A Query Design Guide for Getting Chosen by AI Search
In SaaS GEO, registering service names and feature names alone is not enough.
Designing queries around the contexts in which buyers consult AI
—business challenges, feature comparison, pricing, and adoption decisions—
is the starting point for being chosen by AI.
Users considering SaaS don't necessarily ask AI "Should we adopt ◯◯?" right away.
For example: "What tools can make sales management more efficient?," "Recommend SaaS for small and mid-sized businesses," "What's the difference between CRM and SFA?," "Which suits our company better, ◯◯ or △△?"—in this way, they consult AI in the contexts of business challenges, feature comparison, pricing, and adoption decisions.
With Genview, we design these queries across three stages: TOFU, MOFU, and BOFU.
TOFU: The stage where the buyer doesn't know your service yet
TOFU is the stage where the user hasn't yet decided on a specific SaaS name or company name.
At this stage, you check whether your service makes it into the candidate set for a given business challenge or theme they want to improve.
For example, for a sales management SaaS you would build queries starting from the user's challenge or business objective, such as "how to gain visibility into sales activities"; for a back-office SaaS, "how to make billing operations more efficient"; for a marketing SaaS, "how to nurture leads."
If your company doesn't show up here, from the AI's perspective it may not be recognized as a "representative SaaS that solves that business challenge."
MOFU: The comparison and consideration stage
MOFU is the stage where the user is comparing multiple SaaS products.
At this stage, you check whether the AI can explain the reasons your service is chosen when compared against competitors.
For example, you register category-comparison and competitor-comparison queries such as "Compare recommended CRM tools for me," "What are the key points when choosing a billing management SaaS?," or "Tell me the difference between Company A and Company B."
In MOFU, it's important to look not only at whether your company is included as a comparison candidate, but also at which evaluation axes you are discussed on—features, pricing, ease of use, ease of implementation, support structure, external integrations, security, and so on.
BOFU: The stage just before inquiry or adoption
BOFU is the stage where the user is checking their last concerns right before making an inquiry or adopting.
At this stage, you check whether the AI can accurately explain your service.
For example, these are queries like "What's the reputation of ◯◯?," "Is ◯◯ easy to use even for small and mid-sized businesses?," or "Which is better, ◯◯ or △△?"
In BOFU, you check whether the AI is putting out incorrect or outdated information about pricing, contract period, implementation period, support scope, security, systems it can integrate with, and which companies it is and isn't suited for.
How to think about which queries to register for SaaS
For SaaS, we recommend including at least the following five contexts.
Context
Purpose
Query example
Challenge
See if you enter the candidate set for solving the business challenge
What tools can make sales activities more efficient?
Features
See if you're recommended in the context of needed features
What tool can do customer management and email delivery?
Category
See if you're recognized within the SaaS category
What are some recommended CRM tools?
Comparison
See the reasons you're chosen over competitors
Which is better, Company A or Company B?
Reputation
See if pre-adoption concerns are answered correctly
What's the reputation of ◯◯?
It's important to register not just service names and feature names, but the words a buyer is actually likely to use when consulting AI.
Ready-to-use query examples
TOFU Awareness-stage query examples
What tools can make sales activities more efficient?
Tell me how to make customer management more efficient
What should we do to nurture leads?
What tools can automate billing operations?
How can we make internal information sharing more efficient?
What SaaS can make handling inquiries more efficient?
What tools can we use if we want to stop managing with Excel?
Where should a small or mid-sized business start to advance DX?
How can we improve sales management that depends too much on individuals?
Recommend SaaS that's useful for improving operational efficiency
Is ◯◯ easy to use even for small and mid-sized businesses?
Is ◯◯ expensive?
What kind of companies is ◯◯ suited for?
What is ◯◯'s support structure?
Can ◯◯ integrate with existing systems?
Which is better, ◯◯ or △△?
Tell me the pros and cons of adopting ◯◯
What should we check before contacting ◯◯?
Summary
In SaaS GEO, rather than looking only at service names and feature names, it's important to design queries for each context in which buyers consult AI.
In TOFU, the business challenges and improvement themes of users who don't know your service yet. In MOFU, the consideration axes of users who are comparing multiple SaaS products. In BOFU, the concerns and reputation checks right before an inquiry or adoption.
By registering queries across these three stages, you can see how your service is recognized, recommended, and compared on AI.
Frequently asked questions
Q. Is it enough to register only queries that contain our own service name, like "What's the reputation of ◯◯?"
Queries containing your own service name (BOFU) are essential for checking how AI describes your company, but they aren't enough on their own. Many SaaS buyers consult AI with a business challenge—like "tools that can make sales activities more efficient"—while they still don't know your service name. By also registering TOFU and MOFU queries that don't contain your name, you can check whether AI lists you as a candidate in the early consideration stage.
Q. If we're listed on comparison sites or review sites, will AI recommend us?
Being listed on comparison or review sites contributes somewhat to AI recognition, but it doesn't guarantee a recommendation on its own. Because AI assembles a picture of your service from multiple sources, consistency of information across your own site, comparison sites, and external media builds trust. Along with query design, it's important to organize the feature, pricing, and case-study information on your own site. For more detail, see our overview page on GEO for SaaS.
Q. How can we check whether the queries we registered are producing results?
With Genview, you can continuously measure the appearance status and scores for each registered query across ChatGPT, Gemini, Perplexity, and more. You can track in numbers how AI's description changed—and whether the gap with competitors narrowed—before and after running a measure. We recommend starting with the free plan to check your current AI recognition and understand your starting point. See pricing plan details.