Clear entity associations (defining who I am, what I’m known for, and my core topics).
Since LLMs synthesize information rather than rank pages, I need repeatable, credible mentions across the web to enhance the probability of being cited or referenced in AI answers. Moreover, I’m focused on using my owned media to clearly define my brand entity.
Building authority has become more crucial as my content competes with AI results within the SERP and AI-generated content from other creators.
In short, I need to establish a clear brand identity and produce content so valuable that other experts, journalists, creators, and AI systems frequently reference my brand in discussions relevant to my business.
The Principles and Formatting of AEO-Friendly Content
I rely on many traditional SEO principles as a foundation for AEO-friendly content. Content aligned with Google’s helpful content guidelines, emphasizing value and user experience, appeals to both people and LLMs sourcing expert input.
However, to truly optimize AEO-friendly content, I incorporate formatting that facilitates LLM extraction.
Key formatting principles include:
Clear definitions: Provide concise, clear definitions high on the page:
“X is…”
“Y refers to…”
Structured formatting:
Use descriptive H2s and H3s.
Employ bullet points.
Keep paragraphs short.
Include direct answers under question-based headers.
Explicit context:
Avoid vague pronouns and implied references.
LLMs perform better with explicit, self-contained context.
Summary sections:
TL;DR blocks.
Key takeaways.
FAQs.
Entity reinforcement:
Brand name.
Author expertise and authority.
Brand and author credentials.
By keeping these principles in mind, I can effectively create content that resonates with both AEO requirements and user expectations.
The Specific Objectives for Your AEO Content to Address
To focus solely on AEO, I approach content with these objectives:
Be highly citable: Provide original data or perspectives that are valuable for media such as podcasts, expert roundups, or contributor columns.
Be highly quotable: Deliver at least one clear, insightful quote.
Be specific: Address specific questions that AI systems would seek to answer. Articulate and answer a question verbatim within the content.
Be clear: Clearly define topics for easy extraction.
To meet these goals, I think beyond blog posts to create “reference-grade” assets like:
Practical Steps to Build AEO Authority with Content
Here’s how I turn those principles into a repeatable process:
Research keywords where bloggers and journalists seek references (often including “statistics” or “reports”). I utilize resources like Reddit, Quora, X, Ahrefs, and Exploding Topics.
From those keywords, develop a list of topics my team can provide valuable insights on.
Compile a list of writers and journalists who cover those topics.
Conduct interviews with expert resources to gather content.
Refine content into contemporary insights using Google Trends and social listening.
Example: Collect tips from an expert to help hay fever sufferers (niche audience) sleep better (core topic) during high pollen periods (relevance).
Pitch to writers and journalists on the urgency and uniqueness of my content.
Engage with these writers on social media to build relationships for future opportunities.
Writing for AEO is aligned with writing for humans. It incorporates many of the SEO fundamentals meant to engage actual users.
Despite differences in how LLMs extract and process content, keeping these nuances in mind helps me refine my content approach for both AEO and human users.
With a well-defined brand on my owned media and a strong understanding of AEO principles, I’m ready to leverage my team’s expertise for superior visibility in the AI search landscape.
AI is revolutionizing how we discover, search, and purchase—it’s all happening at lightning speed. If we can’t clearly articulate the problem our brand solves, AI won’t be able to either.
I’ve noticed that customer journeys are now condensed into a single decision-making instance. David Edelman describes this as a blending of behaviors that traditionally occurred separately.
As decisions become more instant, it’s essential that I clarify what my brand can solve for the customer. Yet, too often, I find myself increasing activity rather than honing the strategy behind it.
Edelman, in his March 2026 Think with Google essay, emphasizes the rapid blending of streaming, scrolling, searching, and shopping behaviors, propelled by generative AI.
This insight shows that the traditional linear journey from awareness to purchase is outdated. Now, users multitask across platforms, fluidly moving between entertainment and intent.
The realization hit home when I learned people are using AI search engines to pose complex, emotionally rich queries, expressing context and urgency rather than just keywords.
AI processes these queries, breaking them into multiple streams and quickly synthesis results—a task that once required numerous browser tabs and hours is now done in seconds.
From this, I understand two things:
The competition now revolves around how well brands serve as solutions to specific needs, not just as products.
The demand framework is simultaneous—creating, capturing, and converting demand can no longer occur in sequence.
