Prompt Library

ChatGPT Prompts for Marketing Strategy

30 copy-paste prompts

Thirty structured prompts to turn ChatGPT into a strategy partner — sharpen positioning, define your ICP, build messaging, pick channels, plan your GTM, and set marketing OKRs and budgets.

In short: This page contains 30 copy-paste ready prompts, organized into 6 categories with a description and pro tip for each. The first 15 prompts are free instantly — no signup needed. Hand-curated and tested by the AI Academy team.

By Louis Corneloup · Founder, Techpresso
Last updated ·Hand-curated & tested by the AI Academy team

Positioning & Messaging

5 prompts

Positioning Statement Builder

1/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Target customer: [TARGET CUSTOMER] Primary alternative customers use today: [STATUS QUO / COMPETITOR] Key differentiated capability: [DIFFERENTIATOR] </context> <task> 1. Write a positioning statement using the format: For [target] who [need], [PRODUCT] is the [category] that [key benefit], unlike [alternative], because [reason to believe]. 2. Produce three alternative versions, each leaning on a different competitive frame (category creation, category leadership, anti-incumbent). 3. For each version, list the assumption it depends on and one piece of evidence we would need to defend it. 4. Flag any word that overclaims or is unverifiable. 5. Recommend the strongest version with a one-paragraph rationale. </task>

A defensible positioning statement plus three framed alternatives with assumptions and a recommendation.

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Pro tip: Paste your homepage hero copy into the context so ChatGPT can contrast your stated positioning with your actual positioning.

Message House Architecture

2/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Top three customer pains: [PAIN 1], [PAIN 2], [PAIN 3] </context> <task> 1. Build a message house: one overarching value proposition (the roof), three supporting pillars (the walls), and proof points under each (the foundation). 2. For each pillar, write a one-sentence claim and two proof points (feature, outcome, or stat placeholder). 3. Map each pillar to which of the three customer pains it resolves. 4. Write a single boilerplate paragraph that compresses the whole house into 50 words. 5. Note any pillar that lacks a credible proof point so we can prioritize gathering evidence. </task>

A complete message house with value prop, pillars, proof points, and a 50-word boilerplate.

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Pro tip: Ask ChatGPT to rewrite the roof statement in the voice of a skeptical buyer to stress-test whether it sounds like marketing fluff.

Value Proposition by Segment

3/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Segments to address: [SEGMENT A], [SEGMENT B], [SEGMENT C] </context> <task> 1. For each segment, write a tailored value proposition (one sentence) that names the segment's specific job-to-be-done. 2. List the top benefit, the main objection, and the proof that overcomes it per segment. 3. Identify where the segment value props conflict and recommend how to sequence them without diluting the master positioning. 4. Suggest one headline per segment suitable for a landing page. 5. Rank the segments by likely conversion ease and explain the ranking. </task>

Segment-specific value propositions, objections, proof, and landing-page headlines.

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Pro tip: Feed ChatGPT real quotes from sales calls per segment so the value props use the customer's own language.

Category & Frame Decision

4/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Categories we could claim: [CATEGORY 1], [CATEGORY 2], [CATEGORY 3] </context> <task> 1. Evaluate each candidate category on three axes: demand (do buyers search for it), competition (how crowded), and fit (does [PRODUCT] credibly own it). 2. Score each axis 1-5 and total the scores in a table. 3. Recommend whether to compete in an existing category, reframe an adjacent one, or create a new one. 4. Describe the messaging implications of the recommended choice. 5. List the risks of the chosen frame and an early signal that would tell us it is not working. </task>

A scored category decision with a recommended frame and risk signals.

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Pro tip: Ask ChatGPT to estimate search demand qualitatively, then verify the real numbers in a keyword tool before committing.

Tagline & Elevator Pitch Set

5/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Positioning statement: [POSITIONING] </context> <task> 1. Generate ten taglines ranging from descriptive to evocative, each under seven words. 2. Write three elevator pitches: a 10-second version, a 30-second version, and a 60-second version. 3. Ensure every option is consistent with the positioning statement and avoids generic claims like best or leading. 4. For the top three taglines, note the emotion or belief each one targets. 5. Recommend one tagline and one pitch length for cold outbound and explain why. </task>

Ten taglines and three elevator pitches at increasing lengths, all positioning-aligned.

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Pro tip: Tell ChatGPT to reject any tagline that could equally apply to a competitor — specificity is the test.

