IKEA Data Analysis: Sweden vs. Spain

Project overview

This project focuses on comparing household and lifestyle dynamics between Sweden and Spain, based on insights from IKEA’s Life at Home 2023 report. By analyzing data on household size, living arrangements, and renovation trends, I aimed to reveal cultural contrasts between Northern and Southern Europe.

Objective

Identify and analyze cultural and lifestyle differences in “life at home” across two European regions.

Data Source

IKEA’s Life at Home report, 2023 edition.

Scope

Focus on household size, the prevalence of one-person households, and renovation preferences to assess regional lifestyle trends.

To gain meaningful insights, I followed a structured approach to data preparation, analysis, and visualization.

• Data Preparation:
  Cleaned and prepared the data, addressing issues like miscategorized generational data.
• Hypothesis:
  Formed five key assumptions about household differences between the regions.
• Data Analysis:
  Used Looker Studio for visualization, comparing Swedish and Spanish household trends across key variables.

The project presented technical and analytical challenges that required careful management.

• Data Accuracy:
  Ensured data quality by excluding ambiguous data points.
• Tool Limitations:
  ‍Overcame technical difficulties in Looker Studio to produce accurate visualizations.
• Cultural Interpretation:
  Analyzed survey responses without reinforcing stereotypes, ensuring unbiased insights.

The comparative analysis revealed both expected and surprising insights into household dynamics in Sweden and Spain. While some assumptions about cultural differences were validated, others highlighted unexpected similarities, showcasing the complexity of household behaviors in different regions.

• Household Size:
  Swedish households were generally smaller, aligning with assumptions about Northern European living arrangements.
• One-Person Households:
  ‍Contrary to expectations, the prevalence of one-person households was similar in both countries, suggesting shared lifestyle trends.
• Gender Trends:
  Women were more likely than men to live alone in both Sweden and Spain, though specific rates varied.
• Urban vs. Rural Differences:
  Unique regional trends emerged, with Sweden showing more small households in towns and Spain in urban areas.
• Generational Differences:
  Younger Swedes tended toward independent living, whereas young Spaniards were more likely to live with family.

The project outcome offered actionable insights for understanding regional lifestyle nuances and household trends, which could inform future strategies for companies like IKEA when designing region-specific products or conducting market research. The analysis also highlighted areas for improving data collection methods to better capture the diversity of household preferences.

• Enhanced Visual Clarity:
  Created visualizations that presented cross-regional household patterns, making complex data more accessible and easily interpretable.
• Data-Driven Recommendations:
  Suggested modifications to survey options, such as adding choices for smaller home updates or redecorations, to capture a fuller spectrum of household preferences beyond major renovations.
• Validated and Refined Assumptions:
  Provided IKEA with clear, data-backed insights on household size and living arrangements, challenging and refining assumptions about differences between Northern and Southern Europe.

Working on this assignment was a valuable hands-on experience in data analytics, providing me with practical skills in data preparation, analysis, and interpretation, as well as a deeper understanding of cultural nuances across European households.

• Technical Skill Growth:
  Gained proficiency in Looker Studio for data visualization, learning to navigate and resolve technical issues like system crashes.
• Data Cleaning and Integrity:
  The project underscored the importance of rigorous data cleaning, especially when handling assumptions and categorizations like generational labels. I learned firsthand how critical it is to address inconsistencies early on to ensure the validity of later analysis.
• Cultural Sensitivity and Interpretation:
  I sharpened my skills in approaching data without bias, recognizing how assumptions can influence data interpretation. This experience taught me to question initial biases and work toward a balanced, culturally aware perspective on data findings.
• Survey Design Insights:
  Realized the impact of survey structure on data quality, such as the importance of offering varied response options to capture a fuller range of household behaviors.