High-Performance GTM Strategy and
Execution Prompts for B2B SaaS

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)

To establish an accurate ICP, you'll need to analyse your highest-performing customers against your lowest-performing ones. Collect:

• High-Value Customer Cohort: 10 customers with the highest LTV, shortest sales cycles, and minimal support requirements.
• Low-Value Customer Cohort: 10 customers who churned quickly or required disproportionately high support relative to their contract value.
• Technographic Data: Identify the technology stack your high-value customers use (e.g., Salesforce, HubSpot, Slack).
• Purchase Triggers: Document the specific business events that occurred immediately before purchase (e.g., new executive hire, funding round, organisational restructure).

THE PROMPT:

You are a Go-to-Market Consultant and Data Analyst for [COMPANY NAME]. Our sales team reports [e.g., declining lead quality, and marketing spend on lookalike audiences is generating poor conversion rates.] Your objective is to audit our customer base and define a data-validated ICP focused on high-intent, high-value segments.

CONTEXT:

• Our Solution: [brief description]
• Current ICP: [e.g., "HR Tech companies in North America"]
• Goal: [e.g., "Increase average contract value (ACV) by 30%"]
• Challenge: [e.g., "We're acquiring too many small accounts that churn within 3 months"]

YOUR TASK:

Compare the high-value cohort against the low-value cohort to identify the differentiating characteristics that indicate customer-solution fit.

1. CRM & SUCCESS DATA COLLECTION:

Paste your LTV data, industry segments, and customer health metrics here:

[PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Profitability Benchmark: Which company size or industry demonstrates the highest LTV to CAC ratio? [AI WILL IDENTIFY]
• Sales Velocity Signal: Which leads progress through the sales pipeline 2x faster than average? What shared characteristics do they have? [AI WILL ANALYSE]
• Technology Integration Pattern: Do our best customers use a specific platform or tool that integrates with our solution? [AI WILL DETERMINE]
• Negative Indicators: What are the 3 warning signs that consistently predict high-maintenance, low-value customers? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Targeting Gap:
  • Stated Target: [e.g., "Innovative Startups"]
  • Actual Buyers: [e.g., "Late-stage legacy companies"]
  • The Gap: [AI ANALYSES THE NEED TO PIVOT MESSAGING TO STABILITY RATHER THAN INNOVATION]
• The Value Gap:
  • Current Acquisition Profile: [e.g., Low price, high volume]
  • Desired Acquisition Profile: [e.g., High price, high retention]
  • The Gap: [AI ANALYSES WHY WE ARE ATTRACTING THE LOWER-VALUE SEGMENT]

4. SPECIFIC QUESTIONS TO ANSWER:

• What are the minimum firmographic requirements for a lead to warrant a sales call?
• What trigger event (e.g., Series B funding, new CTO hire) increases purchase likelihood by 5x?
• Which job titles serve as champions versus end users in the buying process?
• Is our ICP best defined by industry classification or by operational characteristics (culture, technology stack, business model)?

5. ANALYSIS PRINCIPLES:

• Prioritise Precision: An effective ICP excludes as much as it includes.
• LTV over Volume: 10 customers at $10K with low churn outperform 100 customers at $1K with high churn.
• Look for "Clusters": Identify underserved industries where we have strong success rates but limited market presence.

6. OUTPUT FORMAT:

The ICP Definition

• Ideal Customer Profile: A detailed breakdown of the 5 defining characteristics of our best-fit customers.
• Disqualification Criteria: 5 characteristics that should remove a lead from consideration.
• Targeting Parameters: Specific search filters for LinkedIn Sales Navigator and Apollo to identify this ICP.
• Messaging Framework: 3 industry-specific challenges our ICP prioritises solving.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)

To identify the underlying job your product fulfills, move beyond feature discussions and collect these qualitative inputs from 8–10 recent customers:

• Triggering Event: What business or personal situation prompted the customer to seek a new solution?
• Risk Concerns: What concerns did they have when evaluating your product? (e.g., "Data security" or "Stakeholder buy-in").
• Previous Solution: What method were they using before? (Include manual processes, spreadsheets, or competing tools)
• Success Definition: How do they describe successful outcomes in their own words?

THE PROMPT:

You are a Senior User Researcher and JTBD Consultant for [COMPANY NAME]. We need to understand the fundamental job our customers are hiring us to do, enabling us to build more relevant features and create more effective messaging. Your task is to use the Forces of Progress framework to analyse customer interviews and identify the primary Job to be Done.

CONTEXT:

• Our Solution: [brief description]
• Current "Assumed" Job: [e.g., "To manage tasks"]
• Customer Segment: [e.g., "Marketing Managers at mid-market firms"]
• Primary Alternative: [e.g., "Spreadsheets or Trello"]

YOUR TASK:

Analyse the provided interview transcripts to extract the underlying Jobs, Pains, and Gains, with emphasis on the emotional and social drivers behind purchase decisions.

1. TRANSCRIPT DATA COLLECTION:

Paste your customer interview notes and switching stories here:

[PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Push Factors: What specific frustrations with their previous solution compelled them to seek an alternative? [AI WILL IDENTIFY]
• Pull Factors: Which specific capability or promise of our product was most compelling? [AI WILL ANALYSE]
• Adoption Barriers: What concerns nearly prevented them from making the purchase? [AI WILL DETERMINE]
• Legacy Behaviors: What previous workflows or habits do they still default to despite adopting our solution? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Social Job Gap:
  • Functional Need: [e.g., "Generate a report"]
  • Social Need: [e.g., "Present as the most prepared stakeholder in meetings"]
  • The Gap: [AI ANALYSES HOW TO MAKE PRODUCT OUTPUT MORE PRESENTATION-READY]
• The Emotional Job Gap:
  • Functional Need: [e.g., "Secure my data"]
  • Emotional Need: [e.g., "Reduce work-related anxiety during personal time"]
  • The Gap: [AI ANALYSES THE NEED FOR PROACTIVE STATUS NOTIFICATIONS]

4. SPECIFIC QUESTIONS TO ANSWER:

• What is the primary job? (Format: "Help me [action] so that I can [outcome] despite [constraint].")
• What are the 3 supporting jobs that enable the primary job?
• What is the primary obstacle in the customer's current workflow? (e.g., "Manual data entry").
• If our product were unavailable tomorrow, what would customers revert to using?

5. ANALYSIS PRINCIPLES:

• Outcomes Over Features: A "Job" is never "to use a dashboard"; it's "to know the status of my team at a glance."
• Prioritise Recent Switchers: The most valuable insights come from customers who recently changed their behavior.
• Distinguish Initial from Ongoing Value: Purchasing the software is the initial decision; consistent daily use represents sustained value.

6. OUTPUT FORMAT:

The JTBD Strategy Framework

• Job Statement: A definitive Job to be Done statement for our primary persona.
• Forces of Progress Map: A structured summary of Push factors, Pull factors, Anxieties, and Habits.
• Job-Aligned Roadmap: 3 feature improvements that address the core job better than alternatives.
• Enhanced Interview Protocol: 5 additional questions to validate these findings with future customers.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)

Gather these foundational inputs to ensure the analysis is grounded in actual customer behavior:

• Our Assumed Buyer Journey: [Describe how you believe customers currently move through the funnel, from initial awareness through to purchase and activation. Include the key touchpoints, content interactions, and decision points at each stage. For example: customers typically discover us through paid advertising, engage with educational content, request a demo, enter a trial period, complete vendor evaluation, and then move through contract negotiation and onboarding.]
• Conversion Bottlenecks: Where do users abandon the process in your product or website? (e.g., "50% drop-off at the integration setup step")
• Customer Feedback: Direct quotes from customers about confusion, obstacles, or unclear moments during their first week.
• Sales Cycle Data: How many days or weeks typically pass between initial demo and closed deal?

THE PROMPT:

You are a Lead UX Researcher and Customer Journey Architect for [COMPANY NAME]. While we have a defined customer journey on paper, our conversion and activation rates indicate customers are experiencing significant friction. Your task is to map the actual customer journey based on behavioral evidence and identify where we're losing purchase intent and momentum.

CONTEXT:

• Our Solution: [brief description]
• Primary Activation Goal: [e.g., "Get the user to upload their first dataset"]
• Current Journey Hypothesis: [e.g., "Ad → Demo → Trial → Purchase"]
• Known Friction Point: [e.g., "Security review adds 3 weeks to sales cycle"]

YOUR TASK:

Synthesise behavioral data and qualitative feedback to create an "Actual vs. Assumed" Journey Map that identifies the specific moments where customer motivation decreases.

1. JOURNEY DATA COLLECTION:

Paste your analytics summaries, interview transcripts, and funnel performance data here:

[PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Undocumented Steps: What actions are customers taking that aren't reflected in our assumed journey? (e.g., "Consulting peer reviews" or "Requesting sandbox access") [AI WILL IDENTIFY]
• Frustration Points: At which stage do customers express the most concern or difficulty? [AI WILL ANALYSE]
• Communication Gaps: Where in the journey are we providing insufficient guidance when customers need it most? [AI WILL DETERMINE]
• Feedback Timing Issues: Are we requesting feedback at suboptimal moments? (e.g., requesting NPS before value realisation) [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Time-to-Value Gap:
  • Expected Time to Value: [e.g., "Immediate"]
  • Actual Time to Value: [e.g., "12 days"]
  • The Gap: [AI ANALYSES THE NEED FOR INCREMENTAL VALUE MILESTONES]
• The Knowledge Gap:
  • Assumed User Expertise: [e.g., "Advanced"]
  • Actual User Expertise: [e.g., "Beginner to Intermediate"]
  • The Gap: [AI ANALYSES THE NEED FOR ENHANCED IN-APP GUIDANCE AND DOCUMENTATION]

4. SPECIFIC QUESTIONS TO ANSWER:

• Where is the critical activation moment where a user becomes 80% likely to convert?
• How many navigation or information dead-ends exist in our current documentation or trial experience?
• Is our sales-assisted approach creating friction for self-serve users?
• What external factors (competitive alternatives, internal approval processes) are interrupting the customer journey?

5. ANALYSIS PRINCIPLES:

• Customer Perspective: Evaluate the journey from the viewpoint of a time-constrained user with competing priorities.
• Non-Linear Progression: Recognise that customers move back and forth between stages (e.g., from contract review back to feature evaluation).
• Cumulative Friction: Small obstacles compound; identify the breaking point where customers disengage.

