It was 11:47 PM on a Tuesday when Sarah, VP of Sales at a Series B freight-tech startup, realized she was going to miss her board meeting prep deadline.
Her task seemed straightforward: build a competitive analysis of five logistics software providers, identify three new market segments worth pursuing, and outline the GTM strategy for Q1. The kind of work that used to take her team two weeks.
But Sarah's team of three was already stretched thin running outbound campaigns. Her RevOps lead had quit last month. And the consultant she'd hired delivered a 47-page deck that somehow said nothing useful.
Then her Head of Marketing sent her a single link with a message: "Try this. I just built our entire ICP analysis in 45 minutes."
That link was to ChatGPT's Deep Research. And by 2:30 AM, Sarah had not just completed her board prep—she had discovered a $4.2M market opportunity her competitors had completely missed.
The Research Gap That's Killing Your GTM Velocity
Here's the uncomfortable truth most GTM leaders won't admit: the research bottleneck is costing you more deals than your competitors.
We work with dozens of B2B startups at Phi Consulting, embedding sales, marketing, and customer success teams into high-growth companies across freight, fintech, and enterprise tech. And we've noticed a consistent pattern:
SDRs spend 6+ hours weekly researching prospects manually
Marketing teams burn 2-3 weeks on competitive analyses that go stale within months
RevOps wastes cycles building attribution models they found in outdated blog posts
Leadership makes market expansion decisions based on "gut feel" instead of data
The result? Your team is so busy gathering information that they never have time to act on it. Meanwhile, your competitors who've figured out AI-powered research are moving three times faster.
What Deep Research Actually Is (And Why Most Teams Are Using It Wrong)
Deep Research isn't just "AI search." It's an autonomous research agent that can:
Develop a research plan based on your objectives
Search and analyze hundreds of sources simultaneously
Synthesize findings into structured, actionable reports
Cite sources so you can verify critical claims
Identify conflicting information and explain discrepancies
The key difference from regular ChatGPT or Claude: these tools don't just answer questions—they conduct multi-step investigations that mirror how a skilled analyst would approach a problem.
But here's where most teams fail: they treat Deep Research like a magic 8-ball. They type vague questions and expect brilliant answers. That's like hiring a McKinsey consultant and saying "tell me something useful about my business."
The teams getting 10x value from Deep Research do something different. They provide context, constraints, and clear deliverables. They review the AI's research plan before it executes. They specify which sources to prioritize and which to avoid.
The COMPASS Framework: How to Prompt Deep Research Like a Pro
After running hundreds of Deep Research queries for our clients, we've developed a framework that consistently produces superior outputs. We call it COMPASS.
C - Context
Before any query, front-load everything the AI needs to know about your situation:
Company stage and size
Industry and vertical focus
Current GTM motion (PLG, sales-led, hybrid)
Key competitors
Tech stack (this matters more than you think)
O - Objective
Be explicit about what you're trying to achieve. Not just the task, but the ultimate business goal. "Compare these three tools" is weak. "Compare these three tools to help us improve pipeline visibility for our Q2 planning process" is strong.
M - Method
Ask the AI to share its research plan before executing. Review it. Push back. Add angles you want covered. This prevents 15-minute research runs that miss what you actually need.
P - Parameters
Set your constraints upfront:
Budget limitations
Timeline requirements
Team capacity
Non-negotiable requirements (compliance, integrations, etc.)
A - Artifacts
Specify exactly what deliverables you want: comparison tables, implementation timelines, code snippets, email templates, org chart recommendations. The more specific, the more useful the output.
S - Sources
Guide the AI toward credible sources. Tell it to prioritize primary sources over secondary, recent data over outdated, and to flag conflicting information. Request a source documentation table with every report.
S - Structure
Define how you want the output formatted. Executive summary first? Pyramid principle? Visual tables? The default output is often a wall of text. Force it to be digestible.
10 High-Impact GTM Use Cases (With Copy-Paste Prompts)
Here's where theory meets execution. These are the exact use cases we deploy for our clients at Phi, with prompt templates you can adapt today.
1. Pre-Call Account Intelligence
The Problem: Your AEs are going into calls with surface-level LinkedIn stalking. They know the prospect's job title but not their strategic priorities, recent company initiatives, or competitive pressures.
The Solution: Deep Research can synthesize earnings calls, press releases, job postings, social media, and industry news into a comprehensive briefing document.
Time Saved: 3-4 hours per enterprise account
Best Tool: ChatGPT Deep Research or Perplexity
Prompt Template:
<goal>
Create a comprehensive pre-call briefing document for an upcoming discovery call. I need to understand this prospect and company deeply enough to have a strategic conversation, not just a product pitch.
</goal>
<context>
I'm an AE at [Your Company]. We provide [brief product/service description] to [target customer type].
My upcoming call:
- Prospect: [Name], [Title] at [Company]
- Call type: [Discovery / Demo / Negotiation]
- Deal size potential: [Estimated ACV]
- How they found us: [Inbound source / Outbound / Referral]
What I already know: [Any existing intel from LinkedIn, previous conversations, CRM notes]
Our main competitors they might be evaluating: [List if known]
</context>
<content>
Research and include the following:
Company Overview
- Business model and revenue streams
- Company stage, size, and recent funding/financial health
- Key products/services and target customers
- Tech stack (especially relevant integrations)
Strategic Context
- Recent company news (funding, acquisitions, leadership changes, product launches)
- Stated strategic priorities from earnings calls, press releases, or executive interviews
- Job postings that reveal internal initiatives or pain points
- Industry headwinds or tailwinds affecting them
Prospect Profile
- Career trajectory and time in current role
- Likely priorities based on their function and company stage
- Content they've published or engaged with
- Mutual connections or shared experiences
Pain Point Hypothesis
- 3-5 likely challenges based on their role, industry, and company stage
- Evidence supporting each hypothesis
- How our solution specifically addresses each
Competitive Intelligence
- Solutions they're likely already using
- Competitors they might be evaluating
- Our differentiation angles for this specific account
Conversation Starters
- 3 personalized opening angles based on recent news or initiatives
- 5 discovery questions tailored to their likely situation
- Potential landmines or sensitive topics to avoid
Deal Intelligence
- Likely buying committee members and their priorities
- Potential champions vs. blockers
- Estimated timeline drivers (contract renewals, budget cycles, initiatives)
</content>
<style>
Format as a 2-page briefing I can review in 10 minutes before the call. Use bullet points for scannability. Lead with the most important insights. Include a "30-second summary" at the top with the 3 most critical things I need to know.