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Audience & Personas

5 prompts

Ideal Customer Profile Definition

6/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Current best customers: [3-5 EXAMPLE CUSTOMERS OR TRAITS] </context> <task> 1. Define the ICP across firmographic, demographic, and behavioral dimensions in a table. 2. List five qualifying signals that indicate a strong fit and five disqualifying signals that indicate a poor fit. 3. Estimate the relative size of this ICP versus the broader market (qualitatively) and note the risk of niching too narrowly. 4. Identify the trigger event that typically makes this ICP start looking for a solution. 5. Recommend two adjacent ICP segments worth testing next. </task>

A structured ICP with qualifying and disqualifying signals, trigger events, and adjacent segments.

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Pro tip: Paste a list of your top 20 accounts and ask ChatGPT to reverse-engineer the common traits into the ICP table.

Buyer Persona Deep Dive

7/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Persona role: [JOB TITLE / ROLE] </context> <task> 1. Build a persona profile covering goals, daily frustrations, success metrics they are measured on, and where they spend professional attention. 2. List their top three objections to a product like [PRODUCT] and the reframe for each. 3. Map their information diet: which channels, communities, and influencers they trust. 4. Write a one-line emotional summary of what this persona truly wants beneath the functional need. 5. Suggest the single message most likely to make this persona stop scrolling. </task>

A rich buyer persona including goals, objections, information diet, and a hook message.

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Pro tip: Ask ChatGPT to write a day-in-the-life narrative for the persona to surface friction points you can target in copy.

Jobs-To-Be-Done Mapping

8/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Primary persona: [PERSONA] </context> <task> 1. Identify the functional, emotional, and social jobs the persona is trying to get done. 2. For each job, write a JTBD statement in the format: When [situation], I want to [motivation], so I can [expected outcome]. 3. Rank the jobs by how poorly current alternatives serve them. 4. Map each high-opportunity job to a [PRODUCT] capability that addresses it. 5. Recommend which job should anchor the core marketing narrative and why. </task>

A jobs-to-be-done map with ranked opportunities and the anchor job for messaging.

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Pro tip: Have ChatGPT separate the job from your product so you market the outcome, not the feature.

Anti-Persona & Disqualification

9/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Common bad-fit leads we attract: [DESCRIPTION] </context> <task> 1. Define two anti-personas: who should NOT buy [PRODUCT] and why. 2. List the surface signals that make these look like good prospects but turn out to churn or complain. 3. Suggest copy and qualification questions that gently filter them out before they enter the pipeline. 4. Estimate the cost of serving anti-personas (support load, churn, reviews). 5. Recommend where in the funnel to place the disqualification without harming top-of-funnel volume. </task>

Two anti-personas with filtering copy and funnel placement to reduce bad-fit leads.

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Pro tip: Ask ChatGPT to turn each anti-persona into a polite headline that repels them while reassuring your real ICP.

Buying Committee & Influence Map

10/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Deal type: [SELF-SERVE / SALES-LED] </context> <task> 1. Map the typical buying committee: economic buyer, champion, end user, and blockers. 2. For each role, state their primary concern, what convinces them, and what kills the deal. 3. Identify which role marketing should target first to create internal momentum. 4. Recommend one content asset per role that moves them forward. 5. Note the likely sequence in which these roles get involved. </task>

A buying-committee map with per-role concerns, convincers, and recommended content.

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Pro tip: For self-serve products, ask ChatGPT to collapse the committee into the one person who can swipe a card.

Channel Strategy

5 prompts

Channel Prioritization Matrix

11/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Budget level: [BUDGET] Team size: [TEAM] </context> <task> 1. List eight plausible acquisition channels for [PRODUCT]. 2. Score each on a 1-5 scale for reach, cost efficiency, intent quality, and time-to-results in a table. 3. Weight the scores toward our stated budget and team constraints and produce a ranked shortlist of three. 4. For the top channel, outline the first 30-day experiment to validate it. 5. Flag any channel that looks attractive but is a poor fit for our ICP and explain why. </task>

A weighted channel matrix with a top-three shortlist and a first experiment.

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Pro tip: Ask ChatGPT to apply the bullseye framework — many channels, then a focused few — so you avoid spreading thin.

Channel-Message Fit Check

12/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Channels in use: [CHANNEL 1], [CHANNEL 2], [CHANNEL 3] </context> <task> 1. For each channel, describe the audience mindset and the format that performs there. 2. Rewrite our core message to fit each channel's native style and length. 3. Identify any channel where the message and audience intent clash, and recommend a fix. 4. Suggest the single metric that best signals message-fit per channel. 5. Recommend one channel to double down on and one to pause. </task>

Channel-native message variants plus a double-down and pause recommendation.

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Pro tip: Give ChatGPT a top-performing post per channel so it can match the voice rather than guess.