6. OUTPUT FORMAT:

Actual vs. Assumed Journey Audit

• Journey Reality Map: A stage-by-stage breakdown of actual user actions, thoughts, and emotional states.
• High-Friction Areas: 3-5 priority areas where the journey is currently failing customers.
• Critical Obstacles: Specific UI/UX or process barriers to address immediately.
• Optimisation Priorities: 4 high-impact improvements to reduce time to value.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)

To understand churn beyond surface-level reasons, gather these inputs to paste into the prompt:

• Exit Interview Data: Specific reasons provided by the last 10–15 customers who canceled.
• Engagement Decline Patterns: Data showing when users reduced or stopped product usage (e.g., "Activity ceased after 45 days").
• Support Interaction Summary: Common technical issues or feature requests from customers who churned.
• Competitor "Pull": Information on where customers migrated after leaving your platform.

THE PROMPT:

You are a Senior Retention Strategist and Customer Success Architect at [COMPANY NAME]. Our churn rate is negatively impacting Net Revenue Retention (NRR), and we need to shift from reactive intervention to proactive prevention. Your task is to analyse our churn data to identify underlying dissatisfaction signals and build a systematic mitigation strategy.

CONTEXT:

• Our Solution: [brief description]
• High-Risk Segment: [e.g., Small business owners or Enterprise IT leads]
• Most Common Stated Reason: [e.g., "Missing features" or "Budget constraints"]
• Internal Hypothesis: [e.g., "Onboarding complexity is a primary factor"]

YOUR TASK:

Analyse the provided churn signals to identify root causes of customer departure and design a retention strategy that addresses the pre-cancellation phase.

1. CHURN DATA COLLECTION:

Paste your exit surveys, support tickets, and usage logs here:

[PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Behavioral Decline Indicators: Which usage patterns (or absence of activity) predicted churn weeks before the cancellation request? [AI WILL IDENTIFY]
• Expectation Misalignment: Where did our marketing messaging fail to match the actual product experience? [AI WILL ANALYSE]
• Value Realisation Failure: At what specific point in the first 90 days did customers stop perceiving ROI? [AI WILL DETERMINE]
• Support Experience Quality: How many negative support interactions did churned customers have compared to retained customers? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Onboarding Gap:
  • Customer Expectation: [e.g., "Operational within 2 days"]
  • Actual Experience: [e.g., "Implementation requires 4 weeks and technical resources"]
  • The Gap: [AI ANALYSES THE NEED FOR SELF-SERVICE IMPLEMENTATION TOOLS]
• The Feature Adoption Gap:
  • Typical Platform Utilisation: [e.g., "10% of available capabilities"]
  • The Gap: [AI ANALYSES THE NEED FOR TARGETED FEATURE EDUCATION PROGRAMS]

4. SPECIFIC QUESTIONS TO ANSWER:

• What are the measurable early warning indicators we can automate in our CRM to alert Customer Success teams?
• How do we distinguish between poor-fit churn (wrong customer profile) and experience-driven churn (execution issues)?
• What is the minimum value a customer must achieve in Week 1 to remain a customer through Year 1?
• How can we redesign our retention playbook to emphasise value delivery rather than price concessions?

5. ANALYSIS PRINCIPLES:

• Behavioral Evidence First: Prioritise actual usage data over exit survey responses.
• Segment-Specific Analysis: Examine churn by industry and company size to identify if specific segments fail at higher rates.
• Underlying Causes: If customers cite "Price," investigate whether the actual issue is insufficient perceived value.

6. OUTPUT FORMAT:

Churn Mitigation & Retention Strategy

• Early Warning System: 5 specific behavioral triggers (e.g., "No login activity for 7 consecutive days") that should initiate proactive outreach.
• Onboarding Enhancement Plan: 3 specific improvements to the first 14 days of the customer lifecycle.
• Value-Based Retention Framework: A structured approach for Customer Success Managers to use during cancellation conversations.
• Product Feedback Process: A system for channeling feature-related churn insights to the Product team.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Gather these post-decision inputs to enable accurate analysis of deal outcomes:

• Closed Won Examples (5 deals): CRM notes and sales notes showing the decisive moment or demo transcript
• Closed Lost Examples (5 deals): CRM notes + sales notes (e.g., "Lost to Competitor X" + demo context)
• Customer Perspective (5 lost deals): Direct post-decision feedback from prospects (email or call notes)
• Feature Discussion Log: The 3 features prospects consistently asked about during demos.
• Pricing Discussion Notes: When and how prospects raised cost concerns during the sales process

THE PROMPT:
You are a Senior Product Marketing Manager and Sales Strategist for [COMPANY NAME]. We are losing deals in the final stage, and our "Closed-Lost" reasons in the CRM are too generic to drive improvement. Your task is to analyse our Win/Loss data to uncover the decision drivers and build a framework for improved sales execution.

CONTEXT:

• Our Solution: [brief description]
• Primary Competitor: [e.g., "Legacy incumbent" or "Low-cost disruptor"]
• Win Rate Trend: [e.g., "Down 10% in the last quarter"]
• Most Common Loss Reason: [e.g., "Prospect chose status quo"]

YOUR TASK:
Analyse the provided deal notes and transcripts to identify the critical moments that led to wins or losses.

1. WIN/LOSS DATA COLLECTION:
Paste your sales notes, prospect emails, and CRM data here: [PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Conversion Catalyst: In our wins, what was the specific moment or capability that secured commitment? [AI WILL IDENTIFY]
• Critical Gap: In our losses, what was the essential requirement we failed to address effectively? [AI WILL ANALYSE]
• Competitive Positioning: How are competitors framing their advantage against us? (e.g., "Too expensive" or "Too complex") [AI WILL DETERMINE]
• Inertia Factor: Why are prospects choosing to maintain their current process instead of adopting our solution? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Messaging Gap:
  • Our Primary Message: [e.g., "Superior user interface"]
  • Their Primary Concern: [e.g., "Data security and compliance"]
  • The Gap: [AI ANALYSES THE NEED TO PRIORITISE SECURITY IN INITIAL DEMOS]
• The Value Perception Gap:
  • Our Positioning: [e.g., Premium solution]
  • Their Perception: [e.g., Over-engineered for their use case]
  • The Gap: [AI ANALYSES THE NEED FOR SIMPLIFIED PACKAGING OR TIERED OFFERINGS]

4. SPECIFIC QUESTIONS TO ANSWER:

• What single element, when addressed in a sales conversation, increases our win rate by 50%?
• Which stage of the sales funnel has the highest "leakage," and why?
• Are we losing due to missing capabilities (product gap) or inadequate demonstration (sales gap)?
• Which objection are we consistently failing to address effectively?

5. ANALYSIS PRINCIPLES:

• Decode Surface Responses: When a prospect cites "budget constraints," they often mean "insufficient business priority." Determine why it wasn't prioritised.
• Identify Win Patterns: Do we consistently win when specific personas are involved in the decision?
• Outcome-Oriented Thinking: Customers purchase outcomes, not products. What transformation do our winning customers seek?

6. OUTPUT FORMAT:
Win/Loss Strategic Analysis

• Win Framework: 3 essential actions or messages every salesperson must deliver to close deals successfully.
• Loss Analysis: A categorised breakdown of the top 3 failure reasons (Product limitations, Process issues, Persona misalignment).
• Competitive Response Guide: 5 specific responses to the most common competitor positioning claims.
• Status Quo Disruption Strategy: A narrative framework to demonstrate the cost and risk of inaction to prospects.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Before running this prompt, gather market positioning data to copy-paste. Collect:

1. Search Intent: List 5–10 keywords people use to find you. Do they use an established category name (e.g., CRM) or a problem description (e.g., "how to track sales calls")?
2. Competitive Landscape: List 3 competitors. Do you offer the same solution they do, or do you solve the problem in a fundamentally different way?
3. Customer Understanding: Notes from Sales on how frequently prospects ask, "So, are you like [Competitor X]?"
4. Analyst Coverage: Are there existing Gartner/Forrester categories for what you do?

THE PROMPT:
You are a Category Designer and Go-to-Market Strategist for [COMPANY NAME]. We are at a strategic decision point: should we compete for market share in an existing category or invest in defining a new one? Your task is to analyse our market signals and provide a definitive category strategy recommendation.

CONTEXT:

• Our Current Positioning: [e.g., Project Management Software]
• Existing Approach: [How people solve the problem today]
• Our Approach: [Our unique mechanism/technology]
• Strategic Objective: [e.g., Establish market leadership in a niche or disrupt an established market]

YOUR TASK:
Analyse the market signals and competitive data below to recommend either a "Category Creation" or "Category Entry" strategy.

1. MARKET SIGNAL DATA COLLECTION:
Paste your search intent and competitor notes below:
MARKET DATA: [PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Buyer Awareness Level: Are buyers aware of the problem or actively searching for solutions? [AI WILL IDENTIFY]
• Category Association Risk: How easily can we be positioned within an existing category? [AI WILL ANALYSE]
• Market Language Ownership: Does an established competitor own the terminology and conversation in this space? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Differentiation Gap:
  • Category Standard: [e.g., "Speed and Efficiency"]
  • Our Value Proposition: [e.g., "Strategic Outcomes"]
  • The Gap: [AI ANALYSES IF THIS REPRESENTS A NEW CATEGORY OR AN INCREMENTAL IMPROVEMENT]

4. SPECIFIC QUESTIONS TO ANSWER:

• Market Transformation Test: Does our product fundamentally change how the problem is solved, or does it improve the existing approach?
• Education Investment: If we create a new category, will the investment required to educate the market outweigh the first-mover advantage?
• Search Demand: Is there sufficient search volume for the problem to support a new category, or should we leverage existing category traffic?
• Third-Party Validation: Can we realistically secure analyst and media recognition for this new category?

5. ANALYSIS PRINCIPLES:

• Investment Requirements: Category creation requires market education; category entry requires competitive selling. Recommend based on available resources.
• Problem Intensity: Category creation requires a widely recognised problem with the existing approach. Assess whether the current solution is problematic enough.
• Naming Clarity: Ensure any new category name follows an intuitive "Adjective + Noun" structure that is immediately understandable.

