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Step 5: Share

Guidelines from the assignment

  • Guiding questions
  • Were you able to answer the business questions?
  • What story does your data tell?
  • How do your findings relate to your original question?
  • Who is your audience? What is the best way to communicate with them?
  • Can data visualization help you share your findings?
  • Is your presentation accessible to your audience?
  • Key tasks
  1. Determine the best way to share your findings.
  2. Create effective data visualizations.
  3. Present your findings.
  4. Ensure your work is accessible.
  • Deliverable
  • Supporting visualizations and key findings

A reminder of the problem I am trying to solve.

Bellabeat is an established company focused on health smart products for women. The problem that I am trying to solve is the main trends in smart device usage found in Bellabeat's competitor's data that can be applied to Bellabeat customers to influence their marketing strategy.

A list of the key trends I found in the data

  • From the fitabase dataset:
  • There are some days where people are more active and/or burn more calories on average.
  • There is a negative correlation between TotalMinutesAsleep and SendentaryMinutes.
  • The data is not very significant, but most of the participants have more of their daily steps every day around 12:00 and 19:00.
  • Very active people make the most steps on Saturdays, while sedentary people make the least on Sundays.
  • Although calories and steps are in general correlated, active (and highly active) people have many days with a low-calorie count.
  • From the apple dataset:
  • There are significant differences in running activities between males/females.
  • There is a difference between males/females for the calories expended in the different activities.
  • There is a clear difference in the resting heart rate between men/women in all the activities.
  • These differences are more substantial in people below 35; these discrepancies are insignificant when looking only at participants above 35 years old.
  • From the fitbitGrades dataset:
  • For healthy or average participants, the GPA increases with the number of steps; the trend is more significant in females.

Story found in data

  • It is not very significant, but tracker device consumers are more active on Tuesdays and Saturdays, while they are less active on Sundays.

  • Tracker device consumers burn on average fewer calories on Sundays.

  • Consumers are more active (more steps, more intensity, burn more calories) every day between 8:00 and 19:00. Being Wednesday at 17:00 and Saturday at 13:00, the two times where consumers are more active than usual on average.

  • There are no significant differences in sleep patterns between days of the week; on average, Sunday is the day when consumers spend more time awake.

  • Besides the consumer behavior, I found interesting trends in data between men/women that the marketing department can exploit to sell our products:

    • The number of calories burned by men and women are different when doing similar activities.
    • The resting rate between men/women is different.
    • These differences are more robust in people below 35 years old.
    • Number of steps is correlated with GPA; this relation is more substantial in women.

Story to tell

  • Bellabeat has two products that track activities, sleep, and stress. The stakeholders want to gain insights into how the consumers are using these products.
  • The Fitbit data analyzed is not significant, but minor trends can be found: the day of the week when people are more active/burn more calories and therefore are more prong to use the Bellabeat products.
  • There are no significant differences in tracker activity per time of the day; however, two distinguished times when customers are more active are: Wed 17:00 and Saturday 13:00.
  • Customers, on average, spend more time awake on Sunday than any other day.
  • Major differences in calorie burn and heart rate are found using apple watch data between women/men. The marketing department can target this to emphasize these differences when targeting women's products.
  • There is mild evidence that minutes of being active can improve intellectual activities; this relation is more substantial for female consumers.

DataViz and critical findings