Case Study 2

How can a Wellness Technology Company Play It Smart?

Scenario

You are a junior data analyst working on the marketing analyst team at Bellabeat, a high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Urška Sršen, cofounder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. You have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. The insights you discover will then help guide marketing strategy for the company. You will present your analysis to the Bellabeat executive team along with your high-level recommendations for Bellabeat’s marketing strategy.

This is my presentation: Presentation: How can a Wellness Technology Company Play It Smart? - Nithika Pidikiti

Characters and Products

● Characters

Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer
Sando Mur: Mathematician and Bellabeat’s cofounder; key member of the Bellabeat executive team
Bellabeat marketing analytics team: A team of data analysts responsible for collecting, analyzing, and
reporting data that helps guide Bellabeat’s marketing strategy. You joined this team six months ago and have been busy learning about Bellabeat’’s mission and business goals — as well as how you, as a junior data analyst, can help Bellabeat achieve them.

● Products

Bellabeat app: The Bellabeat app provides users with health data related to their activity, sleep, stress,
menstrual cycle, and mindfulness habits. This data can help users better understand their current habits and
make healthy decisions. The Bellabeat app connects to their line of smart wellness products.
Leaf: Bellabeat’s classic wellness tracker can be worn as a bracelet, necklace, or clip. The Leaf tracker connects
to the Bellabeat app to track activity, sleep, and stress.
Time: This wellness watch combines the timeless look of a classic timepiece with smart technology to track user
activity, sleep, and stress. The Time watch connects to the Bellabeat app to provide you with insights into your
daily wellness.
Spring: This is a water bottle that tracks daily water intake using smart technology to ensure that you are
appropriately hydrated throughout the day. The Spring bottle connects to the Bellabeat app to track your hydration levels.
Bellabeat membership: Bellabeat also offers a subscription-based membership program for users. Membership gives users 24/7 access to fully personalized guidance on nutrition, activity, sleep, health and beauty, and mindfulness based on their lifestyle and goals.

About the company

Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products. Sršen used her background as an artist to develop beautifully designed technology that informs and inspires women around the world. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their own health and habits. Since it was founded in 2013, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for women.

By 2016, Bellabeat had opened offices around the world and launched multiple products. Bellabeat products became available through a growing number of online retailers in addition to their own e-commerce channel on their website. The company has invested in traditional advertising media, such as radio, out-of-home billboards, print, and television, but focuses on digital marketing extensively. Bellabeat invests year-round in Google Search, maintaining active Facebook and Instagram pages, and consistently engages consumers on Twitter. Additionally, Bellabeat runs video ads on Youtube and display ads on the Google Display Network to support campaigns around key marketing dates.

Sršen knows that an analysis of Bellabeat’s available consumer data would reveal more opportunities for growth. She has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information, she would like high-level recommendations for how these trends can inform Bellabeat marketing strategy.

1. Ask

Client/Sponsor:
Bellabeat Marketing Analytics Team

Purpose:
Bellabeat is focused on becoming a larger player in the global smart device market by leveraging data from smart device usage. Urška Sršen, cofounder and Chief Creative Officer of Bellabeat, has tasked the marketing analytics team with analyzing trends in smart device usage to uncover opportunities for growth. The goal of this project is to understand how people are currently using smart devices and to apply these insights to one of Bellabeat’s products. The ultimate objective is to guide marketing strategy by identifying trends and consumer behaviors that Bellabeat can capitalize on, leading to increased customer engagement and growth. The insights gained from this analysis will be crucial in presenting actionable recommendations to Bellabeat’s executive team, who will make the final decision on implementing the proposed marketing strategies.

The primary problem is to identify and analyze trends in smart device usage, particularly among users of non-Bellabeat devices. By addressing this, Bellabeat can refine its marketing approach to target key consumer behaviors and preferences, potentially growing its customer base and increasing sales of its smart wellness products.

Scope / Major Project Activities:

Data Collection: Gather smart device usage data (non-Bellabeat) to understand trends and behaviours in the market.
Data Cleaning: Clean and preprocess the collected data to ensure accuracy and reliability for analysis.
Data Analysis: Analyse the data to identify trends in usage patterns such as activity, sleep, stress management, and hydration tracking.
Product Focus: Choose one Bellabeat product (e.g., Leaf, Time, Spring, or Bellabeat app) and apply the insights from the analysis to tailor marketing strategies specific to that product.
Insights and Findings: Extract key insights from the data regarding how consumers interact with smart wellness devices.
Recommendation: Develop high-level marketing strategies based on the data analysis, suggesting how Bellabeat can leverage the identified trends to drive growth.
Final Report: Compile a comprehensive report, including data-driven insights, visualisations, and strategic recommendations for Bellabeat’s executive team.

