Issue XI: Past the Playbook
Welcome to the difficult part.
Note - The first draft of this issue was written in Early December 2025
It’s been an interesting year.
Not because marketing suddenly stopped working, but because the conditions it works under have changed in very real ways. Marketing did not break. The environment shifted. And once you look at it honestly, it becomes clear that marketing was always going to get harder.
There are more products in the market than ever before. More startups. More SaaS tools. More creator products. More AI products. More companies chasing growth. At the same time, there are more marketers, more agencies, more freelancers, and more automated tools producing marketing at scale.
That alone creates pressure.
Attention has not increased. Time has not increased. People have not suddenly become more interested in buying new things. So what you get is density. Too many messages fighting for the same limited mental space.
When density increases, efficiency drops.
This is why many marketers felt like they were doing everything right this year, but results still softened. Campaigns that used to work felt weaker. Channels that felt reliable became unpredictable. Costs went up. Conversions felt harder to explain.
Then came the structural issues on top of that.
Tracking continued to deteriorate. Apple’s privacy changes weakened Meta’s attribution. Cookie-based targeting became unreliable. Last-click attribution told less of the story than it used to. Marketers were forced to make decisions with incomplete data, while being asked to justify performance more than ever.
At the same time, platforms made their incentives clear.
Organic reach declined across social platforms. This was not accidental. Platforms are businesses. Paid distribution is how they make money. Organic reach still exists, but it is no longer something you can depend on consistently. You can win moments, not systems, if organic is all you rely on.
Clicks also dropped in places people did not expect.
AI changed how people search and discover information. Instead of clicking ten links, users now ask one question and get one answer. That removes traffic from the open web and concentrates power elsewhere. Marketing did not disappear, but the path to discovery changed.
Old assumptions quietly stopped being true.
Another layer to this is geography and context.
Global marketing playbooks do not translate cleanly into local markets. In places like Nigeria, high inflation, low purchasing power, infrastructure gaps, and trust issues all shape how marketing actually performs. You cannot simply copy what works in the US or Europe and expect the same outcome.
One of the clearest lessons from this year is that offline and online channels work best together, especially in markets where trust and familiarity matter. Digital-only strategies can work, but they are rarely sufficient on their own. Real-world touchpoints, referrals, and physical presence still influence conversion in ways dashboards cannot fully capture.
Attribution struggles to account for this.
Someone might see a billboard, hear about a product from a friend, watch a video later, and finally convert through a paid ad. Most systems only record the last step. That does not mean the other steps did not matter. It just means they were invisible.
This is why attribution feels frustrating. Not because it is useless, but because it is incomplete.
Fraud also increased. Fake leads, low-quality traffic, inflated metrics. As pressure to show growth rises, so does noise inside the data. This further weakens trust in surface-level numbers and forces marketers to look deeper.
So when people say “marketing is harder now,” they are right. But not because marketers suddenly forgot how to do their jobs.
Marketing is harder because:
The market is crowded
Attention is fragmented
Tracking is imperfect
Platforms prioritize paid distribution
AI is reshaping discovery
Local realities complicate execution
And all of this is happening at the same time.
The marketers who performed best this year were not necessarily the ones who found new hacks. They were the ones who treated marketing as a system, not a collection of channels. They focused on first-party data. They built workflows. They combined automation with human judgment. They stopped expecting clean attribution and started designing around uncertainty.
That shift is the real story of this year.
From Channels to Systems: Engineering Marketing Again
Once you accept that marketing is getting harder for structural reasons, the next question becomes obvious.
How should marketers respond?
The mistake many people make at this point is looking for better tactics. A new channel. A new ad format. A new growth hack. But the shift that is happening is not tactical. It is foundational.
Marketing is quietly moving back toward its technical roots.
If you go far enough back in digital marketing, marketers were much closer to the product and the technology than they are today. They were setting up websites themselves. Installing analytics. Managing servers. Configuring email systems. Even when they were not writing code, they understood how the systems worked.
Over time, marketing became more abstracted.
Tools became easier. Platforms became more automated. Teams grew. Developers handled technical work. Marketers focused on copy, creatives, and channels. That division worked for a while.
It works less well now.
The current environment rewards marketers who understand systems, not just surfaces. Funnels, data flows, automations, attribution logic, APIs, tagging, workflows. These are no longer optional knowledge areas. They are leverage.
This does not mean every marketer needs to become an engineer. It does mean marketers need enough technical understanding to design systems, debug problems, and communicate clearly with developers or tools.
Waiting is expensive.
Waiting for a developer to set up a simple tracking event. Waiting for engineering to build a landing page test. Waiting for data teams to pull insights. In fast-moving markets, speed compounds. Teams that move slowly lose optionality.
