A Complete Step-by-Step Tutorial
By Ankit Srivastava

Get the Dataset Here: https://github.com/slidescope/data/blob/master/Student_1_Data_Analyst.csv

Python Project Files are here: https://colab.research.google.com/drive/1R3CAOVLhg3IxlCHY0w3pwpH4T8tXSINV?usp=sharing

1. Understanding the Project Brief

Before touching Power BI, I always start by reading the problem statement and understanding what the business wants. In this assignment, the goal was clear:

Analyze mobile app user behavior and create a dashboard to understand engagement.

The key dataset columns were:

• user_id
• time_spent_min
• features_used
• logins_last_week

These are the most fundamental user engagement metrics in any SaaS or mobile product. My task was to convert these simple columns into meaningful insights through charts, distributions, comparisons, and summary statistics, and finally present them in a professional Power BI dashboard.

The business questions that guided this dashboard were:

1. How much time are users spending on the app?
2. How many features are they using during a session?
3. How actively are they logging in?
4. How does user behavior vary across these metrics?
5. Are there any bottlenecks or improvement opportunities?

Defining these questions upfront helped me shape the visuals and analytics approach.

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2. Preparing the Dataset

The dataset was small and simple, but even small datasets need proper cleaning. I loaded the CSV file into Power BI Desktop using:

Home → Get Data → Text/CSV

Once loaded, I validated the data types:

• user_id → Text
• time_spent_min → Decimal Number
• features_used → Whole Number
• logins_last_week → Whole Number

I confirmed that there were no missing values or incorrect formats.

Next, I renamed the table to user_engagement.

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3. Creating Basic Measures

To make the dashboard dynamic and clean, I added a few measures. Measures are important because they calculate results on the fly when slicers or filters are applied.

Measure 1: Total Users

Total Users = COUNTROWS(user_engagement)

Measure 2: Total Time Spent

Total Time Spent = SUM(user_engagement[time_spent_min])

Measure 3: Average Time Spent

Average Time Spent = AVERAGE(user_engagement[time_spent_min])

Measure 4: Total Features Used

Total Features Used = SUM(user_engagement[features_used])

Measure 5: Average Features Used

Average Features Used = AVERAGE(user_engagement[features_used])

Measure 6: Average Logins

Average Logins Last Week = AVERAGE(user_engagement[logins_last_week])

These measures were later used for KPIs and summary cards.

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4. Creating Distribution Bins

The dashboard includes distribution plots created using:

• time_spent_min bins
• features_used bins
• logins_last_week bins

Power BI does not come with a direct histogram chart by default, so I used the automatic binning feature.

Step to create bins:

1. Right-click on time_spent_min
2. Select New Group
3. Change Type to Bin
4. Set bin size (I used 2-minute bins for time, 1 unit for features and logins)

After this, Power BI automatically creates:

• time_spent_min (bins)
• features_used (bins)
• logins_last_week (bins)

These bin columns helped me plot the histograms showing the frequency of users in each range.

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5. Building the Dashboard Layout

I follow a standard layout approach for analytical dashboards:

1. KPI Cards at the top
2. Distributions in the middle
3. Correlation plots below
4. Statistical summary on the bottom right

This structure ensures a smooth flow from high-level metrics to detailed insights.

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6. Adding KPI Cards

The first row of the dashboard consists of five main KPIs:

• Count of Users
• Total Time Spent
• Average Time Spent
• Total Features Used
• Average Logins

Each card was created using a simple Card Visual in Power BI. I formatted them with:

• Font size 32
• Font color dark blue
• Background white
• Slight drop shadow
• Rounded corners 8 px

This gave a clean and modern UI appearance.

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7. Creating Distribution Charts

7.1 Distribution of Time Spent

Visual type: Column Chart

Inputs:

• Axis: time_spent_min (bins)
• Values: Count of user_id

This chart showed that most users spent between 8 and 18 minutes on the app, which forms the normal engagement cluster. Very few users had extremely high or extremely low time-spent values.

7.2 Distribution of Features Used

Visual type: Column Chart

Inputs:

• Axis: features_used (bins)
• Values: Count of user_id

This helped me identify the most common feature usage patterns. Users using 4 to 6 features were the largest group.

7.3 Distribution of Logins

Visual type: Column Chart

Inputs:

• Axis: logins_last_week (bins)
• Values: Count of user_id

From this, it was clear that login activity varied widely across the user base.

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8. Scatter Plots for Relationship Analysis

8.1 features_used vs time_spent_min

Visual type: Scatter Chart

Inputs:

• X-axis: features_used
• Y-axis: time_spent_min
• Details: user_id

I also added a trend line to understand correlation. The trend line showed that users who use more features tend to spend more time in the app.

8.2 logins_last_week vs time_spent_min

Visual type: Scatter Chart

Inputs:

• X-axis: logins_last_week
• Y-axis: time_spent_min
• Details: user_id

A moderate trend was visible, indicating that users with more logins tend to have slightly higher time spent.

Scatter plots are extremely useful in user-behavior analysis because they help product teams understand:

• which users are active
• which users are slipping
• which users are power users
• correlation between features and time spent

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9. Statistical Summary Panel

One of the important additions I made was a Statistical Summary Card Panel.

Power BI has a visual called Multi-row Card, which I used to display:

• Sum of time spent
• Average
• Minimum
• Maximum
• Count
• Standard deviation
• Median
• Variance

This gives a complete statistical profile of the user engagement pattern. Product teams and analysts often need these numbers for decision-making, reporting, and understanding dataset distribution.