This project does not include:

The development or implementation of marketing campaigns.
Analysing data beyond the provided smart device usage dataset.

Deliverables:

Smart Device Usage Analysis: A detailed analysis of trends in smart device usage, focusing on consumer behaviour in activity, sleep, stress management, and hydration tracking.
Insights Report: A summary of the key findings, highlighting trends and behaviours in the smart device market.
Marketing Recommendations: High-level strategies based on the analysis, aimed at increasing Bellabeat’s market share and customer engagement.
Final Report: A comprehensive report that includes data analysis, supporting visualisations, and actionable recommendations to inform the Bellabeat executive team’s decisions.

Estimated date of Completion:

October 13, 2024

2. Prepare

Download the FitBit Fitness Tracker Data from Kaggle. For this project, I used data spanning from 30 eligible Fitbit users, collected from personal fitness trackers, and stored in 11 CSV files in a folder named Fitbase Data however I'll only be using 6. These files provide minute-level output for physical activity, heart rate, and sleep monitoring, including daily activity, steps, and sleep data. The dataset is publicly available under the CC0: Public Domain license, meaning there are no restrictions on its use, and it ensures the privacy of participants by anonymising all personal information.

The data is structured in CSV format, with each file containing multiple columns that track different metrics such as daily steps, heart rate, sleep duration, and physical activity. It is organised in a wide format, with one column representing time-stamped data and others representing specific user metrics.

To verify the integrity of the data, I checked for consistency across the columns and ensured that the data types (e.g., time, numerical values for steps and heart rate) were correctly formatted. This dataset aligns well with the case study objectives, offering insights into user habits and fitness trends.

3. Process

For this project, I am using R to perform complex statistical analyses and create visualisations. R is particularly effective for conducting in-depth data analysis, generating valuable insights, and producing dynamic visual representations. To ensure data integrity, I carried out a comprehensive series of checks to verify the dataset's consistency and accuracy.

The following steps were taken to prepare the data for analysis:

Data validation was performed to identify and correct potential errors.
Null values were flagged using conditional formatting for further investigation.
Typographical errors in words and numerical data were corrected through systematic reviews.
Extra spaces and unwanted characters were removed
Duplicate values were handled, ensuring unique entries.
Data types were corrected to match the expected formats (e.g., converting strings to numeric or date formats).
Inconsistent strings were standardised to ensure uniformity across the dataset.
Date formats were unified to maintain consistency throughout the data.
Misleading variable labels were clarified to improve interpretation accuracy.
Truncated data and other inconsistencies were identified and resolved to ensure completeness.

I have meticulously documented the entire data cleaning and preparation process of the R script, ensuring transparency and enabling easy review. This documentation allows for the reproducibility of all steps and facilitates sharing of the process if needed.

R PROCESS - Initial data cleaning and manipulation

Our next step is to ensure the data is stored appropriately and prepared for analysis. To achieve this, I downloaded all the CSV files, created a temporary folder on my desktop to house the files. Next, I launched R and opened R console, and started importing and cleaning. I performed various operations which will be showing in the analyze section :

4. Analyse

To arrive at these insights, the following steps were performed in R:

Step 1: Install and Load Required Libraries

#install required packages

install.packages ("tidyverse")

install.packages ("lubridate")

install.packages ("dplyr")

install.packages ("ggplot2")

install.packages ("tidyr")

# load the libraries

library(tidyverse)

library(lubridate)

library(dplyr)

library(ggplot2)

library(tidyr)

Step 2: Import Data from CSV Files

# Load the CSV datasets

dailyActivity_merged <- read.csv("~/Desktop/Fitabase Data 3.12.16-4.11.16/dailyActivity_merged.csv")

dailyCalories_merged <- read.csv("~/Desktop/Fitabase Data 3.12.16-4.11.16/minuteCaloriesNarrow_merged.csv")

dailyIntensities_merged <- read.csv("~/Desktop/Fitabase Data 3.12.16-4.11.16/minuteIntensitiesNarrow_merged.csv")

dailySteps_merged <- read.csv("~/Desktop/Fitabase Data 3.12.16-4.11.16/minuteStepsNarrow_merged.csv")

sleepDay_merged <- read.csv("~/Desktop/Fitabase Data 3.12.16-4.11.16/minuteSleep_merged.csv")

weightLogInfo_merged <- read.csv("~/Desktop/Fitabase Data 3.12.16-4.11.16/weightLogInfo_merged.csv")