As I think of Walt Kelly’s Pogo, I’m reminded of the risk of mistaking busyness for progress. His words cut deep: ‘Having lost sight of our objectives, we redoubled our efforts.’
I see brands scrambling to generate content tailored for this new speed of decision-making, yet without clear strategic goals, it’s just activity for activity’s sake.
While the compressed customer journey is an opportunity for brands with precise positioning, it’s a trap for those without clear direction. Inconsistent brand signals lead to confusion.
Edelman highlights this issue by suggesting that brands should be seen as ‘the sum of signals’ that reveal them as solutions. I realized the journey compression issue isn’t just technological; it’s about setting clear objectives.
A question I continually ask is: What specific situation does my brand best address? If I can’t answer that concisely, AI certainly won’t be able to.
As I delve into the world of e-commerce, I’m constantly amazed by how paid search can transform business growth. Platforms like Google Shopping and Amazon Ads are game-changers, offering high conversion rates and efficient spending when campaigns are crafted thoughtfully.
These platforms are adept at capturing high-intent demand, providing the crucial data to expand my campaigns. They connect search queries directly to revenue streams, letting me pinpoint which terms are boosting sales so I can allocate my budget wisely.
However, the true test lies in organizing campaigns to effectively leverage this data.
Why does paid search excel in e-commerce? It’s all about intent and data. Google and Amazon thrive on search-driven environments. When someone seeks a product, they’re clearly expressing their needs. I don’t need to make inferences; I’m delivering precisely what customers want.
Moreover, Google Shopping and Amazon Ads offer unparalleled keyword-level revenue data. This insight helps me understand conversion rates and costs better. Amazon, in particular, shines with its granular product and category level revenue visibility.
Together, this data forms a powerful feedback loop. By analyzing which terms tie back to revenue, I can strategically shift my spending and enhance my return on ad spend (ROAS) over time. On Amazon, higher conversion rates even boost organic rankings, reducing future acquisition costs.
My success in search campaigns hinges on creating multi-funnel structures. While the concept remains consistent, execution varies based on campaign types, settings, and bidding strategies.
I implement campaign architectures that utilize wide-net, low-cost discovery initiatives to explore the search landscape. High-intent converters funnel into dedicated performance campaigns with strategic bidding. This approach not only strengthens ROAS but also enhances rankings and fosters scalable growth.
Embarking on Google Shopping, the priority sculpting method, inspired by Martin Roettgerding, is invaluable. Utilizing a three-layer campaign structure, I route keywords into distinct campaigns based on their performance.
This strategy optimizes spending on discovery keywords and directs investment toward high-performing, high-intent terms. The Google Shopping priority settings are pivotal; high-priority campaigns initially serve at lower bids.
Layer 1 focuses on capturing branded search traffic through a Performance Max campaign, maintaining an assetless format to focus on shopping inventory and avoid bleeding into other channels.
Layer 2, the catch-all, casts a wide net, experimenting with search terms to gather conversion data, while Layer 3 dedicates budget to best-performing terms, aligning with high-ROAS strategies.
Amazon’s multi-tier campaign structure offers its own set of advantages, like higher conversion rates and the intricate connection between ad spend and organic rankings. Campaigns are organized at the SKU level, employing research, ranking, and performance tiers.
Each tier serves a unique purpose, managed by differing advertising cost of sales (ACOS) targets, tailored for profitability. The research tier explores broad keyword possibilities, performance tiers maximize returns on proven converters, and ranking tiers drive organic positions aggressively.
Both Google Shopping and Amazon Ads offer unique opportunities in the e-commerce landscape. Whether aiming for short-term gains on Amazon or long-term brand building via Google, using these platforms synergistically can propel a business to new heights.
I remember the days when a Google search was akin to embarking on a quest for information. It was an adventure of navigating various links and forming my own opinions.
Nowadays, tools like AI Overviews, ChatGPT, and Perplexity condense all that information into a single, simplified answer. This transformation often strips away the finer details while amplifying certain perspectives.
This shift has redefined online reputation management. Now, search engines not only present information but shape the underlying narratives. This raises the stakes for brands, as even a top-ranking status doesn’t guarantee influence if AI stories tell a different tale.
For brands, the game has changed. Being number one doesn’t ensure visibility and influence anymore. The underlying narrative holds far greater power.
