Marketing Data Analyst

Task Objective: The goal of this task is to simulate the initial phase of marketing analytics by collecting, cleaning, and preprocessing publicly available marketing campaign data. You will explore data from different online sources, apply data cleaning techniques, and prepare the dataset for further analysis. This task will strengthen your skills in data extraction, data cleaning, and data transformation.

Expected Deliverables: A single comprehensive file (preferably in CSV or Excel format) containing the cleaned and preprocessed dataset along with a detailed report (document file) explaining the steps and techniques applied during the process.

Key Steps:

• Identify and select publicly available datasets related to marketing campaigns or online advertisement metrics.
• Extract the required data attributes (e.g., impressions, clicks, conversions, campaign cost).
• Perform data cleaning by removing duplicates, handling missing values, and correcting inconsistent data entries.
• Document each step in a detailed report, including screenshots, code snippets, or pseudocode where applicable.
• Ensure the final dataset is structured and formatted for analysis.

Evaluation Criteria: Your submission will be evaluated on the completeness and accuracy of the data cleaning steps, clarity of the report, quality of the preprocessing, and the overall organization of both the dataset and documentation. The final mastery of data preprocessing in a marketing context will be key.

This task is designed to be executed over a span of approximately 30 to 35 hours. Ensure that your work is well-documented, replicable, and clearly explains how you derived each conclusion from the raw data.

Task Objective: In this task, you will focus on visualizing marketing campaign performance metrics by creating interactive dashboards and data visualizations. The aim is to use graphic representations to identify trends, outliers, and patterns in your collected data. You are expected to employ visualization tools that can effectively communicate complex data insights to a non-technical audience.

Expected Deliverables: A package containing both the data visualization files (interactive dashboards or static visualization images in a preferred format such as PNG or PDF) and a design report that explains your rationale, tool usage, and insights derived from the visuals.

Key Steps:

• Review your preprocessed dataset from Week 1 or use another publicly available marketing dataset.
• Select appropriate data visualization tools (e.g., Tableau, Power BI, or Python libraries such as Matplotlib, Seaborn).
• Create multiple visualizations (e.g., time-series charts, bar graphs, scatter plots) that capture important metrics like conversion rates, customer engagement, and ROI.
• Design an interactive dashboard if possible, enabling dynamic filtering and exploration of data.
• Document the visualization process, including design choices, tool selection, and how the visualizations convey the underlying data story.

Evaluation Criteria: Your submission will be assessed on the effectiveness of the visualizations in conveying clear messages, the usability and interactivity of the dashboard, creativity in design, and the clarity of the accompanying report. It should reflect a strong understanding of data storytelling and the ability to translate analytical insights into actionable marketing strategies.

This task should be completed in approximately 30 to 35 hours and must include a file submission with all required documents and visualization files.

Task Objective: This task involves performing an in-depth trend analysis on marketing data to extract valuable insights that could inform strategic decision-making. You will identify trends over time, seasonal patterns, and potential correlations between various marketing metrics. The focus is on gathering insights that can be used to recommend improvements or optimizations in marketing campaigns.

Expected Deliverables: An analytical report (in PDF or Word format) detailing your trend analysis, including charts, graphs, and a set of actionable recommendations based on your insights. In addition, submit supporting analysis files (e.g., code files or spreadsheets) that demonstrate the analytical process.

Key Steps:

• Select a marketing campaign dataset from publicly available resources.
• Conduct a thorough exploratory data analysis (EDA) to identify trends, seasonal variations, and anomalies.
• Apply statistical methods where applicable to substantiate your findings.
• Create visualizations that highlight key trends and patterns using tools of your choice.
• Develop a set of actionable insights and recommendations for optimizing marketing efforts.

Evaluation Criteria: Submissions will be evaluated based on the depth of analysis, the clarity and effectiveness of visualizations, the relevance and feasibility of the recommendations, and the quality of the supporting documentation. Your ability to derive data-driven insights that can directly impact marketing strategy is crucial.

This practical task is expected to require roughly 30 to 35 hours of dedicated work. Ensure that all your analysis steps are clearly documented and reproducible through your submitted file attachments.

Task Objective: The purpose of this task is to simulate marketing campaign optimization using data-driven strategies. You will develop a simulation model that tests different marketing strategy variables, such as budget allocation, channel mix, and target segmentation. The goal is to evaluate the potential impact of adjustments in campaign strategies on key performance metrics. This exercise is designed to foster predictive analytics skills and strategic thinking in the context of marketing.

Expected Deliverables: A comprehensive file package including a simulation model file (e.g., Excel with embedded scenarios, Python script, or R notebook) and a detailed report outlining the methodology, simulated scenarios, analysis results, and strategic recommendations. Ensure that your file submission clearly demonstrates your simulation process and findings.

Key Steps:

• Review publicly available marketing performance metrics and relevant benchmarks.
• Develop a hypothesis about how changes in various marketing parameters might affect performance outcomes.
• Create a simulation model that tests these hypotheses under different scenarios (e.g., varying budget allocations, channel optimizations).
• Analyze simulated results and compare performance metrics across scenarios.
• Document your simulation approach, assumptions made, model limitations, and strategic recommendations based on the simulated results.

Evaluation Criteria: Your work will be evaluated based on the clarity of your simulation methodology, the robustness of the model, the accuracy of your scenario analysis, and the practical applicability of your recommendations. The simulation should be comprehensive and demonstrate a clear understanding of how data can drive strategic decisions in marketing campaigns.

This exercise is designed to be completed within 30 to 35 hours. Ensure that every step of your simulation is transparent and well-documented in your final submission.