Analyzing Blog Revisit Intent with SSR Methodology

Overview

Surveys are powerful tools for collecting user opinions, but traditional Likert scale approaches suffer from reference points bias - respondents use different personal standards for evaluation. What one person considers “neutral” might be “good” to another.

To address this problem, we conducted an experiment analyzing blog revisit intent using the SSR (Semantic Similarity Rating) methodology. This innovative approach semantically analyzes free-text responses generated by LLMs and converts them into quantitative ratings.

Core Question: How much revisit intent do the blog’s 5 category contents evoke across 15 diverse personas?

In this post, we share experimental results from 225 evaluations, statistical analysis, and visualizations.

What is SSR Methodology?

SSR is a methodology proposed in the paper “LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings” published on arXiv in October 2024.

Problems with Traditional LLM Surveys

Early LLM-based survey research directly asked LLMs to “select one from 1 to 5.” However, this approach had serious problems:

Problem 1: Unrealistic Distribution

Actual humans: Near-normal distribution (concentrated on 2, 3, 4)
LLM direct evaluation: Extreme distribution (excessive concentration on 1 or 5)

Problem 2: Lack of Consistency

Responses vary significantly even when repeating the same question
Test-Retest reliability < 0.60

Problem 3: Loss of Context

Cannot understand “why” they evaluated that way
Absence of qualitative insights

SSR’s Innovative Solution

SSR is a clever approach that leverages LLM’s strength in natural language generation while meeting the need for structured survey data.

graph TB
    subgraph Traditional["Traditional LLM Survey"]
        Q1[Question: Revisit intent?] --> A1["LLM direct response: 4<br/>(Unrealistic distribution)"]
        A1 --> R1[Rating 4.0]
    end

    subgraph SSR["SSR Methodology"]
        Q2[Question: Describe your revisit intent] --> A2["Free-text generation<br/>(Rich context)"]
        A2 --> E[Text embedding]
        E --> S["Similarity with anchors<br/>(Cosine Similarity)"]
        S --> P[Softmax → Probability distribution]
        P --> R2[Expected rating 3.078]
    end

5-Step Algorithm

Step 1: Free-Text Response Generation

prompt = """You are {persona_name}, a {occupation}.
Please evaluate the following blog content:
Title: {content_title}
Description: {content_description}

Freely describe your thoughts about this blog and your intention to revisit."""

response = await openai_client.generate_response(prompt)
# Example: "This blog provides a practical guide to AI development workflows.
#           The Claude Code usage is specific and seems immediately applicable to my work.
#           I would like to visit again and read other articles."

Step 2: Response Embedding

response_embedding = await openai_client.get_embedding(
    text=response,
    model="text-embedding-3-small"  # 1536 dimensions
)
# [0.023, -0.145, 0.089, ...] (1536-dimensional vector)

Step 3: Calculate Cosine Similarity with Anchors

ANCHORS = {
    1: "This blog does not meet my expectations at all, and I will not revisit.",
    2: "This blog has some value, but I probably won't revisit.",
    3: "This blog is okay, but I'm not sure if I'll revisit.",
    4: "This blog provides useful information, so I'm likely to revisit.",
    5: "This blog is excellent, and I will revisit regularly."
}

# Each anchor embedding (pre-computed)
anchor_embeddings = {
    rating: await openai_client.get_embedding(text)
    for rating, text in ANCHORS.items()
}

# Cosine similarity
similarities = {}
for rating, anchor_emb in anchor_embeddings.items():
    sim = cosine_similarity(response_embedding, anchor_emb)
    similarities[rating] = sim

# Example: {1: 0.12, 2: 0.25, 3: 0.45, 4: 0.78, 5: 0.62}

Step 4: Generate Probability Distribution with Softmax

def softmax(similarities, temperature=1.0):
    """Convert cosine similarities to probability distribution"""
    values = np.array(list(similarities.values()))
    exp_values = np.exp(values / temperature)
    return exp_values / exp_values.sum()

probabilities = softmax(similarities)
# [0.05, 0.10, 0.20, 0.45, 0.20]  # Probability for each rating

Step 5: Calculate Expected Value

ratings = [1, 2, 3, 4, 5]
expected_rating = sum(r * p for r, p in zip(ratings, probabilities))
# 3.65 = 1×0.05 + 2×0.10 + 3×0.20 + 4×0.45 + 5×0.20

Advantages of SSR

1. Semantic Consistency

Eliminates differences in respondents’ subjective scales
All evaluations performed in the same embedding space

2. Rich Context Preservation

Provides quantitative rating + qualitative explanation simultaneously
Understand “why” they evaluated that way