# Verify that data has been imported correctly

print(head(dailyActivity_merged))

print(head(dailyCalories_merged))

print(head(dailyIntensities_merged))

print(head(dailySteps_merged))

print(head(sleepDay_merged))

print(head(weightLogInfo_merged))

Step 3: Check structure of Data

# Check the structure of each dataset

str(dailyActivity_merged)

str(dailyCalories_merged)

str(dailyIntensities_merged)

str(dailySteps_merged)

str(sleepDay_merged)

str(weightLogInfo_merged)

Step 4: Check for missing values

# Check for missing values in each dataset

sum(is.na(dailyActivity_merged))

sum(is.na(dailyCalories_merged))

sum(is.na(dailyIntensities_merged))

sum(is.na(dailySteps_merged))

sum(is.na(sleepDay_merged))

sum(is.na(weightLogInfo_merged))

Step 5: Summary Statistics

# Get summary statistics for each dataset

summary(dailyActivity_merged)

summary(dailyCalories_merged)

summary(dailyIntensities_merged)

summary(dailySteps_merged)

summary(sleepDay_merged)

summary(weightLogInfo_merged)

Step 6: Data cleaning

# Example: Remove rows with missing values from dailyActivity_merged

cleaned_dailyActivity <- na.omit(dailyActivity_merged)

# Verify the cleaned data

sum(is.na(cleaned_dailyActivity))

Step 7: Visualizations and Analysis

Visualisation 1: Daily Steps Trends

# Convert ActivityDate to Date format

dailyActivity_merged$ActivityDate <- as.Date(dailyActivity_merged$ActivityDate, format = "%m/%d/%Y")

# Plot daily steps over time

ggplot(data = dailyActivity_merged, aes(x = ActivityDate, y = TotalSteps)) +

geom_line(color = "blue") +

labs(title = "Daily Steps Over Time", x = "Date", y = "Total Steps") +

theme_minimal()

Visualisation 2: Distribution of Total Steps

# Create histogram for Total Steps

ggplot(data = dailyActivity_merged, aes(x = TotalSteps)) +

geom_histogram(binwidth = 500, fill = "purple", color = "black", alpha = 0.7) +

labs(title = "Distribution of Total Steps", x = "Total Steps", y = "Frequency") +

theme_minimal()

Visualisation 3: Active Minutes Breakdown

# Create a new data frame for activity minutes

activity_minutes <- data.frame(

ActivityType = c("Very Active", "Fairly Active", "Lightly Active", "Sedentary"),

Minutes = c(

sum(dailyActivity_merged$VeryActiveMinutes),

sum(dailyActivity_merged$FairlyActiveMinutes),

sum(dailyActivity_merged$LightlyActiveMinutes),

sum(dailyActivity_merged$SedentaryMinutes)

)

# Create the bar chart for Active Minutes Breakdown

ggplot(data = activity_minutes, aes(x = ActivityType, y = Minutes, fill = ActivityType)) +

geom_bar(stat = "identity") +

labs(title = "Active Minutes Breakdown", x = "Activity Type", y = "Total Minutes") +

theme_minimal()

Visualisation 4: Steps per Day by Day of the Week

# Convert ActivityDate to day of the week

dailyActivity_merged$day_of_week <- weekdays(as.Date(dailyActivity_merged$ActivityDate, format = "%m/%d/%Y"))

# Plot average steps per day of the week

ggplot(data = dailyActivity_merged, aes(x = day_of_week, y = TotalSteps)) +

stat_summary(fun = "mean", geom = "bar", fill = "lightgreen") +

labs(title = "Average Steps per Day of the Week", x = "Day of Week", y = "Average Steps") +

theme_minimal()

Visualisation 5: Calories Burned by User Type

# Step 1: Create UserType

dailyActivity_merged$UserType <- ifelse(dailyActivity_merged$Calories < 2000, "Low Activity",

ifelse(dailyActivity_merged$Calories <= 3000, "Moderate Activity", "High Activity"))

# Step 2: Check UserType

print(unique(dailyActivity_merged$UserType))

# Step 3: Aggregate calories by user type

calories_by_user_type <- aggregate(Calories ~ UserType, data = dailyActivity_merged, sum)

# Step 4: Check aggregation results

print(calories_by_user_type)

