AI in Marketing: Use Cases and Ready-Made Prompts

AI in marketing is too often used as a fancy copywriter: type a prompt, get a text. That approach quickly trips over quality, factual errors and moderation risks. This article covers practical use of AI in marketing across five areas: content and SMM, advertising, SEO, email and analytics. Each scenario comes with a ready-made prompt you can copy and adapt to your product, plus an FAQ at the end.

How to use AI in marketing: four roles

AI works reliably in marketing when you split its work into roles and never mix them in one dialog:

• generator — quickly drafts options: offers, topics, structures. The goal is coverage, not final quality;
• editor — cleans the output: removes promises, value judgments and wording that is risky for ad moderation. It invents nothing, it only checks;
• analyst — hunts for overlaps, conflicts and risks in material that already exists;
• formatter — turns the result into a working artifact: a table, a brief, a sitemap, a script.

Each role is a separate dialog — ideally a separate model: the generator needs breadth, the editor needs rigor. The output of every step is not a "finished text" but input for the next step; the final call stays with a human.

Below are five scenarios built on this scheme.

Content and SMM: plan, posts, short videos

Content is the most common scenario. AI shines here not by "writing posts for you" but by being systematic: a series instead of scattered publications, one idea — one video.

Suggest a social media content plan for [product: what it does, for whom].
Needed:
- 10 post topics: explainers and practical breakdowns, no selling;
- 10 short video ideas: one idea = one clip, 15–30 seconds.
No CTAs, promises or competitor comparisons.
Format: two tables | Topic | Format | What the reader learns |

Run the resulting list through the editor in a second dialog: "remove promotional wording, keep the educational value." For video scripts, ask separately for the hook, shot logic and on-screen text — then the result goes straight to the video editor.

Ads: offers, wording cleanup, A/B hypotheses

In advertising, AI covers three steps: offer variants, editing to pass moderation, and test hypotheses.

Generate ad copy for [product]. Needed:
- 3 offer angles targeting different pain points;
- for each: 3 headlines, body copy, CTA.
No numbers, benefit promises or comparisons. Tone: business-like, calm.

Step two is the editor: "remove benefit promises, value judgments and risky wording, format 'before → after'." The "before → after" format matters: every edit is visible and easy to roll back.

Step three is hypotheses: "formulate A/B hypotheses, one hypothesis = one variable, no assumptions about the outcome." Single-variable testing is the golden rule: if a variant changes both the headline and the CTA, the test proves nothing.

SEO: keywords, clusters, meta tags

SEO is a structure problem, and this is where AI delivers the most measurable result in marketing: from a keyword list to a sitemap in a few steps.

Collect SEO queries for [product and its features]. Needed:
- informational, practical ("how to…") and commercial queries;
- phrasings for roles: [e.g. owner, accountant, manager].
For each query specify intent, funnel stage (TOFU/MOFU/BOFU)
and a draft cluster.
Format: | Query | Intent | Stage | Cluster |

Then down the pipeline: clustering ("one cluster — one intent, with a page type, priority and H1 for each"), meta tags ("title and description with no promises, strictly matching intent") and an audit ("find duplicates and cannibalization risk, suggest fixes"). Each step is a new dialog whose input is the table from the previous one.

Email: welcome, reminders, reactivation

Emails are scenario work: each one solves a single task and lives inside a sequence.

Build email sequence scenarios for [product]: welcome, a reminder
about an unfinished step, reactivation after a pause.
For each email: goal, message logic, one functional CTA
(check, continue, learn) — no urgency or pressure.

After generation comes the usual cleanup: strip manipulation and "last chance" wording, tie each email to an event in the product rather than the calendar. The final step is the send-ready structure: subject line, block order, CTA.

Analytics and reports

AI won't replace your analytics stack, but it is good with qualitative data: it structures observations, spots contradictions and prepares conclusions for discussion.

Propose a monthly marketing report structure for [product]:
blocks, what each analyzes, which management question it answers.
No KPIs or numbers — only observations, patterns and conclusions.

As a second step, hand the model a filled-in report and ask: which conclusions are justified, where over-generalization is a risk, what clarifying questions to ask the team. That turns AI into a "second opinion" before decisions, not a generator of pretty slides.

Creatives: images for banners and posts

Visuals are a separate layer: first a text model assembles a banner brief (format, key phrase, visual idea, what must not be shown), then an image model — Nano Banana, Seedream or Midjourney — renders variants from that brief. In GPTunneL, text and image models live in one interface and share one balance, so the whole "offer → brief → banner" chain comes together without switching services.

Which AI models to use for marketing

There is no one-size-fits-all model — different pipeline roles call for different ones. All of these are available in GPTunneL from a single balance:

Current prices for every model are on the pricing page: pay per use, no subscriptions.

FAQ

Where do I start using AI in marketing? Pick one task that already eats your time — say, the content plan — and run it through the "generator → editor" pipeline. Once the chain works, save the prompts as templates and add the next scenarios.

Do I need a course on AI in marketing? Not to get started: the prompts in this article cover the basic scenarios. Training pays off later, when you scale processes across a team. Practice beats any course: run the same prompt on two or three models and compare the results.

What is neuromarketing and how is it related? Neuromarketing studies how a buyer's brain reacts to advertising — it has nothing to do with neural networks. This article is about generative AI models as a marketer's working tool.

Will AI replace marketers? No: the model generates and structures, but the decisions — which offer to launch, what to publish, where the risk is — stay with a human. What changes is speed: routine goes to the model, strategy and control stay with the marketer.

How much does it cost? GPTunneL has no subscription — you pay per request, and the combo of "a cheap model for drafts + a flagship for the final pass" saves real budget. Per-model prices are in the catalog.

Try it on your own task

Take any prompt from this article, plug in your product and run it in GPT 5.6 Sol — in GPTunneL it works with no VPN needed, no subscriptions, pay per use. Right next to it, in the same chat, are Claude, Gemini and DeepSeek: compare their answers on one task and assemble your own marketing pipeline. For teams and companies there is GPTunneL business access with a shared balance and spend control.