01The Problem
You're doing more marketing.
Is it creating more customers?
"We have a strong offering. We spend on content, campaigns, ads and sales. Everyone is working hard, but we still can't say where our next 100 qualified opportunities will come from."
What founders and CMOs tell us, every week
"We can't tell who's actually in market."
Clean-tech founder
"Buyers wait days for a demo, then go quiet."
SaaS founder
"Leads go cold between marketing and sales."
Marketing lead, Fortune 500
"We can't prove what drove the deal."
CMO, B2B services
The cause is usually fragmentation, not your team: content, outreach, website, AI tools and CRM data running as separate activities, with nothing connecting them or learning from the results.
02How Buyers Changed
Your customers have changed
how they buy.
They research on their own, compare you across channels, and form a preference before they ever talk to your team. More disconnected campaigns won't change that.
of B2B buyers prefer an overall rep-free buying experience, and 73% actively avoid suppliers who send irrelevant outreach.
Earn trust and answer questions before asking for a meeting.
Gartner, 2025 ↗of buyers had ranked their shortlisted vendors by preference before contacting any of them.
Win preference early. A meeting alone can't create it.
6sense, 2025 ↗channels used by B2B buyers across a purchase, up from five in 2016.
Connect marketing, sales, content and every digital touchpoint.
McKinsey B2B Pulse, 2024 ↗Methodology: separate surveys with different samples. The numbers should not be added together, and none of them is an endorsement of 10ex. Full sources are in the references.
03Buyer Psychology
Discovery shifted.
Psychology didn't.
Your buyers still decide the way people always have. What changed is where it happens: they now ask AI and peers before they ask you. These four forces explain why the shortlist forms before your first call, and where you need to show up.
Availability Heuristic
Tversky & Kahneman (1973) · Applied to AI-mediated buying
People judge likelihood by how easily examples come to mind. In the AI era, the examples that come to mind are curated by AI recommendation, not personal memory. If your brand is in the AI answer, it is cognitively available. If it is not, it rarely makes the buyer's shortlist, however good the product.
Inpulse application: Show up in AI answers at P0 and P1, before the buyer starts choosing. The brand that gets into those answers first is the one that comes to mind.
Mere Exposure Effect
Zajonc (1968) · B2B application across the awareness ladder
Familiarity breeds preference. People are more likely to choose options they have encountered before, even without conscious recall of the encounter. Community presence, LinkedIn content, and AI citation all compound mere exposure. The brand seen at P0 and P1 is preferred at P4, often without the buyer knowing why.
Inpulse application: Sequence channels so buyers see you at every awareness stage before they say they want to buy. The goal is familiarity.
Authority Bias in AI Recommendations
Milgram (1963) · Updated for AI-mediated trust
People defer to perceived authority. When an AI system recommends a vendor, it carries the implicit authority of the AI itself. Being cited by ChatGPT or Perplexity borrows the trust people place in those systems. Buyers tend to treat an AI citation the way they once treated an analyst mention.
Inpulse application: Each AI citation lends you the AI's authority, and it keeps working after you earn it.
Social Proof in Dark Social
Cialdini (1984) · Dark social application
People follow what trusted peers are doing. In the AI era, social proof has migrated to dark social: Slack communities, Reddit threads, private forums where no tracking pixel reaches. This is where buyers check their choices with peers. Brands that already take part in these communities get mentioned there.
Inpulse application: Take part in the communities where peers compare options at P1 and P2. That influence happens long before it shows up in your attribution reports.
04A System, Not an Activity
Stop restarting growth.
Start compounding it.
Campaigns end, and the learning usually ends with them. A growth system keeps what each campaign taught you. The difference starts with the questions your team asks every week.
Activity-led thinking
System-led thinking
What campaign should we launch?
Which buyer problem should we solve and learn from?
How many leads did we collect?
Which buying groups and qualified opportunities progressed?
How many emails did we send?
Did relevant buyers get useful engagement?
How much content did we publish?
Are we answerable, discoverable and credible?
Did this month perform?
What knowledge compounds into next month?
What new AI tool do we need?
Which capability, data flow or decision is broken?
Marketing is no longer just what you do. It is a growth capability you build.