AI Narrative Formation: Crafting User Answers
AI platforms now utilize what I like to call ‘AI narrative formation.’ This process crafts the responses we receive from various search engines. Let me walk you through how this system works.
Source Pooling
These systems pull content from numerous sources. Contrary to expected reliance on peer-reviewed articles, they gather data from Reddit, YouTube, and social platforms like Instagram and TikTok.
Signal Weighting
Not all sources are equal. Often, a popular yet low-quality source can outweigh a singular, credible entry. A bustling Reddit thread with negative feedback might overshadow a well-researched Wikipedia page.
Narrative Compression
The summarization process compresses diverse inputs, often losing nuance along the way. Complex reputations are simplified into general statements like, ‘Users find this company untrustworthy.’
Continued Reinforcement
These summaries transcend their original context, getting shared and re-shared across social media. As these echoes return as new data, they further entrench the narratives in AI responses.
Unraveling a Finance Company’s Reputation in AI Search
To illustrate AI narrative formation, consider a recent case I worked on involving a financial company, which we’ll call Company X.
Company X’s reputation remained strong on traditional SERPs. High Trustpilot ratings and reputable endorsements were the norm until Google AI Overview threads surfaced a forgotten Reddit forum rife with grievances against them.
The AI Overview skewed the narrative, suggesting Company X had unresolved customer service issues, even though these concerns had been addressed years prior. This created a skewed perception that was hard to counteract.
The Amplified Risk from AI Searches
AI dramatically increases reputational risk through several mechanisms:
The Spread of Negative Narratives: Negative content surfaces faster and more prominently than before.
AI Hallucinations: Despite growing awareness, AI inaccuracies continue to deceive.
The Snowball Effect: Repeated narratives gain momentum, complicating reputation management efforts.
It has become evident that in ORM, repetition often overrides accuracy.
Auditing AI-Generated Narratives: A Step-by-Step Approach
Let’s consider a situation involving an AI-generated narrative challenge faced by CEO X of a well-known SaaS company.
After an out-of-context quote from CEO X’s podcast appearance went viral, AI summarized him unfavorably. Quickly, his reputation transformed negatively across major platforms.
Step 1: Mapping Queries
I initiated a process to understand what queries AI outputs were generating about CEO X. This helped identify the underlying issues.
Step 2: Capturing Outputs
Identifying repeated claims revealed how CEO X was perceived. Narratives from Google AI and ChatGPT were consistently portraying him negatively.
Step 3: Delving Through Sources
The next step involved examining the quality of sources contributing to these narratives, often outdated or lacking accuracy.
Step 4: Analyzing the Narrative Gap
This involved assessing discrepancies between AI narratives and his actual reputation, contextualizing the initial quote, and examining the long-standing perception of CEO X.
Step 5: Correcting and Replacing Sources
Finally, I focused on directly addressing, correcting, and replacing those negative narratives. This involved engaging directly with platforms that contributed to the misinformation and reinforcing positive content elsewhere.
A New Perspective: From SEO to Narrative Management
The focus has shifted from merely achieving top SEO rankings to understanding and adapting to narrative shifts. We must rethink our strategy from content engagement to managing the narratives AI disseminates.
To succeed, it’s important to reinforce AI systems with quality inputs, including crafting high-quality content, pursuing credible mentions, disseminating structured data, and managing misinformation directly.
I was surprised when despite all the right moves—maintaining a fast website, creating comprehensive content, and achieving a top 10 ranking—my site didn’t show up in Google’s AI Overview. It turns out that high rankings don’t guarantee AI Overview visibility.
This issue isn’t about how well my content ranks, but rather how it’s retrieved. Understanding this distinction is vital for anyone involved in SEO today.
AI Overviews prioritize content that offers the clearest, most usable answers, rather than just relying on high-ranking signals.
If my content doesn’t meet this standard, my search ranking becomes irrelevant. I realized I needed to understand where things were going wrong to make sure my content appeared in more AI Overviews.
The ranking-citation gap is real — and growing
The overlap between AI Overview citations and organic rankings increased from 32.3% to 54.5% between May 2024 and September 2025, according to BrightEdge. Although positive, this means that many AI Overview citations still come from pages not ranked at the top. Google often chooses pages that better suit the AI Overview format.