6. OUTPUT FORMAT:
Category Strategy Recommendation

• Strategic Recommendation (Create vs. Enter) with confidence level assessment.
• Category Positioning: A proposed category name or label for our market position.
• Messaging Shift: How to update our homepage and core messaging based on this decision.
• 12-Month Implementation Plan: Key milestones for establishing this market position.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
To evaluate your value proposition effectively, the AI needs to compare what you say against what you deliver. Collect:

1. Current Value Proposition: Copy-paste your current homepage headline or "About Us" statement.
2. Customer Validation: 3–5 direct quotes from customers explaining their purchase decision.
3. Supporting Evidence: List 3 claims you make (e.g., "We are the fastest") with corresponding proof (e.g., "Average setup time is 4 hours").
4. Market Skepticism: List 3 instances where prospects questioned or didn't believe your value claim.

THE PROMPT:
You are a Messaging Strategist and Brand Auditor for [COMPANY NAME]. Our current value proposition feels undifferentiated and is failing to distinguish us in a crowded market. Your task is to evaluate our claims and help us build a substantiated value proposition that competitors cannot replicate.

CONTEXT:

• Our Product: [brief description]
• Our Target Audience: [e.g., Head of Engineering at Scale-ups]
• Current Value Proposition: [Paste current statement]
• Competitive Messaging: [What competitors typically say, e.g., "We save you time"]

YOUR TASK:
Subject our current messaging to rigorous evaluation to identify unsupported claims and replace them with evidence-based value statements.

1. VALUE DATA COLLECTION:
Paste your current claims and customer evidence below:
PROPOSITION INPUTS: [PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Differentiation Assessment: Which parts of our message could be used by any of our top 5 competitors? [AI WILL IDENTIFY]
• Evidence Validation: Which of our claims lack supporting data or proof points? [AI WILL HIGHLIGHT]
• Specificity Analysis: Are we using vague descriptors (e.g., innovative, robust, seamless) instead of concrete outcomes? [AI WILL ANALYSE]

3. GAP ANALYSIS:

• The Value Perception Gap:
  • What we emphasise: [Insert Claim]
  • What customers actually prioritise: [AI EXTRACTS FROM QUOTES]
  • The Gap: [AI ANALYSES THE MISALIGNMENT]

4. SPECIFIC QUESTIONS TO ANSWER:

• Outcome Clarity: When we present our value proposition to a prospect, can we provide a specific, quantifiable answer to "What does this mean for my business?"
• Opposing Position Test: If we stated the opposite of our value proposition, would it represent a viable alternative business strategy? (If not, our claim is too obvious to be meaningful.)
• Substantiation Check: Do we have concrete evidence for our most significant claim?
• Buyer Alignment: Does this value proposition resonate with the person experiencing the problem or the person controlling the budget?

5. ANALYSIS PRINCIPLES:

• Distinctive over Superior: Being "better" is subjective; being "different" is demonstrable.
• Outcomes over Features: Focus on what customers achieve, not just what the product does.
• Enduring Relevance: Is this value proposition tied to a lasting business need or a temporary market trend?

6. OUTPUT FORMAT:
Value Proposition Audit & Refinement

• Evaluation Results: (Pass/Fail assessment for each current claim.
• Refined Value Proposition: A new, single-sentence core value statement.
• Evidence Documentation:: A table mapping our 3 main claims to 3 specific proof points.
• Messaging Standards: A list of overused terms to avoid that diminish differentiation.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Gather the following for your top 3 competitors:

1. Competitor Messaging: Copy-paste their main homepage headlines and value propositions.
2. Customer Complaints: Go to G2 or Capterra and find 5–10 low-rated reviews for your competitors. What are the recurring pain points?
3. Feature Comparison: List 2 capabilities you have that they don't, and vice versa.
4. Pricing Models: How do they charge compared to your pricing structure?

THE PROMPT:
You are a Competitive Intelligence Lead for [COMPANY NAME]. We're frequently compared to [COMPETITOR NAME] on price alone. Your task is to conduct an audit that identifies our distinct market position where we are the clear choice for our ICP.

CONTEXT:

• Our Company: [brief description]
• Primary Competitor: [The #1 competitor]
• Strategic Positioning Goal: [e.g., Be the "Premium" choice or the "Easy-to-use" choice]

YOUR TASK:
Analyse all the competitor weaknesses and messaging to define our differentiated positioning strategy.

1. COMPETITIVE DATA COLLECTION:
Paste competitor taglines and review data below:
COMPETITIVE LANDSCAPE: [PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Common Messaging Themes: What is the dominant message all our competitors are using? [AI WILL IDENTIFY]
• Unaddressed Pain Points: What are customers complaining about in reviews that no competitor is addressing in their marketing? [AI WILL ANALYSE]
• Strategic Focus Areas: Are competitors prioritising feature quantity while we could emphasise outcome quality? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Market Perception Gap:
  • How the market views the market leader: [e.g., "The established, complex choice"]
  • How we can be positioned: [AI ANALYSES THE CONTRAST]
  • The Gap: [AI IDENTIFIES THE AVAILABLE POSITIONING OPPORTUNITY]

4. SPECIFIC QUESTIONS TO ANSWER:

• What is the primary frustration for our competitor's customers? (Is it complexity? High cost? Poor support?)
• Scale as Liability: How can we reframe their size as a disadvantage (e.g., they are bureaucratic, we are responsive)?
• Review Analysis: Is there a valuable feature customers mention that is difficult to access or use in our competitor's product?
• What is our sustainable advantage? Something we have that is structurally difficult for them to replicate quickly.

5. ANALYSIS PRINCIPLES:

• Avoid Direct Competition: If the market leader emphasises "Reliability," don't compete on "More Reliability." Differentiate on "Speed," "Value," or "Specialisation.
• Enable Migration: Provide compelling reasons for customers to switch from their current solution.
• Define Non-Targets: Identify who the competitor serves best, so we can focus on underserved segments.

6. OUTPUT FORMAT:
Competitive Differentiation Strategy

• Competitive Positioning Mapping: A visual or textual 2x2 matrix showing our position relative to competitors.
• Competitive Response Framework: 3 conversation guides for Sales when prospects mention specific competitors.
• Differentiated Positioning Statement: A 2-sentence statement that defines our distinct market position.
• Content Strategy: 3 content topics that address competitor weaknesses and attract their browned off customers.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
You'll need performance data that reveals which messages are resonating with your audience. Gather what you have from these sources:

1. SEO Performance: List 5–10 ranking page titles/meta descriptions + their Click-Through Rates (CTR) + Landing Page URL. Include search position if available.
2. Ad Performance: List 3–5 ad headlines + CTR + Landing Page URL.
3. Email Outreach: List 2–3 subject lines and their reply rates + Linked landing page.
4. Customer Language: 5–10 descriptions from customers about how they explain your product to colleagues.

THE PROMPT:
You are a Growth Marketer and Analyst for [COMPANY NAME]. We have a product that works, but we need to determine if our messaging is driving growth or limiting it. Your task is to assess our Message-Market Fit (MMF).

CONTEXT:

• Target Segment: [e.g., Series B SaaS Founders]
• Core Message Tested: [e.g., "The only automated tax tool for SaaS"]
• Conversion Goal: [e.g., Demo Booked]

YOUR TASK:
Analyse the engagement data and customer language to determine if our message is resonating with our target market.

1. ENGAGEMENT DATA COLLECTION
Paste your SEO CTR, ad, email, or landing page data below (URL required for all):
PERFORMANCE LOG: [PASTE DATA HERE]

2. PATTERN ANALYSIS

• High-Performance Elements: Which specific words, phrases, or angles drove the highest CTR/reply rate? Normalise for SEO position when comparing. [AI WILL IDENTIFY]
• Generic Messaging Impact: Are our high-impression/low-CTR messages too broad or undifferentiated? [AI WILL ANALYSE]
• Customer Language Alignment: Does our successful messaging match the terminology customers use in their own descriptions? [AI WILL COMPARE]
• SEO Position Analysis: Which titles over/under-perform relative to their ranking position? [AI WILL CALCULATE]

3. GAP ANALYSIS

• The Intent-Messaging Gap:
  • What the buyer is trying to solve: [e.g., "Save time on taxes"]
  • What we are communicating: [e.g., "Compliance and Safety"]
  • The Gap: [AI ANALYSES THE DISCONNECT]

4. SPECIFIC QUESTIONS TO ANSWER

• Priority Alignment: Does our message address a problem they are currently accountable for solving?
• SEO-Specific: What messaging generates clicks even from lower positions (position 5+)? That indicates strong message resonance.
• Click-to-Convert Analysis (requires URLs): For each high-CTR title/headline, analyse the linked URL/page:
  • Does the page headline match the promise from the title/meta description?
  • Is the core benefit from the click reinforced above the fold?
  • Does the page content feel disconnected from the search/ad result?
• Language Adoption: When customers talk about us, do they use our marketing language, or their own terminology?
• Audience Specificity: Does the message strongly resonate with a specific segment or weakly appeal to everyone?

5. ANALYSIS PRINCIPLES

• Performance over Preference: Prioritise CTR data over internal brand preferences. For SEO, weight by position-normalised performance.
• Clarity over Creativity: A straightforward, clear message typically outperforms clever but ambiguous messaging in MMF tests.
• Urgency Assessment: Does the message convey "Must-Have" or "Nice-to-Have"?
• SEO Performance Indicators: Titles with strong CTR from position 3+ outperform position 1 titles with average CTR.
• Promise Alignment: High CTR combined with low conversion indicates messaging that promises what the page doesn't deliver.

6. OUTPUT FORMAT
Message-Market Fit Assessment

• The MMF Score: (1–10) with detailed justification.
• High-Performing Message Analysis: Breakdown of top-performing messaging angle (SEO + paid).
• Low-Performing Message Analysis: Messages to discontinue immediately.
• Click-to-Conversion Gaps: Specific promise/delivery disconnects found on high-CTR pages.
• Testing Recommendations: 5 new meta titles + 3 ad headlines to test based on customer language alignment.
• SEO Messaging Priorities: Rank current titles by messaging effectiveness (position-adjusted CTR).

