ChatGPT, Gemini, Perplexity: these are the new operating environments. Your content must be invokable inside them, or no one will see it.

At SMX Advanced, I broke down how to build an AI visibility engine: a system for making your net-new facts reusable by humans and agents across synthesis-first platforms.

It goes beyond publishing to show how teams can deploy structured content that survives LLM compression and shows up for buyers during their purchasing decisions.

It’s what we’re building with clients and inside XOFU, our LLM visibility GPT.

Here’s how it works.

Find the FLUQs (Friction-Inducing Latent Unasked Questions)

Friction-Inducing Latent Unasked Questions are the unasked questions your audience doesn’t know about. But if left unanswered, they can derail the entire buying process.

Costing you existing and future customers.

FLUQs live in the gap between what’s known and what’s required, often right where AI hallucinates or buyers hesitate.

That’s the zone we’re scanning now.

We explored this with a client that’s a prominent competitor in the online education space. They had the standard FAQs: tuition, payment plans, and eligibility. 

But we hypothesized that there were numerous unknown unknowns that, when discovered, could negatively impact new students. We believed this would negatively impact existing and future enrollments. 

Mid-career students going back to school weren’t asking:

• Who watches the kids while I study for the next 18 months?
• Who takes on extra shifts at work?
• How do I discuss schedule flexibility with my boss?

These aren’t theoretical questions. They’re real decision-blockers that don’t reveal themselves until later in the buying cycle or after the purchase. 

And they’re invisible to traditional SEO.

There’s no search volume for “How do I renegotiate domestic labor before grad school?” 

That doesn’t mean it’s irrelevant. It means the system doesn’t recognize it yet. You have to surface it.

These are the FLUQs. And by solving them, you give your audience foresight, build trust, and strengthen their buying decision.

That’s the yield. 

You’re saving them cognitive, emotional, reputational, and time costs, particularly in last-minute crisis response. And you’re helping them succeed before the failure point shows up.

At least, this was our hypothesis before we ran the survey.

Where FLUQs hide (and how to extract them)

You go where the problems live. 

Customer service logs, Reddit threads, support tickets, on-site reviews, even your existing FAQs, you dig anywhere friction shows up and gets repeated.

You also need to examine how AI responds to your ICP’s prompts:

• What’s being overgeneralized? 
• Where are the hallucinations happening?

(This is difficult to do without a framework, which is what we’re building out with XOFU.)

You have to be hungry for the information gaps. 

That’s your job now. 

You aren’t optimizing content for keywords anymore. This ain’t Kansas. We’re in Milwaukee at a cheese curd museum, mad that we didn’t bring a tote bag to carry 5 pounds of samples.

You’re scanning for information your audience needs but doesn’t know they’re missing

If you’re not finding that, you’re not building visibility. You’re just hoping someone stumbles into your blog post before the LLM does.

And the chances of that happening are growing smaller every day.

There are four questions we ask to identify FLUQs:

1. What’s not being asked by your ICP that directly impacts their success?
2. Whose voice or stake is missing across reviews, forums, and existing content?
3. Which prompts trigger the model to hallucinate or flatten nuance?
4. What’s missing in the AI-cited resources that show up for your ICP’s bottom-funnel queries?

That last one’s big. 

Often, you can pull citations from ChatGPT for your category right now. That becomes your link building list. 

That’s where you knock. 

Bring those publishers new facts and information. 

Get cited. 

Maybe you pay. Maybe you guest post. 

Whatever it takes, you show up where your ICP’s prompts pull citations.

This is what link building looks like now. We’re beyond PageRank. We’re trying to gain visibility in the synthesis layer. 

And if you’re not on the list, you ain’t in the conversation.

Prove FLUQs matter with facts (FRFYs)

Once you’ve spotted a FLUQ, your next move is to test it. Don’t just assume it’s real because it sounds plausible. 

Turn it into a fact.

That’s where FRFYs come in: FLUQ Resolution Foresight Yield. 