Read both columns below. You will recognise your current operating model in one of them.
Fragmented Marketing
Plenty of activity, not much pipeline.
- Hire a sales rep and expect revenue without a demand engine behind them
- Do keyword research and publish 30 blog posts a month, then hope something ranks
- Run paid ads to a landing page with a form gate nobody wants to fill
- Attend four conferences a quarter and call the badge scans a pipeline
- Send weekly nurture emails to a list that stopped reading months ago
- Build personas from demographics instead of real buying signals
- Measure MQLs that sales never calls back
- Report quarterly metrics after the damage is already done
- Ignore AI engines entirely because "SEO still works"
- Wait for inbound and wonder why the funnel is drying up
If five or more sound familiar, your playbook is out of date.
Agentic Flywheel
Every action feeds the next. Effort compounds instead of expiring.
- Detect buying signals before the buyer announces intent
- Score every account in real time with the Inpulse Score
- Show up as the answer in AI engines before your competitors do
- Publish content mapped to buyer awareness, not an editorial calendar
- Engage at the moment the signal fires, not when the cadence says so
- Let agents handle execution so the team focuses on strategy
- Build presence in communities where P0 buyers form opinions
- Feed every outcome back so the next cycle is sharper than the last
- Replace form gates with working-session demos that deliver value first
- Build demand while preference is still being formed, not after the shortlist closes
The flywheel below shows how this runs.
05Your Demand Growth Flywheel
Six moves.
One loop that compounds.
Sense, Engage, Show, Convert, Learn, Expand, then back to Sense. Each turn of the wheel keeps what the last one built. Turn 1 lays the foundation, turn 2 makes demand repeatable, and by turn 3 the engine runs itself while your team steers.
Powered by Inpulse™ · agents + a GTM pod, one data loop
01 Sense · Signal intelligence
Know which accounts are in market and why, on the day it happens.
What we execute on turn 1
Define your ICP and buying groups
Deep research on your market, rivals and best customers.
Map the questions buyers ask AI
Track the prompts your buyers ask ChatGPT, Perplexity and Gemini.
Tap any stage on the wheel.
Outcomes at turn 1
5 → 1
angles tested → one working positioning
1
brand voice, trained on your own knowledge
25
buyer questions to AI tracked from day one
Foundation: know your buyer, sharpen the message, connect your data.
Typical targets. We set yours from your baseline in week 1.
06Proof
How customers moved up
the L1 → L3 ladder.
Each row is a real customer journey. The outlined cell marks where they are today.
BlueDrop
Clean-tech
▲ +3$1M+ in pipeline
30+ MQLs a month
CloudNuro
AI governance
▲ +2Workflows shipping
Scaling outbound
TechOrigins
IT consulting
▲ +1Brand voice live
MQLs warming
Your company →
▲ Find where you start in a working session
We plot it with you in week 1
07You Own the Engine
You own the engine.
We help you build it.
Assess, build, activate, then improve. Six two-week sprints, usable work every sprint, and a review at the end of each four-week increment. Everything we build stays yours: the knowledge, the workflows and the data.
Define the Problem
Map the Baseline
Define Experiments
Configure Agents
Deploy the Agents
Close the Learning Loop
Improve · continuous. After day 90 the results of every cycle reset the backlog. That is the next turn of the flywheel, from L1 toward L3.
Cadence adapted from SAFe® for Marketing (Scaled Agile, 2020). Run small experiments, review the result, and improve the next sprint.
Part II
The Inpulse methodology
The thinking behind the flywheel. Inpulse brings together Eugene Schwartz's five stages of buyer awareness (1966), Basadur's Simplex problem-solving process, Cialdini's principles of persuasion and research on generative engine optimisation. The name comes from the Sanskrit spanda, the first pulse before motion: the signal a buyer gives off before intent becomes visible.
08Why Legacy GTM Breaks
Google-era demand capture
no longer works.
Buyers increasingly start with an AI answer instead of a page of search results, and an answer does not send them to your site.
For two decades, growth in B2B looked the same: rank on Google, drive traffic, nurture leads, close deals. Agencies, tools, conferences and playbooks were all built on one assumption: buyers search, find you, and decide.