DELIVERABLE: A comprehensive message-market fit assessment that reveals whether your messaging resonates with buyers and provides specific recommendations for improvement.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Gather these late-stage decision inputs to copy-paste into the prompt:

1. Stalled Deal Analysis: Notes on deals that reached advanced stages but stopped progressing. What was the last question or concern they raised?
2. Champion Resource Requests: What materials did your last 3 buyers request to present to decision-makers? (e.g., Security documentation, implementation timelines, ROI models).
3. Customer Success Evidence: 3–5 specific examples of how customers achieved measurable results using your solution (quantifiable outcomes).
4. Implementation Concerns: A list of the actual steps required to transition from a competitor or manual process to your product.

THE PROMPT:
You are a Senior Content Strategist and Sales Enablement Lead for [COMPANY NAME]. Our top-of-funnel traffic is strong, but our "Opportunity-to-Close" rate is stagnant because we aren't providing buyers the specific assets they need to finalise high-stakes decisions. Your task is to design a bottom-of-funnel strategy that builds confidence and removes every remaining adoption barrier.

CONTEXT:

• Our Solution: [brief description]
• The Hidden Decision-Maker: [e.g., The CFO or IT Director who doesn't attend calls but must approve]
• Primary Decision Barrier: [e.g., "Fear that implementation will fail" or "Uncertainty about long-term ROI"]
• Current BOFU Assets: [e.g., One generic case study and a pricing page]

YOUR TASK:
Analyse the provided sales friction and champion needs to build a decision-enablement content strategy that accelerates the final stage of the buyer's journey.

1. DECISION-FRICTION DATA COLLECTION:
Paste your stalled deal notes, Champion requests, and technical concerns below:
SALES & SUCCESS FEEDBACK: [PASTE NOTES HERE]

2. PATTERN ANALYSIS:

• Internal Selling Gaps: What resources does our Champion need to secure stakeholder buy-in? [AI WILL IDENTIFY]
• Implementation Concerns: Where in the setup process do prospects express the most concern? [AI WILL ANALYSE]
• Competitive Dynamics: Are we losing deals to competitors, or to inertia and maintaining the status quo? [AI WILL DETERMINE]
• Validation Requirements: Which specific proof points (Security, Scalability, ROI) are most frequently requested by late-stage evaluators? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• The Confidence Gap:
  • Buyer's Late-Stage Concern: [e.g., "This will take 6 months to implement"]
  • Our Current Content: [e.g., "We mention 'Fast Implementation' in a bullet point"]
  • The Gap: [AI ANALYSES THE NEED FOR A DETAILED "FIRST 30 DAYS" IMPLEMENTATION ROADMAP]
• The Decision-Maker Gap:
  • Who we engage: [The User]
  • Who approves: [The Executive]
  • The Gap: [AI ANALYSES THE NEED FOR EXECUTIVE-FOCUSED SUMMARY VERSIONS OF OUR CONTENT]

4. SPECIFIC QUESTIONS TO ANSWER:

• Social Proof Relevance: Do we have case studies that match the prospect's specific industry and company size?
• Trust Infrastructure: Can we create a dedicated resource hub (e.g., 'Security & Trust Center' or 'Technical Implementation Guide')?
• Inaction Cost Framework: How do we demonstrate the ongoing cost of delaying the decision?
• Migration Simplification: What specific content makes switching feel less risky than maintaining the current approach?

5. ANALYSIS PRINCIPLES:

• Evidence over Claims: Prioritise implementation guides and technical documentation over promotional content.
• Skeptic-Focused Content: Create content specifically for gatekeepers whose role is to identify risks (IT, Legal, Procurement).
• Champion Empowerment: Every asset should be easily shareable and help the internal advocate build a compelling case.

6. OUTPUT FORMAT:
BOFU Decision-Enablement Strategy

• Champion Resource Kit: 3 assets (e.g., Executive Summary Deck, ROI Calculator, Business Case Template).
• Risk Reduction Content: 2 assets (e.g., Implementation Roadmap, Security/Compliance Documentation).
• Proof Documentation: A strategy for industry-specific case studies that detail operational changes and measurable outcomes.
• Re-engagement Sequence: A 3-step email series for stalled opportunities that provides additional decision-making resources rather than status check-ins.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
To get meaningful output, feed the AI your unique worldview, how you challenge convention and see things others overlook. Gather:

1. Contrarian Position: What does everyone in your industry believe that you think is incorrect or incomplete?
2. Executive Insights: 3–5 bullet points from your founder or lead subject matter expert about the future of the industry.
3. Industry Realities: What is an uncomfortable truth about your industry that is rarely discussed openly?
4. Legacy Approach: What is the traditional way of doing things that your solution is replacing?
5. Current Keyword Performance: List your top 5–10 ranking keywords + monthly search volume + your current position. (Get this from Google Search Console or SEMrush.)

THE PROMPT:
You are a Content Director for [COMPANY NAME]. We want to move beyond generic content and start publishing material that our ICP actually saves and shares. You are building a Thought Leadership Strategy that challenges conventional thinking.

CONTEXT:

• Our Market: [Industry]
• Our Core Perspective: [What we stand for]
• Our Main Competitors: [Names]
• The Market Gap: [e.g., "Everyone is talking about AI, but no one is addressing the data ethics implications."]

YOUR TASK:
Synthesise our internal beliefs and industry trends into a high-authority content roadmap that positions us as the "Intellectual Leader" of the category.

1. NARRATIVE DATA COLLECTION:
Paste your subject matter expert notes and contrarian views below:
SME INPUTS: [PASTE NOTES HERE]

2. PATTERN ANALYSIS:

• Industry Misconception: What is the most significant misconception currently prevalent in our industry? [AI WILL IDENTIFY]
• Strategic Content Gap: What high-level strategic guidance are buyers seeking but not finding? [AI WILL ANALYSE]
• Distinctive Perspectives: Which internal viewpoints are most differentiated and defensible? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• Search Volume vs. Strategic Value:
  • High-volume keywords (The Standard Topics): [AI IDENTIFIES]
  • Low-volume / High-intent Narrative (The Authority Topics): [AI IDENTIFIES]
  • The Differentiation Opportunity: [AI ANALYSES HOW TO ESTABLISH DISTINCT POSITIONING]

4. SPECIFIC QUESTIONS TO ANSWER:

• What is our core narrative position? (The perspective we are committed to advancing.)
• Who are we advocating for? (Usually the ICP facing a specific challenge or inefficiency.)
• What proprietary data or frameworks can we provide that competitors cannot? (Benchmarks, research, analysis.)
• Influence Factor: Why would a VP-level buyer forward this content to their executive team?

5. ANALYSIS PRINCIPLES:

• Perspective over Information: Don't just explain what something is; explain why it matters and how to evaluate it strategically.
• Targeted Positioning: Be willing to take positions that may not resonate with everyone outside our ICP.
• Credibility Markers: Support every claim with data, proprietary frameworks, and expert validation.

6. OUTPUT FORMAT:
Thought Leadership Strategy

• Core Narrative Statement: A 3-paragraph perspective document that defines our market position.
• Anchor Content Assets: 3 comprehensive resources (whitepapers or research pieces) that establish our point of view.
• Editorial Plan: 12 weeks of topics organised by strategic narrative themes.
• Distribution Approach: How to adapt this content for LinkedIn, email, and media outreach.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
To provide the AI with the right source material, export or transcribe 5–10 recent sales discovery calls or customer success interviews. Create a document with the following sections to copy-paste:

1. Initial Problem: How the customer describes their challenge before they found you.
2. Value Recognition Moment: The specific phrase they used when they understood your value.
3. Frustration Points: Exact words used to describe their issues with competitors or current workarounds.
4. Desired Outcome: How they describe their ideal state in their own industry terminology.

THE PROMPT:
You are a Conversion Copywriter and GTM Strategist for [COMPANY NAME]. We want to move beyond marketing language and build our landing pages and ads using the exact language our buyers use. Your task is to mine the provided data for linguistic patterns that drive emotional resonance and conversion.

CONTEXT:

• Our Product: [brief description]
• Target Persona: [e.g., CFOs at Mid-Market Tech firms]
• Current Messaging: [e.g., "We provide end-to-end financial visibility"]
• The Goal: Find the "messy" language buyers use to describe this.

YOUR TASK:
Analyse transcripts and notes provided to create a "Buyer Language Bank" that will inform all future content and SEO copy.

1. LINGUISTIC DATA COLLECTION:
Paste your transcripts or interview notes below:
TRANSCRIPT DATA: [PASTE TRANSCRIPTS HERE]

2. PATTERN ANALYSIS:

• Problem Description Patterns: How do they describe the pain? Look for metaphors or emotional language. [AI WILL IDENTIFY]
• Category Terminology: What do they call our category when they aren't using our official name? [AI WILL EXTRACT]
• Feature References: What do they actually call our features in conversation? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• Corporate vs. Conversational:
  • What we say: [FROM CONTEXT]
  • What they say: [AI EXTRACTS FROM DATA]
  • The Language Disconnect: [AI ANALYSES THE GAP]

4. SPECIFIC QUESTIONS TO ANSWER:

• What is the primary obstacle in their narrative? (Is it a person, a process, or a specific tool?)
• What are the credibility signals? (Phrases that indicate we understand their world and aren't just another vendor.)
• What are the disconnect triggers? (Jargon we use that causes them to disengage.)
• The Specifics Analysis: Do they mention specific numbers, dates, or repetitive tasks that we can use in our messaging?

5. ANALYSIS PRINCIPLES:

• Direct Quotes over Paraphrasing: Prioritise exact customer language over summaries.
• Frequency and Intensity: Look for phrases used often, but also for phrases used with strong emotional weight.
• Journey-Specific Language: Group language by the stage of the journey (Awareness vs. Decision).

6. OUTPUT FORMAT:
Buyer Language Bank Report

• Headline Options: 10 customer-language headlines for our home page.
• Pain Point Vocabulary: A list of 20 phrases buyers use to describe their challenges.
• Feature Translation Guide: A table converting our technical features into buyer-focused outcomes.
• Content Topics: 3 SEO topics based on questions buyers asked directly during the calls.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
To build an SEO strategy that drives actual pipeline provide the AI with business-aligned data:

1. Keyword Performance Data: 5-10 seed keywords you must own + monthly search volume + current ranking position + competitor ranking. (From Ahrefs, SEMrush, or Google Search Console.)
2. Search Questions: 5 specific questions your ICP types into Google when they need a solution.
3. Competitor URLs: 2-3 competitors who are ranking well in search.
4. Product Capabilities: Your 3 most valuable features that solve these problems.