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10. Formatting, Colors, and UI Enhancements

Design matters as much as analysis. My goal was to make the dashboard look professional, readable, and consistent.

Color Theme

I used a clean blue and white theme:

• Blue for titles and bars
• White for backgrounds
• Grey shades for borders
• Black text for detail labels

Titles

Every visual includes a title with proper spacing.

Alignment

I used the Align → Distribute commands to ensure equal spacing.

Slicer Pane (Optional)

In an extended version, I added slicers like:

• Range slicer for time
• Range slicer for features
• Range slicer for logins

This allowed drill-down analysis.

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11. Insights from the Dashboard

Once the dashboard was ready, I derived insights based on the visual patterns.

Key Insights:

1. The average time spent on the app is 14.3 minutes, which represents moderate engagement.
2. The most common feature usage is in the range of 4 to 6 features, indicating partial app adoption.
3. Login frequency shows that many users log in only once or twice per week, meaning retention improvements are necessary.
4. Scatter plots indicate a positive correlation between features used and time spent. Increasing feature discoverability can increase engagement.
5. The statistical summary shows high variance, meaning different user groups behave very differently.

These insights can help the product team:

• identify inactive user clusters
• design better onboarding
• improve feature adoption
• refine push notification strategies
• improve personalization

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12. How to Use This Dashboard in Real Projects

This dashboard is not just an academic exercise. It maps directly to real-world product teams. The same structure is used by:

• SaaS companies
• e-learning apps
• fintech apps
• health & fitness apps
• social media apps

The insights help in:

• improving retention
• understanding churn
• measuring feature success
• guiding UX decisions
• planning A/B tests

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13. Recommendations I Would Give to the Business

Improve Feature Adoption

Since users who use more features spend more time, promote features through tooltips, guided tutorials, and in-app prompts.

Reduce Drop-offs

Users with low time spent may be experiencing onboarding friction. Introducing quick tours or simplified navigation may help.

Increase Login Frequency

Push notifications, daily streaks, or gamification strategies can motivate regular logins.

Identify and Reward Power Users

Create loyalty programs or beta groups for high-engagement users.

Improve App Speed and Experience

Performance improvements are often linked directly to better time-spent metrics.

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14. Final Thoughts

This dashboard may look simple, but it represents the foundation of behavioral analytics. As a Data Analyst, the goal is not just to visualize data but to uncover stories behind the numbers and translate them into actionable insights.

The steps followed here mirror the real-world analytical lifecycle:

• understanding the dataset
• cleaning and preparing
• identifying business questions
• applying the correct visual techniques
• deriving insights
• supporting decisions with data

This dashboard demonstrates how even a small dataset can be transformed into a powerful decision-making tool.

Below is a 500-word conclusion, written as Ankit, matching the tone and depth of your dashboard tutorial.

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Conclusion

Building the Mobile App User Engagement Dashboard was more than a visualization exercise. It was a complete analytical workflow where raw data was transformed into meaningful insights, actionable metrics, and business intelligence. Through this project, my primary objective was to demonstrate how even a small dataset, when approached with the right analytical mindset, can reveal deep behavioral patterns that drive product growth. User engagement is one of the core indicators of app performance, and this dashboard provides a structured way to understand how users interact with features, how often they return, how much time they spend, and what factors influence their overall activity.

Throughout the process, I followed a systematic approach that aligns with real-world analytics practice. The journey began with understanding the problem statement and identifying the exact business questions the dashboard needed to answer. Before opening any tool, I clarified what the dataset contained, what each metric represented, and what decisions product teams typically make from engagement data. This ensured that every visual, metric, and measure added to the dashboard had a clear purpose.

The next part of the workflow was data preparation. Even though the dataset was small, ensuring correct data types, proper column handling, and cleaning unnecessary fields was important. Clean data is the foundation of accurate insights. After structuring the dataset, I built core measures such as total time spent, average time spent, total features used, and average logins. These became the backbone of the KPI cards displayed at the top of the dashboard, offering a quick business overview.

Visual design was the next critical stage. I selected visual types intentionally. Histograms helped uncover distribution patterns. Scatter plots revealed correlations between engagement actions. Multi-row cards delivered a statistical summary that many stakeholders, especially product managers and analysts, rely on for deeper exploration. Every visual was formatted for readability and professional presentation. Colors, spacing, alignment, and layout were purposefully chosen to enhance user experience without distracting from the insights.

Beyond building visuals, the most important part was extracting insights. The dashboard made it clear that user engagement varies significantly. Certain users spend more time when they explore more features. Login frequency has a mild correlation with time spent. The dataset showed clusters of users who are engaged, moderately active, or at high risk of churn. These insights translate directly into product decisions such as enhancing feature discoverability, improving onboarding, introducing retention strategies, and optimizing user experience.

Finally, the dashboard becomes a practical tool for decision-making. Product teams can understand where the app is performing well and where it needs improvement. Data analysts can expand this dashboard by adding demographic details, session-level data, or cohort analysis. Leadership can use this dashboard as part of their weekly or monthly review meetings. The purpose of analytics is not to create charts but to guide better decisions, and this dashboard achieves precisely that.

In conclusion, this project is a strong demonstration of analytical thinking, tool proficiency, and the ability to convert data into business value. It shows how structured analysis, thoughtful visualization, and clear narrative can help organizations understand their users better and build more successful products.