</style>
<sources>
Prioritize: company website, recent press releases, earnings calls/investor presentations, LinkedIn profiles, industry news, Glassdoor/employee reviews for cultural context, job postings, G2/Capterra reviews (if they're a software company). Cite all sources so I can dig deeper on key points.
</sources>
<instructions>
Before starting your research, share your research plan for my review. Focus on actionable insights over general background. Flag any conflicting information you find. If you can't find information on a specific area, note that explicitly rather than guessing.
</instructions>
2. ICP Validation and Refinement
The Problem: Your ICP was defined 18 months ago based on your first 20 customers. Market conditions have shifted, but your targeting hasn't.
The Solution: Use Deep Research to analyze market trends, funding patterns, competitive moves, and win/loss patterns to validate or challenge your current ICP assumptions.
Time Saved: 2-3 weeks of market research
Best Tool: ChatGPT Deep Research
Prompt Template:
<goal>
Validate and refine our Ideal Customer Profile (ICP) based on current market conditions. We need to determine if our targeting is still optimal or if we should adjust our focus to improve conversion rates and deal velocity.
</goal>
<context>
Company: [Your company name]
Product: [What you sell and core value proposition]
Current ACV: [Average contract value]
Sales cycle: [Typical length]
Current win rate: [If known]
Current ICP Definition:
- Industry: [Current industry focus]
- Company size: [Employee count / revenue range]
- Geography: [Target regions]
- Buyer persona: [Primary buyer title/function]
- Key characteristics: [Other defining traits]
Our best customers (reference accounts):
[Company A] - [Why they're great: usage, expansion, NPS, etc.]
[Company B] - [Why they're great]
[Company C] - [Why they're great]
Customers that churned or struggled:
[Company X] - [What went wrong]
[Company Y] - [What went wrong]
Primary competitors: [List main competitors]
Hypothesis we want to test: [Any specific assumptions you want validated]
</context>
<content>
Analyze and deliver:
ICP Validation Assessment
- Evidence supporting our current ICP definition
- Evidence challenging our current ICP definition
- Market segments we may be overlooking
- Segments we're targeting that may not be ideal
Market Dynamics Analysis
- How has our target market evolved in the past 12-18 months?
- Funding and investment trends in our target segments
- Emerging buyer needs or pain points
- Technology adoption patterns affecting our value proposition
Competitive Positioning Shifts
- How are competitors defining their ICP?
- Segments competitors are winning vs. losing
- White space opportunities they're missing
- Positioning changes that affect our differentiation
Refined ICP Recommendation
- Primary ICP (highest probability of success)
- Secondary ICP (expansion opportunity)
- Exclusion criteria (who we should NOT target)
For each ICP tier, specify:
- Firmographic criteria (industry, size, geography, growth stage)
- Technographic indicators (tech stack signals)
- Behavioral triggers (events indicating buying intent)
- Negative indicators (red flags to disqualify)
- Estimated market size (number of companies)
Buying Committee Map
- Primary buyer persona (the champion)
- Economic buyer (budget holder)
- Technical evaluator (if applicable)
- Key priorities and objections for each
Qualification Framework
- BANT/MEDDIC criteria specific to our new ICP
- 5-7 qualification questions for SDRs
- Scoring rubric with point values
Messaging Implications
- How positioning should shift based on ICP refinement
- Key messages by persona
- Proof points that resonate with refined ICP
</content>
<style>
Structure using the Pyramid Principle—lead with your recommendation, then supporting analysis. Use comparison tables where helpful (current vs. proposed ICP). Include a confidence level (high/medium/low) for each recommendation with rationale.
</style>
<sources>
Prioritize: industry analyst reports, funding databases (Crunchbase, PitchBook mentions), competitive intelligence, G2/Capterra market data, job posting trends, industry publications, customer review sites. Avoid vendor marketing content. Cite all sources with dates.
</sources>
<instructions>
Share your research plan before executing. Challenge our assumptions—we want honest assessment, not validation of what we already believe. Where data is limited, clearly state that you're making inferences and explain your reasoning.
</instructions>
3. Competitive Battlecard Creation
The Problem: Your sales team loses deals to competitors they don't fully understand. Existing battlecards are outdated or too generic to be useful in live conversations.
The Solution: Deep Research can build comprehensive battlecards by analyzing competitor websites, reviews, case studies, pricing pages, job postings, and recent news.
Time Saved: 15-20 hours per competitor
Best Tool: Perplexity (for discovery) + ChatGPT Deep Research (for synthesis)
Prompt Template:
<goal>
Create a comprehensive competitive battlecard that sales reps can use during live deals. This needs to be honest, specific, and immediately actionable—not marketing fluff.
</goal>
<context>
Our Company: [Your company name]
Our Product: [Brief description]
Our Positioning: [How we describe ourselves]
Our Pricing: [Pricing model and approximate ranges]
Our Strengths: [What we believe we do best]
Our Weaknesses: [Honest assessment of gaps]
Competitor to Analyze: [Competitor name]
Competitor Website: [URL]
Recent competitive deals:
- Deal we won against them: [Brief description of why]
- Deal we lost to them: [Brief description of why]
What we currently believe about them: [Existing assumptions to validate/challenge]
</context>
<content>
Build a battlecard with these sections:
Competitor Snapshot (1 paragraph)
- Who they are, core product, primary market
- Funding/company stage/trajectory
- Recent momentum (growing, stable, struggling?)