When you resolve a FLUQ, you’re filling a gap and giving your audience foresight. You’re sparing them cognitive, emotional, reputational, and temporal costs.

Especially during a last-minute crisis response.

You’re saving their butts in the future by giving them clarity now.

For our client in online education, we had a hypothesis: prospective students believe that getting admitted means their stakeholders (their partners, bosses, coworkers) will automatically support them. We didn’t know if that was true. So we tested it.

We surveyed 500 students. 

We conducted one-on-one interviews with an additional 24 participants. And we found that students who pre-negotiated with their stakeholders had measurably better success rates.

Now we have a fact. A net-new fact. 

This is a knowledge fragment that survives synthesis. Something a model can cite. Something a prospective student or AI assistant can reuse.

We’re way beyond the SEO approach of generating summaries and trying to rank. We have to mint new information that’s grounded in data.

That’s what makes it reusable (not just plausible).

Without that, you’re sharing obvious insights and guesses. LLMs may pull that, but they often won’t cite it. So your brand stays invisible.

Structure knowledge that survives AI compression

Now that you’ve got a net-new fact, the question is: how do you make it reusable?

You structure it with EchoBlocks.

You turn it into a fragment that survives compression, synthesis, and being yanked into a Gemini answer box without context. That means you stop thinking in paragraphs and start thinking in what we call EchoBlocks.

EchoBlocks are formats designed for reuse. They’re traceable. They’re concise. They carry causal logic. And they help you know whether the model actually used your information.

My favorite is the causal triplet. Subject, predicate, object. 

For example:

• Subject: Mid-career students
• Predicate: Often disengage
• Object: Without pre-enrollment stakeholder negotiation

Then you wrap it in a known format: an FAQ, a checklist, a guide.

This needs to be something LLMs can parse and reuse. The goal is survivability, not elegance. That’s when it becomes usable – when it can show up inside someone else’s system.

Structure is what transforms facts into signals. 

Without it, your facts vanish.

Where to publish so AI reuses your content

We think about three surface types: controlled, collaborative, and emergent:

• Controlled means you own it. Your glossary. Help docs. Product pages. Anywhere you can add a triplet, a checklist, or a causal chain. That’s where you emit. Structure matters.
• Collaborative is where you publish with someone else. Co-branded reports. Guest posts. Even Reddit or LinkedIn, if your ICP is there. You can still structure and EchoBlock it.
• Emergent is where it gets harder. It’s ChatGPT. Gemini. Perplexity. You’re showing up in somebody else’s system. These aren’t websites. These are operating environments. Agentic layers.

And your content (brand) has to survive synthesis.

That means your fragment – whatever it is – has to be callable. It has to make sense in someone else’s planner and query.

If your content can’t survive compression, it’s less likely to be reused or cited, and that’s where visibility disappears.

That’s why we EchoBlock and create triplets. 

The focus is on getting your content reused in LLMs.

Note: Tracking reuse is challenging as tools and tech are new. But we’re building this out with XOFU. You can drop your URL into the tool and analyze your reuse. 

Test if your content survives AI: 5 steps

Do this right now:

1. Find a high-traffic page.

Start with a page that already draws attention. This is your testing ground.

2. Scan for friction-inducing fact gaps.

Use the FLUQs-finder prompting sequence to locate missing but mission-critical facts:

Your proposed prompt structure is deeply practitioner-aware and already aligned with SL11.0 and SL07 protocol logic. Here’s a synthesis-driven refinement for role-coherence and FLUQ-sensitivity:

---

Refined prompts with emission-ready framing

Input type 1: Known materials

• Prompt:
  “Given this [FAQ / page], and my ICP is <insert ICP>, what are the latent practitioner-relevant questions they are unlikely to know to ask — but that critically determine their ability to succeed with our solution? Can you group them by role, phase of use, or symbolic misunderstanding?”

