Main Goal: The main goal of this project is to develop a comprehensive model for understanding the impact of advertising on organic rankings and optimizing pricing strategies to enhance profitability and organic rank performance on Amazon. This involves leveraging data from various sources, building predictive models, and integrating web scraping to gather competitor insights.

Project Background and Context: In the competitive e-commerce landscape, optimizing product listings and pricing strategies is crucial for achieving high organic rankings and profitability. This project aims to analyze the contribution of advertising to organic rankings, assess the impact of pricing changes, and develop a data-driven strategy for keyword targeting and pricing optimization. By using descriptive, predictive, and prescriptive analytics, we can derive actionable insights to improve our advertising efficiency and overall performance.

Project Goals and Deliverables:

• Develop a web scraper to collect data on organic rankings and competitor performance.
• Analyze the relationship between advertising spend and organic rankings.
• Build predictive models to forecast organic rank based on conversion rate (CVR), sales velocity, and advertising spend.
• Analyze the impact of pricing changes on various performance metrics.
• Provide recommendations for keyword targeting, advertising spend, and pricing strategies.
• Create interactive dashboards to visualize data and insights.

Value to the unboxme: This analysis will help optimize advertising spend, improve organic rankings, enhance profitability, and make data-driven decisions for keyword targeting and pricing strategies. The insights gained will contribute to better resource allocation and improved competitive positioning in the market.

Data Sources and Variables:

• Search Query Reports: Variables include search terms, impressions, clicks, conversions, and advertising spend.
• Sales Data: Variables include product ID, sales volume, revenue, CVR, and sales velocity.
• Competitor Data: Scraped variables include organic rankings, pricing, and product details.

Data Granularity and Time-Period:

• Granularity: Daily data for a 2-3 month period.

Methods and Techniques:

• Statistical Modeling: Regression analysis, classification.
• Machine Learning: Predictive modeling, clustering.
• Exploratory Data Analysis: Statistical methods, visualizations.
• Interactive Dashboards: Data visualization platforms.

Tasks for Learners

1. Data Collection and Cleaning:

• Collect data from multiple sources.
• Clean the data, removing duplicates, missing values, and resolving inconsistencies.

1. In-Depth Data Analysis:

• Perform data analysis using spreadsheets to uncover patterns, trends, and insights.
• Utilize formulas, functions, and pivot tables to calculate metrics such as revenue, customer segmentation, and product performance.

1. Descriptive and Diagnostic Analysis:

• Perform analysis using SQL to gain insights.

1. Statistical Modeling:

• Apply statistical modeling techniques to identify factors influencing sales and customer behavior.

1. Exploratory Data Analysis (EDA):

• Uncover correlations and relationships between variables using statistical methods and visualizations.

1. Interactive Dashboards:

• Create interactive dashboards to visually represent the data.

Support for Learners

Weekly Check-ins and Ad-Hoc Meetings

• Platforms: Zoom, Microsoft Teams

Bi-Weekly Workshops/Brainstorming Sessions

• Platforms: Slack, Zoom, Microsoft Teams

Weekly Office Hours for Technical Guidance

• Platforms: Slack, Microsoft Teams, Email

Bi-Weekly Progress Reviews

• Platforms: Zoom, Microsoft Teams

Access to Data and Tools

• Platforms: AWS S3, Google Cloud Storage, Tableau, Power BI, Python, R, SQL

Documentation and Collaboration

• Platforms: Confluence, Google Drive, SharePoint

I will be available in whatever way the team would like to work on this project with any training resources, reports and workflow requests.

Learners will connect directly with the employer for mentorship and supervision throughout the project.

• Access to Resources: Provide learners with access to relevant data, documentation, and tools necessary.
• Subject Matter Expertise: Assign experienced data professionals as mentors to guide learners in understanding our support environment, identifying pain points, and developing effective solutions.
• Collaboration Opportunities: Facilitate collaboration between learners and team members to gather insights, validate recommendations, and ensure alignment with organizational goals and standards.
• Technical Guidance: Offer technical assistance and guidance to learners during the implementation phase, helping them overcome challenges, troubleshoot issues, and ensure successful deployment of new solutions.
• Feedback and Review: Regularly review project progress, provide constructive feedback on proposed recommendations and implementation efforts, and offer insights for continuous improvement.