This trend varies by industry. In ecommerce, the overlap stayed almost flat over time, while in YMYL categories like healthcare, insurance, and education, it remained between 68%-75%.
High ranking and visibility don’t always align. I’ve seen scenarios where I rank second but remain invisible, while sometimes ranking on the second page gets more visibility in an AI Overview.
1. Your content answers the wrong version of the question
AI Overviews are often triggered by long-tail, conversational searches. These drive 57% of AI Overviews, whereas commercial queries less so, according to Semrush.
Google’s AI looks for content matching user intent, not just the keywords. For instance, a query about managing remote teams may overlook my page if it primarily discusses “project management software.”
2. You’ve buried the answer
If I start with too much context and not enough answer, search systems move on. They extract clean, immediate information. If my response isn’t close to the top, it gets skipped.
3. Your structure is opaque to AI systems
AI systems need clear, self-contained answers with concise paragraph structure and heading hierarchies. Overly complex narratives confuse AI, even if the content is accurate.
4. Your E-E-A-T signals aren’t visible at the content level
Google emphasizes E-E-A-T signals for quality. These need to be explicit in the content, beyond domain authority. Each page needs to establish credibility independently.
Who wrote it?
Where did the data come from?
Does it demonstrate field expertise?
Such signals are crucial in YMYL content where misinformation risks are high.
5. You’re targeting queries that don’t trigger AI Overviews
Before optimizing for AI, I check if my queries trigger Overviews. As of late 2025, they appeared in 16% of searches, but not evenly across types.
Transactional queries, navigational searches, and local searches trigger fewer Overviews. If my traffic is commercial, the lack of a citation might not reflect my content quality but the nature of the query.
What the data tells us about the impact of this shift
The stakes are high. Seer Interactive found AI Overviews reduced CTRs for informational queries by 61% between June 2024 and September 2025. Brands featured in Overviews, however, experienced a 35% increase in CTR.
As Pew Research noted, only 8% of users clicked a traditional result when AI Overviews were present. Without being cited, I could miss not just the Overview visibility but also clicks from organic listings.
How to optimize for retrieval, not just rankings
Rewrite introductions: Provide a direct answer immediately. Context can follow later.
Restructure headings: Make them specific and complete. Each section should operate independently.
Add explicit expertise signals: Use author details, original insights, and reliable sources to enhance credibility.
Audit query triggers: Check if queries trigger AI Overviews and study cited source structures.
Expand topical coverage: Don’t focus excessively on a single page. Deliver comprehensive knowledge across your topic.
AI Overviews show the split between content quality and ranking signals. High rankings used to equal quality, but now they don’t guarantee AI compatibility.
Ranking still matters, but understanding AI identification and retrieval processes is critical for visibility today. We can no longer rely solely on top rankings to bring visibility.
To improve AI Overview inclusion, I focus on understanding how AI systems extract information, making content adjustments accordingly.
Expanding beyond paid social? Discover how I learned to structure campaigns, control spend, and unlock demand without depending solely on the Meta playbook.
My paid social campaigns were thriving. I understood my audience intimately, had a tight creative process, and watched results improve each year. Naturally, when leadership proposed expanding into Google Ads, I was thrilled—envisioning it as a new revenue channel.
But sticking to our existing strategy only led to difficult conversations. Google demands different tactics—intent signals and campaign structures vary, and common budget-draining mistakes aren’t always obvious. Many brands mirroring their Meta strategy end up with flashy dashboards but disappointing balance sheets.
From my experiences, six frequent mistakes can cause substantial damage before they’re even noticed. They’re what I’ve seen most often with ecommerce brands transitioning to Google Ads—and each error is reversible.
Mistake 1: Treating Google like a retention channel
Utilizing Google Ads for retention and brand defense is possible, but relying solely on it as a strategy is problematic. I often notice brands new to the platform diving straight into Performance Max. Initially, the ROAS shines bright, making everyone happy. However, when the right question surfaces—”Are we truly growing or just capturing purchases?”—issues arise.
For example, a client approached me with branded search and retargeting doing most of the work in PMax—a mere tax on demand already created elsewhere, leading to stagnant revenue. Although ad spend was soaring, growth wasn’t.
Acquiring new customers requires a different setup, like:
Shopping campaigns to highlight products to new audiences.
Search campaigns centered on non-branded, high-intent keywords.