Input type 2: Ambient signal

• Prompt:
  “My ICP is <insert ICP>. Based on this customer review set / forum thread, what FLUQs are likely present? What misunderstandings, fears, or misaligned expectations are they carrying into their attempt to succeed — that our product must account for, even if never voiced?”
  
• Optional add-on:
  “Flag any FLUQs likely to generate symbolic drift, role misfires, or narrative friction if not resolved early.”

---

Drop it into this PARSE GPT.

Sources include:

• Reviews and forum threads.
• Customer service logs.
• Sales and implementation team conversations.

3. Locate and answer one unasked but high-stakes question

Focus on what your ICP doesn’t know they need to ask, especially if it blocks success.

4. Format your answer as a causal triplet, FAQ, or checklist

These structures improve survivability and reuse inside LLM environments.

5. Publish and monitor what fragments get picked up

Watch for reuse in RAG pipelines, overview summaries, or agentic workflows.

The day Google quietly buried SEO

We were in Room B43, just off the main stage at Google I/O.

A small group of us – mostly long-time SEOs – had just watched the keynote where Google rolled out AI Mode (it’s “replacement” for AI Overviews). We were invited to a closed-door session with Danny Sullivan and a search engineer.

It was a weird moment. You could feel it. The tension. The panic behind the questions.

• “If I rank #1, why am I still showing up on page 2?”
• “What’s the point of optimizing if I just get synthesized into oblivion?”
• “Where are my 10 blue links?”

Nobody said that last one out loud, but it hung in the air.

Google’s answer?

Make non-commoditized content. Give us new data. Ground AI Mode in fact.

No mention of attribution. No guarantees of traffic. No way to know if your insights were even being used. Just… keep publishing. Hope for a citation. Expect nothing back.

That was the moment I knew the old playbook was done.

Synthesis is the new front page. 

If your content can’t survive that layer, it’s invisible.

Appendix

1. Content Metabolic Efficiency Index (useful content theory)

💾

Grappling with innovation and changing consumer attitudes is second nature to marketers, who have already lived through many technological shifts over the past two decades. But forecasting where things are going is especially hard when it comes to modern AI, which has such unusual, non-deterministic properties. You can’t just extrapolate from the state of AI today to understand where AI is going to be in five years (or one…); during this sort of a platform shift, you need to take a deeper first-principles look.

Some things won’t change. Consumers will always want products, services and experiences that resonate and meet their needs. Marketers will always want easier, faster and more effective ways to connect with consumers. But the technologies that mediate that relationship are primed to shift in the coming years in major, unprecedented ways — impacting how marketers do their work, and the customer experiences they’re able to deliver.

How the marketer experience will evolve: Less rote work, more creativity

The history of marketing is built around constant evolution. But the scale and complexity of the change triggered by the rise of modern AI may test even seasoned customer engagement teams. To thrive, marketers need to open themselves up to new skills, perspectives and capabilities that will allow them to do more with less.

This change is already underway. As marketers take advantage of AI, they’re spending less time on rote tasks (like manual message creation) and more on strategy and creative work — from brainstorming innovative campaigns to deepening their testing and optimization strategy. These efficiency gains will grow as AI becomes a more prominent part of the customer engagement process, allowing brands to set goals and guardrails, then empowering their AI solutions to independently consume context, make decisions, and act on marketers’ behalf. 

Today, that might look like training basic agents on your brand’s voice to ensure that message content is consistently on brand. But as we gain trust in AI’s ability to operate unsupervised over longer time horizons and to handle complex projects, more marketers will be able to shift their focus to strategy and effective management of the AI resources at their disposal to enable AI decisioning and other essential optimizations.

How team experiences will evolve: Humans and AI agents working side by side

Marketing is a collaborative art, where building a successful customer engagement program often depends as much or more on marketers’ ability to work together effectively as it does on their individual skills. But while AI may help marketers to work with internal stakeholders more effectively, its biggest unlock is the ability to be a direct “teammate” to marketers themselves. And by leveraging AI’s ability to create countless agents that can support customer engagement, even entry-level marketers will likely find themselves essentially operating as a “manager” of a team of autonomous subordinates. 