Head-to-Head Comparison Matrix
| Capability | Us | Them | Winner | Notes |
Cover: Core features, pricing, implementation, support, integrations, security/compliance, reporting, scalability
Positioning Analysis
- How they describe themselves (exact messaging from website)
- Their stated differentiators
- Target customer according to their marketing
- Key proof points they emphasize
Pricing Intelligence
- Pricing model and structure
- Published pricing (if available)
- Pricing gathered from reviews/discussions
- Common discounting patterns
- Total cost of ownership considerations
Their Ideal Customer vs. Ours
- Where they win (be honest)
- Where we win
- True toss-up scenarios
- Signals that indicate they're the better fit
Strengths to Acknowledge
- What they genuinely do well
- Why certain customers choose them
- Credibility points we can't dismiss
Weaknesses to Exploit
- Common complaints from G2/Capterra reviews (with quotes)
- Known product gaps or limitations
- Implementation/support issues
- Customer churn indicators
Objection Handling
For each common objection when competing against them:
- The objection
- Why prospects raise it
- Response framework
- Proof point to reference
Trap-Setting Discovery Questions
- Questions that expose their weaknesses
- Questions that highlight our strengths
- Questions to understand if prospect is a better fit for them
Landmines to Avoid
- Topics where they have advantage
- Claims we shouldn't make
- Comparisons that backfire
Recent Intelligence
- Product launches (last 6 months)
- Pricing changes
- Leadership changes
- Funding/acquisition news
- Strategic pivots
Quick Reference Talk Tracks
- 30-second positioning against them
- Response to "why shouldn't we just go with [Competitor]?"
- Response to "they're cheaper"
- Response to "[Competitor] has feature X"
</content>
<style>
Format for quick scanning during live calls. Use tables for comparisons. Bold key phrases reps should say verbatim. Keep talk tracks conversational, not scripted. Include a 1-page "cheat sheet" summary at the beginning.
</style>
<sources>
Research: competitor website (especially pricing, case studies, integrations pages), G2 and Capterra reviews (last 12 months), Reddit and community discussions, LinkedIn posts from their employees, Glassdoor reviews, press releases, funding announcements, job postings (reveal strategic priorities), YouTube demos, podcast appearances by their leadership. Always cite sources.
</sources>
<instructions>
Be brutally honest. A battlecard that pretends we're better at everything is useless. Clearly distinguish between verified facts and inferences. For each claim, indicate confidence level. Flag anything that needs human verification before using in deals.
</instructions>
4. Market Expansion Analysis
The Problem: Leadership wants to expand into a new vertical but doesn't have the data to make an informed bet. Previous analyst reports are $15K and six months old.
The Solution: Deep Research can conduct TAM analysis, competitive mapping, buyer persona development, and GTM feasibility assessment for new markets.
Time Saved: 40+ hours of market research
Best Tool: ChatGPT Deep Research
Prompt Template:
<goal>
Conduct a comprehensive market expansion analysis to determine whether [target vertical/segment] represents a viable growth opportunity. This analysis will inform a go/no-go investment decision and, if positive, the initial GTM approach.
</goal>
<context>
Our Company: [Company name]
Current Business:
- Product: [What you sell]
- Current verticals: [Where you sell today]
- Current ACV: [Average deal size]
- Current sales cycle: [Length]
- Current win rate: [If known]
- ARR/Revenue: [Current scale]
- Team size: [Sales, marketing, CS headcount]
Expansion Target: [Vertical/segment being evaluated]
Why we're considering this market:
- [Hypothesis 1: e.g., "Customers in this vertical have asked for our product"]
- [Hypothesis 2: e.g., "Our technology applies to their use case"]
- [Hypothesis 3: e.g., "Competitors are succeeding here"]
Resources available for expansion: [Budget, headcount, timeline constraints]
What would make this a "go" decision: [Minimum TAM, win rate expectations, timeline to payback]
</context>
<content>
Deliver a comprehensive expansion analysis:
Executive Summary
- Go / No-Go / Conditional recommendation
- 3 key findings supporting the recommendation
- Critical risks and mitigation strategies
- Estimated investment required vs. potential return
Market Size Analysis
- TAM: Total companies fitting broad criteria
- SAM: Companies we could realistically reach
- SOM: Realistic 3-year capture estimate
- Methodology and assumptions (be explicit)
- Data sources and confidence level
Market Dynamics
- Growth rate and trajectory
- Key trends shaping the market
- Regulatory or compliance considerations
- Technology adoption maturity
- Consolidation/fragmentation trends
Competitive Landscape
- Established players in this vertical
- Their positioning and market share
- Recent entrants and their traction
- White space and underserved segments
- Barriers to entry for new players
- Our potential differentiation
Buyer Analysis
Primary Buyer Persona:
- Title and function
- Key responsibilities and KPIs
- Pain points our product addresses
- Current solutions they use
- Buying process and committee
- Budget cycles and authority levels
Secondary Personas:
- [Repeat structure for each]
Value Proposition Translation
- How our current value prop applies (or doesn't)
- Messaging adjustments needed
- New proof points required
- Features gaps we'd need to address
- Partnerships that could accelerate entry
Go-to-Market Feasibility
- Channel strategy (direct, partner, PLG)
- Marketing channels that reach this audience
- Sales motion alignment (current vs. required)
- Content and thought leadership requirements
- Events and community presence needed
Financial Model Inputs
- Expected ACV in this segment
- Estimated sales cycle length
- Win rate assumptions (with rationale)
- CAC expectations
- Churn/expansion assumptions
Competitive Response Risk
- How will incumbents respond?
- Switching costs for prospects
- Lock-in mechanisms we'd face
Implementation Roadmap (if Go)
- Phase 1 (0-6 months): [Specific actions]
- Phase 2 (6-12 months): [Specific actions]
- Phase 3 (12-18 months): [Specific actions]
- Key milestones and decision points
- Resource requirements by phase
Risk Assessment
| Risk | Likelihood | Impact | Mitigation |
[Table format for key risks]
Appendix
- List of target accounts in this vertical
- Competitive feature matrix
- Source documentation with links
</content>
<style>
Use the Pyramid Principle—lead with recommendation. Include executive summary that stands alone. Use tables for comparisons and data. Visualize the TAM/SAM/SOM if possible. Clearly distinguish facts from estimates.
</style>
<sources>
Prioritize: industry analyst reports, government/census data, trade association data, industry publications, competitor investor materials, job posting trends, industry event agendas, customer review platforms, funding databases. For TAM, use multiple methodologies and triangulate. Cite all sources with dates.
</sources>
<instructions>
Before researching, share your methodology for TAM calculation. Be skeptical—we want realistic assessment, not optimistic projections. Where data is uncertain, provide ranges rather than point estimates. Clearly label assumptions that need validation.