Content-Led Growth Plan

13/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Expertise we can credibly publish on: [TOPICS] </context> <task> 1. Define three content pillars tied to buyer search intent and our expertise. 2. For each pillar, list five article or asset ideas mapped to funnel stage (TOFU, MOFU, BOFU). 3. Recommend a publishing cadence realistic for our team size. 4. Identify the one BOFU asset most likely to drive conversions and why. 5. Suggest how to repurpose each pillar across at least two distribution channels. </task>

A content strategy with pillars, funnel-mapped ideas, cadence, and repurposing.

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Pro tip: Ask ChatGPT to label which ideas are likely to rank versus likely to convert — they are rarely the same piece.

Paid Acquisition Allocation

14/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Monthly paid budget: [BUDGET] Target CAC: [TARGET CAC] </context> <task> 1. Recommend a starting budget split across the most plausible paid channels for our ICP. 2. For each channel, state the expected role (demand capture vs demand generation) and a rough CAC expectation. 3. Define a testing structure: which variables to hold constant and which to vary. 4. Set the kill criteria and the scale criteria for each channel. 5. Outline how to reallocate budget after the first 30 days based on results. </task>

A paid budget split with channel roles, test structure, and kill/scale criteria.

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Pro tip: Tell ChatGPT to reserve 20 percent of budget for experiments so winning channels never starve the unknowns.

Owned Audience Strategy

15/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Existing owned assets: [EMAIL LIST / COMMUNITY / FOLLOWING] </context> <task> 1. Recommend which owned channel (newsletter, community, etc.) to invest in given our assets and ICP. 2. Define the value exchange: why the audience subscribes and stays. 3. Outline a 90-day plan to grow the owned audience without paid spend. 4. Suggest how the owned audience feeds the rest of the funnel. 5. Define the leading metric of owned-audience health and a warning sign of decline. </task>

An owned-audience plan with value exchange, 90-day growth, and health metrics.

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Pro tip: Ask ChatGPT how to convert one-time campaign traffic into a recurring owned audience you do not have to re-buy.

Go-To-Market

5 prompts

GTM Motion Selection

16/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Price point: [PRICE] Sales complexity: [LOW / MEDIUM / HIGH] </context> <task> 1. Compare three GTM motions for [PRODUCT]: product-led, sales-led, and community/marketing-led. 2. Score each against our price point, sales complexity, and ICP buying behavior in a table. 3. Recommend the primary motion and the secondary motion that supports it. 4. List the three capabilities we must build to run the chosen motion well. 5. Identify the biggest risk of the recommended motion and an early indicator it is failing. </task>

A scored GTM motion recommendation with required capabilities and risks.

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Pro tip: Ask ChatGPT to map your actual price-to-touch ratio — high price with low touch usually signals a missing motion.

Launch Plan Sequencer

17/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] What is launching: [FEATURE / PRODUCT] Launch date: [DATE] </context> <task> 1. Build a phased launch plan: pre-launch, launch week, and post-launch with goals for each phase. 2. For each phase, list the channels, assets, and owners needed. 3. Define the single primary success metric and two supporting metrics. 4. Identify dependencies and the riskiest item on the critical path. 5. Recommend one tactic to extend launch momentum beyond week one. </task>

A phased launch plan with channels, owners, metrics, and momentum tactics.

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Pro tip: Have ChatGPT build the plan backward from launch day so deadlines and dependencies surface naturally.

Beachhead Market Selection

18/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Candidate beachhead segments: [SEGMENT 1], [SEGMENT 2], [SEGMENT 3] </context> <task> 1. Evaluate each candidate beachhead on urgency of need, ease of reach, willingness to pay, and reference value. 2. Score and rank them in a table. 3. Recommend the single beachhead to win first and explain the wedge strategy into it. 4. Describe how winning this beachhead opens the adjacent market. 5. List the proof points we would need from the beachhead to expand credibly. </task>

A ranked beachhead selection with a wedge strategy and expansion path.

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Pro tip: Ask ChatGPT to choose the segment that is small enough to dominate but connected enough to expand from.

Pricing & Packaging Narrative

19/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Current tiers: [TIER DETAILS] </context> <task> 1. Review whether the current packaging aligns with how the ICP perceives value. 2. Recommend a value metric that scales with customer success. 3. Suggest a tier structure (names, who each is for, the upgrade trigger between them). 4. Write the one-line story that justifies the jump to the highest tier. 5. Flag any tier likely to cause decision paralysis or cannibalization. </task>

A packaging review with value metric, tier structure, and upgrade narrative.

