STQP — Building Positioning Around Customers’ Decision Questions

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STQP is a positioning model proposed by Jace Shim in AI 승자독식 (Raonbook Plus, 2026). It keeps the Segmentation, Targeting, and Positioning sequence systematized by Philip Kotler and Kevin Lane Keller, and inserts Question Mapping between Targeting and Positioning. Q connects the decision questions and constraints of a target customer with the brand's claims and supporting evidence. [1][2]

In one sentence: STQP designs positioning around the questions target customers actually ask — often to an AI assistant — rather than around a single broad statement that tries to cover every decision at once. [1]

STQP is not an official framework of any search engine or AI service. It is my proposed extension of STP for markets in which customers describe their situation in long, specific questions before they ever see a brand's own positioning statement. [1]

Why revisit STP now?

In a positioning workshop with a translation-collaboration software company that helps businesses expand abroad, the target customer was clear: small and mid-sized companies doing business overseas. Yet everyone stalled when it came to the positioning statement. Faced with "the most convenient translation-collaboration tool for SMEs doing business abroad," no one could say what "convenient" actually meant.

When three months of customer inquiries were pinned to the wall, a different picture emerged. Customers were asking: "Can I show external translators only the documents they need?", "Can product terminology stay consistent across languages?", and "If an approved translation changes later, is there a history?" The same target customers were asking entirely different questions to make entirely different decisions.

Customers in front of an AI assistant describe their circumstances in full sentences from the start. A generative system may break such a question into several sub-questions and look for evidence that answers each one. If a positioning statement does not connect to those sub-questions, the answer has nowhere to use it. A step that fills the gap between the target customer and the position — what the customer actually asks — has become necessary.

The original theory: segmentation, positioning, and STP

Segmentation, the first step of STP, was introduced by Wendell R. Smith in the Journal of Marketing in 1956 as a marketing strategy distinct from product differentiation. [3] Smith proposed dividing a heterogeneous market into groups with similar needs. Positioning was popularized by Al Ries and Jack Trout as the work of defining the place a brand occupies in the customer's mind. [4]

Philip Kotler and Kevin Lane Keller organized these ideas into a three-step sequence: segment the market, select the target, and position the brand in the target customer's mind. [2] STP remains the basic grammar of marketing and is taught in the opening chapters of marketing courses around the world.

In its traditional form, STP moves directly from "for whom" to "in what position." When customers searched with short keywords, the keyword "accounting software for small businesses" and the positioning statement "the most economical accounting software for small businesses" met at roughly the same altitude. STQP leaves S and T untouched and inserts a step that the era of AI answers demands.

What STP misses in the age of AI

In front of an AI assistant, a customer asks from a much more specific place: "We are a twelve-person design studio, many of our clients pay from abroad, and we don't have a dedicated accountant." Against that question, "the most economical accounting software for small businesses" floats far too high. A further problem is that one target group contains several different decisions.

When one positioning statement tries to represent every decision, only vague words such as "convenient" survive. The missing element is the question itself: which decision the target customer is making, under which conditions, and what they need to know.

Information retrieval research supports this distinction. Yixing Fan and colleagues showed that relevance is judged differently in document retrieval, question answering, and conversational response retrieval. [5] A customer in front of an AI assistant is looking for an answer, not a document.

What is STQP? The definition

STQP (Shim, 2026, Chapter 6): a strategy model that designs positioning in four steps — Segmentation, Targeting, Question Mapping, and Positioning — by connecting the decision questions and application conditions of target customers with the brand's strengths and evidence. [1]

Question Mapping: the third step of STQP. It identifies which decisions target customers are making, under which conditions, and what they ask, then links those questions to the company's strengths and evidence. Its output is a question map that connects customer, decision, condition, claim, and evidence. [1]

Question Mapping is not the practice of generating a long list of questions with AI and turning them into blog titles. Customer research was always part of STP, and STQP does not claim to have discovered it. The difference is that STQP makes the link between questions, conditions, claims, and evidence a separate decision step with its own deliverable.

How does STQP differ from STP?

