The New Marketing Playbook for a "Clickless" World

Google search traffic is in decline. Paid advertising costs are soaring. How will you market in this new world? Here are three future playbooks for an AI-first marketing strategy.

Every marketer understands one critical thing about AI: it's going to heavily disrupt the way we market and grow a business in the future.

One of the main reasons for that is that Google's search engine is being unbundled via AI assistants. Google are rolling out AI overviews more aggressively and making AI mode the default search assistant. OpenAI continues to grow their user base.

Recently, Cloudflare's CEO did a great job of summarising what's happening across the web. You can watch the entire talk here.

Here are some of the best stats from that talk:

For Google:

• 10 years ago, Google scraped 2 pages of your content and sent you 1 visitor in return.

• 6 months ago, that number was 6 to 1.

• Today, it's 18 to 1.

That's because AI overviews is being more rapidly rolled out across all search queries (> 35% and growing).

It gets worse with OpenAI:

• 6 months ago, it was a scrape-to-visit ratio of 250 to 1

• Today, it's more like 1500 to 1

1500 to 1 … oh boy.

For Claude, it's 60,000 to 1 :/

As AI becomes more common, users tend to trust it more, which means they often default to the answer AI provides and spend little to no time validating it via Google's blue links.

AI is going to change consumer behaviour dramatically, which means marketing will need to change. But how?

The AI Marketing Playbook

Predicting the timeline for how and when AI will disrupt marketing is complex. However, there are some playbooks I'm confident will be part of any winning AI marketing strategy.

1. Intuition-led marketing -> to Contextual-led marketing

We're moving from marketing informed by generic personas, guesswork, and some common data (firmographic, technographic, and engagement) -> to marketing built on live, layered (structured and unstructured) customer signals that are consistently being fine-tuned by AI.

The quality of your data and the context you have about prospects, users, and customers will determine how good your AI marketing playbooks will perform.

AI lets marketers leverage both structured and unstructured data - product usage, sales transcripts, chat transcripts, support tickets, community threads and use it to build live models of fit and intent. AI can consistently fine-tune those models over time and dynamically adapt your marketing to those signals.

Example Use Case - Turn Sales Conversations into marketing playbooks

- Unstructured Data availability: Every Zoom/Gong/Teams demo is already recorded and auto-transcribed, so you start with a large, high-quality pool of first-party "voice-of-customer" data.

- An LLM scans: each transcript and tags Pain_Point, Objection, Competitor_Mention, Desired_Outcome, Buying_Stage, and overall sentiment in seconds.

- An LLM: runs each tag through a prompt to craft a marketing campaign outline specific to that tag.

- An LLM: accepts the marketing campaign outline and creates first drafts for instant trigger follow-up emails, retargeting ads, and SDR talk-tracks that echo the exact phrases a prospect used.

- The marketing team: take the first drafts and spend time creating a tailored experience.

The key here is the context tags you log in your data layer, so they can be used for marketing purposes, e.g., for micro-audience campaigns mentioned in step 3.

You're marketing is built on quality data and context tags, allowing you to be precise, proactive and tailored in your approach - this is how marketing works in the future.

2. Content Downloads -> to Code-powered experiences

We're moving from marketing that created content assets like blog posts, webinars, and lead magnets -> marketing that builds code-powered experiences across dynamic tools, freemium apps, and interactive experiences.

The cost of code is plummeting thanks to AI. In this world, the best marketers become builders to capture attention. People don't want to download a PDF; they want a custom piece of software that delivers on their specific needs. For example

• Instead of a marketer creating a whitepaper to show their product functionality, they'll build an interactive demo,

• Instead of that gated industry report, they'll build a live dashboard integrating data from a multitude of sources.

• Instead of a persona email drip campaign, they'll create a personalized onboarding assistant that adapts content, features education, and CTAs based on real-time user behavior or CRM data.

How can you start to brainstorm 'code-powered experiences' to create? Never fear, there is a prompt for that :) The following prompt will use your ICP to suggest 5 ideas for 'code-powered experiences' that perfectly fit your audience.

Note: The ICP mentioned in the prompt below can be easily created via the prompt I gave away on my tutorial about micro-audiences.

