The document outlines an analytics team's data exploration and model development approach. It analyzes customer demographics like age and gender breakdowns, wealth segmentation, car ownership by state and industry. Key findings include that the 40-47 age group purchases the most bikes, female customers account for 50.6% of purchases, and NSW has the most potential customers despite lower car ownership rates there. Various charts and data are presented on customer attributes to understand purchasing patterns.

1. The Analytics Team
Sprocket Central Pty Ltd
Data Analytics Approach
KPMG [junior analyst Team]

2. Agenda
1. Introduction
2. Data exploration
3. Model Development
4. Interpretation

3. Data exploration
Customer Age Distribution
★ Mostly 40 - 47 age groups are frequent customers.
★ 48 - 59 age group has big drop in percentage.
★ The red line shows the 3 years bike related purchase
Items
★ There is slightly increase in 59 to above age groups .
★ Percentage below 25 age not change. OLD
NEW

4. Wealth Segment (By gender)

5. Gender Analysis
● As per new customer data Female with 50.6% purchase with 25,212 bikes.
● Male contributes to 47.7% purchase with 23,765 bikes.

6. Welth Segmentation
Mass Customer is highest in all ages after that we focus on
High Net Worth

7. Car Ownership (By State)
● NSW State has more potential customers .
● As per new data owners visit less than who do not own car.

8. Car Ownership by Industry
● As per new data most of customers are from Financial Services and Manufacturing industries.
● Rank and Value columns are highly correlated (-0.98) .

9. Apendix
All support items in the attachment.