A Marketing Strategy With ChatGPT in 20 Minutes: Real Prompts and Common Mistakes

Marketers who build strategies with ChatGPT in 20 minutes and those who get 15 pages of fluff are doing the same thing, just in completely different ways. The difference lies in how they structure their requests, and in understanding what AI does well and what still needs human judgment.

Why ChatGPT produces platitudes

The problem is not the neural network but the request. When a marketer writes "write a marketing strategy for a SaaS company," the model knows nothing about the audience, the competitors, or the product's current position in the market. In response, it produces a generic structure: a nicely formatted set of points from any textbook. AI works as an intelligent assistant, not as a strategist with access to your CRM.

To get a useful result, you first have to give it context: a description of the typical customer, examples of real objections raised in demos, and the main differences from competitors. This is called contextual prompting, and it is exactly the step where most marketers stop too early.

"The neural network doesn't know your business.

Your job is to explain it the way you would explain it to a new marketer on their first day."

A working workflow: a chain instead of one request

Instead of one big request, a chain of short ones works better. Each step builds on the result of the previous one and refines the context for the next. A sample structure for a SaaS product:

• Product and audience: "Here is our product, here is our ICP, here are the three main customer pain points, what we hear on every demo"
• Competitors: "Here are three key competitors and their positioning. How do we differ from them?"
• Positioning: "Suggest 3 positioning options, from conservative to aggressive"
• Channels: "Which channels work for B2B SaaS with this ICP? Explain the logic behind each choice"
• Critique: "What looks weak in this strategy? Where might we be wrong?"

The last step is often skipped, and that is a mistake. The model is good at finding holes in its own reasoning if you explicitly ask it to play the skeptic. It is one of the most underrated techniques for using ChatGPT on strategic tasks.

Where AI still falls short

The honest answer: wherever the task requires data the model does not have. Conversion forecasts, specific market figures, how an audience will react to a new offer: AI does not calculate these, it guesses based on general patterns. If you take those numbers as fact, the strategy ends up resting on attractive assumptions.

The second trap is a tendency to agree. By default, the model backs the user. Write "I think we need TikTok" and you get a detailed justification. The workaround is simple: ask the opposite question. "Why might TikTok not suit a B2B SaaS company with a 3-month sales cycle?" will get you a far more useful answer.

The third problem is hallucination in competitive analysis. If you ask ChatGPT to describe competitors "from memory," it often invents details about their features and prices. The rule is simple: paste all competitor information into the prompt yourself, and do not trust the model's internet search without checking it.

What this means

ChatGPT does not replace a marketer. It removes the blank-page problem and speeds up structured thinking. Teams that have learned to give AI the right context and to build the conversation as a chain of requests spend 3–5 times less time on strategy drafts. The key is not the tool but how exactly you talk to it, and what you are ready to explain to it.