Data Analysis of the Impact of Brand Name Queries on CTR, Conversion Rate, and Brand-Specific Funnel Performance

Overview

This project analyzes the impact of brand names in search queries on Click-Through Rate (CTR) and Conversion Rate (CVR) using web log data. The primary objective is to test the hypothesis that search queries containing brand names exhibit a higher CTR compared to non-brand queries. The analysis employs exploratory data analysis (EDA) and statistical hypothesis testing to identify patterns in brand-related queries and their influence on key performance metrics. Additionally, brand-specific funnel performance is assessed to provide targeted optimization strategies.

Data Source

• Dataset: Kaggle - Amazon Advertising Performance Metrics
• Link: Amazon Advertising Performance Metrics

Requirements

To run the analysis, the following Python libraries are required:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• scipy
• statsmodels
• TfidfVectorizer

Warning: Ensure that the dataset file is located in the same directory as the Jupyter notebook file. If the dataset path is incorrect, errors may occur during execution.

Summary of Insights

Analysis Objectives

• Examine the impact of brand names in search queries on CTR and CVR.
• Compare performance metrics (CTR, CVR) between brand and non-brand queries.
• Conduct statistical validation using Mann-Whitney U Test to assess significance.
• Perform brand-specific funnel performance analysis.

Key Findings

• Search queries containing brand names showed, on average, a 15% higher CTR than non-brand queries.
• Brands exhibited different performance patterns across the conversion funnel:
  • High CTR & High CVR (e.g., Bunmo): Strong overall performance.
    • Strategy: Maintain brand trust and expand product visibility through high-quality content and user engagement strategies.
  • High CTR & Low CVR (e.g., Chewigem, Oombee): High initial engagement but low conversion.
    • Strategy: Optimize landing pages and checkout processes to reduce drop-offs, introduce targeted promotions, and refine product positioning.
  • Low CTR & Low CVR (e.g., Fidgetland, Tangle, Speks): Weak engagement and conversion rates.
    • Strategy: Increase brand awareness through SEO improvements, influencer marketing, and paid search campaigns to drive qualified traffic.
• Brand-specific funnel weaknesses were identified, highlighting areas for improvement in conversion optimization.

Recommendations & Future Work

• Data Expansion: Incorporate additional behavioral and demographic data to enhance analysis depth.
• Addressing Skewness & Imbalance: Improve parametric testing by resolving dataset imbalances.
• Advanced Modeling: Apply NLP techniques to further analyze user intent and query structures.
• Marketing Optimization: Use insights to refine brand marketing and ad targeting strategies.

This study demonstrates how brand-related search queries drive higher engagement and provides actionable strategies for improving CTR and conversion performance. Future research should focus on refining analytical methodologies and integrating more diverse datasets, including demographic information and details on the pages where conversions were initiated, to better capture consumer behavior and optimize marketing strategies.