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Unlocking SiriusXM Success in R: Data-Driven Strategies for Promotional Optimization

In our in-depth analysis of customer data, we've unearthed valuable skills in R that can guide SiriusXM's promotional strategy. From driving habits and income disparities to music preferences and subscription patterns, our findings provide a comprehensive understanding of customer behaviour. Explore the nuances of each segment, identify key drivers of subscriptions, and discover the demographic groups that are more likely to engage. Let these insights be the compass guiding SiriusXM towards a more targeted and effective promotional approach.

Problem Description:

The R Programming assignment involves exploring a dataset related to music preferences and subscription patterns. The dataset contains information on customers, including demographic details, driving habits, music preferences, and subscription status. The goal is to analyze the data and draw insights to make recommendations for SiriusXM's promotional approach.


  1. Data Exploration:
    • The dataset consists of 895 customers and 10 variables.
    • Summary statistics and structure of the dataset were examined, revealing no missing values.
  2. Driving Habits Analysis:
    • Males drive longer distances than females, and the difference is statistically significant.
    • A Welch Two Sample t-test was performed, resulting in a p-value of 0.003268.
  3. Income Analysis:
    • Females have a statistically significant higher household income than males.
    • A Welch Two Sample t-test yielded a p-value of 0.00557.
  4. Commute Analysis:
    • While females commute more, the difference is not statistically significant.
    • A chi-squared test showed a p-value of 0.2448.
  5. Driving and Music Enthusiasm Analysis:
    • Males have a significantly higher enthusiasm for driving and music.
    • Both drivingEnthuse and musicEnthuse showed significant differences (p-values < 0.05).
  6. Subscription Analysis:
    • Females tend to subscribe to music more, but the difference is not significant.
    • A chi-squared test resulted in a p-value of 0.1863.
  7. Segment Analysis:
    • Segments were analyzed based on various factors like income, miles driven, and demographics.
    • Significant differences were found in miles driven and household income among segments.
  8. ANOVA Tests:
    • ANOVA tests were conducted to determine significant differences in miles driven and income across segments.
    • Significant differences were observed for both miles driven and income.
  9. Visualization:
    • Mean income with confidence intervals for each segment was visualized.
  10. Two-Way ANOVA:
    • A two-way ANOVA was performed to examine the relationship between income, subscription, and segments.
    • No significant interaction was found between segments and subscriptions.
  11. Model Building:
    • A stepwise procedure was used to build a model to explain miles driven.
    • The final model included subscribeToMusic and Segment variables.
  12. Total Subscribers Analysis:
    • A chi-squared test confirmed a significant difference in the total number of subscribers.
  13. Subscribers vs. Non-Subscribers Analysis:
    • Properties of subscribers (subYes) and non-subscribers (subNo) were analyzed, revealing differences in income, miles driven, and other factors.
  14. Recommendations:
    • SiriusXM should target commuters, individuals with no kids at home, and those with higher income for promotions.
    • These groups show a higher probability of subscribing based on the analysis.

In summary, SiriusXM should tailor its promotional efforts based on customer segments and demographics, focusing on driving habits, music preferences, and income levels for a more effective marketing strategy.