Imagine creating a whole team of agents, with one tasked with personalizing product recommendations, one that QAs messages to ensure they’re formatted and built correctly, one that handles translations and another that reports back at the first sign of campaign underperformance. By supplementing your existing capabilities with agents, you aren’t just reducing the burden on yourself and your human colleagues; you’re also building a digital institutional memory, training these “teammates” with context and goals and reward functions to be able to keep supporting your efforts and driving value even as human coworkers come and go and your team’s goals shift and evolve with time.

AI and customer engagement: How brands can win the future

For years, marketers have sought the ability to truly personalize communication on a 1:1 basis across an audience of millions, and to do it swiftly, efficiently and at scale. This was the Holy Grail of marketing, but due to the limitations of technology it simply wasn’t achievable for even the most advanced teams. That’s all being made possible by AI decisioning, a powerful new type of functionality that can force multiply brands’ marketing performance and creative impact while delivering what their customers want and need.

Previously, a brand trying to win back lapsing customers had a long journey ahead of it. It might start by leveraging a churn propensity model to identify which customers are most likely to churn, then use a product prediction model to figure out what products to highlight in order to tempt them to return. From there, they’d need to run a series of A/B tests in order to figure out which offers and channels will work best. But while taking that approach is a traditional best practice, it only got brands so far — they could target micro-segments on the right channel with the right offer, but truly 1:1 engagement was still out of reach.

AI decisioning represents a new way forward when it comes to personalization. This approach leverages reinforcement learning, where AI agents learn from consumer behavior and learn over time how to maximize rewards (such as conversions or purchases) in order to optimize the KPIs that have the biggest impact through ongoing, autonomous experimentation. That means AI decisioning can seamlessly determine not only the next best product offer for those lapsing users, but also the best channel, the optimal time of day or day of week, the frequency that makes the most sense, the message most likely to drive ideal outcomes, and any other dimension that could impact whether a recipient takes a given action. 

Even better, because AI agents are constantly experimenting in the background, the model can continuously adapt to shifting consumer preferences and behavior. And because these models use first-party data about every available customer characteristic, AI decisioning makes it possible to engage with individuals in a true 1:1 way, rather than relying on segments. The result is exceptional relevance and responsive experiences for individual consumers, something that’s only possible because of AI.

Final thoughts

With any major technology shift, it isn’t enough to just plan for the obvious outcomes — you must ensure you can react effectively to the changes that no one knows are coming. To succeed, brands need to pay careful attention to the arc of this new technology. Responding to a platform shift can’t be a one-and-done thing, and brands that create a five-year plan without building in regular pulse points and adjustments are going to quickly find themselves falling behind their more agile, flexible peers. 

To see the full benefit of AI in their customer engagement efforts, brands also need to look beyond AI. After all, AI isn’t a shortcut, it’s an amplifier — and the AI you use for customer engagement is only ever going to be as good as the infrastructure supporting it. An exceptional AI feature isn’t going to feel exceptional to consumers if it’s built on architecture that can’t take action in real time or can only deliver experiences in a single, prescribed way. Make sure your AI tools are built on a strong foundation and have the infrastructure they need to shine; otherwise, you may never fully achieve what’s possible.

Curious to learn more about how Braze is thinking about AI and customer engagement? Check out our BrazeAIᵀᴹ page.

The holiday season is make-or-break for email marketers. With inboxes bursting from October through New Year’s, even your most dazzling email content could disappear into spam folders if your deliverability isn’t solid.

Mailbox Providers (MBPs), such as Gmail, Yahoo and Outlook, receive an overwhelming volume of emails during peak seasons. Their systems work harder to protect their users and reward senders who follow best practices with more reliable inbox placement.

The good news? You can stay ahead with a few strategic steps. Here are four essential tips to boost your email delivery rate and ensure your campaigns reach the inbox this holiday season.