That assumption no longer holds. When a CIO asks an AI system "what is the best SaaS cost governance platform," the reply is an answer with a recommendation in it, not a list of links. Often nobody visits your content or fills your form, because the shortlist formed before your website loaded.
The broadcast era
Outbound
- ·Interrupt strangers at scale
- ·Rent attention through ads
- ·Cold call from purchased lists
- ·Spray and pray messaging
- ·Measure impressions and dials
- ·Spend more than the competition
- ·Volume over precision
The search era
SEO / Inbound
- ·Wait for the Google search
- ·Optimize keywords and backlinks
- ·Gate content behind forms
- ·Nurture leads on your timeline
- ·Measure traffic and MQLs
- ·React to stated demand
- ·Hope inbound keeps flowing
The AI era
Inpulse
- ·Detect the signal before the search
- ·Optimize to be the AI answer (GEO)
- ·Everything open, machine-readable
- ·Engage when the intent pulse fires
- ·Measure GEO rank and signal score
- ·Anticipate latent demand
- ·Run a loop that learns and compounds
09Strategic Pillars
What the system needs
to work.
These ten requirements define the system: how buyers access value, how AI can understand you, and how the motion improves over time.
01 · Acquisition · Self-Serve
Zero-friction product access
Let buyers understand, try, and buy without waiting for sales. Use fast signup, a sandbox, credits, and marketplace entry points.
Why it matters: Buyers see value before friction
02 · Interface · Website
Agent-ready website
Your site should answer questions clearly for buyers and AI. Use structured Q&A, open comparisons, transparent pricing, and machine-readable pages.
Why it matters: If AI cannot explain you, buyers will not find you
03 · Interface · Agent
One interface for every question
A frontline agent should handle discovery, pricing, comparisons, and demo guidance in one conversation, then route deeper questions when needed.
Why it matters: The experience feels like advice
04 · Interface · Knowledge
Digital twins for company judgment
Capture how leadership explains vision, pricing, objections, and ROI so agents can respond with company-specific judgment.
Why it matters: Company knowledge becomes available on demand
05 · Acquisition · GEO
Show up in AI answers
Structure pages so AI systems can cite them with confidence. Publish answer-first, category, and comparison pages.
Why it matters: AI answers shape the shortlist
06 · Compounding · Content
Always-on content
Answer market questions every week instead of waiting for campaign cycles. Website, social and events run as one continuous loop that improves each cycle.
Why it matters: Presence compounds
07 · Compounding · Experiments
Experiments at scale
Test angles, offers and channels in parallel, small and fast. Agents run the variants; the pod keeps what wins and drops the rest.
Why it matters: Learning speed beats planning
08 · Compounding · Credibility
Lead with proof
Use customer logos, concrete outcomes, and short proof points before long narratives.
Why it matters: Credibility lowers friction
09 · Compounding · Learning
Learn from outcomes
Trace every meeting and deal back to the signal, content and touch that moved it, then feed that into the next turn.
Why it matters: Every turn starts smarter
Data Strategy: Fuel for the AI Engine
More real data makes the system better.
- Capture every customer question, objection, and use case
- Build a thorough set of competitor comparisons
- Continuously train agents with real interactions and product updates
- Ingest market shifts, pricing changes, and regulatory moves
- Feed conversion outcomes back into scoring and content models
- Treat data as an asset that grows in value, not as a report
Metrics That Matter: AI-Age KPIs
Track whether the system is improving.
- Percentage of buyer questions answered instantly
- Time to first value (TTFV) from signup to outcome
- Self-serve conversion rate (no human touch)
- AI mention frequency across LLM outputs
- GEO ranking: top-3 position in AI responses
- Trial-to-paid conversion rate
- Social velocity: first two-hour engagement rate
10Agile Lean Mindset
Mindset first.
No system works if the team running it thinks in campaigns. Inpulse requires a shift from planning certainty to learning velocity. The framework, drawn from SAFe, defines what that shift looks like in practice.
11Problem Definition
Before strategy,
solve the right problem.
Most GTM work fails because the team framed the wrong problem. Start by defining the buyer job clearly.