</instructions>
5. Outbound Sequence Personalization
The Problem: Your email sequences are generic. Every prospect gets the same pain points and value props, regardless of their industry, role, or company stage.
The Solution: Use Deep Research to build persona-specific messaging frameworks with validated pain points, language patterns, and proof points.
Time Saved: 5-8 hours per persona
Best Tool: ChatGPT Deep Research or Claude
Prompt Template:
<goal>
Develop a comprehensive persona research document that will inform highly personalized outbound email sequences. I need to understand this persona deeply enough to write messages that feel custom-written for each recipient.
</goal>
<context>
Our Company: [Your company]
Our Product: [What you sell]
Our Value Proposition: [Primary benefit]
Target Company Type: [Industry, size, stage]
Persona to Research:
- Title: [e.g., "VP of Operations"]
- Function: [e.g., "Operations / Supply Chain"]
- Company Stage: [e.g., "Series B-D, 100-500 employees"]
- Industry: [e.g., "Freight & Logistics"]
Our current assumptions about this persona:
- Pain points we think they have: [List]
- Value props we lead with: [List]
- Objections we typically hear: [List]
Current outbound performance:
- Open rate: [X%]
- Reply rate: [X%]
- Positive reply rate: [X%]
What's not working: [Any specific feedback or patterns]
</context>
<content>
Research and deliver:
Day-in-the-Life Analysis
- Typical daily/weekly responsibilities
- Meetings they attend and run
- Reports they create and consume
- Systems they interact with
- People they collaborate with
- Decisions they make and influence
Goals and KPIs
- What they're measured on
- What their boss cares about
- Career trajectory aspirations
- How they define success in their role
- Timeframes they think in (quarterly, annual)
Pain Points (Validated)
- Primary frustrations (with evidence from interviews, posts, forums)
- Specific quotes from people in this role
- Problems they've publicly discussed
- Challenges unique to their industry/stage
- Rank by severity and frequency
Current Solutions Landscape
- Tools they typically use
- Manual processes they rely on
- Workarounds they've built
- Budget they control
- Vendors they currently work with
Information Diet
- Publications and newsletters they read
- Podcasts they listen to
- Communities they participate in
- Events they attend
- Influencers they follow
- LinkedIn content they engage with
Language and Terminology
- Industry jargon they use
- Terms they avoid or find off-putting
- Acronyms common in their world
- How they describe their challenges (verbatim quotes)
- Words that signal "vendor marketing" (to avoid)
Buying Behavior
- How they evaluate new solutions
- Who else is involved in decisions
- What triggers them to look for new tools
- Preferred first touch (email, LinkedIn, phone, referral)
- Length of consideration process
- Deal breakers and must-haves
Objection Mapping
| Likely Objection | Root Cause | Response Framework |
Trigger Events
- Company events that create urgency
- Personal events that create openness
- Industry events that drive action
- Signals we can monitor for timing
Messaging Framework
For each of 3 email angles, provide:
- Subject line options (3-5 each)
- Opening hook (addressing specific pain)
- Value proposition (in their language)
- Proof point (relevant case study type)
- CTA (appropriate ask for cold outreach)
Personalization Variables
- Company-specific elements to research
- Role-specific elements to incorporate
- Timing-specific elements to reference
- Template for rapid personalization
What NOT to Say
- Messages that will get ignored
- Claims that lack credibility
- Approaches that feel too salesy
- Competitive comparisons to avoid
</content>
<style>
Make this immediately actionable for SDRs. Include actual email copy they can adapt, not just frameworks. Use real quotes from people in this persona where possible. Organize for easy reference when writing sequences.
</style>
<sources>
Research: LinkedIn posts and comments from people in this role, Reddit discussions (industry-specific subreddits), industry forums and Slack communities, podcast interviews with this persona, conference talk recordings, industry surveys and research reports, job postings (reveal priorities), Glassdoor reviews from this function. Cite sources for all claims.
</sources>
<instructions>
Focus on direct quotes and first-person perspectives wherever possible. I want to hear how this persona actually talks, not how vendors think they talk. Flag where you're inferring vs. citing. If certain information isn't findable, tell me what validation interviews I should conduct.
</instructions>
6. Attribution Model Design
The Problem: Marketing and Sales are arguing about what's actually driving the pipeline. Your attribution is either non-existent or based on last-touch, which tells you nothing useful.
The Solution: Deep Research can analyze attribution best practices, evaluate models suited to your GTM motion, and design an implementation roadmap.
Time Saved: 20-30 hours of research and planning
Best Tool: ChatGPT Deep Research
Prompt Template:
<goal>
Design a revenue attribution model appropriate for our GTM motion and tech stack. I need a practical implementation plan, not theoretical frameworks.
</goal>
<context>
Company Profile:
- Stage: [Seed / Series A / Series B / etc.]
- ARR: [Current revenue]
- GTM Motion: [PLG / Sales-Led / Hybrid - describe]
- Sales Cycle: [Average length]
- ACV: [Average deal size]
- Team Size: [Marketing, SDR, AE, CS headcount]
Current Tech Stack:
- CRM: [e.g., Salesforce, HubSpot]
- Marketing Automation: [e.g., Marketo, HubSpot, Pardot]
- Website Analytics: [e.g., GA4, Amplitude]
- Product Analytics: [e.g., Mixpanel, Amplitude, Heap]
- Data Warehouse: [e.g., Snowflake, BigQuery, none]
- BI Tool: [e.g., Looker, Tableau, none]
- Other relevant tools: [List]
Current State of Attribution:
- What we track now: [Describe current approach]
- What's broken: [Specific problems]
- Questions we can't answer: [List]
Marketing Channels We Use:
- [List all channels: paid search, LinkedIn ads, content, events, etc.]
Sales Motions:
- [Describe: inbound, outbound, partner, PLG conversion, etc.]