1. Understand How Deliverability Really Works

Deliverability goes beyond pressing send. It’s the difference between your email being delivered and it actually landing in the inbox. Each send passes through two main stages:

Stage 1: Delivery. Your email is transmitted to an MBP (like Gmail or Outlook) and either accepted or rejected. Hard bounces occur when an address is invalid. Soft bounces occur when an inbox is temporarily unavailable (for example, due to full storage).

Stage 2: Inbox placement. Once accepted, the provider decides where your message goes: inbox, promotions tab or spam. This judgment is based on factors like authentication, sender reputation and recipient engagement.

During peak holiday months, email traffic can double or triple — especially around major shopping days. MBPs must protect users from unwanted or malicious emails, which means even legitimate senders face heightened scrutiny. Understanding this process helps marketers plan more strategically and avoid looking “spammy” to the algorithms that decide inbox fate.

For a deeper dive, check out Email Deliverability: What It Is and Why It Matters.

2. Build and maintain a stellar sender reputation

Sender reputation is your credibility score with mailbox providers. Think of it as your brand’s trust rating in the email world. A strong reputation earns you consistent inbox access; a weak one can land even your best content in spam.

Two factors carry the most weight:

• Audience engagement. High open and click rates tell MBPs your messages are wanted. They also measure dwell time (how long emails are open), whether recipients add you to contacts or delete messages unopened. These small actions add up to big reputation signals.
  
• List quality. Healthy lists equal healthy results. The holidays often bring a surge in signups, but not all contacts are equal. Focus on quality over quantity. Use permission-based opt-ins, utilize welcome series to set expectations, secure your forms with ReCAPTCHA and regularly review your list based on audience engagement. Remember that every contact should have opted in through a compliant process. If you’re collecting new subscribers during the holidays, follow with an automated welcome email that confirms expectations and builds immediate trust.

To keep your reputation strong:

• Re-engage inactive subscribers early. Start your warm-up campaigns before the rush to re-spark engagement.
  
• Clean your list regularly. Remove dormant contacts who haven’t interacted in months.
  
• Honor unsubscribes immediately. A fast, frictionless opt-out keeps you compliant and builds trust.
  
• Authenticate your domain. Proper SPF, DKIM, and DMARC settings are table stakes for modern deliverability.

By maintaining good list hygiene and engagement practices, your emails are far more likely to land where they belong. Refresh your email list-building skills with Campaign Monitor’s quick guide. 

3. Avoid sudden strategy changes.

As the holidays heat up, it’s tempting to ramp up your send volume or reach out to older contacts. But sudden shifts in cadence, audience size, or content tone can raise red flags. MBPs track consistency. If your patterns change abruptly—say, doubling your frequency in one week—it may look like your account was compromised or that you’re engaging in spammy behavior.

Gmail’s “Manage Subscriptions” feature now allows users to unsubscribe from multiple senders quickly and easily. This means your content needs to be relevant and valuable to keep subscribers engaged.

Keep your program steady and predictable with these basics:

Do:

• Keep a consistent sending cadence.
• Warm new segments gradually.
• Offer subscribers control through preference centers or “opt-down” options instead of forcing them to unsubscribe.
• Test new creative or messaging with smaller sample groups before scaling.

Don’t:

• Send to third party, purchased or dormant lists
• Reactivate old segments without a re-permission strategy outside of campaigns
• Change sending domains without a well-thought-out and phased warm-up plan
• Ignore warning signs like rising bounce and spam complaint rates or declining open rates

Leverage Campaign Monitor’s “Month-to-Month Holiday Guide for Busy Marketers” to stay on track and on time with relevant holiday messaging.

4. Monitor your metrics closely.

Holiday email marketing is not a set-it-and-forget-it operation. Even high-performing senders can experience fluctuations in inbox placement, open rates, or complaints.

Keep a close watch on:

• Bounce rate: Hard bounces above 2% signal data or list issues. Investigate immediately.
  
• Complaint rate: Keep it below 0.1%. High complaints damage reputation fast.
  
