Introduction

Decathlon, a renowned sports retailer, strives to not only sell products but also retain the loyalty of its younger clientele. To achieve this goal, it is crucial to monitor the performance of its various brands using data-driven insights. This blog will break down the different datasets and their significance in creating a comprehensive dashboard for brand performance analysis.

Click here to access the case study.

Purpose of the Data

The provided data can be leveraged for various analyses to monitor and enhance the performance of the Domyos brand. Key analyses include customer segmentation, product performance monitoring, web traffic and engagement analysis, store performance evaluation, and detailed transaction analysis. By using tools like SQL, Python, Tableau, Power BI, Excel, and Google Analytics, Decathlon can gain valuable insights into customer behavior, sales trends, and overall brand performance, ultimately driving better decision-making and customer loyalty.

Case Study Objective

This case study aims to build intuitive and actionable insights/conclusions from the provided data. By conducting a thorough analysis, we can derive meaningful findings that will help Domyos understand and improve its market performance. The ultimate goal is to create a comprehensive dashboard that provides clear and actionable insights to support strategic decision-making and enhance customer loyalty.

This multi-faceted data analysis approach ensures that Domyos continues to meet the evolving needs of its customers while maintaining its competitive edge in the fitness market.

Key Datasets

1. d_customers
2. flow_audience
3. d_business_unit
4. d_sku
5. f_transaction_detail

Dataset Descriptions

1. d_customers

This dataset contains information about customers who hold loyalty cards. Here are the key fields:

• loyalty_card_num (numeric): Unique number identifying the customer’s loyalty card.
• year_birthdate (numeric): Customer’s year of birth.
• gender_id (numeric): Gender of the customer (1 = male, 2 = female).
• language_id (varchar): Customer’s preferred language.
• email_is_valid (boolean): Validity of the customer’s email address.
• mobile_is_valid (boolean): Validity of the customer’s mobile number.
• loyalty_card_creation_date (datestamp): Date when the loyalty card was created.
• but_idr_business_unit_usual (numeric): ID of the usual business unit/store.
• but_idr_business_unit_creator (numeric): ID of the store where the loyalty card was created.
• cnt_country_code_creator (varchar): Country code where the account was created.
• optin_sport (boolean): Opt-in status for receiving sports-related communications.
• optin_review (boolean): Opt-in status for receiving review requests.
• optin_event (boolean): Opt-in status for receiving event-related communications.
• last_purchase_date (datestamp): Date of the last purchase made by the customer.

2. flow_audience

This dataset provides information about web traffic and product views:

• hit_date (date): Date of the web consultation.
• url_referer_host (varchar): URL of the visited site.
• product_id_model (numeric): Model ID of the product viewed.
• product_instock (varchar): Availability status of the product.
• device_type (varchar): Type of device used for the web consultation.
• avg_note (decimal): Average rating of the product.
• nb_reviews (numeric): Number of reviews for the product.
• tot_visitors (numeric): Total number of unique visitors.

3. d_business_unit

This dataset includes details about the business units (stores):

• but_idr_business_unit (numeric): Unique ID of the store.
• but_num_typ_but (numeric): Type of store (7 = physical, 50 = digital).
• cnt_idr_country (numeric): Country ID where the store is located.
• cnt_country_code (varchar): Country code.
• but_name_business_unit (varchar): Name of the store.
• but_postcode (numeric): Postal code of the store.

4. d_sku

This dataset contains details about the products:

• sku_idr_sku (numeric): Unique ID of the SKU (Stock Keeping Unit).
• mdl_num_model_r3 (numeric): Model number.
• mdl_blue_product (boolean): Indicates if the product is blue.
• fam_num_family (numeric): Family number to which the product belongs.
• family_label (varchar): Label of the product family.
• sdp_num_sub_department (numeric): Sub-department number.
• sdp_label (varchar): Label of the sub-department.
• dpt_num_department (numeric): Department number.
• dpt_label (varchar): Label of the department.
• unv_num_univers (numeric): Universe number.
• unv_label (varchar): Label of the universe.
• pnt_num_product_nature (numeric): Product nature number.
• product_nature_label (varchar): Label of the product nature.
• Category_label (varchar): Category to which the product belongs (e.g., textile, footwear).
• brd_type_brand_libelle (varchar): Brand type label (e.g., Decathlon products or international brands).
• sku_date_end (datestamp): Date when the product was discontinued.

5. f_transaction_detail

This dataset provides detailed transaction information:

• the_transaction_id (varchar): Unique ID of the transaction.
• tdt_date_to_returned (datestamp): Date of the return.
• the_date_transaction (datestamp): Date of the transaction.
• the_date_authorized (datestamp): Date of payment authorization.
• sku_idr_sku (numeric): SKU ID.
• but_idr_business_unit (numeric): Store ID.
• ctm_customer_id (numeric): Customer ID.
• ctm_zip_code (numeric): ZIP code for order delivery.
• the_to_type (varchar): Type of transaction (physical or digital).
• the_transaction_status (varchar): Status of the transaction (sale or return).
• tdt_type_detail (varchar): Type detail of the transaction.
• f_to_tax_in (decimal): Revenue from the transaction.
• f_qty_item (numeric): Quantity of items in the transaction.

Leveraging Domyos Data for Business Intelligence

What Can Be Done with This Data?

1. Customer Segmentation and Analysis

• Objective: Identify key customer segments based on demographics, purchasing behavior, and loyalty.
• Action: Use the d_customers dataset to group customers by age, gender, and location. Analyze purchasing patterns and loyalty card activity to understand different customer segments.
• Tools: SQL for data extraction, Python (pandas, matplotlib) for analysis and visualization, and Tableau for creating customer segmentation dashboards.

2. Product Performance Monitoring

• Objective: Track the sales performance of different products and categories.
• Action: Utilize the d_sku and f_transaction_detail datasets to analyze sales by product categories, families, and SKUs. Monitor stock status and product availability.
• Tools: SQL for querying sales data, Python (pandas, seaborn) for detailed analysis, and Power BI for dynamic product performance dashboards.

3. Web Traffic and Engagement Analysis

• Objective: Understand customer engagement through web traffic data.
• Action: Analyze the flow_audience dataset to track unique visitors, device types, and popular products. Correlate web traffic with sales data to understand the impact of online engagement on sales.
• Tools: Google Analytics for detailed web traffic data, SQL for data extraction, and Tableau for web engagement dashboards.

4. Store Performance Analysis

• Objective: Evaluate the performance of physical and digital stores.
• Action: Use the d_business_unit and f_transaction_detail datasets to compare sales performance by store type and location. Identify top-performing stores and regions.
• Tools: SQL for querying store data, Excel for initial data exploration, and Power BI for interactive store performance reports.

5. Transaction Analysis

• Objective: Monitor transaction volumes, revenue, and return rates.
• Action: Leverage the f_transaction_detail dataset to analyze transaction details, including sales, returns, and revenue. Track transaction status to identify trends and issues.
• Tools: SQL for detailed transaction queries, Python (pandas, numpy) for statistical analysis, and Tableau for comprehensive transaction dashboards.

Tools Required

1.SQL

2. Python

3. Tableau or Power BI

4. Excel

Expectations

• Data Collection
• Data Modelling
• Data Preparation
• Exploratory Data Analysis (EDA)
• Defining Key Performance Indicators (KPIs)
• Dashboard Design
• Providing Actionable Insights and Recommendations

Click here to access the case study.