# Wine Label Design Testing AI Agent Connect

> Design valid A/B testing protocols for wine labels using statistical experimental design.

## Overview
- **Category:** marketing
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_7axOAo7hMCfpJ5AIbxb0fsshf2Ua1XxTELtT9ibx/ai-agent-connect
- **Tags:** wine, ab-testing, packaging, consumer-research, experimental-design

## Description

This MCP server provides specialized tools for wine packaging experts to design and analyze A/B tests. Use `calculate_experiment_requirements` to determine the necessary sample size and test duration for statistical validity. You can use `evaluate_label_performance` to compare two design variants and determine if a winner is statistically significant. Additionally, `simulate_shelf_impact` allows you to adjust predicted scores based on lighting or retailer context, while `segment_test_results` helps break down consumer responses by demographic attributes like age or region.

## Tools

### calculate_experiment_requirements
Determines the foundational parameters needed to launch a valid A/B test

### evaluate_label_performance
Compares the performance of two label variants against the target metric

### segment_test_results
Breaks down the experimental results into specific consumer groups

### simulate_shelf_impact
Adjusts predicted performance based on environmental and consumer context

## Prompt Examples

**Prompt:** 
```
I want to test a new label for a premium Chardonnay. What sample size do I need for a 95% confidence level and 80% power to detect a 5% difference in shelf appeal?
```

**Response:** 
```
To detect a 5% difference in shelf appeal with 95% confidence and 80% power, you will need a required sample size of 1,536 participants, with an estimated test duration of 12 days.
```

**Prompt:** 
```
Variant A scored 7.5 and Variant B scored 8.2. Variant A had 500 participants and Variant B had 500 participants. Is there a winner?
```

**Response:** 
```
Yes, Variant B is the winner. The metric difference is 0.7, and the result is statistically significant.
```

**Prompt:** 
```
A label has a base score of 75. How will it perform in a premium boutique setting?
```

**Response:** 
```
In a premium boutique setting, the adjusted score for the label is 84.5 due to the positive context multiplier.
```

## Frequently Asked Questions

**How do I know if my sample size is large enough?**
You can use the `calculate_experiment_requirements` tool. By providing your target metrics, desired power, and confidence level, it will return the exact required sample size needed for a valid test.

**Can I account for different retail environments?**
Yes, the `simulate_shelf_impact` tool allows you to adjust baseline performance scores based on the shelf context, such as whether the environment is dimly lit or a premium boutique.

**How do I compare two different label designs?**
Use the `evaluate_label_performance` tool. Input the scores and sample sizes for both Variant A and Variant B to determine if the difference is statistically significant.