Dattva Blog · September 23, 2026
Money Prompts: How to Identify the Buyer Queries That Actually Matter
A Money Prompt is a specific buyer query, phrased the way a real prospect would type it into ChatGPT or Perplexity, used to track whether a brand is cited in AI-generated answers. Unlike a keyword list built for SEO, Money Prompts are locked for a fixed period so that movement in AI visibility can be measured against a stable, comparable baseline.
Why Money Prompts Are Not the Same as SEO Keywords
An SEO keyword list is built around search volume and competition, terms enough people type into Google to be worth targeting. A Money Prompt is built around buyer intent phrased conversationally, closer to a full sentence than a keyword fragment, because that is how people actually talk to ChatGPT. "Best cloud consulting firm India" and "which cloud consulting firm should I hire for a mid-sized enterprise in India" might target the same buyer, but only one reflects how a real person phrases the question inside a chat interface, and AI models respond differently to each.
I have seen teams hand over a spreadsheet of SEO keywords expecting it to double as a Money Prompt list, and it never works cleanly. The same brand can be well cited for the keyword phrasing and completely absent for the natural-language version, because the model is not matching against a search index, it is generating a fresh answer to whatever was actually asked.
Running a free diagnostic against a first draft of this list is a fast way to see where a brand currently stands.
The Five Types of Money Prompts Worth Tracking
A category query tests whether a brand appears for a broad, generic version of the buyer's need, something like naming the best provider in a category for a given year. A problem-specific query tests a narrower requirement, such as a specific certification or compliance need layered on top of the category. A comparison query directly names two brands and tests how the AI describes each relative to the other. A branded query asks the AI what a specific company specialises in, testing whether the brand is described accurately when asked about directly. A geography-specific query adds a location constraint, testing whether local relevance changes which brands the model surfaces.
Together, these five types cover most of the ways a real buyer's question actually gets phrased, and I rarely see a category where all five types return the same result for a given brand.
Why Locking the List Matters
Money Prompts are agreed at the start of an engagement and locked for a fixed period, typically 90 days, specifically so that tracking measures real movement rather than a moving target. Swapping prompts mid-engagement, even with good intentions, makes it impossible to say honestly whether visibility improved, because the comparison is no longer apples to apples. This is the same discipline behind any controlled measurement: change the fixes, not the yardstick.
When I have inherited a tracking sheet from a team that kept swapping queries every few weeks, the resulting data was close to useless for showing whether anything actually improved, even when real progress had been made.
How Many Money Prompts a Brand Actually Needs
Ten Money Prompts, covering all five types across the brand's core buyer segments, is typically enough to give a representative read on AI visibility without becoming unmanageable to track weekly across four platforms. Fewer than that risks missing an important buyer angle entirely; significantly more starts to dilute attention across too many low-priority queries instead of the handful that actually drive pipeline.
It is also worth revisiting the list roughly once a year, even though it stays locked within any single 90-day tracking cycle. A category can shift meaningfully over twelve months, a new compliance requirement can become standard, a new competitor can enter the market, or a brand's own product focus can change enough that the original ten prompts no longer reflect the questions a real buyer would actually ask.
A Worked Example of Building a Money Prompt List
Consider a mid-sized cybersecurity vendor selling managed detection and response services to enterprises in India. A category query might name the best managed detection and response provider in India. A problem-specific query could narrow this to a provider with ISO 27001 certification serving BFSI clients. A comparison query might directly ask how this vendor compares with a named competitor. A branded query would simply ask what the company specialises in. A geography-specific query could ask for the best cybersecurity vendor for enterprises in Bengaluru specifically.
When I run this exercise for a real brand, it usually surfaces two or three additional angles worth tracking that were not obvious at the outset, often a specific industry vertical the company serves particularly well, or a certification that matters more to buyers than the marketing team initially assumed. In one recent batch of ten client Money Prompt lists I helped build this quarter, exactly zero were finalised on the first draft (Dattva internal programme data), which tells me the exercise itself is doing real work, not just formalising something the team already knew.
What Happens Once the List Is Set
Each Money Prompt gets checked weekly across ChatGPT, Perplexity, Gemini, and Claude, with the results compiled into a factual weekly record: was the brand cited, which competitors appeared, and which sources were cited instead. A full cross-platform audit runs at Day 30, Day 60, and Day 90, comparing results against the original baseline to produce concrete before-and-after evidence rather than an interpreted score.
Dattva's ongoing AI visibility monitoring runs this exact cadence for every client engagement, with every result reproducible by anyone typing the same prompt into the same platform. The same underlying discipline is what makes how ChatGPT, Perplexity, and Gemini decide which brands to cite worth checking platform by platform rather than as one blended number.
Conclusion
A Money Prompt list is only useful if it reflects how real buyers actually phrase their questions and stays fixed long enough to measure genuine movement. Building the wrong list, one built like an SEO keyword sheet instead of a set of real buyer questions, produces tracking data that looks precise but measures the wrong thing entirely.
Frequently Asked Questions
How is a Money Prompt different from a search keyword?
A Money Prompt is phrased conversationally, the way a buyer would ask ChatGPT a question directly, while a search keyword is typically a shorter fragment optimised for search engine matching rather than natural conversation.
Who decides which Money Prompts to track for a brand?
They are typically agreed jointly between the brand and whoever is running the AI visibility engagement, based on the actual questions a real buyer in that category would ask, then locked for the tracking period.
Can Money Prompts be changed partway through a tracking period?
Changing them undermines the comparison, since movement can only be measured honestly against a fixed baseline. New prompts can be added for future tracking cycles once the current period ends.
How many AI platforms should each Money Prompt be checked against?
Checking across all four major platforms, ChatGPT, Perplexity, Gemini, and Claude, gives the fullest picture, since citation sourcing differs meaningfully between them and a brand can be visible on one while invisible on another.
Do Money Prompts need to include the brand's own name?
Yes, at least one branded query is worth including to test whether the AI describes the brand accurately when asked about it directly, alongside category, comparison, and geography-based queries.
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