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Pro tip: Ask ChatGPT to name each tier after the customer outcome rather than feature counts to make value obvious.

90-Day GTM Roadmap

20/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Primary GTM goal: [GOAL] </context> <task> 1. Build a 90-day GTM roadmap broken into three 30-day sprints, each with a single theme. 2. For each sprint, list the top three initiatives and the outcome that defines success. 3. Identify the dependencies between sprints. 4. Recommend what to deliberately NOT do in the first 90 days. 5. Define the one metric that proves the GTM is working by day 90. </task>

A themed 90-day GTM roadmap with sprint outcomes, dependencies, and a north-star metric.

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Pro tip: Ask ChatGPT to defend the not-doing list — saying no to good ideas is what makes a 90-day plan achievable.

Competitive Analysis

5 prompts

Competitor Teardown

21/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Competitor: [COMPETITOR] </context> <task> 1. Summarize the competitor's positioning, target customer, and pricing model from the information provided. 2. List their three clearest strengths and three exploitable weaknesses. 3. Identify the message they own in the market and the gap they leave open. 4. Recommend how [PRODUCT] should position against them without copying. 5. Note where direct comparison helps us and where it legitimizes them. </task>

A competitor teardown with strengths, weaknesses, owned message, and counter-positioning.

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Pro tip: Paste the competitor's homepage and pricing page text so ChatGPT analyzes their real claims, not its training data.

Competitive Battlecard

22/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Key competitor: [COMPETITOR] </context> <task> 1. Build a sales battlecard: where we win, where we lose, and the truthful framing for each. 2. List the three objections prospects raise that favor the competitor and the rebuttal for each. 3. Provide three trap-setting questions that expose the competitor's weaknesses. 4. Write the one-sentence why-us summary a rep can say on a call. 5. Note any claim we should avoid making because it is easy to disprove. </task>

A sales battlecard with win/loss framing, objection rebuttals, and trap questions.

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Pro tip: Ask ChatGPT to keep rebuttals honest — battlecards that overclaim erode rep trust and lose deals.

Market Gap Finder

23/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Main competitors: [COMPETITOR 1], [COMPETITOR 2], [COMPETITOR 3] </context> <task> 1. Map what each competitor emphasizes in their messaging. 2. Identify the customer needs that NO competitor is clearly addressing. 3. Assess whether each gap is unserved because it is hard, unprofitable, or simply overlooked. 4. Recommend the gap [PRODUCT] is best positioned to own. 5. Draft the positioning angle that claims this gap. </task>

A messaging-gap analysis with the best gap to own and a positioning angle.

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Pro tip: Ask ChatGPT to distinguish a true gap from a graveyard — some gaps are empty because customers do not care.

Win/Loss Insight Synthesizer

24/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Win/loss notes: [PASTE NOTES OR THEMES] </context> <task> 1. Cluster the win/loss notes into recurring themes. 2. Separate reasons we control (messaging, product, price) from reasons we do not (timing, budget). 3. Rank the controllable loss reasons by frequency and impact. 4. Recommend one positioning or messaging change per top loss reason. 5. Identify the strongest recurring win reason we should amplify in marketing. </task>

Synthesized win/loss themes with controllable fixes and a win reason to amplify.

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Pro tip: Even without formal notes, paste a few sales call summaries and ask ChatGPT to infer the patterns.

Competitive Response Playbook

25/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Competitor move: [LAUNCH / PRICE CUT / REPOSITIONING] </context> <task> 1. Assess how threatening the competitor move actually is to our ICP and revenue. 2. Recommend whether to respond directly, reframe, or ignore — with reasoning. 3. If responding, draft the core counter-message that stays on our strengths. 4. List the internal stakeholders to align and the customer-facing channels to update. 5. Define a timeframe and a signal that tells us the response worked or failed. </task>

A competitive response playbook with a respond/reframe/ignore decision and counter-message.

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Pro tip: Ask ChatGPT to argue the case for ignoring first — most competitor moves do not deserve a public reaction.

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Goals & Budget

5 prompts

Marketing OKR Builder

26/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Company goal this quarter: [COMPANY GOAL] </context> <task> 1. Translate the company goal into one marketing objective and three measurable key results. 2. Ensure each key result is an outcome (pipeline, revenue, activation) rather than an activity (posts published). 3. For each key result, set a baseline placeholder and a target, and name the owner. 4. Identify the leading indicator that predicts each key result early. 5. Flag any key result marketing cannot influence alone and note the cross-team dependency. </task>

A marketing OKR set with outcome-based key results, leading indicators, and dependencies.

