Analyzing Smart Device Usage Data for Bellabeat
Welcome to the presentation for the analysis of smart device usage data conducted for Bellabeat, a leading manufacturer of health-focused products for women. As an aspiring data analyst, I've delved into the trends in smart device usage and explored how these insights can inform Bellabeat's marketing strategy.
Business Task Summary
The goal of this analysis is to understand trends in smart device usage, their relevance to Bellabeat customers, and how they can influence the company's marketing strategy.
Data Sources
The primary data source used for this analysis is the FitBit Fitness Tracker Data, which contains minute-level output for physical activity, heart rate, and sleep monitoring from thirty Fitbit users. These anonymous users consented to have their data tracked for 30 consecutive days from April-May 2016. The data was made publicly available through Mobius on Kaggle.
Data Preparation
The FitBit data was downloaded and stored appropriately. It was organized in a structured format, and cleaning was performed to address any errors or inconsistencies.
Data Analysis
Several key insights were derived from the analysis:
Total distance vs total steps showed a positive correlation, indicating that as the number of steps increases, so does the total distance covered.
Sedentary minutes and total steps had a generally negative correlation, suggesting that higher levels of sedentary behavior are associated with fewer steps taken.
Total steps and total minutes asleep exhibited no significant correlation.
Calories and sedentary minutes showed a somewhat negative correlation, implying that higher calorie burn is associated with lower levels of sedentary behavior.
Time in bed and total minutes asleep had a very strongly positive correlation, indicating that more time spent in bed is associated with longer sleep durations.
Data Visualization and Key Findings
The visualization above illustrates the positive correlation between time in bed and total minutes asleep.
The visualization above illustrates the negative correlation between sedentary minutes and total steps taken in a day.
The visualization above illustrates the positive correlation between distance traveled and steps taken.
The visualization above illustrates the negative correlation between sedentary minutes and calories burned. Some potential factors as to why the plot shows a few groupings are each participant’s BMI, resting metabolism, and diet. These factors fall beyond the scope of the data gathered.
To further explore sleep patterns, I plotted the average number of minutes slept per weekday for random participants over the span of one month. This analysis revealed variations in sleep duration throughout the week.
The general pattern observed is that each individual participant has a few days per week where they sleep more than all other days of the week. These days of more sleep are usually consecutive, and commonly fall on weekends.
High-Level Recommendations
Based on the analysis, here are some high-level recommendations for Bellabeat's marketing strategy:
Emphasize the importance of increasing physical activity to cover more distance, aligning with the positive correlation found between total distance and total steps.
Develop targeted campaigns to reduce sedentary behavior, considering the negative correlation observed between sedentary minutes and total steps/calories burned.
Focus on promoting healthy sleep habits, leveraging the strong positive correlation between time in bed and total minutes asleep.
Emphasize promotions and incentives for sleep-related products and stress reduction techniques, particularly on days when participants tend to get less sleep. Offer rewards or affirmations for days where participants get more sleep.
Conclusion
In conclusion, the analysis of smart device usage data provides valuable insights for informing Bellabeat's marketing strategy. By understanding trends in user behavior, Bellabeat can tailor its products and messaging to better meet the needs of its customers.