• Unsubscribe rate: A spike suggests your cadence or messaging may be off.
  
• Open rates by domain: If Gmail opens drop sharply, but others stay steady, it may indicate inbox filtering specific to that provider.
  
• Spam trap hits: Hitting recycled or inactive addresses means your list hygiene needs work.
  
• Reputation data: Tools like Google Postmaster provide insights into domain health and spam reputation.

These numbers tell a story — one that can guide smarter, real-time adjustments. 

Learn how Campaign Monitor’s Campaign Score feature helps you improve campaign performance with best practice benchmarks and personalized suggestions.

Bringing it all together

Landing in the inbox is no longer a guarantee — it’s a privilege earned through consistent, trustworthy practices. As you prepare for the holidays, focus on these four deliverability foundations:

1. Understand the system. Learn how MBPs evaluate senders and adapt your approach accordingly.
   
2. Guard your reputation. Build and maintain clean lists and engaged audiences.
   
3. Keep it steady. Avoid sudden spikes in send volume or frequency.
   
4. Watch your data. Monitor metrics constantly and act fast when something looks off.

The combination of smart strategy, authentic engagement and proactive monitoring sets you up for success—even in the busiest inbox season of the year.

Campaign Monitor makes these best practices easy to implement with intuitive tools that help you segment, automate, and analyze your messaging so you can focus on creating content your audiences want to open.

When done right, deliverability isn’t a technical hurdle—it’s the key to turning holiday emails into lasting customer relationships.

Ready to land in the inbox and make this your most successful season yet? 

“Sleigh” holiday emails with Campaign Monitor’s Annual Essentials Plan for just $26.10/mo.

Martech debt builds up through manual reporting, fragile integrations, and silos. These issues fragment customer data, break campaign attribution, and force teams to rely on shadow spreadsheets to fill gaps between platforms.

Current maturity models focus on technology adoption (hello AI!) rather than business outcomes. This misses the structural shift required to escape this cycle.​

Semrush Enterprise evaluates maturity across five interconnected pillars:

• Search
• Traffic
• Behavior
• Social
• Brand

Progress means moving from patchwork operations to a unified engine where insight, execution, and impact connect and scale together for strategic effect.

Marketing maturity progresses through five interconnected levels, each marked by deeper integration and growing automation roles across digital marketing specialties. 

Level 1: Siloed

Teams hit individual goals but miss collective impact. 

Teams operate as isolated units, protecting their own metrics while critical insights die inside departmental boundaries. Individual goals get hit while campaigns lose ground because no one understands cross-pillar impact. 

Silos block productive feedback loops: teams deepen expertise but miss the compounding lift when signals transfer. 

Isolated metrics become absolute targets (Goodhart’s Law), pushing teams to game numbers at the expense of real growth while eroding the unified experience customers expect. 

At this level, every pillar runs its own optimization race, blind to system impact and blind to what real performance should look like. The results?

• Fragmented customer data.
• Inconsistent messaging across touchpoints.
• Broken attribution.

Spot the symptoms of a siloed marketing department

Siloed operations generate specific, identifiable symptoms such as:

• SEO and PPC campaigns run in parallel; no knowledge is shared.
• Funnel drop-offs are reported but never explained. 
• PR teams measure media coverage volume but have no concept of SEO outcomes.
• The content team drives engagement, but the data isn’t handed off, so no other team learns. 

Concrete example : the Lidl case study

A viral TikTok case study presented by Mathilde Høj from TRANSACT Denmark at BrightonSEO demonstrated how TikTok content can dramatically impact search behavior and website traffic. 

SEO and social media teams operated in silos, meaning when viral TikTok content drives massive search demand, the brand lacks the cross-functional collaboration needed to capitalize on it. 

The disconnect becomes particularly costly when organic social teams identify what’s resonating with audiences in real-time, but paid/performance teams and SEO teams have no visibility into these insights to act quickly. 

Ultimately, siloed workflows prevent brands from delivering a unified customer journey across discovery, consideration, and conversion. 