Basadur's Simplex Creative Problem Solving process identifies that most organizations jump to solution-finding before they have accurately defined the problem. His research found problem-finding, more than idea generation, to be the weakest capability in most organizations. The four-phase model below shows where most B2B marketing teams start, and where Inpulse starts instead.
Basadur Simplex Applied to Inpulse Value Prop Discovery
Start at Phase 1. Do not proceed to Phase 4 until Phases 1 through 3 are complete.
Phase 1
Problem Finding
What is the actual problem your buyer has? Not what they tell you, but what causes them to lose sleep? Use the 5 Whys. Interview ten customers using Jobs-to-Be-Done format. Do not assume the problem is what you think it is.
Phase 2
Fact Finding
What signals exist in the market right now? Mine AI query patterns, community forums, job postings, and review platforms before forming a hypothesis. Signal data should inform your problem framing, not confirm it.
Phase 3
Problem Definition
Restate the problem as a "How might we..." question. Your value proposition lives here, in a sharper statement of the problem your product is built to solve, not in a feature list.
Phase 4
Idea and Action
Now build the Inpulse motion. Every signal map, GEO content strategy, channel sequence, and agent architecture flows from the problem definition you arrived at in Phase 3. Strategy without this is guesswork.
"Creative problem solving is not about generating ideas. It is about finding the right problem to solve in the first place, and most organizations never actually do that."
Min Basadur, Applied Creativity (1995) · basadur.com/ourprocess
Applied to Inpulse: before building your signal map, your GEO content strategy, or your agent architecture, run this exercise. What job is your buyer actually trying to get done? Not the search term, and not what their job title implies. Every motion that follows starts from that answer.
12Buying Signals
Intent does not appear.
It is mined.
Buyers leave signals before they book a demo: in AI queries, communities, hiring, and tool adoption. The job is to detect them early enough to act.
Gartner research puts 57 to 70 percent of the B2B buying process before a buyer contacts a vendor. Those buyers leave signals almost everywhere except your CRM. The question is whether your system is set up to catch them.
Behavioral Signals
Digital footprints during the problem-awareness phase. Job postings reveal organizational pain before the buyer acknowledges it internally. Technology installs show stack evolution. LinkedIn activity spikes indicate buying committee formation. You don't have to wait for a search.
Contextual Signals
Market moments that create predictable buying windows. Contract renewals, regulatory shifts, competitor pricing increases, organizational restructuring. They are tied to dates, so they can be watched. Inpulse maps them so you arrive weeks before the RFP is written.
Conversational Signals
Questions buyers ask AI systems, community forums, and peer networks. This is the most direct form of intent. When your ICP asks ChatGPT "how do I reduce Microsoft 365 costs," that is a buying signal with a timestamp. The goal is to be in the answer they get.
Structural Signals
Company traits that predict buying readiness: growth velocity, tech stack, buying committee formation, M&A activity. Unlike demographics, they change as the company changes, so the same account can score very differently from one week to the next.
13Content Drives Conviction
Reach them before
they reach for a solution.
Each awareness stage needs different content. The job is to move buyers forward, not to publish for volume.
Problem Unaware
~40% of addressable market
The buyer feels pain but has not named the problem. This is the biggest segment and the one most teams ignore.
Content to build
Goal: Make AI engines cite your data when buyers first describe the symptom
Problem Aware
~25% of addressable market
The buyer has named the problem and is learning how to think about it. The brand that shapes this stage has an advantage later.
Content to build
Goal: Become the explanation AI gives when buyers ask how to think about the category
Solution Aware
~20% of addressable market
The buyer knows solutions exist and is comparing categories. If you are missing from AI answers here, you are often missing from the shortlist.
Content to build
Goal: Define the evaluation criteria before competitors do and own the comparison in AI responses
Product Aware
~10% of addressable market
The buyer knows your product. Proof now matters: reviews, demos, case studies, and ROI.
Content to build
Goal: Close the gap between interest and proof with evidence the buyer can verify
Most Aware: Ready to Buy
~5% of addressable market
The buyer is ready to buy. They need price, terms, risk reduction, and a reason to move now.
Content to build
Goal: Remove the last friction between belief and purchase
"The buyer who finds you at P4 is comparing you to four others. The buyer who found you at P0 is already convinced you understand their world."