[Table 1] STP vs. STQP by step — the deliverable added by Question Mapping (Q)

Step	STP (Kotler & Keller)	STQP (Shim)	Deliverable
S — Segmentation	Divide customers into similar groups	Same	List of segments
T — Targeting	Choose the group to focus on	Same	Definition of the target customer
Q — Question Mapping	(No separate step)	Map the target's decision questions and conditions and link them to strengths and evidence	Question map
P — Positioning	Define the place in the target customer's mind	Define the position around the questions the brand can answer most convincingly	Positioning statement and evidence list

Source: STP after Kotler & Keller (2016). Question Mapping and deliverables from Shim (2026), Chapter 6. [1][2]

The "Same" in the S and T rows matters. STQP does not replace STP; it inherits the long-standing discipline of dividing markets and choosing targets. The new Q step produces a question map, and the P step produces not only a positioning statement but also an evidence list that supports every claim in that statement.

How does Question Mapping differ from keyword research?

[Table 2] Keyword research vs. Question Mapping — a map of broad interest and a map of specific decisions

Dimension	Keyword research	Question Mapping
What it examines	The words customers use and search volume	The decisions, conditions, and constraints behind those words
Typical sources	Keyword tools, related searches	Sales calls, support logs, on-site search, reviews, AI question experiments
Unit of analysis	Words and short phrases	Sentences, situations, decisions
What it reveals	Where market interest is, and how much	What customers must confirm before deciding
How it becomes content	Pages that contain the keyword	Explanations that pair each question with an answer, conditions, and evidence
Relationship	A map of broad interest	A map of specific decisions inside that interest

Source: Shim (2026), Chapter 6. Differences in relevance across retrieval tasks after Fan et al. (2021). [1][5]

Keyword research and Question Mapping are not rival methods. Keyword research identifies broad areas of interest; Question Mapping digs into the structure of decisions within them. The same keyword, "inventory management software," covers both a seller opening its first warehouse and a manufacturer consolidating stock across several countries.

The three relationships in a question map

Customer and decision.

Whose decision does the question support? Within one company, the end user asks about ease of use, the purchasing lead asks about cost and contract terms, and the information security manager asks where data is stored.

Decision and condition.

Under which headcount, region, schedule, or budget does the problem arise? When the conditions change, the right product and the evidence needed also change.

Condition and claim with evidence.

Under those conditions, what does the product provide, and which material proves it? When a question cannot be answered, an explanation gap goes to the content backlog, and a capability gap goes to the product backlog.

[Table 3] Question map template — inventory management service (hypothetical example)

Question cluster	Representative question (observed / AI-generated)	Whose decision	Conditions	Our claim	Evidence	Owning page
External sharing	"Can suppliers see order status without seeing our costs?" (observed)	Operations lead	Many external partners; cost data must stay private	Item-level limits on what external accounts can view	Permission settings screen; applicable plan	Permissions guide
Multiple warehouses	"Can I see stock in three warehouses on one screen?" (observed)	Logistics manager	Two or more warehouses	View stock by warehouse and in total	Feature description; customer case	Multi-warehouse guide
First adoption	"How long does it take to move inventory we track in spreadsheets?" (observed)	Owner	Existing spreadsheets; no dedicated staff	Bulk spreadsheet import	Import procedure; average time required	Onboarding guide
Cost	"What is the total cost for 15 users?" (AI-generated)	Purchasing lead	15 users; annual contract	Per-user pricing, no extra fees	Price list; worked example	Pricing page

Source: Illustrative example based on Shim (2026), Chapter 6. [1]

A question map must always distinguish questions actually observed from candidates generated with AI. AI-generated questions can sound plausible without showing real demand. Any row with an empty evidence cell stays out of the positioning statement until the evidence exists.

Which questions should a brand compete on?

