Start with the offer and the marketing decision
Before choosing an AI tool for marketing, define whom you want to reach, what you sell and which decision needs improvement. You may need to understand objections, test a message or analyse a campaign. A concrete objective makes it possible to request useful work and assess whether the response helps the business progress.
If a business sells a service that is difficult to explain, producing more ads can repeat the same confusion. I would start by collecting questions from sales conversations: whom it serves, what it includes, how much customer involvement it requires and how a successful delivery is recognised. That information helps build the message.
In my 2022 book, defining the audience, offer and value proposition precedes many execution decisions. AI can help organise questions and compare alternatives, but needs the context that distinguishes the business. Text that could apply to any company rarely explains why someone should choose one particular provider for the work.
The page on online business and sales develops that foundation. Here, I focus on using AI within marketing work: preparing hypotheses, producing materials, analysing information and connecting campaign activity with sales follow-up and service delivery. Those connections influence what information needs to be collected and reviewed.
Prepare data that supports useful questions
Marketing analysis with AI needs data with clear periods, sources and meanings. Confirm what each column represents and whether important stages of the sales journey are missing. State the decision you want to make, so the model can organise information without confusing advertising activity with the results the business ultimately needs.
In Sextas Ímpares #142, about analysing advertising with Claude, I showed that an export may contain only visible columns. The file supplied shapes the analysis. If relevant data is absent, a well-written answer can still omit the information required for a decision. The class is in Portuguese.
For example, an ad may generate inexpensive contacts that the team cannot turn into conversations. Another may bring fewer contacts but requests better matched to the service sold. Comparing them requires connecting campaign, contact and sales progress while respecting differences in time periods and how long requests take to develop.
The page on AI data analysis for businesses explains how to prepare those questions and check calculations. AI can help locate patterns and formulate hypotheses. A decision requires confirming that the data represents the journey being assessed and considering whether another explanation could account for the result.
Create ad variations with meaningful differences
Ad variations should explore different reasons for someone to pay attention, with a promise consistent with the offer. Use AI to develop angles, examples and presentation formats from truthful information. Review each version’s meaning before comparing its performance with other campaign messages, so the test examines a difference that could inform your decisions.
In class #136, about producing images for ads, I worked on preparing ideas and materials. The broadcast title refers to volume, but the criterion relevant here is diversity of propositions. Changing a background colour while keeping the same message may add little to what the business learns.
Imagine a process-implementation service. One ad could start with time lost copying information; another with difficulty following requests; a third with dependence on one person to find data. Those are different hypotheses. The advertised service needs to be capable of addressing the problem presented in each version of the message.
Keep the rationale for each test and review images, copy and landing page together. A generated image should not simulate a testimonial, customer or result that never existed. The article on using AI beyond the chatbot helps explore the working process that supports creation and review before publication.
Preserve your voice and check the content produced
Marketing content made with AI should preserve the thinking of the person or business signing it. Give the model concrete examples, criteria and sources, and review claims before publication. The review needs to confirm meaning and authenticity, alongside correcting spelling or improving presentation, because polished writing can still misrepresent the business.
In Sextas Ímpares #143, I discussed communication beginning to sound alike when everyone uses AI in the same way. That concern also guides my website. I want readers to recognise decisions, experiences and the reasons behind an opinion, rather than a collection of interchangeable statements.
If I request a text about automation, I provide the process, the difficulty and what was observed. I then check whether the draft invented benefits, removed a condition or turned a possibility into certainty. A stronger-sounding sentence can sell an idea that the business is not equipped to deliver.
I also separate the content of an older class from its current application. Tools, offers and examples may have changed. When returning to a principle from the 2022 book, I identify that context and add current reasoning. This curation preserves useful knowledge without presenting old instructions as if they had just been verified.
Connect campaigns with sales capacity and margins
Marketing performance should be assessed alongside sales follow-up and the capacity to deliver what was sold. Observe contacts, proposals, sales and the effort needed to serve customers. AI can help organise those signals, but the criteria should reflect the economics and actual operating conditions of the business whose results are being assessed.
In class #151, about advertising management in the AI era, I discuss the strategic role of advertising professionals. Knowing tools is part of the work. Understanding the offer, the customer and the consequences of decisions helps determine what to do when the data points towards a problem.
Increasing campaign investment while the team responds slowly can increase the amount of unattended work. If delivering the offer requires substantial intervention for every sale, margins may limit growth. The article on growing a business with attention to margins, teams and AI develops this connection between demand and operational capacity.
To understand where the transition from contact to customer is failing, also read turning leads into customers in a service business. AI can prepare information for analysis, but the conclusion needs to include what happens in conversations and in the operation after the advertising click.
Choose between services, implementation and learning
The appropriate support depends on whether you need growth management, technical implementation or skill development. SpartAds works on growth for service businesses and ecommerce, with a dedicated AI and automation area. At Laboratório da IA, I review projects and decisions in two mentoring sessions each month, connecting business reasoning with practical application.
If you need to improve acquisition, your offer and sales, present the context to SpartAds for business growth. If the need is already defined as an integration or automation, use the SpartAds AI area. The destination should match the work you want assessed.
To learn while working on your project, explore AI and business mentoring. To prepare several people in one organisation, consider corporate AI training. The choice should reflect who will execute afterwards and how much support is needed to turn the learning into work that gets used.
Bring concrete examples to that discussion: a campaign, a sales difficulty or a repeated task. My experience in digital since 2012 helps connect those situations to the business. AI adds execution and analysis capacity, and I want that capacity used to improve decisions whose consequences you can recognise and follow over time.