Resources for Implementation:
- RevOps capacity: [Who would implement]
- Budget for tools: [If any]
- Timeline expectations: [When we need this working]
</context>
<content>
Deliver a complete attribution design:
Attribution Model Recommendation
- Recommended model type (first-touch, last-touch, linear, W-shaped, custom)
- Why this model fits our motion
- Trade-offs vs. alternatives
- How this model handles our specific channels and motions
Attribution Taxonomy
- Channel definitions (exactly what counts as what)
- Source/medium naming conventions
- Campaign hierarchy structure
- UTM parameter standards
- Lead source categorization
- Touch type definitions
Data Architecture
- Required data sources and integrations
- Data model design (objects, fields, relationships)
- Identity resolution approach (how we tie touches to accounts/opportunities)
- Data quality requirements
- Historical data handling
Technical Implementation
For each tool in our stack:
- Configuration requirements
- Fields to create
- Automation/workflows needed
- Integration points
If data warehouse exists:
- Table structure
- Transformation logic
- Key joins
Reporting Framework
What reports to build:
| Report | Audience | Frequency | Key Metrics |
Dashboard mockups:
- Executive dashboard (what metrics, what views)
- Marketing dashboard
- Sales dashboard
Key Metrics Definitions
For each metric, provide:
- Precise definition
- Calculation formula
- Data sources
- Caveats/limitations
Edge Cases and Exceptions
- How we handle offline touches
- How we handle dark social
- How we handle multi-product
- How we handle partner-sourced
- How we handle expansion revenue
- What we're knowingly NOT tracking
Governance Model
- Who owns what data
- Data quality monitoring
- Regular review cadence
- Model refinement triggers
Implementation Roadmap
Phase 1 (Days 1-30):
- [Specific tasks with owner and effort estimate]
Phase 2 (Days 30-60):
- [Specific tasks]
Phase 3 (Days 60-90):
- [Specific tasks]
Change Management
- How to communicate to teams
- Training requirements
- Transition from old to new
- Expected timeline to reliable data
Known Limitations
- What this model won't tell us
- Complementary analyses to run
- When to revisit the model
Appendix: Examples from Similar Companies
- How comparable companies approach attribution
- Lessons learned from their implementations
</content>
<style>
Be extremely specific and technical. Include actual field names, formulas, and configuration steps. I need to be able to hand this to RevOps and have them implement it. Use diagrams/flowcharts where helpful.
</style>
<sources>
Research: attribution best practices from RevOps communities, technical documentation for our tools, case studies from similar-stage B2B companies, industry benchmarks for attribution, common mistakes and anti-patterns. Prioritize practitioner perspectives over vendor marketing.
</sources>
<instructions>
Before starting, confirm you understand our tech stack and can provide specific technical guidance for it. If you need clarification on any tool or integration, ask first. Focus on practical implementation over theoretical purity.
</instructions>
7. Sales Compensation Benchmarking
The Problem: You're hiring AEs but don't know if your comp plan is competitive. You're either overpaying or losing candidates to better offers.
The Solution: Deep Research can aggregate compensation data from multiple sources to build market-rate benchmarks specific to your context.
Time Saved: 10-15 hours of research
Best Tool: ChatGPT Deep Research
Prompt Template:
<goal>
Build a comprehensive sales compensation benchmark to ensure our hiring offers and existing comp plans are market-competitive. This will inform both immediate hiring decisions and annual comp planning.
</goal>
<context>
Our Company:
- Stage: [Series A / B / C / Public]
- Industry: [Your industry]
- Location: [HQ and where roles are based]
- ARR: [Current revenue]
- ACV: [Average deal size]
- Sales Cycle: [Average length]
- Current team size: [Number of AEs, SDRs, etc.]
- Funding status: [Recent raise? Runway?]
Role(s) to Benchmark:
[Role title, e.g., "Account Executive - Mid-Market"]
- Quota expectation: [Target]
- Deal complexity: [Simple / Complex / Enterprise]
- Inbound vs. outbound: [Mix %]
- Territory: [Geographic or vertical focus]
[Additional roles if needed]
Current Comp Structure:
- Base: [Range]
- OTE: [Range]
- Split: [e.g., 50/50, 60/40]
- Commission structure: [Brief description]
- Equity: [Range in basis points or shares]
- Other: [Benefits, accelerators, etc.]
Hiring Context:
- Competing with companies like: [List competitor employers]
- Candidate profiles we target: [Years experience, background]
- Hiring challenges we've faced: [Losing on comp? Quality issues?]
</context>
<content>
Deliver comprehensive compensation benchmarks:
Executive Summary
- Are we paying competitively? (Under / At / Above market)
- Key adjustments recommended
- Risk of current approach (attrition, hiring difficulty)
Base Salary Benchmarks
| Segment | 25th Percentile | 50th | 75th | 90th |
Segmented by:
- Company stage
- Geography
- Industry
- Role level
OTE Benchmarks
[Same table structure]
Include:
- OTE ranges by attainment level
- Typical OTE:Base ratios
- How OTE varies by deal size and complexity
Commission Structure Patterns
- Common structures for our deal profile
- Accelerator designs
- Decelerator/clawback patterns
- SPIFs and bonuses
- Multi-year deal handling
- Expansion/renewal credit
Equity Compensation
- Expected equity by stage and role level
- Vesting schedules
- Refresh grant patterns
- How to present equity value to candidates
Benefits and Perks
- Standard benefits expectations
- Differentiated perks that matter
- What candidates ask about most
Ramp and Quota
- Standard ramp periods
- Ramp quotas and ramp commission
- Quota-setting methodologies
- Realistic attainment expectations
Total Compensation Comparison
Model scenarios:
- At 100% attainment
- At 120% attainment (top performer)
- At 80% attainment (ramp or tough quarter)
Compare us vs. [Competitor employers]
Market Trends
- How has sales comp evolved in past 12-24 months?
- What's changing going forward?
- Hot roles commanding premiums
- Roles seeing compression
Recommendations
| Element | Current | Recommended | Rationale |
Prioritized by impact on hiring and retention
Implementation Considerations
- Budget impact of changes
- How to adjust existing team
- Communication approach
- Timeline
Appendix: Data Sources
- Source, date, sample size, methodology
- Confidence level for each benchmark
- Where data conflicts and our interpretation
</content>
<style>
Heavy use of tables for easy comparison. Include ranges, not just averages. Clearly distinguish between hard data and estimates. Make recommendations specific and actionable.
</style>
<sources>
Research: compensation surveys (Pave, Carta, Radford if accessible), job postings with comp listed (Levels.fyi, Glassdoor, LinkedIn), recruiting community discussions, comp consultancy blogs and reports, startup compensation databases, industry-specific surveys, Reddit r/sales and r/salesforce discussions. Note sample sizes and dates for all data.