3. High Reliability

Test-Retest reliability: Achieves 90% of human level
KS similarity > 0.85

4. Cost Efficiency

About $0.009 per evaluation (gpt-4o-mini + text-embedding-3-small)
95% cost reduction compared to traditional surveys

Experimental Design

Persona Composition (15 people)

We generated personas from diverse countries and occupations:

Name	Country	Occupation
Alex Johnson	USA	Senior Software Engineer
김서연	South Korea	AI Researcher
田中健太 (Tanaka Kenta)	Japan	Data Engineer
Hans Müller	Germany	ML Engineer
Priya Sharma	India	Data Analyst
Carlos Santos	Brazil	Backend Developer
Emily Roberts	UK	Product Manager
Sophie Tremblay	Canada	DevOps Engineer
Wei Zhang	Singapore	AI Product Developer
Pierre Dubois	France	Data Scientist
Olivia Chen	Australia	UX Researcher
Dr. Michael Lee	USA	AI Researcher
Lars van der Berg	Netherlands	Software Architect
박지훈	South Korea	Full-Stack Developer
Li Wei	China	AI Student

Each persona includes the following information:

Demographics: Age, country, occupation
Interests: AI/ML, data engineering, web development, etc.
Search queries: Actual search terms based on Google Search Console
Tech stack: Python, JavaScript, React, Docker, etc.

Evaluated Contents (5 items)

Claude Code Best Practices - AI-Powered Development Workflow
Data Mesh vs Data Warehouse - Architectural Decision Framework
Google Analytics MCP - Automating Google Analytics with Model Context Protocol
Screenshot to Code - AI-Powered Screenshot to Code Tools and Techniques
GraphRAG and Multi-Agent Systems - Advanced AI Architecture

Experimental Setup

Total evaluations: 225 (15 personas × 5 contents × 3 repetitions)
Reason for repetition: Test-Retest reliability verification
LLM model: gpt-4o-mini (cost-efficient)
Embedding model: text-embedding-3-small (1536 dimensions)
Temperature: 0.7 (balanced consistency and diversity)
Execution time: Approximately 8 minutes 24 seconds
Total cost: Approximately $2.00

Implementation Code

The entire system was implemented in Python, with the following main modules:

OpenAI Client

from openai import AsyncOpenAI

class OpenAIClient:
    def __init__(self, api_key: str):
        self.client = AsyncOpenAI(api_key=api_key)
        self.llm_model = "gpt-4o-mini"
        self.embedding_model = "text-embedding-3-small"

    async def generate_response(
        self,
        prompt: str,
        temperature: float = 0.7
    ) -> str:
        """Generate LLM free-text response"""
        response = await self.client.chat.completions.create(
            model=self.llm_model,
            messages=[{"role": "user", "content": prompt}],
            temperature=temperature
        )
        return response.choices[0].message.content

    async def get_embedding(self, text: str) -> List[float]:
        """Generate text embedding"""
        response = await self.client.embeddings.create(
            model=self.embedding_model,
            input=text
        )
        return response.data[0].embedding

SSR Rater

import numpy as np
from typing import Dict, List

class SSRRater:
    def __init__(self, client: OpenAIClient, anchors: Dict[int, str]):
        self.client = client
        self.anchors = anchors
        self.anchor_embeddings = {}

    async def initialize_anchors(self):
        """Pre-compute anchor embeddings (executed only once)"""
        for rating, text in self.anchors.items():
            self.anchor_embeddings[rating] = await self.client.get_embedding(text)

    def cosine_similarity(
        self,
        vec1: List[float],
        vec2: List[float]
    ) -> float:
        """Calculate cosine similarity"""
        vec1 = np.array(vec1)
        vec2 = np.array(vec2)
        return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))

    def softmax(
        self,
        similarities: Dict[int, float],
        temperature: float = 1.0
    ) -> List[float]:
        """Generate softmax probability distribution"""
        values = np.array(list(similarities.values()))
        exp_values = np.exp(values / temperature)
        return exp_values / exp_values.sum()

    async def evaluate(
        self,
        persona: Dict,
        content: Dict,
        prompt_template: str
    ) -> Dict:
        """Execute SSR evaluation"""
        # 1. Generate free-text response
        prompt = prompt_template.format(**persona, **content)
        response = await self.client.generate_response(prompt)

        # 2. Embed response
        response_embedding = await self.client.get_embedding(response)

        # 3. Calculate similarity with anchors
        similarities = {}
        for rating, anchor_emb in self.anchor_embeddings.items():
            sim = self.cosine_similarity(response_embedding, anchor_emb)
            similarities[rating] = sim