THE PROMPT:
You are an SEO Strategist and GTM Specialist for [COMPANY NAME]. We want to own the search visibility for the specific problems our product solves. Your task is to build a GTM-aligned Topic Cluster Strategy.

CONTEXT:

• Our Core Solution: [brief description]
• Primary Keyword Category: [e.g., Project Management Software]
• Target Audience: [e.g., Creative Agency Owners]
• Current SEO Status: [e.g., "Ranking for our brand name, but nothing else."]

YOUR TASK:
Create a semantic cluster map that links educational (Top-of-Funnel) content to problem-solving (Middle-of-Funnel) content, leading directly to our product.

1. KEYWORD & PROBLEM DATA COLLECTION:
Paste your seed keywords and competitor notes below:
SEO INPUTS: [PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Search Intent Classification: Are people looking for a definition, a template, or a tool? [AI WILL CATEGORISE]
• Untapped Keyword Opportunities: What related terms are our competitors not targeting? [AI WILL IDENTIFY]
• High-Value Opportunities: Which problem-focused keywords have manageable competition but high business value? [AI WILL IDENTIFY]

3. GAP ANALYSIS:

• Volume vs. Intent:
  • High Volume / Low Intent (The Low-Conversion Keywords): [AI IDENTIFIES]
  • Low Volume / High Intent (The High-Conversion Keywords): [AI IDENTIFIES]
  • The Content Authority Gap: [AI ANALYSES WHICH PILLAR POSTS WE ARE MISSING]

4. SPECIFIC QUESTIONS TO ANSWER:

• What is the pillar page? (The comprehensive guide that anchors the cluster.)
• What are the supporting articles? (8–10 focused pieces that link back to the pillar.)
• What is the lead capture asset? (What can we offer: template, checklist, framework?)
• Internal Link Strategy: How do we flow SEO authority from the blog to our product pages?

5. ANALYSIS PRINCIPLES:

• Topic Coverage over Keyword Density: Focus on comprehensive subject coverage, not just keyword optimisation.
• GTM Relevance: If a keyword has high volume but zero relevance to our product, exclude it.
• User Journey Design Ensure every supporting article has a clear path to the next stage of the funnel.

6. OUTPUT FORMAT:
GTM SEO Topic Cluster Map

• Pillar Page Structure: Outline for our 3 most important cornerstone pages.
• Cluster Content Plan: 10–15 specific article titles with target keywords and intent classification.
• Internal Linking Architecture: A visual or textual guide on how to link these pages for maximum SEO impact.
• Lead Capture Strategy: Recommended content offers for each cluster.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
To build an AI search optimisation strategy, provide the AI with the following training data inputs:

1. AI Platform Testing Data: 5-10 core category questions you've tested on ChatGPT, Perplexity, and Claude + the AI's current responses + whether your brand was mentioned. (Document exact queries and full responses.)
2. Competitor AI Presence: 2-3 competitors who are being cited by AI platforms when answering relevant queries.
3. Solution Context: Your 3 most distinctive capabilities that differentiate you from alternatives in your space.

THE PROMPT:
You are an AI Search Strategist for [COMPANY NAME]. We need to ensure our brand and solution appear in AI-generated responses when potential customers ask AI assistants for recommendations. Your task is to build an AI Visibility Optimisation Strategy.

CONTEXT:

• Our Core Solution: [brief description]
• Website URL: [URL]
• Primary Problem Category: [e.g., Customer Support Automation]
• Target Audience: [e.g., Customer Success Leaders at SaaS Companies]
• Current AI Visibility Status: [e.g., "Never mentioned in ChatGPT responses about our category"]

EXISTING CONTENT INVENTORY:

List your current authoritative content assets:

• Research/Whitepapers: [e.g., "2024 Customer Support Benchmark Report"]
• Case Studies: [Number and brief description]
• Documentation: [e.g., "Public API docs, Help Center"]
• Thought Leadership: [e.g., "Monthly blog covering industry trends"]
• Industry Presence: [e.g., "Quoted in TechCrunch 2x, featured in G2 reports"

COMPETITOR CITATION EXAMPLES:

Paste 2-3 examples of how competitors are being cited:

• Competitor A: [What query triggered their mention + what content was cited]
• Competitor B: [Same format]

YOUR TASK:
Create a content and authority strategy that positions our brand as the reference answer when AI platforms respond to queries in our problem space.

1. AI PLATFORM TESTING DATA COLLECTION:
Paste your tested queries and AI responses below:
AI VISIBILITY INPUTS: [PASTE DATA HERE]

2. PATTERN ANALYSIS:

• Response Pattern Classification: When do AI platforms recommend specific brands vs. generic advice? [AI WILL CATEGORISE]
• Citation Source Analysis: What types of content are AI platforms citing (research papers, case studies, documentation, news)? [AI WILL IDENTIFY]
• Visibility Gap Opportunities: Which problem areas in our domain generate AI responses without specific vendor recommendations? [AI WILL IDENTIFY

3. GAP ANALYSIS:

• Authority vs. Visibility:
  • High Authority / Low Visibility (The Citation Gap): [AI IDENTIFIES]
  • High Visibility / Low Relevance (The Positioning Gap): [AI IDENTIFIES]
  • The Trusted Source Gap: [AI ANALYSES WHICH AUTHORITATIVE CONTENT ASSETS WE ARE MISSING]

4. SPECIFIC QUESTIONS TO ANSWER:

• What is the foundational research asset? (The comprehensive study or framework that establishes category authority.)
• What are the supporting validation pieces? (6-8 case studies, comparisons, or benchmarks that reinforce our expertise.)
• What is the citation-worthy offering? (What can we publish: original research, industry benchmark, methodology framework?)
• Authority Flow Strategy: How do we build cross-platform presence that AI models recognise as authoritative?

5. ANALYSIS PRINCIPLES:

• Authoritative Depth over Content Volume: Focus on creating reference-quality content that AI models cite, not just publishing frequently.
• GTM Alignment: If a query pattern doesn't align with our ICP, deprioritise optimisation for it.
• Multi-Platform Validation: Ensure content appears across platforms AI models trust (industry publications, research sites, forums, technical documentation).

6. OUTPUT FORMAT:
AI Search Visibility Strategy

• Authority Content Blueprint: Outline for our 3 most important reference assets that position us as category experts.
• Query Optimisation Plan: 10-15 specific conversational queries to optimise for, with content recommendations and citation strategies.
• Cross-Platform Distribution Map: A guide for where to publish and syndicate content for maximum AI platform recognition.
• Citation Enhancement Strategy: Recommended approaches to earn mentions in third-party authoritative sources that AI models reference.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Export a list of MQLs from the last 90 days. Include these specific columns in your spreadsheet:

1. Lead ID & Company Name
2. MQL Date: (The date they were flagged as marketing qualified)
3. SQL/Opp Date: (The date they were accepted by Sales, if applicable)
4. Lead Source & Recent Conversion: (e.g., "Webinar" or "Pricing Page View")
5. Lead Score: (The point total that triggered the MQL)
6. Job Title & Industry: (For persona matching)
7. Current Status/Disqualification Reason: (e.g., "Nurturing," "Disqualified, No Budget")

THE PROMPT:
You are a RevOps Analyst for [COMPANY NAME]. Our Marketing team is hitting MQL volume targets, but Sales reports insufficient pipeline quality. Your job is to identify the specific quality gaps where the handoff is breaking down.

CONTEXT:

• Target MQL Volume: [e.g., 500/month]
• Actual MQL Volume: [e.g., 550/month]
• Target MQL-to-SQL Conversion: [e.g., 25%]
• Actual MQL-to-SQL Conversion: [e.g., 10%]

YOUR TASK:
Analyse the lead data provided to identify why MQLs are failing to become Opportunities and provide a new quality framework.

1. LEAD QUALITY DATA COLLECTION:
Paste your data below (organised by record):
MQL PERFORMANCE LOG: [PASTE DATA HERE: Include MQL Date, SQL Date, Source, Score, Title, and Disqualification Reason]

2. PATTERN ANALYSIS:

• Low-Converting Sources: Which sources produce high MQL volume but minimal SQL conversion? [AI WILL IDENTIFY]
• High-Converting Profile: Which specific titles/industries convert at a 2x higher rate than average? [AI WILL ANALYSE]
• Primary Rejection Factors: What is the most frequent reason Sales disqualifies an MQL? [AI WILL CATEGORISE]

3. GAP ANALYSIS:

• The Intent Gap: Compare high-intent actions (Demo Request) vs. low-intent actions (Whitepaper). [AI ANALYSES CONVERSION DIFFERENCE]
• The Velocity Gap: Compare the time it takes for an MQL to be accepted vs. those that are disqualified. [AI IDENTIFIES DELAYED FOLLOW-UP PATTERNS]

4. SPECIFIC QUESTIONS TO ANSWER:

• Conversion Baseline: Which Industry + Job Title combinations have a 0% conversion rate regardless of source?
• Time Decay Factor: After how many days does the conversion rate of an MQL drop by 50% or more?
• Scoring Methodology: Do leads that hit the score threshold via multiple small actions (blog views) convert better or worse than a single high-intent action (demo request)?

5. ANALYSIS PRINCIPLES:

• Handoff Quality: Check if disqualification reasons are specific or if Sales is using generic categories.
• Negative Scoring: Identify attributes (e.g., specific job titles or email domains) that should reduce lead scores but are currently being ignored.
• Revenue Weighting: Prioritise sources that lead to the largest deal sizes, not just the most leads.

6. OUTPUT FORMAT:
MQL Quality Audit Report

• Ideal Lead Profile: The 3 attributes most likely to lead to a sale
• Disqualification Criteria: Leads we should stop passing to Sales immediately
• Lead Scoring Adjustments: Specific point-value changes for our scoring model
• Sales Handoff Recommendations: Changes to how and when Sales should engage

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Export a "Deals" report from your CRM for the last 6 months with these columns:

1. Deal Name / ID
2. Lead Source: (e.g., Organic Search, Referral, Paid Social)
3. Total Deal Value: ($ Amount)
4. Deal Stage: (Won, Lost, or Open)
5. Created Date

THE PROMPT:
You are a Marketing Analytics Manager for [COMPANY NAME]. We need to move beyond first-touch attribution and understand the true ROI of our marketing channels to optimise our next quarterly budget.