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Pro tip: Ask ChatGPT to reject any key result that measures effort instead of impact — that is the most common OKR failure.

Budget Allocation Model

27/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Total marketing budget: [BUDGET] Primary goal: [GOAL] </context> <task> 1. Recommend a budget split across channels, content, tools, and experiments aligned to the primary goal. 2. Express each allocation as a percentage and a dollar placeholder. 3. Separate proven spend from experimental spend and recommend a ratio. 4. Identify the line item most likely to be over- or under-funded today. 5. Define the trigger that would justify shifting budget mid-quarter. </task>

A goal-aligned budget split with proven/experimental ratio and reallocation triggers.

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Pro tip: Have ChatGPT pressure-test the split against your goal — a brand goal and a pipeline goal demand very different allocations.

Funnel Metrics & Targets

28/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Known conversion rates: [ANY KNOWN RATES] </context> <task> 1. Map the full marketing funnel from awareness to revenue with the metric at each stage. 2. Using any known rates, model how many top-of-funnel inputs are needed to hit a [REVENUE TARGET]. 3. Identify the stage with the biggest leak and the likely cause. 4. Recommend the single metric to optimize first for the largest impact. 5. Build a simple weekly dashboard list of the five numbers to watch. </task>

A funnel model with input math, the biggest leak, and a five-metric weekly dashboard.

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Pro tip: Give ChatGPT your real conversion rates so the funnel math reflects your business, not industry averages.

CAC, LTV & Payback Analysis

29/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Known unit economics: [CAC / ARPU / CHURN IF KNOWN] </context> <task> 1. Calculate or estimate CAC, LTV, the LTV:CAC ratio, and payback period using the inputs provided. 2. Interpret what the ratios say about how aggressively we can spend on growth. 3. Identify the single lever (CAC, conversion, retention, price) with the highest impact on the model. 4. Recommend a healthy spend ceiling per channel given the payback period. 5. Flag any assumption that, if wrong, would break the model. </task>

A unit-economics analysis with LTV:CAC, payback, the top lever, and a spend ceiling.

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Pro tip: Ask ChatGPT to show the math step by step so you can swap in real numbers and recompute instantly.

Quarterly Strategy Review

30/30

<context> Brand: [BRAND] Product: [PRODUCT] Market: [MARKET] Last quarter results vs targets: [RESULTS] </context> <task> 1. Summarize what worked, what underperformed, and what was inconclusive last quarter. 2. Separate outcomes caused by execution from those caused by strategy. 3. Recommend what to double down on, what to cut, and what to test next quarter. 4. Update the marketing OKRs for the coming quarter based on the learnings. 5. Write a three-sentence executive summary leadership can read in 20 seconds. </task>

A quarterly review separating execution from strategy with next-quarter decisions and an exec summary.

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Pro tip: Ask ChatGPT to be ruthless about cuts — a review that adds initiatives without removing any quietly overloads the team.

Frequently Asked Questions

They are structured instructions that turn ChatGPT into a strategic thinking partner for the high-level decisions behind a marketing plan — positioning, ICP and personas, messaging, channel mix, go-to-market motion, competitive analysis, and OKRs and budget. Instead of asking ChatGPT to write a single post, you ask it to help you decide what to say, to whom, where, and why, then pressure-test those choices before you commit budget and time.
Each prompt has a context block and a task block. Replace the bracketed placeholders like [BRAND], [PRODUCT], and [MARKET] with your real details, and paste in supporting evidence — homepage copy, sales-call quotes, competitor pages, and known conversion rates. The richer the context, the less ChatGPT guesses. Run the prompt, then iterate: ask it to defend its recommendation, argue the opposite, or rewrite for a skeptical buyer.
No. ChatGPT accelerates the thinking — it drafts frameworks, surfaces gaps, and forces structure on fuzzy decisions in minutes instead of days. But it does not know your real numbers, your customers' unspoken context, or your market timing unless you tell it, and it can confidently state things that are wrong. Treat its output as a sharp first draft that a human validates against real data and judgment.
Generic output is almost always a context problem. Feed it specifics: real customer quotes, actual competitor copy, your true unit economics, and concrete constraints like budget and team size. Then ask it to reject any claim that could apply equally to a competitor, and to flag assumptions it cannot verify. Specificity in equals specificity out.
Yes, every prompt on this page is free to copy and use in ChatGPT or any capable AI assistant. They work on free ChatGPT models, though stronger reasoning models handle the multi-step analytical tasks more reliably. For deeper, structured training on AI-assisted marketing, explore the related AI for Marketers track and the full prompt collections linked below.

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