Data Analysis Protocol
Data Analysis Protocol
1. Introduction
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Objective: The purpose of this data analysis is to evaluate the impact of a new marketing campaign on customer engagement and sales. We aim to determine whether there is a statistically significant increase in customer interaction and revenue after the campaign's launch.
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Background: The data was collected from customer interaction logs and sales records over a six-month period before and after the campaign. Previous studies have shown mixed results regarding the effectiveness of marketing campaigns on customer behavior.
2. Data Description
Data Source: Data was sourced from the company's CRM system and sales database.
The dataset includes:
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Customer Interaction Logs: Timestamped records of customer activities on the website, including page views, clicks, and time spent.
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Sales Records: Transaction data including date, product ID, quantity sold, and revenue.
Data Structure:
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Customer Interaction Logs: 50,000 records with variables: CustomerID, Date, PageViews, Clicks, TimeSpent (minutes).
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Sales Records: 30,000 records with variables: TransactionID, Date, ProductID, Quantity, Revenue.
Data Cleaning:
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Handle missing data by imputation for interaction logs and exclusion for sales records.
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Outliers will be identified using z-scores and addressed accordingly.
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Data will be normalized where necessary, particularly in time spent and revenue.
3. Analysis Plan
Descriptive Statistics:
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Calculate mean, median, and standard deviation for interaction metrics (PageViews, Clicks, TimeSpent) and sales metrics (Quantity, Revenue).
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Use frequency distributions to analyze customer activity patterns.
Inferential Statistics:
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Conduct a t-test to compare mean sales before and after the campaign.
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Perform regression analysis to assess the relationship between customer interactions and sales revenue.
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Use chi-square tests to evaluate any significant changes in categorical variables such as customer segments.
Data Visualization:
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Histograms for distribution of page views and time spent.
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Scatter plots to show the relationship between customer interactions and sales.
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Bar charts comparing average sales before and after the campaign.
Software and Tools:
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Data analysis will be performed using R for statistical testing and Python for data manipulation.
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Visualizations will be created using Tableau for interactive dashboards and matplotlib for static graphs.
4. Assumptions and Limitations
Assumptions:
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Data is normally distributed for t-tests.
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Linearity in regression analysis.
Limitations:
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Potential bias due to incomplete customer logs.
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Generalizability may be limited to similar marketing campaigns and customer demographics.
5. Ethical Considerations
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Data Privacy: All customer data will be anonymized before analysis to ensure privacy. Sensitive information such as customer names and contact details will be removed.
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Informed Consent: Participants provided consent for their data to be used for analysis as per the company’s privacy policy.
6. Quality Assurance
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Validation: Perform cross-validation of regression models to assess accuracy and robustness.
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Reproducibility: All analysis code will be documented and stored in a version-controlled repository (GitHub) to ensure reproducibility.
7. Reporting
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Format: Results will be presented in a comprehensive business report, including an executive summary, detailed analysis, and visualizations.
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Interpretation: Results will be interpreted in the context of campaign effectiveness and recommendations will be provided based on statistical findings.
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Visualizations: Visualizations will be integrated into the final report to illustrate key findings and trends.
8. Timeline
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Data Cleaning and Preparation: August 1 - August 15, 2050
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Descriptive and Inferential Analysis: August 16 - August 31, 2050
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Visualization and Reporting: September 1 - September 10, 2050
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Review and Finalization: September 11 - September 15, 2050
9. References
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Smith, J. (2048). Impact of Marketing Campaigns on Customer Behavior. Marketing Journal.
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Doe, A., & Lee, B. (2049). Statistical Methods for Business Analysis. Business Analytics Press.
