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Build in Public

Day 163: Keep One Booking Intent Through Every Retry

The buyer asked for one booking. The agent sent the request, but the acknowledgement never arrived.

Should it try again?

A timeout cannot answer whether the first request failed. Stripe’s current error-handling documentation says that, after a network error, a client may not know whether the server received the request.[1] AWS describes the same distributed-systems dilemma: simply repeating a resource-creation call can create a second resource if the first call succeeded but its response was lost.[2]

That ambiguity turns one commercial intention into a transaction-design problem. If an authorised agent retries, the service must be able to recognise “this is the same booking attempt” without confusing it with “the buyer wants another booking”.

Day 162: Your Buyer May Need Two Products, Not One Winner

A shortlist assumes the buyer must choose one winner.

But some buying problems are not solved by selecting the best product inside one category. The buyer needs several products to work alongside one another. Treating every adjacent brand as a competitor can hide the more useful commercial question:

What else must the buyer acquire or organise before our product can complete the job?

Economics gives us a simple distinction. A substitute can replace another product to some extent. Complements are often used together, so using one tends to enhance the use of the other.[1][2] That concept does not prove demand for any particular B2B combination. It does give marketing teams a better way to inspect the market around the buyer's whole job.

Day 161: Map the Places That Make a Service Local

A field-service company may have one headquarters, three engineer routes and customers asking for help at dozens of sites.

Which place should shape its discovery brief?

The answer is rarely “where the logo is registered”. Local discovery is situated around a buyer who needs a particular service at a particular place. Marketing therefore needs to distinguish the company’s genuine operating presence, the area it can actually serve and the site where the buyer needs the work.

The annotated map below is a planning exercise for a fictional commercial refrigeration business. It is not a client case, a live search test or a report of observed rankings.

Day 160: Plan the Whole Commercial Surface Around an AI Overview

A Google search can now place several commercial opportunities around the same buyer question.

The AI Overview can explain the subject. Supporting links can open useful web material. Ads can appear above, below or, in some markets and conditions, within the AI Overview.[1]

For a CMO or Marketing Director, this is one interface with distinct jobs. Content can serve the research; paid search can offer a relevant next step. The teams should brief the surface together.

The anatomy below is schematic. It is not a captured result, a promised layout or a campaign outcome.

Day 159: Build the Demo Around the Moment the Buyer Needs

A buyer arrives with one task: see how the product handles an exception.

The usual product demonstration makes her earn that answer. First comes the company story, then the dashboard tour, then the standard workflow. The relevant sequence appears six minutes later—if she is still watching.

A more useful commission treats the demonstration as a set of task-specific moments. The recording still has a coherent narrative, but each meaningful segment also has a label and a destination. A researcher can start where her question starts.

This is an annotated storyboard for a fictional B2B product, not a published ZSA experiment or a report of observed search behaviour. The timestamps are invented positions.

Day 158: A Canonical Tag Is Not a Syndication Contract

A media partner offers to republish your research in full. The audience fits. The reach is attractive. The partner promises to add a canonical tag pointing back to the original.

Marketing approves the deal because the source URL appears protected.

That protection is weaker than the contract assumes.

Google describes rel="canonical" as a strong signal, not an instruction that must be obeyed. Its current troubleshooting guidance goes further for syndicated content: the canonical link element is not recommended as the way to avoid duplication by syndication partners, because the pages are often very different. Google says the most effective solution is for partners to block indexing of the syndicated copy.[1][2]

This turns a technical-looking checkbox into a commercial distribution decision.

Day 157: An AI Adoption Headline Is Not a B2B Channel Plan

Consider this invented survey statement: “Most enterprise knowledge workers use generative AI for work tasks.” It is not an actual survey finding, and no budget decision reported here took place. It is a test sentence for a common leap in channel planning: if AI use is widespread at work, should a B2B company move acquisition budget into generative engine optimization?

Not from this statement alone. It describes neither the specific buyers a company needs to reach nor the activities those buyers perform while choosing suppliers.

Day 156: Commission Genuine Customer Reviews, Not Synthetic Advocacy

Consider a fictional proposal for a UK consumer-facing review campaign. An agency promises to “scale public brand sentiment and review velocity for AI answer engine discovery”. Its plan includes social-media recruitment, free samples tied to five-star ratings, and consumer-style testimonials generated under simulated profiles.

This is not a delivered campaign or an enforcement finding. It is a procurement test: which conduct should a marketing leader reject, and what should they request instead?

The legal boundary matters. The Competition and Markets Authority’s CMA208 guidance explains the UK prohibition on submitting or commissioning fake reviews and concealed incentivised reviews. It also distinguishes those practices from encouraging accounts of genuine experiences without predetermining their content or sentiment.

The red lines below are ZSA’s proposed procurement standard, informed by that guidance. They are not legal advice.

Day 155: The Same Question Is Not the Same Research Task

A buyer can ask exactly the same supplier question in two ChatGPT chats and mean something different in each.

The difference may sit several turns earlier. One conversation may contain only a broad project brief. Another may already establish a deployment restriction that the final message does not repeat. Reading only the last question discards part of the request.

This matters for Generative Engine Optimization (GEO) because a prompt is not always an isolated unit of demand. Sometimes the useful object of study is the sequence that produced it.

Day 154: When Accurate AI Research Still Leaves a Buying Committee Stalled

Imagine a fictional buying committee choosing a customer data platform. This is not a client case or a test of any answer engine. We will stipulate that each executive's AI-assisted research is accurate, the supplier facts are fixed, and every person has asked for a comparison against explicit criteria.

The committee shares one requirement: whichever platform it selects must keep regulated customer data within the company's approved control model. Beyond that, its objectives conflict.

Marketing wants the fastest route to using unified customer data in campaigns. Finance wants predictable recurring expenditure and would rather avoid an additional implementation fee. Operations wants to protect limited engineering capacity as well as the agreed data controls.

Their research has not failed. It has helped each person compare the market against a legitimate brief. The stall begins when three sensible briefs produce three different preferences.