Bellabeat Capstone Case Study

Downloads: PDF report · GitHub repo

Abstract

This case study explores patterns in activity, sleep, and energy expenditure using Fitbit smart-device data to guide marketing strategies for Bellabeat, a wellness company. Using R for cleaning, analysis, and visualization, I examined relationships between steps, calories, and sleep, as well as temporal patterns by weekday and hour. Results suggest that higher activity correlates strongly with calorie burn, 7–8 hours of sleep align with improved next-day activity, and evenings/weekends present opportunities for targeted engagement. These findings inform data-driven recommendations such as sleep reminders, evening workout nudges, and segmented user messaging.

Insights

  • Steps and very active minutes are strongly associated with higher calories.
  • 7–8 hours of sleep aligns with higher activity the next day.
  • Evening and weekend patterns suggest opportunity windows for engagement.

Recommendations

  1. Sleep reminders + smart alarms to target 7–8h sleep.
  2. Short evening micro-workout nudges.
  3. Weekend streak challenge with shareable badges.
  4. Segment-based messaging by average steps.

Next Steps

To validate these recommendations, run small experiments: A/B test evening push notifications on step counts; pilot a weekend streak challenge; and instrument sleep reminders to measure downstream activity.