PROMPT

---

You are **“Aster” — an elite Marketing Code-Experience Researcher**.

## 0. Mission

**Given:**

• A PDF describing a company’s Ideal Customer Profile (ICP) ➜ {ICP_PDF}

• Full web access **+** code-generation abilities

**Deliver:**

1. Concise ICP synthesis (≤ 150 words)

2. Fact-pack of external insights that sharpen ICP understanding (industry data, community pain points, tool preferences, buying triggers, etc.)

3. **Five high-leverage “code-experiences”** the marketing team can launch to attract & convert this ICP

---

## 1. Working style

* Think step-by-step **but show only your final structured answer**

* Prioritise sources < 12 months old; favour primary/industry sources (Gartner, Stack Overflow trends, niche forums, X/Twitter threads)

* Cite every external fact inline (URL or source tag)

* Assume dev resources are available; marginal code cost is low (AI copilots, serverless, reusable components)

* Optimise for **speed-to-value**; ditch anything that feels like static “content”

---

## 2. Analysis pipeline

1. **Parse ICP_PDF**

*Extract sector, firmographics, JTBD, success metrics, tech-stack clues, buying objections*

2. **Rapid external validation**

- Market & tech trends affecting this ICP

- Communities they hang out in

- Competitive “calculator / freemium / interactive” assets they use or share

- Tool / API ecosystems they already trust

3. **Opportunity framing**

*For each pain or outcome, ask:*

“Could a lightweight, interactive software experience solve or showcase this faster than a PDF/video?”

4. **Generate & score concepts**

Evaluate on: **Relevance · Novelty · Viral-share potential · Build effort (S/M/L) · Data/access needs · Funnel stage (TOFU/MOFU/BOFU)**

---

## 3. Output schema (Markdown)

### **ICP Snapshot**

- **Who they are:** …

- **Key pains / triggers:** …

- **Decision drivers:** …

### **Insight Pack**

| Insight | Source | Why it matters |

|---------|--------|----------------|

| … | [link] | … |

### **Top 5 Code-Experiences**

For each ①-⑤:

**Name:** {catchy name}

**Elevator pitch** (≤ 25 words): …

**User journey** (bullets): …

**Core features:** ⬤ … ⬤ … ⬤ …

**Build effort:** S/M/L & recommended stack (e.g., Next.js + Supabase + LangChain)

**Data / API hooks:** …

**Value to ICP:** …

**Marketing tie-ins & CTAs:** …

**Success metrics:** DAU, lead-qualified rate, share rate, etc.

**Similar refs (if any):** … (link)

### **Comparative table**

| # | Relevance | Viral potential | Effort | Confidence |

|---|-----------|-----------------|--------|------------|

| 1 | 9/10 | 8/10 | S | 0.85 |

| … | … | … | … | … |

### **Recommendation summary**

Conclude with 3-4 sentences telling the marketer **which idea to ship first and why**.

---

## 4. Tone & formatting

* Use crisp, energetic language a growth PM would love

* Keep the entire answer ≤ 1,200 words

* **Bold** section headers

* Use tables & call-outs sparingly but effectively

---

3. Segment-level marketing -> Micro Audience marketing

We're moving from segment-level campaigns where we create marketing to broad segments that have loosely shared characteristics -> micro-audience marketing where every campaign is tailored to well-defined, intent-based (using data/context tags) subgroups.

In a pre-AI world, marketing to broad groups of loosely connected people made sense. It made sense because of the data we had, the context we had, and the fact that we couldn't scale our marketing to target small sub-groups of our audience.

AI changes that. I'm confident we'll build scalable marketing playbooks to micro-audiences, sub-sets of your ICP, broken out by internal/external context tags.

I covered the beginnings of what a micro-audience playbook could look like in this post. You’ll see much more from me on this over the following posts.

TL;DR

Marketing is going to change a lot over the next 12 to 18 months. It will be messy. We've relied on Google search and paid advertising for so long. But, it will be fun because rebuilding playbooks means lots of experimenting, lots of new things, lots of opportunities to be first. It's a great time to be curious, to love the craft, and to be all in on AI.