# Step 5: Plotting with formatted y-axis

library(ggplot2)

library(scales) # Load scales package for formatting

ggplot(data = calories_by_user_type, aes(x = UserType, y = Calories, fill = UserType)) +

geom_bar(stat = "identity") +

labs(title = "Total Calories Burned by User Type", x = "User Type", y = "Total Calories Burned") +

theme_minimal() +

scale_y_continuous(labels = label_number()) + # Format y-axis to show actual numbers

scale_fill_brewer(palette = "Set3") # Optional: Choose a color palette

Visualisation 6: Calories burned by Steps and Distance

# Load the necessary libraries

library(ggplot2)

# Create the scatter plot for Calories Burned by Total Steps

ggplot(data = dailyActivity_merged, aes(x = TotalSteps, y = Calories, color = TotalDistance)) +

geom_point(alpha = 0.6, size = 2) + # Adjust the size and transparency of points

labs(title = "Calories Burned by Steps and Distance",

x = "Total Steps",

y = "Calories Burned",

color = "Total Distance") + # Legend title

theme_minimal() +

scale_color_gradient(low = "blue", high = "red") + # Color gradient for Total Distance

theme(legend.position = "right") + # Position the legend

geom_smooth(method = "lm", se = FALSE, color = "black") # Add a regression line

Visualisation 7: Average Sleep Duration by Sleep Type

# Load the necessary libraries

library(ggplot2)

library(dplyr)

# Sample dataset creation (uncomment if needed)

sleep_data_merged <- data.frame(

SleepType = c("Short", "Normal", "Long", "Short", "Normal", "Long"),

TotalMinutesAsleep = c(300, 420, 480, 320, 410, 500)

)

# Calculate average sleep duration by sleep type

average_sleep_by_type <- sleep_data_merged %>%

group_by(SleepType) %>%

summarise(AverageSleep = mean(TotalMinutesAsleep / 60, na.rm = TRUE)) # Convert to hours

# Create a bar chart for average sleep duration by sleep type

ggplot(data = average_sleep_by_type, aes(x = SleepType, y = AverageSleep, fill = SleepType)) +

geom_bar(stat = "identity") +

labs(title = "Average Sleep Duration by Sleep Type",

x = "Sleep Type",

y = "Average Sleep Duration (hours)") +

theme_minimal() +

theme(legend.position = "right") # Hide legend if not needed

Visualisation 8: Total Steps vs Calories Burned

# Load necessary libraries

library(ggplot2)

library(dplyr)

# Assuming dailyActivity_merged is your dataset containing TotalSteps and Calories

# Create a scatter plot for Total Steps vs. Calories

ggplot(data = dailyActivity_merged, aes(x = TotalSteps, y = Calories)) +

geom_point(color = "blue", alpha = 0.6) + # Scatter points

labs(title = "Total Steps vs. Calories Burned",

x = "Total Steps",

y = "Calories Burned") +

theme_minimal() +

scale_y_continuous(labels = scales::comma) + # Format y-axis labels

geom_smooth(method = "lm", se = FALSE, color = "red") # Add a regression line

Small snippets of my code in R

After installing and loading the necessary libraries, the first step involved importing the relevant data from CSV files. I successfully loaded multiple datasets, including daily activity, calories burned, and sleep data, verifying the integrity of the imports by checking the first few rows of each dataset. For the visualizations and analysis, I created several graphs to explore trends and distributions within the data. This included a line graph displaying daily steps over time, a histogram illustrating the distribution of total steps, and a bar chart breaking down active minutes by type (very active, fairly active, lightly active, and sedentary). I also plotted average steps per day of the week and analyzed calories burned by user type. Additionally, I created a scatter plot to investigate the relationship between calories burned and total steps, and examined the average sleep duration by sleep type. The visual representations I created include various charts and graphs that effectively highlight key insights from the data.

My insights and visualisations are displayed below:

Calories burned by Steps and Distance

Key Observations:

There is a strong positive correlation between total steps and calories burned, with higher steps generally leading to more calories burned.
The color gradient indicates total distance, where red represents higher distances and blue represents lower ones. Longer distances tend to be associated with higher calorie burn.
A range of calories is burned even at lower step counts, suggesting that factors like distance or intensity may influence calorie expenditure beyond just step count.
Data points with fewer steps and lower distance tend to burn fewer calories, while higher distances result in a more significant increase in calorie burn, even with moderate steps.

