@ Udacity Data Scientist Nano Degree
Customer Segmentation Report for Arvato Financial Services
In this project, I will analyze more than 190,000 demographics records for customers of a mail-order sales company in Germany, comparing it against more than 89,000 demographics information records for the general population.
By analysis these data, mail-order sales companies can better understand the subdivision characteristics of customer groups, and then take more accurate marketing projects. For this project, I was interested in using data analysis to better understand:
（1）Do customers and the general population have different characteristics?
（2）What are the main characteristics of customer groups?
（3）According to these characteristics of customers, how can we subdivide them into subgroups?
Are our customers special?
Now let’s dive deep into this project. Here we analyzed the distribution of five indicators in this two populations, and we find the following conclusions.
（1）. These customers may be richer.
In the picture above, you can see there are five groups in each population, from 1 to 5, means very high, high, average, low, and very low in financial investment. More than half of the customers are “very high”. However, in the public, the corresponding population accounted for about 25%.
In the picture above, you can see there are five groups in each population, from 1 to 5, means very high, high, average, low, and very low in owning house. Top three groups of the customers are “average”,“high” and “very high”. However, in the public, top three groups of the public are “average”,“very low” and “high”.
（2）. These customers may have lower affinity.
In the picture above, you can see there are seven groups in each population, from 1 to 5, means highest, very high, high, average, low, very low and lowest in affinity. In our customers, group 6 is the top 1 group, and far higher than other groups. However, in general population, the gap between groups is smaller.
What are the salient characteristics of customers?
Here, I used unsupervised learning techniques to describe the salient characteristics of the demographics of the company’s existing customers.
（1） Top 1 principal component of customers ——Financial.
Through analysis, we found that the first principal component is mainly about health and financial risk. It is mainly composed of the following features.
VERS_TYP :insurance typology
NATIONALITAET_KZ:nationaltity (scored by prename analysis)
HEALTH_TYP: health typology
ALTER_HH:main age within the household
SEMIO_VERT:affinity indicating in what way the person is dreamily.
（2） No.2 principal component of customers ——Houses.
The second principal component is mainly about houses and family. It is mainly composed of the following features.
PLZ8_ANTG3: number of 6-10 family houses in the PLZ8
ORTSGR_KLS9: classified number of inhabitants
（3） No.3 principal component of customers ——Cars.
The third principal component is mainly about cars. It is mainly composed of the following features.
KBA13_KMH_211: share of cars with a greater max speed than 210 km/h within the PLZ8
KBA13_KMH_250 : share of cars with max speed between 210 and 250 km/h within the PLZ8
KBA05_MOTOR : most common engine size in the microcell
KBA13_HERST_BMW_BENZ : share of BMW & Mercedes Benz within the PLZ8
（3）How to target new customers?
Next, we can train supervised machine learning model to identify the characteristics of consumers, and then use the trained model to identify potential customers from a large number of people.