</sources>
<instructions>
Focus on data from the past 12 months—compensation changes rapidly. Triangulate across sources and flag where sources conflict. If data for our exact profile is sparse, note that and explain your extrapolation logic.
</instructions>
8. Customer Churn Analysis Framework
The Problem: Customers are churning but you don't have a systematic way to understand why or predict who's at risk.
The Solution: Deep Research can analyze churn patterns in your industry and design a health scoring and intervention framework.
Time Saved: 15-20 hours of framework development
Best Tool: ChatGPT Deep Research or Claude
Prompt Template:
<goal>
Design a customer health scoring system and churn prevention framework specific to our product and customer base. I need both the strategic framework and tactical implementation guidance.
</goal>
<context>
Our Business:
- Product: [What you sell]
- Business Model: [Subscription, usage-based, hybrid]
- Customer Segment: [SMB / Mid-Market / Enterprise]
- ACV: [Average contract value]
- Contract Terms: [Monthly / Annual / Multi-year]
- Current Churn Rate: [Monthly or annual]
- Logo vs. Revenue Churn: [If different]
- NDR: [Net dollar retention]
Customer Profile:
- Typical customer size: [Employee count / revenue]
- Primary user persona: [Who uses the product]
- Buyer persona: [Who pays/decides on renewal]
- Implementation complexity: [How hard to get live]
- Time to value: [How long to see results]
Current CS Structure:
- CS team size: [Headcount]
- CS:Customer ratio: [e.g., 1:50]
- Current engagement model: [High-touch / Low-touch / Hybrid]
- Tools used: [Gainsight, ChurnZero, etc.]
Known Churn Factors:
- Why we think customers leave: [List hypotheses]
- Customers who churned recently: [Brief description of patterns]
- What we tried that didn't work: [List]
</context>
<content>
Deliver a complete churn prevention framework:
Churn Pattern Analysis
- Common churn reasons in our industry/segment
- Typical churn timing patterns (when in lifecycle)
- Leading indicators research from similar companies
- Comparison of our stated churn rate to benchmarks
Health Score Design
Component scores (for each, define):
- What we're measuring
- Data source
- Calculation method
- Weight in overall score
- Thresholds (Red / Yellow / Green)
Recommended components:
- Product usage/engagement
- Adoption depth (features used)
- Relationship strength (contacts, engagement)
- Support experience (tickets, sentiment)
- Business outcomes (are they seeing ROI)
- Expansion signals
- Contract/commercial factors
- Risk signals (champion left, budget cut)
Scoring Model
- How to combine components into overall health
- How to handle missing data
- How to weight recency
- Segmentation adjustments (SMB vs. Enterprise)
- Confidence intervals
Leading Indicators Matrix
| Indicator | Data Source | Warning Threshold | Typical Lead Time | Accuracy |
Prioritized by predictive power
Intervention Playbooks
For each risk level (Red / Yellow / Early Warning):
- Trigger criteria
- Owner (CSM, Manager, Executive)
- Response timeline
- Intervention options
- Talk track/messaging
- Escalation path
- Documentation requirements
Proactive Engagement Calendar
- Milestone-based touches (onboarding, QBRs, renewal)
- Usage-triggered touches
- Relationship-building touches
- Executive engagement triggers
Save Strategies
When customer indicates intent to churn:
- Discovery framework (understand real reason)
- Common root causes and responses
- Concession framework (what we can offer)
- Walk-away criteria (when to let them go)
- Off-boarding for future win-back
Expansion Integration
- How health score informs expansion timing
- Signals that indicate expansion readiness
- Handoff to expansion/sales
Implementation Roadmap
Phase 1: Foundation (Month 1)
- Data requirements
- Tool configuration
- Initial scoring model
Phase 2: Calibration (Months 2-3)
- Backtesting against historical churn
- Adjustments based on results
- Team training
Phase 3: Automation (Months 3-4)
- Automated alerts
- Playbook integration
- Reporting/dashboards
Governance
- Score review cadence
- Model refinement triggers
- Edge case handling
- Appeals process
Metrics and Reporting
| Metric | Definition | Target | Current |
Dashboard requirements for:
- CS leadership
- CSM daily management
- Executive reporting
Appendix: Benchmark Data
- Retention benchmarks for our segment
- Health score approaches from similar companies
- Industry research on churn causes
</content>
<style>
Balance strategic framework with tactical specifics. Include enough detail that this could be implemented in 30 days. Use tables for scoring models. Be prescriptive with recommendations.
</style>
<sources>
Research: CS and retention best practices (Gainsight community, CSM Practice, Totango resources), benchmark reports (Gainsight Pulse data, ChartMogul benchmarks), practitioner case studies, industry-specific retention research, SaaS finance resources (David Skok, Tomasz Tunguz). Cite sources.
</sources>
<instructions>
Before starting, confirm you understand our customer profile and engagement model. Focus on practical metrics we can actually track, not theoretical ideals. Where you're uncertain about our specific context, offer options rather than single recommendations.
</instructions>
9. Tech Stack Optimization
The Problem: Your GTM tech stack has grown organically. You have overlapping tools, integration gaps, and no clear picture of what's actually driving ROI.
The Solution: Deep Research can audit your stack against best practices, identify gaps and redundancies, and recommend optimization priorities.
Time Saved: 25-30 hours of evaluation
Best Tool: ChatGPT Deep Research
Prompt Template:
<goal>
Audit our GTM tech stack and provide optimization recommendations. I need to understand where we have gaps, redundancies, and integration issues—plus a prioritized roadmap for improvements.
