OCDevel AI for Marketers

OCDevel

Write the email in your brand's voice, then build the workflow that gets it to the right customer. Learn AI marketing from producing one useful asset to connecting content, campaigns and measurement across your tools. The course starts with audience research, prompts, brand examples and editing, using recognizable work such as landing pages, ad copy, emails and social posts. Those foundations carry into SEO, visibility in AI search, CRM, email and lifecycle marketing, paid advertising and analytics. Build toward no-code and low-code automation, reusable content processes and agents that handle several campaign steps. The emphasis stays on judging the work: whether a claim is supported, the voice fits, the audience is right and the result justifies the cost. Examples explain the prompts, settings and checks behind a marketing task, alongside mistakes such as invented statistics and over-automated email. Relevant news and practical shortcuts add context as platforms change. For in-house marketers, freelancers, agency operators and founders doing their own marketing, no programming experience is assumed. This show's audio is narrated by an AI-generated synthetic voice.

Episodes

  1. 5 days ago

    What Customers Actually Said, and What You Assumed

    A landing-page draft built from traceable facts still doesn't tell you if it sounds like a real contractor, so the chapter builds a sourced file of verbatim customer language, separates observation from inference, and sets up a reusable brand brief so an AI assistant stops inventing personas and starts citing evidence. Episode page & show notes Visit website The founding charter Ledgerlane has a hero section built from traceable numbers, but nothing yet tells you whether the words in it sound like a contractor's own week. Asking Ida for a customer persona produces a fluent, plausible "Dave, forty-one, runs a two-van electrical firm" — and none of it was said by anyone. It was predicted, not gathered, and researchers call the error reification: treating an inferred sketch as data. The fix is a rule that governs everything else here — a line only counts as evidence if you can name the exact source. That means going to where UK trade contractors actually write about money in their own words: review sites like Trustpilot and Google Business Profiles (bimodal, capturing fury and gratitude but nothing ordinary), trade forums such as electriciansforums.net threads on tax and bookkeeping and the ContractorUK accounting and legal board, search phrasing, inbound call notes, competitor FAQ pages, and short customer interviews. Each source is named and its bias made explicit — forums skew toward people trying to avoid paying anyone, reviews toward extremes, inbound notes toward people who already bought. Interviews go wrong through politeness and bad self-prediction, so the chapter turns to the approach from Rob Fitzpatrick's Mom Test method for customer interviews: ask about a specific past occasion, not a hypothetical future, and check whether the person already spent effort on the problem. The evidence gets logged verbatim — typos and jargon intact — using in vivo coding, Saldaña's coding method for qualitative researchers, keeping the customer's own labels rather than paraphrasing them. Every line then gets marked observation or inference, and inferences get a line-count threshold before they're trusted: three independent sources make a pattern, two an emerging finding, one a hypothesis that stays labelled no matter how appealing it is. Explain AI-marketing changes and practical shortcuts Ida gets asked to group the evidence lines by situation, name each cluster from the words inside it, and cite line numbers — removing her room to invent. The result is checked the same way the hero section was: search for the quoted words, and count the cited lines against the actual file, watching for a smuggled-in extra quote that sounds right but isn't there. A reusable brand brief then gets written once into the project's instructions layer — voice rules, a banned-words list, one accepted and one rejected example — while facts stay in the project's files, since Claude Code's CLAUDE.md hierarchy treats instructions and reference material as separate layers that can silently conflict without a stated precedence rule.

  2. 10 Sept

    The Source Packet That Makes a Draft Publishable

    A walkthrough of fixing AI-generated marketing copy for a small bookkeeping business by building a short packet of real facts, audience details, a stated goal, and a genuine tone sample, then loading it into a project and inspecting the resulting draft claim by claim before publishing it, followed by a short note on a recent change to how Claude's memory works. Episode page & show notes Visit website Building a landing page you can defend The chapter walks through fixing a real failure mode in AI-written marketing copy: a fluent draft that invents prices, invents customers, and asks for the wrong action. Working with an invented small business, Ledgerlane Bookkeeping, it builds a short "source packet" — product facts with real prices and explicit exclusions, an audience paragraph, a one-sentence goal for the specific piece of copy, and a real sample of the business's own writing standing in for a tone description. That packet goes into a project, a container that holds standing instructions and reference files so every conversation starts from the same material. The chapter uses Claude Projects as the walkthrough tool, noting the file formats and size limits involved in uploading documents to Claude, and flags the account requirements for the alternative, ChatGPT Projects, plus Gemini's equivalent "Gems." It then runs the same request twice — once with no project and no files, once inside the built project — and lays the two drafts side by side. The bare request produces confident, well-formed prose full of unsourced claims: a made-up company name, an invented discount, a wrong price. The project-backed draft ties every number to a specific line in the product facts, and the chapter inspects it anyway, checking claim by claim, hunting for anything stated with no source, comparing tone against the brand sample, and checking the closing line against the stated goal. Two real problems turn up — a dropped condition on the onboarding timeline and a missing mention of what the service doesn't do — and get fixed in one further pass. The chapter closes by separating out what the model contributes, what the project layer contributes, and what only the supplied packet contributes, and by warning that an assistant's confident description of "your typical customer" is not evidence of anything. Where the assistants changed this month A short update flags a change to how Claude's memory behaves following Anthropic's move to unify memory across chat and Cowork, covered in Anthropic's rundown of Claude's personalization features. Memory now writes during a session rather than in a daily batch, and it's sorted into editable topics. The practical point: brand voice and facts you'd defend to a client belong in files you control, not in whatever a chat happens to remember, and it's worth a quick audit of what's been saved.

  • 2 Episodes

About

Write the email in your brand's voice, then build the workflow that gets it to the right customer. Learn AI marketing from producing one useful asset to connecting content, campaigns and measurement across your tools. The course starts with audience research, prompts, brand examples and editing, using recognizable work such as landing pages, ad copy, emails and social posts. Those foundations carry into SEO, visibility in AI search, CRM, email and lifecycle marketing, paid advertising and analytics. Build toward no-code and low-code automation, reusable content processes and agents that handle several campaign steps. The emphasis stays on judging the work: whether a claim is supported, the voice fits, the audience is right and the result justifies the cost. Examples explain the prompts, settings and checks behind a marketing task, alongside mistakes such as invented statistics and over-automated email. Relevant news and practical shortcuts add context as platforms change. For in-house marketers, freelancers, agency operators and founders doing their own marketing, no programming experience is assumed. This show's audio is narrated by an AI-generated synthetic voice.

Information

  • Creator
    OCDevel
  • Years Active
    2026
  • Episodes
    2
  • Rating
    Clean
  • Copyright
    © All rights reserved 2026, OCDevel
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    OCDevel AI for Marketers