Layered PMax configurations to bypass defaulting to easy conversions.
When Google grants vast access to new audiences, focusing solely on closing disregards most of this opportunity.
Mistake 2: Not knowing how to leverage Google’s core levers
Although paid social expertise is somewhat transferable to Google, I’ve observed four major gaps. Let me share them with you in more detail.
Search intent: Social media ads interrupt, but search ads meet users actively seeking your offerings, transforming campaign structure, ad copy, and keyword targeting entirely.
Data feed optimization: An optimized product feed enhances visibility and targeting in Shopping or Performance Max campaigns.
Keyword research: Understanding match types and search intent is critical for reach and cost efficiency.
Landing pages: Engaging landing pages outperform product pages for high-intent but unfamiliar visitors.
Mistake 3: Allowing operational issues to interrupt campaign momentum
Consistent data is key for Google’s algorithms. Every unintended campaign pause can reset learning, causing weeks of degraded performance and wasted spend.
Common disruptions include:
Payments: Bill lapses, leading to campaign pauses, overshadow the actual cost when factoring in downtime recovery.
Tracking and feed integrity: Broken pixels and feed errors silently degrade performance.
Setting up automated alerts and regular audits can prevent these costly errors.
Mistake 4: Overly granular campaign structures
Detail-oriented advertisers may over-segment campaigns, believing it provides control. However, widespread budget allocation hinders Google’s automation from optimizing effectively.
Instead, tight, well-funded campaigns optimize better and are more manageable.
Mistake 5: Leaving campaigns on Max Conversion Value without ROAS targets
Max Conversion Value aims for conversion volume, neglecting cost efficiency. A realistic ROAS goal encourages the algorithm to maximize efficiency. Setting this correctly is crucial.
Mistake 6: Underfunding campaigns, keeping them in learning mode
Underfunding during the learning phase results in indefinite stalled progress. Adequately funding new campaigns from the outset fosters quicker, more accurate results.
Expanding beyond Meta to include Google is a strategic move, accessing actively expressed demand. These pitfalls aren’t deterrents but guideposts for smoother transitions and optimized strategies.
Tracking my brand’s visibility in AI-powered searches has become an essential part of SEO. However, the available tools often come with hefty price tags, starting around $300 to $500 monthly. For those of us who need custom solutions, these costs can be prohibitive.
I encountered this challenge firsthand. I required a specific tool that wasn’t available within my budget. So, I took matters into my own hands and built one myself, despite not being a developer. With a weekend of effort and dialogue with an AI agent, I crafted an AI search visibility tracker tailored to my needs.
Sharing my experiences, I’ve compiled a guide that I wish I had at the start—a step-by-step playbook for creating a custom tool. This guide navigates through technology, processes, the hiccups I faced, and how to streamline your build.
My main goal was to automate an AI engine optimization (AEO) testing protocol. To achieve comprehensive AI-driven brand visibility, tracking across five critical AI surfaces was necessary:
ChatGPT (via API): Renowned for its conversational AI prowess.
Claude (via API): A significant competitor with a unique response style.
Gemini (via API): Google’s direct model aimed at developers.
Google AI Mode: Enhances Google’s AI search experience with advanced reasoning.
Google AI Overviews: Summaries at the top of search results, prevalent by late 2025.
On top of these, I implemented a custom 5-point rubric for scoring results based on criteria like brand name inclusion and citation quality. With no existing SaaS tools offering this particular mix, the solution was to build one.
This project leveraged vibe coding, translating natural language into functional applications with AI assistance. Amid developers increasingly adopting AI coding and the growing trend of AI-generated code, this approach offered a viable path for a non-developer like me to create an impactful internal tool.
Your tech stack: The three tools you’ll need
To replicate this project while keeping costs manageable, here are the necessary components:
Replit Agent: An online development environment costing around $20/month, enabling application building via description alone.
DataForSEO APIs: The core of this project, allowing data retrieval from various AI platforms, priced on a pay-as-you-go model.
Direct LLM APIs (optional): Establishing direct connections with OpenAI, Anthropic, and Google APIs to verify and correct any discrepancies.
The playbook: A step-by-step guide to building your tool
Building this tool involved clear communication and step-by-step progress. Here’s a structured approach to guide your process:
Step 1: Write a requirements document first
Start by outlining your needs clearly. This document acts as a blueprint covering problems, features, and necessary data. Initial conversations with your AI should revolve around this document to set a solid foundation.
