Blue Puddles

Blue Puddles | Frameworks — Sorilbran Stone
F-006 Positioning Published Proprietary

Blue
Puddles

Not blue oceans. Emerging micro-markets where demand is forming and you’re the only one who’s noticed yet.

Blue Puddles is a market positioning framework for identifying emerging sub-categories in your first-party data — and claiming them before your competitors know the category exists. It’s not a keyword strategy. It’s a market intelligence move that starts in your conversations, not your keyword planner.

Framework ID
F-006 · Blue Puddles
Layer
Layer 03 — Positioning
Status
Published · 2025
Author
Sorilbran Stone · Five-Talent Strategy House
Track
Positioning · Proprietary
Use When
You have first-party conversation data showing an emerging shift in what buyers are asking for — and your capabilities genuinely align with that demand.

What Is a Blue Puddle?

A Blue Puddle is an emerging micro-market inside a larger category — one that surfaces in your first-party conversation data before it appears in keyword volume tools or industry research.

It is a sub-category that’s forming in real conversations — sales calls, lost deals, client proposals — before your competitors have named it or built architecture around it. You catch it because you’re paying attention to what people are actually saying, not what the keyword planner is reporting.

On the page, it functions like long-tail SEO. But the insight that drives it doesn’t come from search data. It comes from patterns in your conversations — and recognizing when those patterns represent a real shift in what buyers want.

Blue Puddles vs. Blue Oceans: Blue Ocean Strategy asks you to find vast uncontested market space. Blue Puddles asks something different — not where is there no competition, but where is demand forming right now, in your actual conversations, that nobody has claimed yet? The puddle is small. That’s the point. Specific enough to own. Real enough to matter.

The Two-Condition Test

A Blue Puddle is only a Blue Puddle when both conditions are true simultaneously. One condition alone is not enough.

Condition 1
Emerging Demand
The micro-market is showing up in your first-party data. Sales calls, lost deal records, client conversations, stakeholder interviews. Not keyword tools. Not industry reports. Actual human language from actual conversations describing real problems that are shifting.
Condition 2
Genuine Capability
You can actually do the work. Receipts — case studies, measurable outcomes, documented proof — are ideal. A proven process is sufficient if proof hasn’t been built yet. Claiming a puddle you can’t fulfill works against you both reputationally and in the retrieval layer.

Demand without capability is just a market opportunity you can see but can’t serve. The machine will eventually find the gap between your claims and your proof, and it will hedge. You’ve actively injected doubt into the retrieval layer — which is harder to undo than never having claimed the puddle at all.

Capability without emerging demand is just a service you offer. Not a puddle. There’s no emerging sub-category to claim — only existing competition in an established lane.

Receipts are ideal. A proven process is sufficient. The key question is whether your capability is genuine — not whether you’ve already documented every win. If you can do the work and demand is forming, the puddle is yours to claim.

The Core Insight: Conversations Precede the Data

Keyword tools measure what people are already searching — which means they measure demand that’s already visible, already competitive, and often already saturated. By the time a micro-market shows up in meaningful keyword volume, the window for positioning is closing.

First-party conversation data captures demand before it becomes formalized search behavior. People describe emerging problems in conversations long before they know how to search for the solution. The pattern shows up in calls first. Then in proposals. Then in lost deals. Then in search volume. Then in competitor positioning.

You want to move at the first signal, not the last.

The competitive window is the gap between when demand shows up in conversations and when it shows up in keyword tools. Blue Puddles is a framework for finding and claiming that gap — while it’s still a gap.

Proof of Concept

The clearest example of Blue Puddles in practice: a high-growth influencer marketing agency where ads were generating clicks but not converting. Something had shifted in what buyers wanted. Rather than adjusting copy based on assumption, the diagnostic went to first-party data — 15 brands analyzed across 24 sales conversations, using AI to extract what the actual asks were in both 2024 lost deals and 2025 active pipeline.