[Table 4] Four criteria for prioritizing questions — how STQP chooses where to compete

Criterion	What it asks	What a high score means	Data to check
Business value	Do customers asking this question matter to revenue?	Winning converts into revenue	Revenue by customer, contract size
Evidence availability	Do we have, or can we soon produce, evidence to answer it?	We can compete immediately	VRIO-D worksheet, test data, cases
Information scarcity	Is a good answer still missing from the market?	We can claim an empty position first	Observed AI answers, competitor content
Repeated inquiry	Do sales and support hear this question often?	It is where customers actually get stuck	Support logs, sales notes

Source: Shim (2026), Chapter 6. [1]

In practice, start with questions that score high on both business value and evidence availability — questions you can win and that pay when you do. Begin with a cluster of five to ten questions, win them clearly, and then widen the scope. Narrow, specific questions often have no good answer yet, which leaves room for smaller companies to take the position first.

Illustration (hypothetical): translation-collaboration software

The translation-collaboration company did not change its customers; it understood the same customers more deeply. Question Mapping revealed three clusters — external sharing permissions, terminology consistency, and approval history — and the team documented each with screenshots and user guides as evidence. It also stated a limit: machine translation alone does not guarantee final quality.

The positioning statement changed from "convenient translation collaboration for global business" to "a translation-collaboration service for small international teams that must manage product terminology and approval records with external translators." The first statement could belong to anyone; the second could belong only to that company. The team also stated the boundary: customers translating short texts on their own may be better served by a simpler tool.

Propositions and limits of STQP

[Table 5] The three propositions of STQP and its scope

Item	Content
Proposition 1 (evidence)	Even with the same target customer, different decision questions and conditions may require different positioning evidence.
Proposition 2 (resources)	When the link between positioning and questions is clear, resources may be concentrated on the evidence needed rather than on content that repeats the same claim.
Proposition 3 (promise)	The closer the conditions a product supports match the conditions a positioning promises, the smaller the gap between customer expectations and experience may become.
Especially useful when	One target group contains different decision structures; customers ask AI long questions
Caution	Fragmenting questions too finely can blur the brand promise
Claims it does not make	That choosing good questions guarantees citation; that content costs fall by a fixed percentage

Source: Shim (2026), Chapter 6. [1]

What changes from question to question is the evidence and the angle of explanation, not the promise of who the company is. Update the question map each quarter from support logs and sales notes, but do not move the central question of the positioning lightly.

Frequently asked questions

Does STQP replace STP?

No. STQP keeps the S and T of STP and adds a Question Mapping step between Targeting and Positioning.

Can AI be used to generate candidate questions?

Yes. Label AI-generated questions as candidates and confirm real demand through support logs or sales notes before prioritizing them.

What if Question Mapping reveals a question we cannot answer?

Send explanation gaps to the content backlog and capability gaps to the product backlog. Do not cover an unsupported feature with positioning language.

What comes after STQP?

Use E-E-A-T-C to review the passages on pages that answer the chosen questions, so that conditions and evidence remain intact when a passage is read on its own.

References and provenance

STQP, Question Mapping, the question map template, the prioritization criteria, and the propositions above are attributed to Jace Shim's Korean-language book AI 승자독식 (Raonbook Plus, 2026), Chapter 6. The established theory and research listed below provide background; they do not constitute independent validation of Shim's extension.

[1] Shim, J. (심준규). (2026). AI 승자독식 [Korean-language book; title translated descriptively as "AI Winner-Takes-All"]. 라온북플러스 (Raonbook Plus).

[2] Kotler, P., & Keller, K. L. (2016). A framework for marketing management (6th ed.). Pearson.

[3] Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21(1), 3–8.

[4] Ries, A., & Trout, J. (1981). Positioning: The battle for your mind. McGraw-Hill.

[5] Fan, Y., Guo, J., Ma, X., Zhang, R., Lan, Y., & Cheng, X. (2021). A linguistic study on relevance modeling in information retrieval. Proceedings of the Web Conference 2021.

The comparison tables are explanatory reconstructions, not reproductions of tables from the cited works. Hypothetical examples are illustrations, not observed AI responses or measured business outcomes.

How to cite this article

Shim, J. (2026, October 16). STQP — Building Positioning Around Customers’ Decision Questions. TSCB Group — INSIGHT B. https://www.tscbgroup.com/single-post/stqp-model-en

When citing the original proposal rather than this web explanation, cite the Korean-language book AI 승자독식 (Raonbook Plus, 2026). The English wording of its title in brackets is descriptive, not a claim about a separately published English edition.