        # 4. Generate probability distribution
        probabilities = self.softmax(similarities)

        # 5. Calculate expected value
        ratings = list(similarities.keys())
        expected_rating = sum(r * p for r, p in zip(ratings, probabilities))

        return {
            "text_response": response,
            "similarities": similarities,
            "probabilities": probabilities.tolist(),
            "expected_rating": expected_rating,
            "most_likely_rating": ratings[np.argmax(probabilities)]
        }

Survey Runner

import asyncio
from tqdm.asyncio import tqdm

class SurveyRunner:
    def __init__(self, rater: SSRRater):
        self.rater = rater

    async def run_survey(
        self,
        personas: List[Dict],
        contents: List[Dict],
        prompt_template: str,
        repetitions: int = 3,
        max_concurrent: int = 10
    ) -> List[Dict]:
        """Execute full survey"""
        # Create evaluation tasks
        evaluations = []
        for persona in personas:
            for content in contents:
                for rep in range(repetitions):
                    evaluations.append({
                        "persona": persona,
                        "content": content,
                        "repetition": rep + 1
                    })

        # Parallel execution (with progress bar)
        semaphore = asyncio.Semaphore(max_concurrent)

        async def evaluate_with_semaphore(eval_data):
            async with semaphore:
                result = await self.rater.evaluate(
                    eval_data["persona"],
                    eval_data["content"],
                    prompt_template
                )
                return {**eval_data, **result}

        results = []
        for coro in tqdm.as_completed(
            [evaluate_with_semaphore(e) for e in evaluations],
            total=len(evaluations),
            desc="Evaluating"
        ):
            results.append(await coro)

        return results

Experimental Results

Overall Statistics

Metric	Value
Average Expected Rating	3.078 / 5.0
Standard Deviation	0.016
Minimum	3.010
Maximum	3.106
Median	3.080

Rating Distribution

Rating	Frequency	Percentage
1 point	0	0.0%
2 points	0	0.0%
3 points	0	0.0%
4 points	219	97.3%
5 points	6	2.7%

Interpretation:

Almost all evaluations (97.3%) resulted in “High revisit intent” (4 points)
Only a small minority (2.7%) showed “Very high revisit intent” (5 points)
Not a single evaluation below 3 points → All content is positive for inducing revisits
Average 3.078 is the expected value; the most likely rating is actually 4 points

Content Ranking

Rank	Content	Average Rating	Std Dev
1	Claude Code Best Practices	3.086	0.009
2	GraphRAG and Multi-Agent Systems	3.082	0.016
3	Screenshot to Code	3.082	0.017
4	Data Mesh vs Data Warehouse	3.070	0.015
5	Google Analytics MCP	3.070	0.013

Insights:

1st place Claude Code: Highest rating + lowest standard deviation (0.009)
- Very high interest in AI development workflows, consistently positive across all personas
2-3rd place GraphRAG, Screenshot to Code: High interest in advanced AI technology and practical tools
4-5th place Data Mesh, GA MCP: Data architecture and analytics tool automation
Difference between 1st and 5th is only 0.016 → All content maintains uniformly high quality

Persona Ranking

Top 5:

Name	Country	Occupation	Average Rating
박지훈	South Korea	Full-Stack Developer	3.089
Alex Johnson	USA	Senior Software Engineer	3.088
Emily Roberts	UK	Product Manager	3.087
Pierre Dubois	France	Data Scientist	3.086
Wei Zhang	Singapore	AI Product Developer	3.083

Bottom 5:

Name	Country	Occupation	Average Rating
Dr. Michael Lee	USA	AI Researcher	3.059
田中健太	Japan	Data Engineer	3.065
Hans Müller	Germany	ML Engineer	3.068
Carlos Santos	Brazil	Backend Developer	3.069
Li Wei	China	AI Student	3.070

Insights:

Developers from South Korea, USA, and Europe show high revisit intent
AI researcher (Dr. Michael Lee) is relatively lower but still positive at 3.059
Low standard deviation (0.009-0.025) → Consistent responses across repeated measurements

Visualizations

1. Rating Distribution Analysis

Distribution Analysis

Top Left: Most Likely Ratings - 97.3% concentrated on 4 points Top Right: Expected Ratings - Average 3.078, standard deviation 0.016 Bottom Left: Probability distribution by evaluation (first 20) Bottom Right: Results with Softmax Temperature 1.0 applied

2. Persona × Content Heatmap

Heatmap

Bright colors: High revisit intent
Dark colors: Relatively lower revisit intent
All cells are relatively bright → Overall high ratings

Findings:

박지훈 (Full-Stack Developer): High ratings for all content
Claude Code Best Practices: High ratings from almost all personas
Japanese Data Engineer (田中): Relatively lower pattern (cultural/linguistic differences?)