CONTEXT:

• Total Marketing Budget: [e.g., $50k/mo]
• Primary Channels Used: [e.g., Google Ads, LinkedIn, Webinars, SEO]
• The Big Question: [e.g., "Is our $10k/mo LinkedIn spend actually creating closed-won deals?"]

1. CHANNEL DATA COLLECTION:
Paste your deal source data below:
DEAL SOURCE SUMMARY: [PASTE SPREADSHEET DATA HERE - Include Source, Value, and Stage]

COST PER CHANNEL (Last 90 Days):

• Channel A: [$ Amount spent]
• Channel B: [$ Amount spent]

2. PATTERN ANALYSIS:

• Volume vs. Value: Which channel creates the most leads vs. which creates the highest value deals? [AI WILL ANALYSE]
• Conversion Efficiency: Which channel has the highest win rate? [AI WILL CALCULATE]
• Supporting Influence: Which channels appear frequently in the history of won deals but rarely get last-touch credit? [AI WILL INFER]

3. GAP ANALYSIS:

• Spend vs. Yield Gap:
  • Highest Spend Channel: [Insert Channel]
  • Highest Revenue Channel: [AI IDENTIFIES]
  • Gap: [AI ANALYSES IF WE ARE OVER-INVESTING IN UNDERPERFORMING CHANNELS]

4. OUTPUT FORMAT:
Channel ROI Scorecard

• A table ranking every channel by ROI (Revenue / Spend).
• Identification of "Scale" channels (High ROI, High Volume).
• Identification of underperforming channels (Low ROI, High Cost).

The Re-Allocation Strategy

1. Budget Shift Plan: How to move 20% of the budget from underperforming to overperforming channels.
2. Testing Pipeline: Which low-cost, high-win-rate channels deserve experimental budget??

SPECIFIC QUESTIONS TO ANSWER:

• What is our blended CAC per channel?
• Which channel has the shortest sales cycle (velocity)?
• If we cut the budget of [Channel X] by 50%, what is the projected impact on pipeline?

ANALYSIS PRINCIPLES:

• Revenue over Volume: Always prioritise "Closed-Won" data over "MQL" volume.
• Attribution Window: Consider that a deal won today may have started 6 months ago.
• Saturation Analysis: Identify if a channel is reaching diminishing returns.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Before using the prompt, create a summary table with the following columns for the last 6–12 months:

1. Month: (e.g., January 2025)
2. Total S&M Spend: (All Marketing ad spend + Sales salaries/commissions + software tools)
3. New Customers Acquired: (Total count of new logos)
4. Total New ARR/MRR: (The dollar value of those new logos)
5. Gross Margin %: (Your revenue minus the cost of serving the customer, usually 70-85% for SaaS)

THE PROMPT:
You are a Growth Analyst conducting a CAC Payback Period Analysis for [COMPANY NAME], a [B2B SaaS/SERVICE] company. Our goal is to determine if our unit economics are sustainable and which segments are the most capital-efficient.

CONTEXT:

• Our Current Estimated CAC: [e.g., $5,000]
• Target Payback Period: [e.g., < 12 months]
• Gross Margin: [e.g., 80%]
• Business Model: [e.g., Monthly Subscription / Annual Contract]

YOUR TASK:
Analyse the spend and revenue data provided below to calculate the actual Payback Period and identify efficiency gaps.

1. FINANCIAL DATA COLLECTION:
Paste your monthly data summary below:
MONTHLY PERFORMANCE DATA: [PASTE TABLE: Month | S&M Spend | New MRR | New Customers]

SEGMENTED DATA (If available):

• SMB Segment Payback: [Cost vs. Revenue]
• Enterprise Segment Payback: [Cost vs. Revenue]

2. PATTERN ANALYSIS:

• Efficiency Trend: Is our CAC increasing or decreasing over time relative to the revenue we generate? [AI WILL ANALYSE]
• Segment Performance: Which customer segment achieves payback the fastest? [AI WILL IDENTIFY]
• Organic vs. Paid Impact: How much of our growth is organic vs. paid, and how does that impact the break-even point? [AI WILL CALCULATE]

3. GAP ANALYSIS:

• The "LTV to CAC" Ratio Gap:
  • Our Target Ratio: [e.g., 3:1]
  • Our Actual Ratio: [AI CALCULATES BASED ON CHURN ESTIMATES]
  • Gap: [AI ANALYSES SUSTAINABILITY]
• Budget Allocation Gap:
  • Where we spend the most: [e.g., Paid Search]
  • Where we get the fastest payback: [AI IDENTIFIES FROM DATA]

4. SPECIFIC QUESTIONS TO ANSWER:

• How many months does it take for a customer to become profitable?
• How does our payback period compare to the B2B SaaS industry benchmark (12-18 months)?
• Which month had the worst efficiency, and what caused the spike in CAC?

5. ANALYSIS PRINCIPLES:

• Gross Margin Adjusted: Always calculate payback based on Gross Profit, not just Revenue.
• Comprehensive Cost View: Include Sales salaries and overhead, not just ad spend.
• Cohort Analysis: Look for changes in payback across different acquisition periods.

6. OUTPUT FORMAT:
Based on the analysis, provide:
Executive Summary (1 page)

• Current Payback Period (in months)
• Efficiency Assessment (Excellent / Sustainable / Needs Improvement)
• Top 2 levers to reduce payback time

Detailed Efficiency Roadmap

1. Segment Prioritisation: Which segments to invest in based on speed of return.
2. Cost Optimisation Opportunities: Identifying "High CAC / Low Value" channels.
3. Growth Projections: If we maintain this payback, what happens to our cash flow in 12 months?

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Export a "Content Engagement" report for your Closed-Won and Closed-Lost deals from the last 6 months. Include these columns:

1. Deal Name & Value
2. Content Asset Name: (e.g., "2025 Industry Report" or "Case Study: Company X")
3. Asset Type: (Webinar, Blog, Whitepaper, Video)
4. Engagement Date: (When they viewed/downloaded it)
5. Deal Created Date: (To see if they saw it before or after the deal opened)

THE PROMPT:
You are a Content Strategist for [COMPANY NAME]. We produce significant content, but we need to determine which pieces are revenue drivers vs. engagement-only assets. You need to correlate content consumption with deal success.

CONTEXT:

• Core Content Goal: [e.g., Generate new leads or accelerate existing deals]
• Primary Channels: [e.g., LinkedIn, SEO, Email Nurture]
• Current Top-Performing Asset (by views): [Name of asset]

YOUR TASK:
Analyse the engagement data to determine which assets initiate deals and which assets close deals.

1. CONTENT ENGAGEMENT DATA:
Paste your data below:
DEAL-TO-CONTENT MAPPING: [PASTE DATA HERE: Deal Name, Asset Name, Asset Type, Engagement Date, Deal Date]

2. PATTERN ANALYSIS:

• Pipeline Initiator: Which assets are most frequently the first touchpoint for a Won deal? [AI WILL IDENTIFY]
• Deal Accelerator: Which assets are consumed after a proposal is sent but before the deal is Won? [AI WILL IDENTIFY]
• Underperforming Assets: Which pieces have high engagement but minimal presence in the journey of Won deals? [AI WILL HIGHLIGHT]

3. GAP ANALYSIS:

• Consumption vs. Conversion: Identify assets with high lead generation but low revenue contribution. [AI ANALYSES ROI]
• Format Preference: Does video, long-form text, or interactive tools (calculators) correlate with higher deal values? [AI DETERMINES PREFERENCE]

4. SPECIFIC QUESTIONS TO ANSWER:

• Deal-Closing Asset: Is there a specific case study that appears in >50% of Won deals in the final 30 days of the cycle?
• Engagement Threshold: How many unique assets does a prospect typically consume before they move from Lead to Opportunity?
• Efficiency Analysis: Does a prospect who consumes [Asset A] have a shorter sales cycle than one who doesn't?

5. ANALYSIS PRINCIPLES:

• Correlation vs. Causation: Don't assume a blog post "caused" the sale; look for repeating patterns across 10+ deals.
• Velocity Metric: Prioritise content that reduces time in each sales stage
• Asset Type vs. Deal Size: Does long-form content (Ebooks/Whitepapers) correlate with larger deal sizes compared to short-form content?

6. OUTPUT FORMAT:
Content ROI Strategy Report

• The Revenue Top 5: The five assets most correlated with closed revenue.
• The Retirement List: Content topics or formats that are not driving any pipeline.
• The Content Gap: Identify what content is missing for specific stages of the funnel.
• Next Quarter Priorities: Which 3 types of assets should we build next based on the data?

Recommended Output Length: 3,500–5,000 characters.

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
If you don't have a lead scoring model yet, you'll need historical data to build one. Export data from your CRM for the last 6-12 months with these columns:

1. Lead ID & Company Name
2. Final Outcome: (Closed-Won, SQL/Opportunity, Disqualified, or Nurture)
3. Firmographic Attributes: (Job title, company size, industry, revenue range, location)
4. Engagement Actions: (List all touchpoints: email opens, content downloads, webinar attendance, pricing page visits, demo requests)
5. Time to Conversion: (Days from first touch to becoming a customer, if applicable)
6. Lead Source: (How they originally found you)
7. Deal Value: (For closed-won deals, the contract value)

THE PROMPT:
You are a Marketing Operations Strategist for [COMPANY NAME]. We currently have no lead scoring model and are passing all leads to Sales, creating friction and wasted effort. Your task is to analyse our historical conversion data and build a predictive lead scoring framework from the ground up that identifies which leads deserve immediate sales attention.

CONTEXT:

• Current Lead Qualification Process: [e.g., "Manual review by Sales" or "Everyone gets a call"]
• Sales Team Capacity: [e.g., "Can handle 50 qualified conversations per month"]
• Average Monthly Lead Volume: [e.g., "300 leads"]
• Strategic Priority: [e.g., "Prioritise enterprise deals over SMB"]

YOUR TASK:
Analyse the historical lead and customer data to reverse-engineer which attributes and behaviors predict conversion, then build a scoring model that automatically identifies sales-ready leads.