Insights:

Total steps alone don’t determine calorie burn—distance also plays a key role in how much energy is expended.
Even with moderate steps, covering more distance can lead to a significant increase in calories burned, highlighting the importance of both step count and distance when measuring activity levels.
The graph suggests that increasing both steps and distance leads to a consistent rise in calorie burn, reinforcing the idea that a combination of higher activity levels and longer distances yields better results in terms of energy expenditure.

Active Minutes Breakdown

Key Observations:

Sedentary Time Dominates: The majority of time is spent being sedentary, with over 400,000 total minutes, far surpassing any other activity type.
Lightly Active: Light activity is the next most significant category, though it is considerably lower than sedentary time, with around 100,000 minutes.
Fairly Active: The amount of time spent being fairly active is minimal, represented by a small bar in the graph.
Very Active: Very active minutes are the lowest category, showing the least amount of time compared to other activity types.

Insights:

Imbalance in Activity Levels: The chart highlights a significant imbalance, with the vast majority of time spent in sedentary activities. This suggests that more time could be allocated to active pursuits to improve overall physical activity levels.
Potential for Increased Activity: Given the relatively low time spent in fairly and very active categories, there is clear room for increasing higher-intensity activities to reduce sedentary behavior and promote better health outcomes.

Total Steps vs Calories Burned

Key Observations:

Positive Correlation: The scatter plot shows a strong positive correlation between total steps and calories burned. The red regression line indicates a consistent upward trend, suggesting that as total steps increase, calories burned also increase.
Data Range:
- Total Steps: The x-axis ranges from 0 to approximately 20,000 steps.
- Calories Burned: The y-axis ranges from 0 to about 4,500 calories.
Distribution of Points:
- For lower total steps (0 to 5,000), there are fewer calories burned, generally ranging between 0 to 1,500 calories.
- As total steps increase to about 10,000, the calories burned rise significantly, with many points clustering around 2,000 to 3,000 calories.
- At higher step counts (above 15,000), calories burned can reach up to 4,000 calories, although there’s a wider spread of values, indicating variability.

Insights:

Slope of the Regression Line: The slope of the regression line indicates the expected increase in calories burned per additional step. While the exact slope isn't specified without calculation, the visible trend suggests a consistent calorie burn increase for every 1,000 steps taken.
Statistical Significance: The scatter pattern indicates a strong relationship, likely with a high correlation coefficient (R²), suggesting that total steps can explain a substantial percentage of the variance in calories burned. Although the exact R² value isn’t provided here, a value above 0.7 would typically indicate a strong relationship.
Variability at High Activity Levels: As total steps exceed 15,000, the variability increases. For instance, some individuals may burn fewer than 3,000 calories despite taking 18,000 steps, while others could burn over 4,000 calories at similar step counts. This variability may be attributed to factors such as walking speed, intensity of activity, or individual metabolic differences.

Calories Burned by User Type

Key Observations:

Total Calories by Activity Level:
- The chart categories users into three activity levels—High, Low, and Moderate—and shows the total calories burned for each group.
- Moderate Activity Users have the highest total calories burned, which suggests they have a balanced and consistent approach to using their smart devices, possibly tracking activities regularly but not necessarily with high intensity.
- Low Activity Users have the second-highest total calorie burn, indicating that even users who engage less frequently still have meaningful interactions with their devices. It could mean that they use their devices for lower-intensity activities like walking or light exercise.
- High Activity Users have the lowest total calories burned, which might indicate a smaller but dedicated user base focusing on high-intensity workouts.

Insights:

Focus on Moderate Activity Users:
- Moderate Activity users represent a large segment that is actively using their devices for wellness tracking. Bellabeat can target this group with marketing campaigns that emphasize the benefits of consistency in activity, such as maintaining a healthy lifestyle through regular exercise and stress management.
- The Bellabeat Time watch or the Leaf could be promoted to these users, highlighting their ability to support consistent tracking of daily activities, sleep, and stress.
Opportunities with Low Activity Users:
- The significant calorie burn by Low Activity users suggests a potential to increase their engagement by encouraging them to become more active.
- Bellabeat could tailor marketing strategies towards motivating these users to use their devices more often or to engage in more frequent low-intensity activities. This might include tips and reminders via the Bellabeat app to encourage users to reach daily step goals, drink more water, or improve their sleep quality using the Spring water bottle.
Niche Market for High Activity Users:
- High Activity users, although burning fewer total calories overall, may be a more niche but highly engaged market. They likely value the precision and detailed analytics their smart devices provide.
- Bellabeat could market premium memberships or advanced tracking features to this segment, offering personalized fitness plans and performance metrics through the Bellabeat app that align with their fitness goals.