This is why treating marketing like an engineering discipline matters.
Engineering thinking forces structure. Inputs, outputs, constraints, feedback loops. It asks how a system behaves over time, not just how a campaign performs in isolation. It encourages iteration, testing, and documentation.
When marketers think this way, automation becomes useful instead of shallow.
Automation is not about doing less work. It is about doing the same work faster and more consistently. Email flows. CRM logic. Lead scoring. Content distribution. Reporting. These are not exciting, but they remove friction and create capacity for higher-quality thinking.
AI fits here, too.
AI is an execution layer, not a strategy engine. It can help you write faster, analyze faster, and prototype faster. It cannot tell you what to build or who to serve. Marketers who confuse execution speed with strategic clarity end up shipping more noise, not better systems.
This is where many teams struggle.
They add AI on top of weak foundations. Poor data. No segmentation. No feedback loops. No clear customer understanding. AI amplifies whatever you already have. If the system is fragile, it breaks faster.
Another important shift is skill valuation.
Skills like copywriting, social media management, and basic content creation are still important. They are just no longer rare. The bar has moved. What used to be impressive is now expected.
This does not devalue creativity. It changes where creativity is applied.
The advantage now comes from creative system design. How channels connect. How users move between touchpoints. How data informs messaging. How offline and online efforts reinforce each other. How feedback loops are built into the workflow.
Marketing is becoming less about shipping campaigns and more about shipping infrastructure.
This also explains why new roles and labels are emerging. Technical marketing. Growth engineering. GTM engineering. Revenue operations. Different names, similar direction.
The common thread is systems ownership.
The marketers who stay ahead will be the ones who understand enough of the stack to build, adapt, and maintain these systems without waiting for permission or perfect conditions.
Winning in 2026: Precision, Data, and Product Thinking
If marketing is becoming more structural and more technical, then the way we operate has to change with it. 2026 will not reward louder marketing. It will reward clearer systems.
The first priority is data. Not more dashboards, but better data.
Third-party data is unreliable. Platform reporting is incomplete. Attribution will remain imperfect. The only durable advantage left is first-party data. Knowing who your users are, how they behave, what they respond to, and how they move through your ecosystem.
This means asking better questions. It means collecting information intentionally. It means designing flows where data is not an afterthought, but a core input into decision-making.
First-party data does not only improve targeting. It improves judgment.
When you understand your audience deeply, you make better creative decisions, better channel decisions, and better product decisions. Data becomes context, not just metrics.
The second priority is precision.
The era of broad, generic messaging is fading. Not because it never worked, but because it is increasingly inefficient. There are too many messages competing at once. Precision cuts through noise better than volume.
This applies to targeting, messaging, and distribution.
Reaching fewer people who care is more valuable than reaching many people who do not. This is especially true in markets where budgets are constrained and trust matters. Precision creates relevance. Relevance creates conversion.
Lead generation fits into this shift.
Leads are not about spam or endless nurture sequences. They are about optionality. If someone is not ready to convert today, can you keep the conversation open in a way that adds value over time?
Communities help, but they do not scale on their own. Social platforms are noisy. Group chats are personal but limited. Email, SMS, and messaging workflows sit in the middle. They allow scale with personalization when designed well.
The key is segmentation.
Not all leads are equal. Not all users need the same message. Systems that fail to recognize this end up wasting attention and trust. Systems that adapt become compounding assets.
Another major shift for 2026 is product thinking.
Marketing cycles are shortening. Channels change faster. Formats decay faster. What worked six months ago may already be stale. This requires marketers to think like product teams.
Ship. Observe. Learn. Iterate.
Instead of annual plans that lock teams into rigid strategies, shorter cycles create flexibility. Reviews every few months force honesty. What is working. What is not. What needs to be rebuilt.
Marketing systems should evolve the same way products do.
AI and automation support this, but they do not replace thinking. AI accelerates execution. It does not decide direction. Teams that rely on AI without clarity move faster in the wrong direction.
Creative thinking still matters, but it has shifted.
Creativity is no longer just about messaging. It is about system design. How offline and online touchpoints reinforce each other. How content ecosystems work together. How distribution, partnerships, and platforms interact.
Attribution will remain hard. This is not a reason to give up. It is a reason to design around reality.
Think of attribution as a system, not a metric. Use it to guide decisions, not to justify them. Accept that some influence will remain invisible, especially across offline and trust-based channels.
The marketers who win in 2026 will not be the ones running the most campaigns. They will be the ones building the most resilient systems.
Systems that learn. Systems that adapt. Systems that respect attention. Systems that compound over time.
Marketing is no longer about shipping campaigns.
It is about shipping marketing systems.