Level 2: Connected

Faster problem solving and leaner workflows 

At Level 2, teams connect some dots manually, creating symbiosis (i.e., interdependent relationships between search, traffic, behavior, social, and brand).

Campaigns can now pivot faster and answer “what’s working?” with a bit more clarity. 

Leaner workflows, selective data sharing, and better targeting all drive sharper engagement and conversions.

In the real world, it can look something like this: 

• When social media shares drive engagement signals to content optimized for search.
• SEO often gains from brand awareness campaigns that increase branded search volume, even when brand teams don’t optimize specifically for organic search.

Search engines value cross-channel signals: social media interactions generate social signals that indirectly influence SEO through increased content reach and backlink opportunities. When users share and engage with content across platforms, it signals relevance and authority. Social media profiles now appear in search engine results pages (SERPs), creating additional brand touchpoints.

Quick win: Pair up two specialties for a quarterly project. Demand a shared outcome and document what worked.

Dig deeper. SEO & Content Playbook for Agencies with Andy Crestodina. 

Level 3: Integrated

Shared KPIs locked by cross-functional playbooks

Integrated marketing teams hit revenue and scaling goals faster because every team stays focused on shared objectives while customizing tactics to get the best results for each channel. 

Every specialist knows where their work plugs into the pipeline. 

Real-time feedback and joint campaign planning become the new default and help achieve compounding results.

Concrete example: automated internal linking

Picsart, a creative design platform serving millions of users across 17 languages, identified pages needing optimization but lacked a systematic way to prioritize internal linking. Scaling manually across 300+ pages would have consumed 12,500+ hours. 

Semrush Enterprise’s Link Recommender deployed 50,000+ contextual links in one week, creating pathways that matched user intent at different journey stages: visitors researching “photo editing” could now flow seamlessly to specific feature pages, then to templates. 

The automation increased clicks by 20% over a period of 2 months.

Senior Product Manager Niels Kaspers emphasized the automation didn’t eliminate the team’s role: it shifted them from tactical linking grunt work to strategic content prioritization and forecasting which new pages would deliver more clicks. 

This demonstrates how Level 3 automation builds bridges between user behavior insights and technical execution while freeing capacity for strategic work. 

Level 4: Predictive

Algorithms detect patterns and forecast outcomes faster than human analysis, enabling proactive resource allocation before opportunities close or risks materialize.

AI forecasts outcomes before execution, freeing strategic capacity

AI models connect signals across pillars to forecast outcomes before they materialize. At this level, the marketing system stops reacting to what happened and starts preparing for what will happen.

Predictive analytics builds on integrated foundations, using cross-channel patterns to anticipate customer behavior, campaign performance, and revenue trajectories before teams execute. 

Instead of fixing problems after they occur, predictive systems surface trends, redirect resources in real time, and enable proactive intervention.

What predictive looks like in practice with Square

What previously took months of manual analysis now happens in seconds. When algorithm updates hit or traffic drops, Square’s teams can open Semrush Enterprise, run the “What Has Happened” automation, and respond before competitors even understand what changed. 

Predictive SEO forecasting shows:

• Which content optimizations will move rankings.
• Which markets offer untapped opportunity.
• Where competitors are gaining ground. 

Systems identify high-impact opportunities across markets and channels automatically, then surface them to teams for strategic execution rather than waiting for manual discovery.

This freed 12 hours per week for strategic work while AI handled diagnostic detection. Square made a point of focusing its attention on content.

Running AI-powered content audits allowed visibility of the competitive gaps and opportunities, which could immediately be deployed into their predictive SEO forecasts. Now they could understand which content changes would move rankings most, allowing prioritization of high-impact optimizations rather than guessing.

These could then instantly be shared across their nine global markets to scale the impact.

The system surfaced “high-impact opportunities across markets” that Square’s human team hadn’t detected, enabling the company to adapt strategies, optimize content, and capture growth opportunities in real time ahead of competitors.