Signal 2024 (Secondary Ask) 2025 (Primary Ask)
Performance Nice to have. Awareness was still the primary goal. Non-negotiable. Brands wanted influencers to generate revenue, not impressions.
Proof Case studies were viewed, not required. Test campaigns, measurable outcomes, and validation were table stakes before engagement.
Content Campaign-specific creative. Reusable asset catalogs — creator content as an ongoing production engine, not a one-time campaign.

The agency already had receipts for performance-driven influencer work — the proof existed, it just wasn’t visible or machine-readable. The move was targeted: a performance marketing navigation tab, a dedicated page, sub-pages for allow listing and related plays, and modular case studies on each performance page.

No competitors were positioning in that specific sub-category. The window stayed open for several months. That was the puddle — and the agency was ready to claim it because both conditions were true.

Why It Works for AI Retrieval

Broad category terms — influencer marketing, content marketing, digital strategy — are contested, commoditized, and hard for machines to use to differentiate one entity from another. When a query comes in about influencer marketing, the machine has thousands of signals pointing in every direction.

A Blue Puddle sub-category has far fewer signals. When you build architecture around it — dedicated pages, case studies, schema-marked content — and those signals point clearly to your entity, the machine can make a confident match. You’re not competing in a crowded retrieval result. You’re the clearest answer to a specific query.

Broad Category Blue Puddle Sub-Category
Influencer marketing Performance-driven influencer marketing
Real estate platform Diaspora property investment platform for emerging Caribbean markets
AI visibility consulting Entity architecture for founder-led businesses hitting the referral ceiling
Content marketing AI-generated content compliance strategy for regulated industries
Specificity reduces competition in the retrieval layer. The machine doesn’t have to choose between hundreds of signals — it has a handful, and yours is the clearest. That’s not luck. That’s architecture built around a puddle you found first.

Finding and Claiming a Blue Puddle — 5 Steps

1
Mine First-Party Conversation Data
Pull from sales calls, lost deal records, client conversations, and stakeholder interviews. Use AI to analyze patterns across a meaningful sample — 15 to 25 conversations minimum. You are looking for asks that are shifting: things that were secondary becoming primary, new problem framings that didn’t exist six months ago, phrases appearing repeatedly that aren’t in your current positioning.
2
Identify the Emerging Pattern
A Blue Puddle candidate is a phrase, problem framing, or sub-category appearing repeatedly across conversations — but not yet claimed by competitors in their positioning or architecture. It should feel specific. If it sounds like a headline you’ve seen before, it’s probably already contested. If it sounds like something you’ve been hearing from clients that nobody has named yet, that’s a puddle forming.
3
Apply the Two-Condition Test
Before building anything, verify both conditions. Is the demand real and emerging — showing up in actual conversations, not just theoretically plausible? And can you genuinely serve it — do you have receipts, or at minimum a proven process? If both are true, proceed. If only one is true, wait or build the capability first.
4
Build the Architecture
Dedicate visible structure to the sub-category: a page, a navigation item, case studies, schema-marked content. The goal is to give machines enough interconnected signals to make a confident association between your entity and this specific sub-category. Don’t just mention it once — build it as a node with corroboration across multiple pages and formats.
5
Monitor and Find the Next One
Blue Puddles have a lifespan. Competitors will follow. Continue watching first-party data for the next pattern forming. The strategic posture is perpetual attention to conversation data — not a one-time positioning exercise. You are always looking for the next puddle while you’re riding the current one.

When You Don’t Have First-Party Data

New businesses don’t have sales calls to analyze. No lost deal records. No client conversations. That’s not a reason to skip puddle identification — it’s a reason to use Reddit instead.

Reddit is where people speak without a filter. They describe their actual problems, their actual frustrations, what they’ve tried, what failed, and what they wish existed. That language is gold — not just for finding puddles, but for writing the copy that makes your positioning land with buyers who recognize themselves in it.

The goal isn’t to read Reddit threads casually. It’s to analyze them systematically — extracting pain points, organizing them by frequency of mention, and reading the pattern that emerges. That pattern is where the puddle is hiding.

