---
title: "Intent·Persona·Product Attribute: How to Turn Purchase Questions into Data"
slug: "intent-persona-product-attributes"
language: "en"
tags: ["구매 문맥 모델","아키텍처 노트","sag 기술","용어와 원리"]
created: "2026-10-06T08:00:00.000Z"
published: "2026-10-08T10:16:45.571Z"
updated: "2026-10-08T10:16:50.748Z"
sample: false
---

# Intent·Persona·Product Attribute: How to Turn Purchase Questions into Data

## What Is a Purchase Context Model?

**It is a way to organize customers’ goals, situations, constraints, and product attributes as question structures.** This note approaches a purchase context model in terms of the responsibilities of its inputs, transformations, and outputs, rather than as a feature name. To trust the analysis results, it must be possible to trace what materials were provided, what was verified, and how far the conclusions can go.

## Why Is This Technology Needed?

The same keyword can reflect different budgets, environments, and decision criteria. Storing only the words misses the actual selection conditions and the product evidence needed.

## Design Principles and Data Flow

Separate goals, situations, customer types, and product characteristics, and connect specifications, policies, and official sources to answers for each set of conditions. Record the units and applicable conditions for numerical values as well.

> **Purchase context** → **Attribute and evidence mapping** → **Answers by condition**

Each stage should not reframe the success of the previous stage as its own outcome. Recording the identifiers and time periods of source materials, along with their verification status, makes it possible to locate where omissions and errors occurred and determine what needs to be checked again.

## Connection to the SAG Architecture

SAG Goal is the starting point for services, customers, questions, markets, and languages. Structured product information is a design direction for strengthening the evidence used to answer questions.

SAG’s operational value lies in connecting these relationships to pages and questions, comparison results, and improvement tasks. Rather than reading numbers alone, customers can review both what needs to be improved and the basis for decisions. Patterns that require further application should be interpreted within the scope of the relevant paragraph.

## Illustrative Example and Decision Criteria

The illustrative conditions of a budget of 200,000 won or less, business travel, light weight, and battery life are more specific than the word “earbuds.” Price, weight, and usage time are comparison attributes, and their recency should also be verified.

The example above is provided to explain the structure and calculations; it is not measured performance for a specific customer. In an actual report, the selected period, target, observation conditions, and original records must be linked so that the same decision can be checked again.

## Practical Verification Checklist

| Process stage | Items to check |
| --- | --- |
| Purchase context | Separate goals, constraints, and attributes |
| Attribute and evidence mapping | Check units and conditions |
| Answers by condition | Link to official information |

Check whether the meaning remains consistent not only with normal inputs, but also with missing materials, duplicate materials, and materials with different conditions. Connecting verification items to task completion criteria can reduce the gap between feature descriptions and actual operations.

## Limitations and Points to Note When Applying

Preparing context does not automatically guarantee that AI advertising will be run or recommendations will be made. Product facts, operational integrations, and conversion measurement require separate verification.

## Research and Official Documentation

- [Schema.org Product](https://schema.org/Product) — An official vocabulary for checking the properties and relationships used to describe product information.

External materials provide background for the design topics above; they do not certify every SAG implementation or customer outcome. The application interpretation and illustrative example in this note are organized according to SAG’s operational structure. Materials checked: 2026-10-06.

## Further Reading and Feature Information

- [Related architecture note](/ko/blog/customer-question-ontology)
- [Try the service connected to the purchase context model](/ko/preview/goal?scenario=cream)
- [Feature-specific FAQ](/en/faq)
- [Consult about implementation scope](/ko#inquiry)


## How to Continue Reading About This Technology

Read about the problems that SEO·AEO·GEO, entities, and JSON-LD each address.

- [Understand the terminology first](/ko/blog?tag=%EC%9A%A9%EC%96%B4%EC%99%80%20%EC%9B%90%EB%A6%AC)
- [Feature information FAQ](/en/faq)