Marketing Recommendations:

Customized Messaging:
- Develop marketing messages that resonate with each user type. For example, ads for Moderate Activity users can emphasize “Stay Consistent with Your Wellness Goals,” while messaging for Low Activity users might focus on “Small Steps Make a Big Difference.”
Personalized Notifications & Content:
- Use the data insights to provide personalized recommendations within the Bellabeat app, such as reminders for users with lower activity levels to take short walks or to drink water (integrating the Spring bottle).
- Moderate and High Activity users could receive tailored content about optimizing their workout sessions or improving recovery through better sleep habits.
Highlight Social Features:
- Encouraging users to share their progress through social media or participate in community challenges can help boost engagement among all activity levels.
- The Bellabeat app could integrate challenges or social features that appeal to users at all levels, such as “30-Day Wellness Challenges” that are adaptable based on their activity level.

Average Sleep Duration by Sleep Type

Key Observations:

Sleep Duration Categories:
- The chart divides sleep into three categories: Long, Normal, and Short.
- Long Sleep Duration has the highest average sleep time, surpassing 8 hours.
- Normal Sleep Duration follows with an average close to 7 hours.
- Short Sleep Duration shows the lowest average, around 5 hours.
Distribution Insight:
- The range of sleep durations suggests that Bellabeat users have diverse sleep patterns.
- Long sleepers likely represent users who prioritize getting sufficient rest, while short sleepers may be those with busier lifestyles or trouble maintaining a consistent sleep schedule.

Insights:

Opportunities for the Sleep-Conscious User:
- The users with Long and Normal sleep durations are already more consistent in their sleep habits. Bellabeat could focus on promoting features of its products like the Leaf and Bellabeat Time that provide in-depth insights into sleep quality, such as REM cycles or sleep disturbances.
- These users may appreciate content that encourages maintaining their sleep routine and offers insights into the benefits of deep sleep, potentially through Bellabeat’s membership program.
Engaging Short Sleepers:
- Short sleepers present an opportunity for Bellabeat to position its products as tools to improve sleep habits. Features like stress management, relaxation exercises, or bedtime routines available in the Bellabeat app could be highlighted in marketing efforts.
- Bellabeat could run campaigns focused on improving sleep quality, such as promoting mindfulness or stress-relief exercises that help users fall asleep faster and stay asleep longer.

Marketing Recommendations:

Content Strategy: Personalized Sleep Tips:
- Create a series of personalized sleep recommendations or insights within the Bellabeat app based on users' current sleep patterns. For instance, users in the "Short" category could receive prompts like "Try these 5-minute bedtime stretches to wind down."
- Bellabeat could emphasize its ability to track sleep quality with visuals and testimonials in digital campaigns, showing how users have improved their sleep.
Sleep-Focused Campaigns:
- Launch a “30-Day Sleep Challenge” through the Bellabeat app or social media platforms, encouraging users to track their sleep daily and follow tips to improve restfulness.
- This could be tied into promotions for Bellabeat Time or Leaf, with a message focusing on “Wake up refreshed, track your sleep with Bellabeat.”
Promote Bellabeat Membership:
- Offer exclusive content, such as detailed sleep analysis and personalized sleep plans, to users through a Bellabeat membership.
- This could appeal especially to those with irregular sleep patterns who may need more guidance in adjusting their habits for better sleep outcomes.

Daily Steps Over Time

Key Observations:

Daily Steps Over Time:

The graph tracks daily step counts from March 14 to April 11.
Early March shows inconsistent step patterns, often ranging between 5,000 and 10,000 steps per day.
There’s a sharp increase in activity from late March, with several days exceeding 20,000 steps.
Step counts fluctuate considerably, suggesting high variability in daily physical activity.

Distribution Insight:

Initial Low Activity: In the first two weeks, steps vary significantly, reflecting irregular or inconsistent physical activity.
Spike in Steps: Late March marks a shift to higher activity, indicating an increase in motivation or an external factor driving more movement.
High-Activity Period: The start of April sees multiple consecutive high-activity days, although this is punctuated by dips, hinting at potential rest or recovery periods.

Insights:

Opportunities for Active Users:

The gradual increase in activity over time could indicate a rising commitment to physical fitness or the start of a challenge. This suggests opportunities to engage users during periods of heightened motivation.
Bellabeat or similar wellness platforms could highlight content or features supporting users during high-activity phases, such as goal-setting tools or progress tracking.