Inpulse Thesis, 2026 · Framework adapted from Schwartz (1966)
14Channel Plan
The right channel
at the right time.
Channels are not interchangeable. Each one works best at a different buyer stage and for a different job.
| Channel | Purpose | Key Signals and Tactics |
|---|---|---|
| GEO and Answer Engine Presence | Be cited in AI answers before the buyer forms a solution preference. Use proprietary data and answer-first structure. | ChatGPT citations, Perplexity answers, Gemini responses, FAQ schema, llms.txt, /research page |
| Community and Dark Social | Show up as an expert, not a brand. Answer threads before dropping links and make promotion rare. | Reddit (r/sysadmin, r/FinOps), LinkedIn groups, Slack communities, practitioner forums, AMAs |
| LinkedIn Content Engine | Build trust before buyers need you. Post consistently, use proprietary data, and earn familiarity before outreach starts. | Daily posts in breaking-news format, benchmark data drops, founder POV, mid-tier influencer amplification |
| Signal-Triggered Outreach | Reach out when a real trigger appears, not on a fixed cadence. Personalize to the event behind the account. | EA renewal triggers, hiring signals, pricing change triggers, M&A triggers, intent spike triggers |
| Paid Ads (Signal-Informed) | Target ads by signals, not demographics. Spend follows signal density instead of fixed budgets. | Signal-triggered LinkedIn ads, retargeting on GEO content visitors, Google Ads on AI query language, account-based display |
| Blog and Long-Form Content | Answer the exact questions AI engines surface in your category. Build posts to be cited, not just read. | Answer-first blog posts, benchmark-driven articles, original research narratives, category-defining frameworks |
| Webinars, Assessments and Trials | Turn belief into pipeline through proof: working-session webinars, free assessments, and self-serve trials. | Working-session webinars, free assessments with real output, self-serve trial, live demos on actual data |
15Execution System
Humans set direction.
Agents execute it.
Each agent owns one job, uses defined inputs, and hands a clear output to the next step.
All agents draw on the same four inputs: company knowledge, domain skills, live context, and feedback from real results.
Signal Enrichment Agent
Owns: signal detection.
Uses: job boards, LinkedIn, review sites, communities, and intent platforms.
Outputs: scored accounts and new signal events.
GEO Monitoring Agent
Owns: GEO visibility.
Uses: target prompts, competitor benchmarks, and live AI outputs.
Outputs: rank changes, citation gaps, and next content tests.
Content Generation Agent
Owns: content production.
Uses: founder voice, proprietary data, and active buyer questions.
Outputs: pages, posts, replies, and email copy.
Community Intelligence Agent
Owns: community opportunities.
Uses: community rules, thread activity, and the signal map.
Outputs: priority threads, draft replies, and reputation signals.
Outreach Sequencing Agent
Owns: outreach timing and sequencing.
Uses: signal triggers, proof points, and account context.
Outputs: personalized outreach matched to the signal.
Loop Optimization Agent
Owns: learning from outcomes.
Uses: conversion data, GEO results, and engagement performance.
Outputs: updated weights, content priorities, and outreach rules.
Shared Inputs
ICP profiles, founder voice, benchmark data, case studies, transcripts, product docs, and win-loss notes
So outputs sound like your company.
GEO structure, signal classification, answer-first writing, trigger mapping, community rules, and attribution logic
So each agent can do one job well.
Live signal feeds, current AI results, active community threads, CRM history, and campaign state
So outputs are current.
Conversion feedback, citation changes, reply rates, engagement quality, and signal accuracy
So the system improves over time.
"The agent that understands your organization's knowledge is worth ten agents that only understand the task. Context is the advantage, not raw capability."
Inpulse Agentic Architecture Principle · 10ex.ai
RReferences
Source Material
Research is the foundation for this strategy, not an endorsement of 10ex. Survey results come from different samples and should not be added together.
AAppendix: Inpulse Lexicon
Vocabulary
The terms that define the Inpulse operating model. Share freely.
Build your
demand engine.
Bring your market, your accounts and your buyers to a working session. We'll find where you stand on the L1 → L3 ladder and plan the first turn together.
Open methodology · share freely · 10ex.ai · v2.0 · October 2026