Data Analysis Concepts & Analytical Mindset - Govarthan-Boopalan/Customer_Behaviour_Analysis GitHub Wiki

These notes explain the rationale, techniques, and thought processes behind the data analysis parts of the ShopEasy scripts. They cover how raw data is extracted, transformed, and interpreted to generate actionable insights, with simple analogies to make each concept more approachable.

1. Understanding the Data

Data Sources & Structure

  • Customer Journey Data:
    Tracks user interactions with products. Key columns include JourneyID, CustomerID, ProductID, VisitDate, Stage, Action, and Duration.
  • Customer Reviews:
    Contains reviews, ratings, and text feedback for products. This dataset is vital for understanding customer sentiment.
  • Demographic Data (Customers & Geography):
    Provides customer profiles (age, gender, location) for audience segmentation.
  • Engagement Data:
    Captures digital interactions (views, clicks) on marketing content, which is crucial for conversion analysis.

Data Quality Considerations

  • Missing Values & Duplicates:
    Data is cleaned before analysis. For example, missing values in numerical fields like Duration are imputed (e.g., replaced with 0) and duplicates are removed.
  • Standardization:
    Text data is standardized (e.g., converting stages and actions to lowercase) to ensure consistency when grouping and filtering.

2. Analytical Mindset & Techniques

A. Aggregation & Grouping

Purpose:
Summarize large datasets to identify patterns and trends.

Techniques:

  • Aggregate Functions:
    Using SQL functions like COUNT(), SUM(), and AVG() to calculate totals, averages, and other metrics.
  • GROUP BY:
    Data is grouped by key attributes (e.g., product name, country, demographic segments) to compare performance across different groups.
  • Conditional Aggregation:
    Using CASE statements (e.g., to distinguish between 'Retained' vs. 'One-Time' customers) for more granular insights.

Analogy:
Imagine hosting a party and wanting to know which snack was the most popular. You’d count how many times each snack was chosen. The snack with the highest count is like the top product—showing where most interactions occur.

B. Segmentation Analysis

Purpose:
Identify how different customer segments behave and perform.

Approach:

  • Demographic Segmentation:
    Dividing customers based on age, gender, and location (e.g., grouping age ranges as <30, 30-45, >45) to assess which group has a higher conversion rate.
  • Behavioral Segmentation:
    Grouping customers by actions (e.g., those reaching the 'Checkout' stage and completing a purchase) to calculate conversion rates.

Analogy:
Think of a school where students are grouped by classes (age groups). To see which class excels in sports, you count how many students participate in sports in each class—similar to analyzing conversion rates within each segment.

C. Conversion Rate Analysis

Concept:
Conversion rate is the percentage of users who take a desired action (like making a purchase) compared to the total number of users or interactions.

Calculation:

  • Numerator: Count of specific actions (e.g., purchases).
  • Denominator: Total number of potential customers or interactions.
  • Example Formula:
    [ \text{Conversion Rate} = \left(\frac{\text{Total Purchases}}{\text{Total Interactions}}\right) \times 100 ]

Usage:
Computed across products, demographics, and marketing channels to highlight performance gaps.

Analogy:
Think of it like a car race where you measure not just the winner, but also factors like acceleration or fuel efficiency. Here, different metrics (e.g., number of purchases vs. total interactions) reveal which products or channels perform best.

D. Sentiment Analysis

Tool Used:
TextBlob is used to analyze customer reviews.

Process:

  • Text Analysis:
    Reviews are analyzed to determine their sentiment polarity.
  • Classification:
    Reviews are categorized as positive, neutral, or negative based on their sentiment score.

Outcome:
Provides qualitative insights into customer satisfaction and helps identify areas for improvement.

Analogy:
Imagine reading letters from restaurant customers. Some praise the food, some are neutral, and some complain. By categorizing these letters, you quickly see whether most customers are happy or if there are common complaints about certain dishes.

E. Combining Data Insights for Recommendations

What It Does:
Aggregates insights from various analyses (trends, segmentation, sentiment) to generate strategic, actionable recommendations.

Concepts:

  • Synthesis of Information:
    Combining insights from different analysis functions to form a comprehensive view.
  • Actionable Insights:
    Translating the data into recommendations, such as which products to promote or which customer segments to target.

Analogy:
This is like a coach reviewing game footage—not only noting what happened but also making recommendations on what to do differently next time. The coach’s game plan is analogous to the recommendations generated by the analysis.

3. Data Analysis Process in the Scripts

Step-by-Step Approach

  1. Extract Data:

    • Load CSV files using Pandas.
    • Connect to the MySQL database.
    • Execute SQL queries using SQLAlchemy or mysql.connector to extract data subsets.

  2. Transform Data:

    • Clean and standardize data (handle missing values, convert strings, remove duplicates).
    • Aggregate data using SQL's GROUP BY and aggregate functions to generate summaries.

  3. Analyze & Interpret:

    • Customer Trends:
      Identify top-performing products and regions and calculate retention rates.
    • Segment Performance:
      Break down data by demographics to determine conversion rates across different segments.
    • Purchase Drivers:
      Analyze engagement data to see which marketing content drives purchases.
    • Sentiment Analysis:
      Use TextBlob to gauge customer sentiment from reviews.

  4. Generate Reports:

    • Convert processed data into tables and visual elements with ReportLab.
    • Compile insights and recommendations into professional PDF reports.

4. Key Thinking Behind the Analysis

  • Data-Driven Decision Making:
    Every query and analytical step is designed to produce insights that directly influence business strategies. Quantifying customer behavior helps form a factual basis for decision-making.

  • Iterative Exploration:
    Multiple queries examine data from different angles (e.g., overall trends and demographic segments) to ensure robust conclusions.

  • Comparative Analysis:
    Ranking and sorting data (e.g., products by conversion rates) help quickly identify strengths and weaknesses.

  • Actionability:
    The ultimate goal is not just to present data but to generate clear recommendations for product improvement, targeted marketing, or customer retention.

  • Real-Time Adaptability:
    Using dynamic queries to pull live data ensures that insights remain current and actionable.

Conclusion

The data analysis segments in the ShopEasy scripts illustrate a systematic approach to transforming raw data into actionable insights. By combining aggregation, segmentation, conversion analysis, sentiment analysis, and dynamic recommendations, the analysis not only reveals customer behavior patterns but also drives business strategies. Whether you think of it as preparing a multi-course meal or reviewing game strategies, each step builds on the previous one to serve up clear, actionable insights.

Feel free to reference these notes as you delve deeper into data analysis projects or extend the current ShopEasy scripts!