About the Company: Bellabeat is a technology based health and lifestyle company that allows women to make informed decisions about their health.
Task: Identify opportunities for growth and recommendations for the Bellabeat marketing strategy improvement based on user trends.
Stakeholders:
- Urška Sršen – Bellabeat co-founder and Chief Creative Officer
- Sando Mur – Bellabeat co-founder and key member of Bellabeat executive team
- Bellabeat Marketing Analytics team
Case Study Preparation
For this study I was provided with a FitBit Fitness Tracker Data. This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.
This dataset is stored in Kaggle and was made available through Mobius.
While the sample sizes from the data are limited, I am proceeding with a view that the data was collected in a good way, and the data provided is valid and considered to be sufficient for the purposes of today’s findings.
In addition I have pulled some additional data directly from Google Trends.
And finally, in helping to prepare my findings I also conducted a review on bellabeat.com, as well as within a number of the brand’s social media channels.
Case Study Process
For this effort I utilized R.
Installed R Packages
- tidyverse
- here
- skimr
- janitor
- lubridate
- ggpubr
- ggrepel
- stats
- purr
- tibble
- ggplot2
- dplyr
The primary data sets I relied on to produce my results:
- dailyActivity_merged.csv
- dailySteps_merged.csv
- sleepDay_merged.csv
Import Data
“`{r}
daily_activity <- read.csv(“dailyActivity_merged.csv”)
“`
“`{r}
sleep_day <- read.csv(“sleepDay_merged.csv”)
“`
“`{r}
daily_step <- read.csv(“dailySteps_merged.csv”)
“`
Preview Data
head(daily_activity) 3X
Column Review
colnames(daily_activity) 3X
Summary Stats
n_distinct(daily_activity$Id)
n_distinct(sleep_day$Id)
n_distinct(daily_step$Id)
Summary Stats Output
[1] 33 [1] 24 [1] 33
Combined Data
combined_data <- merge(sleep_day, daily_activity, by=”Id”)
n_distinct(combined_data$Id)
[1] 24
colnames(combined_data)
[1] "Id" "SleepDay" [3] "TotalSleepRecords" "TotalMinutesAsleep" [5] "TotalTimeInBed" "ActivityDate" [7] "TotalSteps" "TotalDistance" [9] "TrackerDistance" "LoggedActivitiesDistance" [11] "VeryActiveDistance" "ModeratelyActiveDistance" [13] "LightActiveDistance" "SedentaryActiveDistance" [15] "VeryActiveMinutes" "FairlyActiveMinutes" [17] "LightlyActiveMinutes" "SedentaryMinutes" [19] "Calories"
Analysis
Fitbit Tracker Data rendered in R
ggplot(data=combined_data, aes(x=TotalSteps, y=Calories)) + geom_point()+ geom_smooth() +
labs(title=”Calories and Total Steps”)
From the above chart we can see a clear connection where as Total Steps increases, the amount of Calories increases.
Google Trends Data
From the Google Trends data, over a 5 year period, we can see that there is a strong overlap between generalized fitness app and personalized health trends.
Product Recommendations Ground in Market Insights
A few recommendations for Bellabeat to Identify opportunities for growth and recommendations for the Bellabeat marketing strategy improvement based on user trends:
Provide your user bases with real-time customized insights, tailored to each unique user that allows for individualized data insights that are further guided and enhanced with generalized data.
In so doing, test with tone to drive consumption and product adoption, but ground this messaging in data driven insights to unlock user appreciation and ongoing positive (health grounded) participation.
In App Prompts Tonal Variants
- Aspirational – you can do it!
- Warning – you are at risk….
- Factual – X% of people that take X steps per day over a 5 day period tend to see Y results
In App Reminders Tonal Variants
- You haven’t logged anything today…
- You are on track with 3 walks already this week. Keep up the good work! Let’s hit the road
- Usually at this time of day you are on the road getting your steps in. (user based insights).
Tie In App Voice Testing with Website Marketing, and Back Again
- As Bellabeat explores a number of data driven messaging guides, consider implementing some of these finding directly into your marketing efforts on your website, and through any paid media efforts.
- As Bellabeat begins to rely on a testing strategy to explore how best to implement a “data-centric user messaging strategy” consider bringing this finding directly into your In App efforts.
- NOTE: Be aware and consider how different demographics might best respond to different tonal prompts (age, region, income for example might yield unique, yet very powerful outcomes)
Rewards Program
Introduce a rewards program – even if the entirety of the program is based on virtual achievements and virtual recognition. This could be a fun addition to the Bellabeat community with some very unique applications around reward structures and vocabulary. Again, ground in at least some part with data encouragements to encourage known behavioral driven outcomes.
Referral Program
Find others that share common interests via a referral program. Could be tied to a rewards program as well. But likely a great source of referrals will be the existing user base as they should be well versed and well aligned with Bellabeat mission
Acknowledgments
As I conclude my case study capstone I would like to acknowledge my appreciation to the many others within the Kaggle community that generously shared their findings, which certainly helped to guide me as I put this work together.