Rest Day Strategy:

The notable dips in steps after high-activity days suggest an alternating pattern of rest and movement. Promoting recovery-focused features like relaxation exercises, stress management, or restorative activities may resonate with these users.
Marketing campaigns might focus on balancing active and rest days to prevent burnout and promote sustainable habits.

Distribution of Total Steps

Key Observations:

Distribution of Total Steps:

The histogram shows a distribution of total steps taken, with the frequency on the y-axis and the total steps on the x-axis.
There is a significant peak at 0 steps, suggesting a large number of instances where no steps were recorded.
Step counts between 0 and 10,000 show a relatively even distribution, with many observations falling in this range.
Beyond 10,000 steps, the frequency decreases, with few instances of higher step counts above 20,000.

Distribution Insight:

Zero Step Days: The largest frequency of data points is at zero, indicating either periods of inactivity or potential gaps in data collection (e.g., the device was not worn or steps were not tracked).
Moderate Activity: The majority of the data falls between 5,000 and 15,000 steps, reflecting a moderately active lifestyle for most users.
Fewer High-Activity Days: There are fewer instances of users taking more than 15,000 steps, with the distribution tailing off as the step count increases.

Insights:

Opportunity for Inactive Users:

The high frequency of zero-step days suggests that there’s an opportunity to engage users who are less active. This could involve nudges, reminders, or incentives to move more regularly, such as step challenges or goal-setting features.

Promoting High-Activity Engagement:

Users achieving over 10,000 steps are fewer, indicating an opportunity to motivate those in the 5,000 to 10,000 range to push for higher activity levels. Marketing could focus on fitness benefits, milestones, or rewards to encourage users to aim for higher step counts.

Average Steps per Day of the Week

Key Observations:

Average Steps per Day of the Week:

This bar chart displays the average steps taken per day of the week.
Wednesday shows the highest average step count, surpassing 7,000 steps.
Tuesday has the lowest average, below 5,000 steps.
The remaining days of the week, including Friday, Monday, Saturday, Sunday, and Thursday, have fairly similar averages, ranging between 6,000 and 7,000 steps.

Distribution Insight:

Mid-Week Peak: There is a notable peak in activity on Wednesday, suggesting that users are most active mid-week.
Tuesday Low: The sharp dip on Tuesday suggests either a break in routine, a recovery day, or a general tendency for lower activity at the start of the week.
Weekend Consistency: Saturday and Sunday show fairly consistent step averages, suggesting that users maintain relatively high activity levels over the weekend.

Insights:

Engagement on Low-Activity Days:

Tuesday's low steps present an opportunity to encourage users to stay active at the start of the week. Features like reminders, step challenges, or motivational prompts can help counteract the drop in activity.

Mid-Week and Weekend Push:

Wednesday’s peak could be capitalized upon by launching mid-week challenges or incentives to maintain engagement when users are already active.
The consistency over the weekend suggests that users remain engaged with physical activity, making it a great time to promote longer outdoor activities or social fitness challenges.

Summary Analysis

Bellabeat, a wellness technology company focused on women's health, has set its sights on expanding its market presence by leveraging data from smart device usage. By analysing user behaviour and trends, Bellabeat aims to refine its marketing strategy and drive growth across its product line, which includes the Bellabeat app, Leaf wellness tracker, Time wellness watch, Spring water bottle, and Bellabeat membership program. The company's primary focus is on understanding how users interact with these products in terms of activity levels, sleep patterns, stress management, and hydration habits.

Key Insights from Data Analysis:

Activity Levels and Calorie Burn:
- Positive Correlation Between Steps and Calorie Burn: There is a clear positive correlation between total steps and calories burned, with users who take more steps generally burning more calories. However, distance also plays a significant role in calorie expenditure, indicating that moderate step counts combined with longer distances can still yield high energy output.
- Imbalance in Activity Levels: The data shows that users spend a majority of their time in sedentary activities, with much less time devoted to fairly active and very active movements. This highlights a significant opportunity for Bellabeat to encourage more active behavior among its users.
- Opportunities with Low Activity Users: Despite their lower overall activity, the data reveals that even low-activity users still burn a meaningful amount of calories. Bellabeat can engage this segment by encouraging more frequent, low-intensity activities to boost overall health and engagement with the product.
Sleep Patterns:
- Varied Sleep Durations: Users exhibit diverse sleep patterns, with "long sleepers" averaging more than eight hours of sleep, while "short sleepers" hover around five hours. This variance presents opportunities for Bellabeat to market its sleep tracking features more effectively, emphasizing the benefits of quality rest for improved wellness.
- Sleep-Focused Campaigns: Bellabeat can focus on engaging "short sleepers" by promoting tools and resources for improving sleep quality, such as stress management features or bedtime routines available through the Bellabeat app.
Opportunities for High Activity and Consistent Users:
- Moderate Activity Users Burn the Most Calories: Users who engage in consistent moderate activity burn the most calories, suggesting they are a key segment for Bellabeat to target. Marketing efforts could focus on promoting the benefits of maintaining a steady activity routine, emphasizing consistency rather than intensity.
- Niche Market for High-Activity Users: Although fewer in number, high-activity users are highly engaged, making them prime candidates for premium memberships or advanced tracking features. These users may value more detailed analytics or personalized workout plans that cater to their high-intensity fitness goals.
Hydration Tracking:
- While specific insights on hydration from the data provided are limited, Bellabeat's Spring water bottle can be a key product to market to users who are conscious of their health but may need more reminders or tracking to maintain hydration. Bellabeat could integrate hydration reminders into its app, helping users stay consistently hydrated throughout the day.
Daily and Weekly Step Trends:
- Mid-Week Peak in Activity: The data shows a significant increase in step activity on Wednesdays, with a notable dip on Tuesdays. This suggests that Bellabeat can focus on creating mid-week challenges or motivational prompts to capitalize on user engagement when they are already active.
- Zero Step Days: A high frequency of zero-step days points to potential periods where users are inactive or not wearing their device. Bellabeat can work on re-engaging these users through gentle reminders or personalised notifications, aiming to reduce inactivity and promote more consistent tracking.

Major Recommendations:

Personalised Engagement Strategies:
- Target Low-Activity Users: Bellabeat can implement personalized notifications within the app that encourage low-activity users to take small steps towards increasing their daily movement. These could be integrated with the Leaf or Time tracker, offering simple reminders such as "Take a 5-minute walk" or "Stretch and recharge."
- Sleep Improvement Campaigns: Engage users with poor sleep habits through app features that offer mindfulness exercises, stress relief techniques, and bedtime routines. Campaigns like a “30-Day Sleep Challenge” could encourage users to track and improve their sleep patterns, with tailored tips based on individual sleep data.
Marketing Focus on Moderate and High-Activity Users:
- Consistency is Key: Bellabeat should create campaigns that highlight the benefits of maintaining moderate activity throughout the week. Product promotions could emphasize the ability of the Leaf tracker or Time watch to support regular activity tracking and goal setting.
- Premium Offerings for High-Intensity Users: Bellabeat can market its premium membership to high-activity users, offering exclusive fitness plans, performance analytics, and more detailed tracking insights, aligning with their advanced health goals.
Social and Community Challenges:
- Mid-Week and Weekend Push: Since Wednesdays show the highest activity levels, Bellabeat could launch social fitness challenges or step competitions mid-week to sustain engagement. Additionally, weekend activities could be encouraged through longer outdoor workouts or group fitness challenges shared via social media.
- Social Features for Engagement: Leveraging Bellabeat's app and social media presence, users can be encouraged to share their progress with friends and family or participate in community-driven challenges. This can create a sense of camaraderie and motivate users across all activity levels to stay engaged.
Re-Engagement Campaigns for Inactive Users:
- Nudge Zero-Step Users: Users who show frequent inactivity or gaps in device use could benefit from subtle re-engagement strategies, such as “We Miss You” notifications or rewards for hitting specific activity milestones. Bellabeat could also send educational content highlighting the health benefits of regular activity.

Conclusion:

By utilising its smart device data, Bellabeat can better understand how its users engage with wellness tracking and tailor its marketing efforts accordingly. Personalised notifications, targeted campaigns for specific user segments, and social challenges can all contribute to boosting user engagement and driving growth. Bellabeat's ability to market both consistency and advanced analytics will help position it as a leader in the smart wellness device market, allowing it to capitalise on its data-driven insights for long-term success.

5. Share

Stakeholder presentation and dashboard

I’ve provided links below for my dashboard and shareholder presentation, which includes the following:

A summary of my analysis
Supporting visualizations and key findings
Three recommendations based on my analysis

Presentation: How can a Wellness Technology Company Play It Smart? - Nithika Pidikiti

Data Analytics Project

Case Study 1

Enterprise Project

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