Step 2: Ask the AI, ‘What am I missing?’
Once your needs are outlined, seek the AI’s help in uncovering overlooked areas. Questions like “What am I not accounting for?” can avert common pitfalls and ensure comprehensive planning.
Step 3: Build one feature at a time and test it
Avoid building everything simultaneously. Tackle one small task and test it thoroughly before moving to the next. This methodical approach aids in pinpointing and addressing issues efficiently.
Step 4: Point the agent to the documentation
When integrating APIs, guide the AI using specific documentation. Providing exact URLs ensures accurate implementation and saves time otherwise spent fixing errors.
Step 5: Save working versions
Before introducing significant changes, save copies of your project. In Replit, this is done through “forking.” It’s a precaution against potential new feature-induced disruptions.
Common problems and how to fix them
You’ll likely face technical hurdles. Here are frequent issues with solutions to help you navigate the process smoothly:
Problem
Solution
1. API authentication fails
Provide the exact authentication documentation URL to the agent.
2. Results disappear
Ensure persistent storage by requesting a database from the start.
3. API responses don’t show
Share raw JSON data with the agent to diagnose and fix parsing logic.
4. Model response cut short
Conduct parameter checks post-updates to maintain consistent results.
Evaluating the real costs
Building this tool has clear advantages over purchasing a SaaS solution, notably cost savings. Here’s a breakdown:
Expense
Custom Tool
SaaS
Subscription
$20/month
$500/month
API Usage
$60/month
Included
Total
$80/month
$500/month
Despite the initial time investment, the ability to adapt and tailor the tool outweighs the ongoing costs.
Is building your own tool right for you?
This decision largely depends on your specific needs:
Consider building if:
You require unique testing methods not supported by current tools.
Your agency needs a white-labeled solution.
You prefer cost-effective strategies and are willing to invest time.
Stick with SaaS if:
Your time is more valuable than subscription costs.
You need robust security and customer support.
You find standard features sufficient.
Ultimately, crafting a tool that aligns perfectly with your workflow can provide a distinct edge in the competitive SEO landscape. Welcome to the era of practitioner-developers; it’s time to innovate.
I’ve noticed that when I rely too heavily on micro-conversions, my PPC campaigns don’t quite perform as expected. This often leads to distorted CPA and ROAS figures. Here’s how I’m learning to refine my approach to micro-conversions and align my strategies with real revenue.
AI-powered ad bidding systems are remarkably advanced, yet I find myself grappling with conversion tracking that isn’t as evolved. While ad platforms nudge me to keep track of multiple actions, I’ve heard from experts that it’s actually more beneficial to zero in on final outcomes.
From my experience, neither approach is entirely foolproof. Both over-signaling and under-signaling can impact PPC campaigns negatively. Too many vague micro-conversions can introduce noise, steering the bidding process toward less valuable actions, hampering the actual results. Conversely, with too few signals, the system lacks sufficient data for learning.
This issue becomes particularly apparent in my work with Performance Max and similar setups. The optimization here leans heavily on whatever signals I provide, irrespective of their true business value.
I started reflecting on how micro-conversions can overshadow real conversions, leading me to explore why these bidding systems operate this way and how to create a conversion framework that better aligns signal volume with actual business impact.
The Myth of a ‘Data-Hungry’ PPC Algorithm
I had always believed that algorithms thrive on data, a notion reinforced by platform guides and numerous PPC articles. They often imply that more signals inherently equate to better learning.
Yet, I’ve realized that while bidding systems need a certain signal density, they don’t necessarily gain from indiscriminate micro-conversion logging. More data doesn’t equate to better data.
When I add low-intent or weakly related actions, performance can degrade. The system might start optimizing for actions not aligned with real revenue.
It’s clear to me that these machine-learning systems assess frequency, consistency, and predictability without discerning the strategic relevance of a signal.
My account often contains a blend of meaningful actions like purchases and others less significant, like pageviews. Without a value hierarchy, the algorithm treats all signals as viable targets, leaning toward easy, frequent actions that offer little business value.
As I adjust my approach, I’m finding the need to streamline my focus. By applying disciplined strategies and value-based bidding, I can align my signal structures more effectively with my business outcomes.