Level 5: Autonomous

Spend time on growth, not management.

The autonomous marketing system self-optimizes across all pillars with minimal human input: spend, content, reporting, and optimization adjust in real time without manual intervention. Teams step in by exception when strategic judgment, creative vision, or crisis response requires human expertise. 

Most marketing organizations remain at Levels 2–3. A 2025 automation maturity study found that autonomous operations require foundational work most companies have not completed: 

• Fully integrated cross-channel data.
• Machine learning models trained on business-specific outcomes.
• Governance frameworks defining when systems act independently versus escalating to humans.

Autonomous marketing requires clean, connected data flowing across every channel. This requirement conflicts with the fragmented martech stacks most teams use.

Signals of autonomous operation:

• Campaigns run fully automated with optimization loops adjusting creative, targeting, and budget allocation without manual input​.
• Budgets shift automatically based on real-time ROI calculations, freeing teams to innovate rather than manage spreadsheets​.
• Brand monitoring runs continuously, flagging humans only when risk thresholds are breached​.
• Crisis playbooks trigger automatically from AI pattern detection, replacing reactive emergency meetings​.

What if autonomous operation feels distant?

Identify one high-volume, low-complexity marketing task and automate it with clear exception rules defining when the system escalates to a human. 

Document decision triggers that remain human-only: 

• Brand messaging approval.
• Crisis response.
• Budget reallocation above certain thresholds. 

Most organizations will operate as hybrid systems for years, with autonomous operations handling defined tasks while humans manage judgment calls, cross-functional strategy, and organizational change required to reach full integration.

Marketing maturity is not a technology checklist

Organizations stuck buying tools without integrating systems perpetuate the martech debt cycle. This fragments data and burns out teams while competitors who build connected foundations capture compounding returns.

The path forward starts with an honest assessment: identify which level describes current operations. Then, focus on one cross-functional integration project that demonstrates symbiotic value. 

Progress happens through deliberate structural shifts (e.g., connecting silos, establishing shared KPIs, automating tactical work) not through adding another platform to an already fragmented stack.

Meet the DOOGEE U12, the company’s new Android tablet. It is a direct successor to the DOOGEE U11, which received Android 16 two months ago. The DOOGEE U12 is now here, and it can handle anything you throw at it, basically.

The DOOGEE U12 is here, the company’s brand new tablet

DOOGEE says that the tablet is made for both “busy students and families”, as it’s made for both work and play. The company also announced the worldwide release of this tablet, and we have a special offer for you, which we’ll talk about at the very end.

This tablet has a large 12-inch 2K IPS display, which offers a 90Hz refresh rate. This display is also larger than the one on its predecessor. You’re also getting Certified Eye Comfort technology here, to protect your eyes.

There is a 9,000mAh battery included on the inside, and DOOGEE says it’s an “all-day battery”, as in you won’t have to charge it no matter what you end up doing. It can also charge other devices, act as a power bank.

It comes with expandable RAM and storage

The Unisoc T7255 octa-core chip fuels this device, while you get 6GB of RAM. You can expand that up to 24GB via virtual RAM, though. DOOGEE included 128GB of storage here, though you can expand that up to 2TB via a microSD card.

Android 16 comes pre-installed on this tablet, by the way, the latest version of Android OS. There is also a ‘VIP Edition’ of the tablet that comes with a suite of accessories. Those accessories include a Bluetooth keyboard, a stylus, a mouse, a tempered glass screen protector, and a protective cover.

You’ll also notice that the bezels around the display are not too thin or too thick, which is always nice to see.

You can save quite a bit of cash thanks to these coupons

With that being said, you can purchase this tablet via Amazon as we speak. We also have a 30% off coupon (£100) +5% off code for you to use, in order to get a very nice discount. You’ll see both the purchase link and codes listed below. You can get the tablet with free shipping and an extended warranty. If you’d like to know more about the company’s products, visit DOOGEE’s official website.

Discount coupon (30% off): U30

Discount code (5%): PE9YCPVK

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