1. HISTORICAL CONVERSION DATA COLLECTION:
Paste your lead and customer data below:

CONVERSION HISTORY: [PASTE DATA HERE: Include Lead ID, Outcome, Firmographics, Actions Taken, and Deal Value]

2. PATTERN ANALYSIS:
Based on the data above, analyse and identify the following:

• Winning Firmographic Profile: Which job titles, company sizes, and industries have the highest conversion rates to closed-won? [AI WILL IDENTIFY]
• High-Intent Behaviors: Which specific actions (demo request, pricing page view, comparison page visit) most strongly predict eventual purchase? [AI WILL ANALYSE]
• Low-Value Signals: Which common behaviors (newsletter signup, generic blog reading) have weak correlation with conversion? [AI WILL IDENTIFY]
• Conversion Timeline: What is the typical engagement pattern of a lead that converts vs. one that doesn't? [AI WILL MAP]

3. GAP ANALYSIS:

• Capacity Gap:
  • Current Lead Volume: [Insert Number]
  • Sales Capacity: [Insert Number]
  • The Gap: [AI CALCULATES REQUIRED MQL THRESHOLD TO MATCH CAPACITY]
• Value Alignment Gap:
  • Highest Volume Lead Profile: [AI IDENTIFIES]
  • Highest Value Lead Profile: [AI IDENTIFIES]
  • The Gap: [AI ANALYSES IF WE SHOULD PRIORITISE QUALITY OVER QUANTITY]

4. SPECIFIC QUESTIONS TO ANSWER:

• The MQL Definition: Based on conversion data, what combination of attributes and behaviors should qualify a lead as "sales-ready"?
• The Point Allocation: How many points should each firmographic attribute and behavioral action be worth?
• The Threshold Setting: What total score should trigger MQL status to ensure Sales receives qualified leads within their capacity?
• The Negative Filters: Which attributes should immediately disqualify a lead (e.g., students, competitors, wrong geo)?
• The Decay Mechanism: Should scores decrease if a lead goes inactive? If so, at what rate?

5. ANALYSIS PRINCIPLES:

• Start Simple: Begin with 2 categories (Demographic Fit + Engagement Level) rather than over-engineering.
• Closed-Won Backwards: Weight attributes based on what actual customers had in common, not assumptions.
• Balance Precision and Coverage: Too strict a model misses opportunities; too loose wastes Sales time.
• Segmentation Consideration: Enterprise and SMB buyers may need different scoring thresholds.

6. OUTPUT FORMAT:
Based on the analysis, provide:

1. Scoring Framework: A complete point allocation table for demographic and behavioral attributes.
2. Threshold Recommendations: Suggested score ranges for MQL, SQL, and high-priority leads.
3. Negative Scoring Rules: Attributes that should disqualify or reduce lead scores.
4. Implementation Roadmap: Phased approach to testing and rolling out the new scoring model.
5. Success Metrics: KPIs to measure scoring model effectiveness (MQL-to-SQL rate, Sales acceptance rate, CAC)

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Before using the prompt, create a document containing the following alignment inputs to copy-paste:

1. Funnel Data: MQL-to-SQL conversion rates for the last 3 months.
2. Lead Definition: The current written definition of a "Marketing Qualified Lead" (MQL).
3. Lead Rejection Reasons: A list of 10–20 recent reasons Sales gave for disqualifying leads.
4. Team Feedback: Brief quotes from Sales on lead quality and from Marketing on sales follow-up speed.

THE PROMPT:
You are a Sales Enablement Lead conducting a Sales-Marketing Alignment Diagnostic for [COMPANY NAME], a [B2B SaaS/SERVICE] company. Our goal is to unify the revenue engine by identifying where leads are being lost in the handoff process.

CONTEXT:

• Our Product: [brief description]
• Target Audience (ICP): [who we think we sell to]
• Current Friction Point: [e.g., "Sales says leads are unqualified" or "Marketing says Sales isn't following up"]
• Conversion Benchmark: [Current MQL to SQL %]

YOUR TASK:
Analyse the alignment data and feedback provided below to create a prioritized alignment improvement plan.

1. ALIGNMENT DATA COLLECTION:
Paste your data and feedback below:

QUANTITATIVE FUNNEL DATA:

• Total MQLs (Last 90 Days): [NUMBER]
• MQL-to-SQL Conversion %: [%]
• Top 3 Lead Sources: [Source A, B, C]
• Average Speed-to-Lead: [Time in hours/days]

QUALITATIVE FEEDBACK (SALES):

• Rep #1: [PASTE QUOTE - e.g., "The leads from the webinar are never the right title."]
• Rep #2: [PASTE QUOTE]
• Rep #3: [PASTE QUOTE]

QUALITATIVE FEEDBACK (MARKETING):

• Marketer #1: [PASTE QUOTE - e.g., "Sales takes 3 days to call a demo request."]
• Marketer #2: [PASTE QUOTE]

2. PATTERN ANALYSIS:
Based on the data above, analyse and identify the following:

• Definition Gap: How do the definitions of "Qualified" differ between teams? [AI WILL ANALYSE]
• Rejection Pattern: Why is Sales actually disqualifying leads? (Timing vs. Persona vs. Intent) [AI WILL IDENTIFY]
• Feedback Loop: Is there a formal mechanism for Sales to communicate what's working to Marketing? [AI WILL EVALUATE]

3. GAP ANALYSIS:

• Expectation vs. Reality:
  • Marketing's Expected Lead Quality: [FROM CONTEXT]
  • Sales' Perceived Lead Quality: [AI ANALYSES FROM FEEDBACK]
  • Gap: [AI ANALYSES MISALIGNMENT]

4. SPECIFIC QUESTIONS TO ANSWER:

• What specific lead source is producing the "noisiest" (lowest-quality) leads?
• What is the #1 reason for lead disqualification that can be addressed via marketing filters?
• Is the issue "Lead Quality" (Marketing) or "Lead Follow-up" (Sales)?

5.ANALYSIS PRINCIPLES:

• Focus on revenue impact, not blame assignment.
• Look for "Process Gaps" (missing steps) vs "Skill Gaps" (people issues).
• Prioritise high-intent leads (Demo requests) over low-intent (Ebook downloads).

6. OUTPUT FORMAT:
Based on the analysis, provide:
Executive Summary (1 page)

• Alignment Health Score (1–10)
• Top 3 friction points causing revenue loss
• Immediate improvements for both teams

The Alignment Action Plan

1. Phase 1: The Unified Definition: A proposed "Universal Lead Definition" (ULD).
2. Phase 2: The Service Level Agreement (SLA): Recommended response times and outreach volume.
3. Phase 3: The Feedback Loop: How to systematise lead feedback from Sales to Marketing.

DELIVERABLE: A Sales-Marketing SLA and Alignment Report that can be presented to the VP of Sales and CMO.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Before using the prompt, gather transcripts from 5 "Won" calls and 5 "Lost" calls. Specifically, focus on the sections where the customer discusses:

1. Price/Budget
2. Competitors
3. Features/Technical Gaps
4. Timing/Priority

THE PROMPT:
You are a Sales Coach and Enablement Expert conducting Objection Pattern Mining for [COMPANY NAME]. We need to move beyond surface-level objections and identify the deeper reasons prospects are hesitant.

CONTEXT:

• Our Product: [brief description]
• Top Competitor: [Who we lose to most]
• Typical Price: [e.g., $10k/year]
• Current Top Objection: [What we think it is]

YOUR TASK:
Analyse the provided call transcripts to identify underlying objections and create a high-impact Objection Handling Framework.

1. CALL DATA COLLECTION:
Paste your transcripts or detailed notes below:

WON CALLS (Success Patterns):

• Call #1: [PASTE TRANSCRIPT SNIPPET]
• Call #2: [PASTE TRANSCRIPT SNIPPET]

LOST CALLS (Objection Patterns):

• Call #1: [PASTE TRANSCRIPT SNIPPET]
• Call #2: [PASTE TRANSCRIPT SNIPPET]

2. PATTERN ANALYSIS:

• Implicit vs. Explicit Objections: What are they saying vs. what do they actually mean? [AI WILL ANALYSE]
• The Critical Objection: Which objection consistently leads to a deal stalling or being lost? [AI WILL IDENTIFY]
• The Pivot Point: What did the rep say in Won deals that successfully changed the prospect's mind? [AI WILL EXTRACT]

3. SPECIFIC QUESTIONS TO ANSWER:

• Are we losing on "Value" (ROI) or "Cost" (Budget)?
• Which competitor is mentioned most, and in what context?
• At what point in the call do most objections occur?

4. ANALYSIS PRINCIPLES:

• Distinguish between a "Question" (Curiosity) and an "Objection" (Barrier).
• Identify "Soft Declines" (e.g., "Let me think about it") vs. "Hard Declines" (e.g., "We don't have the budget").

5. OUTPUT FORMAT:
Based on the analysis, provide:
Objection Handling Framework

• List of 3-5 Key Objections:
  • The Root Cause: (Why they are asking this)
  • What NOT to say: (Common mistakes)
  • Winning Response: (A 1–2 sentence script)
  • Value Pivot: (How to steer back to our strengths)

Sales Coaching Recommendations

• Top 3 skills the team needs to develop (e.g., Active Listening, Competitive Positioning).
• Recommended "Discovery Questions" to uncover these objections earlier in the cycle.

DELIVERABLE: A Sales Framework that reps can reference to handle the most common barriers to closing.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Export a report from your CRM showing your "Stage Duration." You need the average number of days deals spend in:

1. Discovery
2. Demo/Evaluation
3. Proposal/Contracting
4. Legal/Security

Also, note the "Fall-off Rate" (Where do most deals die?).

THE PROMPT:
You are a Sales Operations and Enablement Strategist for [COMPANY NAME]. Our pipeline is full, but our "Days to Close" is increasing. We need to identify the bottlenecks slowing deal progression.

CONTEXT:

• Average Sales Cycle: [e.g., 120 days]
• Target Sales Cycle: [e.g., 90 days]
• Average Deal Size: [e.g., $25,000]
• Sales Stages: [List your stages from first meeting to close]

YOUR TASK:
Analyse the pipeline metrics provided below to identify where friction exists and how to reduce it.

