Real-World Data Analysis Workflow from Scratch (EP.1)

Episode 1: From Problem to Plan — Structuring a Data Analysis Project

Welcome to the first episode of my blog series where I walk you through a real-world data analysis project from end to end. In this journey, we'll use real e-commerce data to identify business problems, clean messy datasets, analyze patterns, build predictive models, and create actionable insights.

But before we jump into any code or visualization, we must do something that's often overlooked: understanding the business context and clearly defining the problem.

Why Start With the Problem?

In practical data science, the biggest mistakes happen before you even load your dataset. One of the most common errors is jumping straight into modeling without fully understanding what you're trying to solve. This leads to wasted time, meaningless models, and solutions that don't serve the business.

Think of yourself as a doctor. You wouldn’t prescribe medicine before diagnosing the illness, right? Likewise, our job is to diagnose the business issue and decide how to use data to address it.

The Business Case: E-commerce Repeat Purchases

Let’s say you work for an e-commerce platform. The company has invested heavily in digital marketing to acquire new users. But now there's a concern:

"Customers often buy just once and never return."

This is a classic retention problem, and it's extremely important for long-term business health.

Here's how the issue breaks down:

• Observed phenomenon: First-time buyers are not coming back.

• Impact on business:

  • Fewer repeat buyers mean fewer loyal customers.

  • Low customer lifetime value (CLTV).

  • High marketing spend with diminishing returns.

Our goal is to:

• Identify users most likely to make a repeat purchase.

• Use those insights to design personalized marketing campaigns.

Step-by-Step Framework: Problem Solving in Data Science

Here's the framework I follow before touching any data. This process guides all my projects:

Step 1: Define the Problem

Clearly describe the business issue using data-oriented language.

Example:"Only 15% of customers place a second order within 30 days.""80% of revenue comes from just 20% of users."

Step 2: Quantify the Expected Impact

Why is this problem worth solving?

Increasing repeat purchase rate by 5% could improve monthly revenue by $X.Retention-focused campaigns may reduce CAC (Customer Acquisition Cost).

Step 3: Propose Solutions

Identify multiple approaches. Don’t go all-in on modeling just yet!

Possible strategies:Perform EDA to understand characteristics of repeat buyers.Build a predictive model to score new customers.Use A/B testing to assess campaign impact.

Step 4: Prioritize the Solutions

Which solution is fastest to try with minimal resources?

My approach: Start with quick EDA to gather insight. If that works, move to modeling.

Step 5: Execute the Analysis

Load the data, clean it, explore it. We'll start this in Episode 2.

Step 6: Measure Success

Track clear KPIs. For example:

• Change in repeat purchase rate

• Purchase frequency

• A/B test results

Step 7: Plan for Feedback and Automation

Don’t just stop at analysis. Ask:

• How can we deploy this solution regularly?

• Can we automate it?

• How do we track errors and performance over time?

A Realistic Scenario Example

Let me walk you through a realistic narrative.

Company A is growing fast and acquiring many new users through ads. However, only a small fraction of these users come back. Management is asking: "How can we improve customer retention?"

You come in as the data analyst and propose:

• Step 1: Define the drop-off problem in numbers.

• Step 2: Show how improving retention boosts revenue.

• Step 3: Plan exploratory data analysis (EDA) to identify patterns.

• Step 4: Propose marketing strategies based on insights.

• Step 5: Later, apply predictive modeling to target users.

  
  

Wrapping Up

In this first episode, we’ve defined a real-world business problem and laid the foundation for everything that follows.

This step may feel like planning and paperwork, but it's where clarity is born. Without a solid problem statement and strategy, everything downstream becomes shaky.

In the next episode, we roll up our sleeves and get into data preprocessing — loading, cleaning, checking, and transforming real-world data to get it analysis-ready.

Stay tuned for Episode 2: Data Preprocessing 101!