IMPLEMENTATION OF DATA ANALYTICS USING CRISP-DM METHODOLOGY (CASE STUDY OF BIKE SALES DATA IN EUROPE ON KAGGLE PLATFORM)

Abstract

This study aims to implement data analytics using the CRISP-DM methodology on a European bicycle sales dataset obtained through the Kaggle platform. Data analytics is one of the important elements in strategic decision-making for companies, especially in the bicycle industry which has unique challenges in understanding consumer preferences and market dynamics. This study uses a qualitative descriptive approach with six main stages, namely: (1) Business Issue Understanding to understand the business context using the SMART method, (2) Data Understanding for initial identification of the dataset, (3) Data Preparation for data cleaning, (4) Exploratory Data Analysis using Python and the Pandas, Matplotlib, and Seaborn libraries, (5) Validation to verify hypotheses and revenue trends using BigQuery, and (6) Visualization and Presentation to present the results in the form of an interactive dashboard using Tableau Public. The results of the study show that the Bikes product category is the largest profit contributor, while Accessories and Clothing have potential that can still be optimized. Customer segmentation analysis shows that the Adult age group (35-64) provides the highest profit contribution. Validation of the revenue analysis shows a stable growth trend since 2013, as well as a profit gap between countries that requires a differentiation strategy. This study emphasizes the importance of implementing data analytics with the CRISP-DM approach to support data-based decision making in the bicycle industry, while providing practical recommendations for more effective marketing and business development strategies.

Keyword : CRISP-DM, Data Analytics, Platform Kaggle, SMART Method..