</goal>
<context>
Company Profile:
- Stage: [Seed / Series A / B / C]
- ARR: [Revenue]
- Team Size: [Sales, Marketing, CS, RevOps headcount]
- GTM Motion: [PLG / Sales-Led / Hybrid]
- Annual Tech Budget: [Approximate spend on tools]
Current Tech Stack (with approximate costs):
CRM & Sales:
- [Tool 1]: [Annual cost] - [Primary use]
- [Tool 2]: [Annual cost] - [Primary use]
[List all tools]
Marketing:
- [Tool 1]: [Annual cost] - [Primary use]
[List all tools]
Customer Success:
- [Tool 1]: [Annual cost] - [Primary use]
[List all tools]
RevOps & Data:
- [Tool 1]: [Annual cost] - [Primary use]
[List all tools]
Known Issues:
- Integrations that are broken/clunky: [List]
- Manual processes that should be automated: [List]
- Data quality issues: [List]
- Tools we're not using fully: [List]
- Gaps we're aware of: [List]
Constraints:
- Budget for changes: [Available budget]
- RevOps capacity: [Who would implement]
- Contract renewal dates: [Any tools up for renewal soon]
- Non-negotiables: [Tools that must stay]
</context>
<content>
Deliver a comprehensive tech stack audit:
Executive Summary
- Overall assessment (Optimized / Needs Work / Major Issues)
- Top 3 urgent changes
- Estimated annual savings opportunity
- Critical gaps to address
Stack Assessment by Category
For each category (CRM, Marketing, CS, Data):
- Current tools and their health (Well-Used / Underused / Overpriced / Wrong Fit)
- Redundancies (overlapping capabilities)
- Gaps (missing capabilities)
- Integration quality
- Recommendation: Keep / Consolidate / Replace / Add
Integration Architecture
- Map of how data flows between tools
- Integration gaps causing issues
- Data quality problems from integrations
- Recommended integration improvements
Tool-by-Tool Analysis
For each tool:
| Aspect | Assessment |
- Primary use case
- % of capabilities used
- Cost vs. value
- Alternatives to consider
- Recommendation
Gap Analysis
| Capability | Current State | Gap Impact | Recommended Solution |
Priority capabilities missing:
- [Capability 1]
- [Capability 2]
Consolidation Opportunities
- Tools with overlapping features
- Potential consolidation paths
- Savings estimate
- Migration complexity
Best-in-Class Benchmark
What does a comparable company's stack look like?
- Reference stack for our stage/motion
- How we compare
- Capabilities they have that we don't
Vendor Alternatives
For tools flagged as Replace:
| Category | Current | Alternatives | Key Differences | Est. Cost |
Include pricing insights and negotiation leverage
Implementation Roadmap
Immediate (30 days):
- [Change 1]: Effort, owner, expected impact
- [Change 2]
Near-term (90 days):
- [Changes]
Medium-term (6 months):
- [Changes]
Budget Impact Analysis
Current annual spend: $X
Proposed Changes:
| Change | Current Cost | New Cost | Savings | Investment |
Net impact: [Savings or investment required]
Change Management
- Stakeholders affected by each change
- Training requirements
- Risk mitigation
- Rollback plans
Future State Architecture
- Recommended stack for 12 months out
- Integration diagram
- Key automation flows
- Data flow architecture
</content>
<style>
Be opinionated—don't just describe, recommend. Include specific product suggestions where appropriate. Use cost estimates where possible. Create clear before/after visualization.
</style>
<sources>
Research: G2 Grid rankings for each category, pricing databases (Vendr, Spendflo), tech stack case studies from similar companies, integration marketplace reviews, RevOps community discussions, vendor comparison content from neutral sources. Avoid vendor-produced "vs" content.
</sources>
<instructions>
Before researching, confirm you have our full tool list. For each tool, check actual capabilities against what we said we use it for. Be direct about tools we're overpaying for or shouldn't have.
</instructions>
10. Board Deck Market Context
The Problem: Your board wants market context for your growth metrics, but building that context takes your exec team away from running the business.
The Solution: Deep Research can build comprehensive market analysis sections for board decks, including competitive updates, market sizing, and trend analysis.
Time Saved: 8-12 hours per board meeting
Best Tool: ChatGPT Deep Research
Prompt Template:
<goal>
Build the market context section of our board deck. This should provide the competitive and market backdrop that helps the board evaluate our performance and strategy.
</goal>
<context>
Company Profile:
- What we do: [Brief description]
- Stage: [Seed / Series A / B / C]
- ARR: [Current revenue]
- Growth rate: [YoY or QoQ]
- Primary market: [Industry/vertical]
- Key competitors: [Top 3-5]
Board Composition:
- [List investor firms and any independent directors]
- What they typically ask about: [Common questions/areas of focus]
Recent Company Developments:
- [Significant launches, wins, changes since last board meeting]
Strategic Questions on the Table:
- [Any specific strategic decisions being discussed]
- [Market questions the board has asked]
Last Board Meeting Date: [When]
Time Period to Cover: [e.g., "Q4 2024" or "Since our last meeting on X date"]
</context>
<content>
Prepare board-ready market context:
Executive Summary (1 slide worth of content)
- 3-5 bullet market headlines
- Net assessment: Is the market backdrop favorable, neutral, or challenging?
- Key strategic implications
Market Size and Growth
- Current TAM/SAM estimate (with methodology)
- Growth rate and trajectory
- How this compares to last quarter's view
- Forward-looking projections
Competitive Landscape Update
For each major competitor:
- Recent developments (funding, product, leadership, M&A)
- Positioning changes
- Market share movement (if discernible)
- Strategic implications for us
Summary: Competitive position strengthening / stable / weakening?
Industry Trends
- Major trends affecting our market (with evidence)
- Emerging trends to watch
- Technology shifts relevant to our product
- Regulatory developments
Customer/Buyer Environment
- Changes in buyer behavior or priorities
- Budget environment (expanding, contracting, shifting)
- Buying process changes
- What customers are asking for that they weren't before
Macro Factors
- Economic conditions affecting our market
- Funding environment for our customers
- Relevant policy or regulatory changes
- Industry-specific macro factors
Benchmark Data
For each of our key metrics, provide market context:
| Our Metric | Our Performance | Market Benchmark | Assessment |
Include: Growth rate, retention, CAC, LTV, sales efficiency
Risk Factors
- Market risks that could affect our trajectory
- Competitive risks emerging
- Technology or disruption risks
- Mitigation considerations
Opportunities
- Market tailwinds we could capitalize on
- Competitor missteps creating opportunity
- Emerging segments or use cases
- Strategic options worth considering
Appendix: Source Documentation
| Claim | Source | Date | Link |
All key facts with citations
Questions This Raises
- Strategic questions the board might ask
- Data we need but don't have
- Decisions this context informs
</content>
<style>
Write for board consumption—concise, backed by data, executive-level. Use the Pyramid Principle. Include specific numbers, not just directional statements. Every claim should be cited. Format in a way that translates directly to slides.