The method: Install the Keywords Everywhere Chrome extension. Find 3–5 active subreddits in your category — ideally threads with 20+ comments. Use Keywords Everywhere’s one-click Reddit thread analyzer to synthesize what’s being said. Then run a second prompt asking it to lift out pain points and organize them by frequency of mention, most to least.

Read what’s showing up at the top of that list. That’s what the market is actually saying — not what competitors are positioning around, not what keyword tools are reporting, but what real buyers are frustrated about right now. If something is showing up repeatedly that nobody in your competitive set is addressing directly, that’s a puddle forming.

Platform How to Pull the Data Best For
Reddit Install Keywords Everywhere. Open any thread. One-click analysis. No copying needed. Raw, unfiltered buyer language. Pain points before they’ve been formalized into search behavior.
YouTube Keywords Everywhere works on YouTube pages too. One-click summary of comments and video content. How-to frustrations, feature complaints, workaround culture around existing tools.
LinkedIn No one-click tool. Copy comments manually from a post. Paste into Claude or ChatGPT for analysis. Decision-maker language. How buyers describe problems to peers and leadership.
G2 / Capterra reviews Copy and paste reviews directly. No tool needed. Post-purchase frustrations — the gap between what was promised and what was delivered.

Important: Reddit data is third-party signal. It tells you what the market is saying, not what your buyers specifically are saying. Use it to find puddle candidates, then validate with your own conversations before committing to the architecture. First-party data always outranks Reddit when you have it.

The handled.io example: A hypothetical project management software startup entering a crowded market — Asana, Monday, Notion, Trello. No first-party data. Three Reddit threads, approximately 200 comments analyzed using Keywords Everywhere. Pain points organized by frequency: “it doesn’t scale with how we actually work,” “we tried everything and were never aligned to begin with,” “when we got busy we went back to basics.” The pattern across all three threads was the same — teams weren’t failing because of missing features. They were failing because adoption collapsed under pressure. No competitor was positioning around that gap. The puddle: project management software that survives your busy season — specifically, structuring onboarding around team optimization periods (the slow seasons when teams have capacity to build new habits) rather than feature adoption. Reddit found the puddle. First-party data would have confirmed it faster.

Working Answers to Common Questions

These aren’t theoretical questions — they come up in practice. Here are the working answers based on how Blue Puddles has been applied.

Question Working Answer
How many conversations constitute a meaningful sample? For first-party data: 15–25 conversations is enough to see patterns emerge reliably. For Reddit: 3–5 threads with 20+ comments each gives you a working signal. The threshold isn’t about volume — it’s about whether the same language is appearing across multiple independent sources. When you start hearing the same phrase from people who don’t know each other, that’s a puddle.
Can a puddle be found from industry signals alone, without first-party data? Yes — Reddit, YouTube comments, G2 reviews, and LinkedIn posts are all viable substitutes when first-party data doesn’t exist yet. The tradeoff is signal quality. Third-party sources tell you what the market is saying. First-party tells you what your buyers specifically are saying. Use third-party to find puddle candidates. Use first-party to confirm them before building architecture.
What’s the minimum architecture required to claim a puddle? One dedicated page and one case study is the minimum. But the page alone isn’t enough — it needs to be connected. Internal links from related pages, a mention in your nav if the puddle is strategic enough, and schema markup that names the sub-category explicitly. The machine needs to find the signal from multiple angles, not just one URL.
How do you know when a puddle has matured — when the window is closing? Three signals: competitors start using the sub-category language in their positioning, keyword tools start showing volume for the phrase, and you start seeing generic content appearing around the topic. At that point the puddle is becoming a lane. You’re not too late — but the easy window is closing. This is when you go deeper rather than broader: more case studies, more specificity, more schema. And you start looking for the next puddle forming underneath the one you just owned.
Does claiming puddles accelerate or complicate the MVKG? Accelerates — when done correctly. A well-claimed puddle adds a new, specific anchor to the Specialization node of your knowledge graph, with its own corroborating content cluster. The machine has more to work with, and it’s more specific, which means more confident matching. The risk is claiming puddles that conflict with your existing entity signals. That’s when complexity enters. The rule: a puddle should extend your specialization, not contradict it.