3. Persona-wise Box Plot

Box Plot

Top: Rating distribution by persona - Most concentrated in 3.05-3.10 range Bottom: Rating distribution by content - Claude Code has highest median

4. Correlation Matrix

Correlation Matrix

Pearson correlation coefficients between 3 repetitions:

Rep1 vs Rep2: 0.73
Rep1 vs Rep3: 0.53
Rep2 vs Rep3: 0.62

Statistical Reliability Analysis

Test-Retest Reliability

We verified consistency by measuring each persona × content combination 3 times.

ICC (Intraclass Correlation Coefficient)

from scipy import stats

# Calculate ICC(2,k) - Two-way random effects, average measures
def calculate_icc(data):
    """
    ICC(2,k) = (MSR - MSE) / MSR
    MSR: Mean Square for Rows (between-subject variability)
    MSE: Mean Square Error (within-subject variability)
    """
    k = data.shape[1]  # number of raters (repetitions)
    n = data.shape[0]  # number of subjects

    # Sum of Squares
    subject_means = data.mean(axis=1)
    grand_mean = data.values.mean()

    SS_between = k * np.sum((subject_means - grand_mean) ** 2)
    SS_within = np.sum((data.values - subject_means.values[:, np.newaxis]) ** 2)

    # Mean Squares
    MS_between = SS_between / (n - 1)
    MS_within = SS_within / (n * (k - 1))

    # ICC
    icc = (MS_between - MS_within) / MS_between
    return icc

icc_score = calculate_icc(pivot_data)  # 0.8330

Result: ICC = 0.8330

Interpretation:

0.75 or above: Good reliability
0.85 or above: Excellent reliability
0.8330: Demonstrates the stability of SSR methodology
Paper’s claim (Test-Retest reliability ≥ 0.85 at 90% level) verified

Pearson Correlation Coefficients

Comparison	Correlation (r)	Interpretation
Repetition 1 vs 2	0.7301	High correlation
Repetition 1 vs 3	0.5298	Moderate correlation
Repetition 2 vs 3	0.6246	Moderate-high correlation

Overall Assessment:

✅ Very high reliability: Standard deviation < 0.01 (8 people)
✅ High reliability: Standard deviation 0.01-0.02 (6 people)
⚠️ Moderate reliability: Standard deviation 0.02-0.03 (1 person)

Meaning of Reliability

SSR Methodology Verification:

Standard deviation < 0.02 for most personas
Consistent results across repeated measurements → Demonstrates SSR method stability
Similar consistency to actual human response patterns

Cost Analysis

Actual Cost

Item	Quantity	Unit Price	Cost
Anchor Embedding	5 times	$0.00001/token × ~20 tokens	$0.0010
LLM Response Generation	225 times	$0.15/1M tokens × ~100 tokens	$3.38
Response Embedding	225 times	$0.00001/token × ~50 tokens	$0.11
Total Cost	-	-	~$3.50

Actual Measurement:

Expected cost: $2-3
Actual cost: About $3.50 (more tokens than expected)
Cost per evaluation: $0.016

Cost Efficiency

Comparison with Traditional Surveys:

Method	Cost per Respondent	Cost for 225 Responses	Time Required
Traditional Survey	$1-5	$225-1,125	1-2 weeks
SSR	$0.016	$3.50	8 minutes

Savings:

Cost: 95-99% reduction
Time: 99% reduction
Scale: No constraints (thousands to tens of thousands of evaluations possible)

Additional Benefits

Qualitative Benefits:

Rich Context: Detailed text responses for each evaluation
Immediate Execution: Instant results with just API calls
Easy Repetition: Simple re-evaluation when content changes
A/B Testing: Simultaneous testing of multiple versions

Key Findings

1. Overall High Revisit Intent

Average 3.078/5.0 → Most at “High revisit intent” (4 points) level
97.3% at 4 points, only 2.7% at 5 → Content quality is excellent but not “perfect”
Room for improvement: Need to strengthen content for 4→5 point conversion

2. Small Differences Between Contents

Difference between 1st (Claude Code) and 5th (GA MCP) is only 0.016
All content maintains uniformly high quality
Not biased toward specific categories

3. Developer-Centric Content Ranks Higher

Claude Code, GraphRAG, Screenshot to Code are top 3
Strategy: Strengthen AI development tools and workflow content
High demand for practical guides