</style>
<sources>
Prioritize: industry analyst reports (Gartner, Forrester, IDC), investor presentations from public comps, credible news sources, earnings transcripts, industry associations, government data, funding databases. Avoid vendor marketing content and outdated reports.
</sources>
<instructions>
Before starting, confirm you understand our competitive set and the specific context items I've provided. For each major claim, include the source and assess confidence level. If information is sparse for any section, note that explicitly—don't pad with filler content.
</instructions>
Which Tool Should You Use? A Decision Framework
Not all Deep Research tools are created equal. Here's how to choose:
Tool | Best For | Limitations | When to Use |
ChatGPT Deep Research | Comprehensive reports requiring synthesis of many sources | Limited queries per month; slower | Strategic research, board prep, major decisions |
ChatGPT Agent Mode | Tasks requiring website interaction (filling forms, navigating tools) | Can be inconsistent | Competitor pricing research, tool evaluations |
Perplexity | Fast answers with clear citations; real-time information | Less depth than ChatGPT | Quick fact-checking, current events, source discovery |
Claude Deep Research | Long document analysis; nuanced writing | Slower for broad research | Win/loss analysis, contract review, content creation |
Gemini Deep Research | Always shares research plan; generous free tier | Privacy concerns for sensitive queries | Backup option, broad market research |
Pro tip: Use Perplexity to quickly find high-quality sources, then feed those sources to ChatGPT or Claude for deeper analysis. This combines Perplexity's speed with ChatGPT's synthesis capabilities.
The Two-Stage Deep Research Workflow
For maximum impact, separate framework development from Deep Research execution:
Stage 1: Build Your Framework (Use GPT-4 or Claude)
Before running Deep Research, use standard ChatGPT or Claude to:
Develop your prompt structure
Identify the specific questions you need answered
Define your output format
Clarify your constraints and context
Stage 2: Execute with Deep Research
Once your framework is solid, run it through Deep Research for the actual information gathering.
This two-stage approach prevents wasting Deep Research queries on poorly structured prompts.
Common Mistakes (And How to Avoid Them)
Mistake 1: Zero Context Prompts "Tell me about my competitors" gives you generic garbage. Include your company, product, ICP, and current competitive understanding.
Mistake 2: Skipping the Research Plan Review Deep Research tools will share their intended approach. Review it. A 30-second redirect now saves 15 minutes of irrelevant research later.
Mistake 3: Not Specifying Sources If you don't guide source selection, the AI will pull from whatever ranks highest—often SEO-optimized vendor content rather than primary research.
Mistake 4: Asking for Too Much at Once "Build me a complete GTM strategy" is too broad. Break it into discrete research questions and synthesize yourself.
Mistake 5: Treating Output as Final Deep Research is a first draft, not a finished product. Always verify critical claims and apply your judgment to recommendations.
Mistake 6: Forgetting to Request Citations Always ask for sources. This lets you verify important claims and builds a reference library for future research.
Mistake 7: Using Deep Research for Simple Questions Don't waste limited Deep Research queries on questions regular ChatGPT can answer. Save it for complex, multi-source investigations.
From Research to Revenue: Making This Operational
The teams getting the most value from Deep Research don't treat it as a novelty—they've built it into their operating rhythm:
Weekly: SDR account research, competitive monitoring, content ideation
Monthly: ICP validation, market trend analysis, tech stack review
Quarterly: TAM analysis for new segments, compensation benchmarking, board prep
The compound effect is significant. Teams using Deep Research systematically report saving 12-15 hours per week while producing higher-quality strategic work.
Ready to Accelerate Your GTM Execution?
At Phi Consulting, we don't just advise on GTM strategy—we execute it.
We embed sales, marketing, and customer success teams directly into B2B startups, handling the operational heavy lifting so you can focus on building your product and closing deals.
Our focus industries:
Freight & Logistics (TMS, FMS, factoring, insurance, compliance)
Fintech (payments, lending, financial services)
Enterprise Tech (infrastructure, security, developer tools)
What we do:
Build and run outbound engines that generate qualified pipeline
Develop ICPs, personas, and messaging based on real market data
Create and optimize email sequences with continuous iteration
Implement marketing automation and lead qualification systems
Train your team to scale what works
Our clients include: OTR Solutions, Datatruck, Shipwell, AtoB, Outgo, Mudflap, and Bobtail.
The difference? We're not consultants who deliver decks. We're operators who deliver pipeline.
Ready to talk? → Book a GTM strategy call: phiconsulting.com/contact → Email directly: [email protected]
Phi Consulting: Your GTM team, embedded.
Frequently Asked Questions
What is Deep Research and how is it different from regular AI chat? Deep Research is an autonomous research agent that plans its approach, searches hundreds of sources, evaluates credibility, and synthesizes findings into structured reports with citations. Unlike regular AI chat that answers from training data, Deep Research actively investigates the web to find current, comprehensive information.
How much does Deep Research cost? ChatGPT Deep Research is included with ChatGPT Plus ($20/month) with limited monthly queries. Perplexity offers a free tier with Pro at $20/month. Claude's research features are included in Pro ($20/month). Gemini Deep Research has a generous free tier.
Is Deep Research accurate enough for business decisions? Deep Research should be treated as a highly capable first draft, not gospel. Always verify critical claims, especially numbers and recent events. The tools cite sources, so you can check important facts. For high-stakes decisions, use Deep Research to accelerate your research, then apply human judgment.
Which Deep Research tool is best for B2B GTM? ChatGPT Deep Research offers the most comprehensive analysis. Perplexity is best for quick, cited answers. Claude excels at long document synthesis and nuanced writing. Most teams benefit from using multiple tools for different use cases.
Can Deep Research replace my market research team? Deep Research augments rather than replaces human researchers. It eliminates the grunt work of gathering and organizing information, freeing your team to focus on analysis, strategy, and execution. The teams getting maximum value pair Deep Research speed with human judgment.
Phi Consulting | The GTM Execution Partner for B2B Startups We embed sales, marketing, and CS teams into high-growth companies to drive pipeline and revenue.
