Dattva Blog · July 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.

Running a free diagnostic against a first draft of this list is a fast way to see where a brand currently stands.

This distinction is explored further in Dattva's research on why traditional SEO alone cannot close this specific gap.

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.

More detail on how these five types are chosen is covered in Dattva's approach to building this list.

Producing content targeted at each of these query types is handled through Dattva's content intelligence work.

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.

Restructuring pages around whichever prompts are locked in is the core of Dattva's GEO content engine approach.

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. The right number depends on how many distinct buyer segments and use cases a brand genuinely serves.

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. Treating the list as fixed within a cycle but not fixed forever keeps the tracking relevant without sacrificing the comparability that locking the list during any single measurement period is meant to protect.

A broader comparison of how different providers size this list is covered in Dattva's research on GEO platforms built for mid-market B2B teams.

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 for this brand might be phrased as naming the best managed detection and response provider in India, testing whether the brand appears for the broadest version of its core offering. A problem-specific query could narrow this to a managed detection and response provider with ISO 27001 certification serving BFSI clients, testing whether the brand surfaces for a buyer with a specific compliance requirement layered on top of the category. A comparison query might directly ask how this vendor compares with a named competitor, testing how the AI model describes each relative to the other rather than in isolation. A branded query would simply ask what the company specialises in, checking whether the AI describes its actual service lines accurately rather than confusing it with a similarly named company or an outdated product line. A geography-specific query could ask for the best cybersecurity vendor for enterprises in Bengaluru or Mumbai specifically, testing whether local relevance changes which names the model surfaces compared with the broader India-wide query. Running through this exercise for a real brand 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.

Mapping exactly which source wins each of these queries for a competitor is the purpose of Dattva's citation gap intelligence work.

Brands comparing providers directly may find Dattva's roundup of leading GEO agencies in India a useful reference point.

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 AI visibility monitoring service runs this exact cadence for every client engagement, with every result reproducible by anyone typing the same prompt into the same platform.

Checking each prompt independently across all four platforms follows the same logic as Dattva's multi-model verification methodology.

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

Written by the Dattva Research Team, which runs AI visibility diagnostics and GEO implementation for B2B companies across India, Southeast Asia, and the United States.