1. PIPELINE METRICS COLLECTION:
Paste your data below:

STAGE PERFORMANCE:

• Discovery Stage: [Average Days] | [Conversion Rate to next stage %]
• Demo/Eval Stage: [Average Days] | [Conversion Rate to next stage %]
• Proposal/Negotiation Stage: [Average Days] | [Conversion Rate to next stage %]
• Legal/Closing Stage: [Average Days] | [Conversion Rate %]

STALLED DEAL EXAMPLES:

• Deal A: [Stuck in Stage X for 45 days] | [Reason given by rep]
• Deal B: [Stuck in Stage X for 60 days] | [Reason given by rep]

2. BOTTLENECK ANALYSIS:

• Conversion Bottleneck: Which stage has the lowest conversion rate? [AI WILL IDENTIFY]
• Duration Bottleneck: Which stage has the highest increase in "Days in Stage" compared to last year? [AI WILL ANALYSE]
• External vs. Internal Friction: Is the delay caused by our internal process (Legal/Quotes) or the buyer's process? [AI WILL DETERMINE]

3. SPECIFIC QUESTIONS TO ANSWER:

• How many touches are required in the slowest stage to move a deal?
• If we reduced the Proposal stage by 5 days, what is the projected revenue impact?
• Is there a specific Persona that causes the most delays (e.g., Procurement vs. End User)?

4. ANALYSIS PRINCIPLES:

• Focus on velocity (Speed and Quality).
• Look for manual steps that can be automated or supported with enablement content.
• Prioritise the bottleneck that impacts the highest volume of deals.

5. OUTPUT FORMAT:
Based on the analysis, provide:
Deal Acceleration Roadmap

• Immediate Process Improvements: Changes to internal processes to reduce cycle time (e.g., Automated quote generation).
• Enablement Assets needed: (e.g., "We need a Security FAQ to reduce the Legal stage by 10 days").
• Early Warning System: A guide for reps to identify slow-moving deals before they stall.

Recommended Process Changes

• Specific suggestions for Stage-Gate criteria (what must happen to move a deal forward).

DELIVERABLE: A Deal Acceleration Plan that the Sales and Ops teams can implement to shorten the sales cycle.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
To get the best result, create a simple spreadsheet with these columns and copy-paste it into the "Data Collection" section below.

1. Tool Name: (e.g., Slack, Zoom, LinkedIn Sales Navigator)
2. Monthly/Annual Cost: (What you actually pay)
3. Total Seats Paid: (How many licenses you bought)
4. Active Seats: (How many people logged in during the last 30 days)
5. Primary Purpose: (e.g., "Internal communication" or "Lead sourcing")
6. User Sentiment: (A quick 1-5 star rating from the team)

THE PROMPT:
You are a RevOps analyst conducting a Tech Stack ROI Audit for [COMPANY NAME], a [B2B SaaS/SERVICE] company. We are looking to optimise our software budget by identifying tools that are underused, redundant, or overpriced.

CONTEXT:

• Our Total Software Budget: [e.g., $10,000/month]
• Total Number of Tools: [e.g., 15 tools]
• Primary Goal: [e.g., Reduce spend by 20% or consolidate tools for a cleaner workflow]
• Current Team Size: [e.g., 50 people]

YOUR TASK:
Use the provided tech stack inventory and team feedback to create a prioritised Software Optimisation Action Plan.

1. STACK DATA COLLECTION:
Paste your inventory or audit notes below:

SOFTWARE INVENTORY (Sample Data):
[PASTE YOUR SPREADSHEET DATA HERE - Include Tool Name, Cost, Seats, Usage, and Purpose]

OVERLAP & REDUNDANCY NOTES:

• Tool A vs. Tool B: [e.g., We use both Zoom and Microsoft Teams for video calls]
• Unused Tools: [e.g., We are still paying for a tool that the previous VP of Sales bought but no one uses]
• Contract Status: [e.g., Which tools are up for renewal in the next 60 days?]

TEAM FEEDBACK:
Sales Team:
Open-ended feedback:

• The Must-Haves: [e.g., "We would be unable to function without Slack and our CRM"]
• The Pain Points": [e.g., "Tool X is too complex to use, so we just use Excel instead"]
• Feature Requests: [e.g., "I wish our CRM had built-in calling so we could cancel our separate phone tool"]

Marketing Team: [Same questions]
CS Team: [Same questions]

2. PATTERN & VALUE ANALYSIS:
Based on the data provided, analyse the following:

Utilisation Patterns:

• The Shelfware Index: Which tools have the highest gap between "Paid Seats" and "Active Seats"? [AI WILL IDENTIFY]
• Cost Per Active User: Which tools are costing the most per person actually using them? [AI WILL CALCULATE]

Redundancy Identification:

• Feature Overlap: Where are we paying for two tools that do the same thing? [AI WILL HIGHLIGHT]
• Consolidation Opportunities: Can one existing tool (e.g., HubSpot) replace three smaller tools (e.g., Mailchimp, Calendly, Typeform)? [AI WILL SUGGEST]

Sentiment vs. Spend:

• High Cost / Low Value: Tools that cost significantly but have poor user ratings. [AI WILL IDENTIFY]
• Low Cost / High Value: The high-impact tools we should invest more in. [AI WILL IDENTIFY]

3. ANALYSIS PRINCIPLES:

• Efficiency First: If a tool isn't being used, it doesn't matter how "good" it is, recommend cutting it.
• User Adoption Matters: Favor tools the team actually uses and values, even if they are slightly more expensive.
• Integration over Isolation: Prioritise tools that integrate natively with existing systems.

4. OUTPUT FORMAT (ACTION PLAN):
Based on the analysis, provide a structured Tech Stack Optimisation Plan:

Executive Summary:

• Total Potential Savings: (Estimated $ amount per year)
• Stack Complexity Score: (1–10, how bloated is the stack?)
• Top 3 Immediate Actions: (The biggest wins for the budget)

The "Keep, Cut, Consolidate" Roadmap:

1. THE RETIREMENT LIST (Immediate Savings): Tools with <20% usage or zero ROI.
2. THE CONSOLIDATE LIST (Workflow Optimisation): Tools to merge into larger platforms.
3. THE NEGOTIATE LIST (Seat Adjustments): Tools where we are paying for 50 seats but only using 30.

Renewal Calendar Strategy:

• Prioritised list of which contracts to renegotiate or cancel based on upcoming dates.

DELIVERABLE: A Tech Stack ROI Audit that the Finance or Operations team can use to make immediate budget cuts and tool transitions.

Recommended Output Length: 3,500–5,000 characters

HOW TO PREPARE YOUR DATA (FOR BEGINNERS)
Before using the prompt, create a view or report in your CRM called "Hygiene Audit View." Export the following columns for your 100-500 most recent records into a spreadsheet to copy-paste into the prompt:

1. Unique ID (Record ID)
2. Email Address (To check for formatting/bounces)
3. Company Name (To check for duplicates/naming conventions)
4. Job Title (To check for standardisation)
5. Industry (To check for data completeness)
6. Create Date vs. Last Activity Date (To check for "stale" records)
7. Lead Status/Deal Stage (To check for process alignment)

THE PROMPT:
You are a RevOps Specialist conducting a CRM Data Hygiene Audit for [COMPANY NAME], a [B2B SaaS/SERVICE] company. Our objective is to clean the database to improve sales outreach efficiency and reporting accuracy.

CONTEXT:

• Our CRM: [e.g., HubSpot / Salesforce]
• Total Database Size: [e.g., 10,000 Contacts]
• Primary Issue: [e.g., "Sales team reports outdated data" or "High duplicate volume"]
• Target Data Standard: [e.g., "All Job Titles standardised; no missing Industries"]

YOUR TASK:
Build a comprehensive CRM Audit and Remediation Plan using the sample data and system notes provided below.

1. SYSTEM DATA COLLECTION:
Paste your exported data below (100-500 records):

CONTACT & ACCOUNT DATA
[PASTE SPREADSHEET DATA HERE - Include Email, Company, Job Title, Industry, and Last Activity]
PROCESS & USER NOTES:

• Naming Conventions: [e.g., All-caps names? "Google" vs. "Google Inc." duplicates?]
• Sales Feedback: [e.g., "Calling contacts who left companies" or "Unclear which Job Title field to use"]
• Duplicate Examples: [e.g., "3 records for same person with different emails"]

2. PATTERN ANALYSIS:
Based on the data provided, analyse and identify the following hygiene patterns:

• Data Decay Patterns: What percentage of data is stale (no activity in 6+ months)? [AI WILL ANALYSE]
• Formatting Issues: Where does lack of standardisation create the biggest friction? (e.g., Job Titles, Phone formats) [AI WILL IDENTIFY]
• Missing Information: Which critical fields are most frequently empty? [AI WILL ANALYSE]
• Duplicate Root Causes:Is the mess from forms, manual entry, or integrations? [AI WILL DETERMINE]

3. GAP ANALYSIS:
Analyse the gaps between our target data standard and current state:

• Reporting Reality Gap:
  • What we need to report on: [e.g., Revenue by Industry]
  • What the data supports: [AI ANALYSES IF FIELDS ARE TOO INCOMPLETE TO REPORT]
• Sales Productivity Gap:
  • How sales should operate: [e.g., Fast outreach to new leads]
  • How they're blocked: [AI ANALYSES TIME LOST TO BAD EMAILS/DUPLICATES]

4. OUTPUT FORMAT:
Based on the analysis above, provide:
Executive Summary (1 page)

• Overall System Health Score (A-F)
• Top 3 data issues impacting revenue
• Immediate Quick Wins (cleanup tasks requiring < 1 hour)

Remediation Action Plan (Prioritised)

1. Phase 1: The Cleanup (High Impact/Low Effort)
   • Deduplication logic (which rules to set)
   • Bulk-update recommendations (e.g., "Standardise all 'VP' to 'Vice President'")
2. Phase 2: The Guardrails (System Changes)
   • Which fields should be required?
   • Recommendations for "Drop-down" menus to replace "Free Text" fields.
3. Phase 3: The Maintenance (Ongoing)
   • Weekly/Monthly hygiene checklist for the team.

SPECIFIC QUESTIONS TO ANSWER:

• What is the estimated duplicate rate based on the sample?
• Which fields are bloat (present but unused) and should be hidden?
• What specific validation rules should we implement to prevent bad data entry?

ANALYSIS PRINCIPLES:

• Prioritise revenue-generating data (Emails, Titles) over nice-to-have data (Social profiles)
• Identify human error vs. system error (e.g., poor form configuration vs. inconsistent sales entry)
• Flag data that may violate privacy laws (GDPR/CCPA) like missing opt-in dates.

DELIVERABLE: A technical but actionable CRM Audit Report that an admin can use to start cleaning the system immediately.
Recommended Output Length: 3,500–5,000 characters