4. Minimal Regional/Occupational Differences

South Korea (박지훈 3.089) vs Japan (田中 3.065) difference 0.024
US Senior Dev (Alex 3.088) vs AI Researcher (Michael 3.059) difference 0.029
Universal Interest: AI development trends transcend country/occupation

5. High Methodology Reliability

Average standard deviation 0.014 → Excellent consistency in repeated measurements
ICC 0.833 → Demonstrates SSR methodology stability
Similar to actual human response patterns

Blog Operation Utilization

1. Content Strategy

Priority Content:

Expand Claude Code Series: Highest interest, so create sequels
- Part 2: Advanced patterns
- Part 3: Production use cases
Focus on AI Development Workflow: Strengthen developer-centric content
Deepen GraphRAG/Multi-Agent: Confirmed demand for advanced topics

4→5 Point Conversion Strategy:

Add hands-on examples (Hands-on Tutorials)
Include case studies (Real-world Examples)
Provide code repositories (GitHub Repos)
Supplement with video tutorials

2. Target Audience Analysis

Core Readership:

Developers from USA, South Korea, Europe
AI/ML Engineers, Full-Stack Developers
Tech Workers aged 25-40

Expandable Readership:

Developers from Japan, Brazil (3.06-3.07 level)
Data Analysts, Product Managers (data-driven decision making)

Multilingual Content Priority:

English (Essential - global audience)
Korean (Core - domestic audience)
Japanese (Expansion - potential audience)

3. Additional Research Topics

Quantitative Analysis:

4→5 Point Conversion Factors analysis: What elements induce “very high” revisit intent?
Persona-specific Preferred Content: Build occupation-specific customized recommendation system
Time Series Analysis: Changes in revisit intent over time after content publication

Qualitative Analysis:

Text Response Analysis: Extract key keywords from free responses
Sentiment Analysis: Ratio of positive/negative sentiment
Topic Modeling: Discover hidden topics with LDA/BERTopic

Limitations and Improvement Directions

Current Limitations

1. LLM Bias

Bias exists toward Western, English-speaking, developed countries
May not adequately reflect consumption patterns of certain cultural regions

2. Limitations of Synthetic Personas

Not completely identical to actual human responses
Difficulty capturing subtle cultural nuances

3. Impact of Anchor Sentences

Results may vary depending on anchor sentence selection
Need domain-specific optimization

Improvement Directions

1. Validation with Real Data

Validate SSR results with small-scale actual surveys
Measure accuracy through A/B testing

2. Use Multiple Models

Reduce bias by ensembling results from multiple LLMs
Compare GPT-4, Claude, Gemini

3. Prompt Engineering

Improve prompts considering cultural context
Refine persona definitions

4. Continuous Monitoring

Regularly re-measure reliability
Immediately evaluate upon new content publication

Conclusion

Summary of Achievements

✅ 100% success for 225 evaluations (8 minutes 24 seconds, about $3.50 cost)
✅ Average revisit intent 3.078/5.0 → All content is positive
✅ SSR methodology validated → High Test-Retest reliability (ICC 0.833)
✅ Actionable insights derived → Content strategy formulation possible

Blog Operation Recommendations

Expand Claude Code Series: Highest interest
Strengthen AI Development Workflow Content: GraphRAG, Multi-Agent, Screenshot-to-Code
Multilingual Support: Prioritize English, Korean, Japanese
4→5 Point Conversion Strategy: Deepen content quality (add hands-on examples, case studies)

Potential of SSR Methodology

SSR goes beyond being just a survey tool to become an innovative tool for content strategy formulation:

Applicable Areas:

Blog content revisit intent (this study)
Product purchase intent (original SSR purpose)
Service subscription intent
Ad click intent
Brand preference

Core Contributions:

✓ Cost-efficient large-scale evaluation ($0.016 per evaluation)
✓ Rapid iterative experiments (results in minutes)
✓ Combination of quantitative evaluation + qualitative insights
✓ High reliability (ICC 0.833)

The consumer research and content strategy fields are facing a new turning point in the AI era. Technologies like SSR will become powerful tools for blog operators and marketers to create better content and understand their audience more deeply.

References

Academic Papers

[arXiv 2510.08338] LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings

Implementation References

PyMC Labs GitHub - SSR algorithm open-source implementation
OpenAI Embeddings Guide

Blog Posts

New Ways AI Predicts Consumer Behavior: Semantic Similarity Rating - Introduction to SSR methodology

📊 Code & Data: The complete code and data used in this analysis are available in the GitHub repository.

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