customer churn analysis:_
churn problem considered as losing customers as they stop using the services provided by the business, specially e-commerce platforms. The churn rate is an important metric for e-commerce businesses because it can significantly impact their revenue and profitability. High churn rates can indicate issues with customer service, product quality, or pricing. To combat churn, e-commerce businesses often implement retention strategies, such as targeted marketing campaigns,loyalty programs, and personalized recommendations, to keep customers engaged and satisfied.
dataset overview:_ The data set belongs to a leading online E-Commerce company. An online retail (E commerce) company wants to know the customers who are going to churn, so accordingly they can approach customer to offer some promos.
File Structure:_
CustomerID: Unique customer ID
Churn Churn: Flag
Tenure: Tenure of customer in organization
PreferredLoginDevice: Preferred login device of customer
CityTier: City tier
WarehouseToHome: Distance in between warehouse to home of customer
PreferredPaymentMode: Preferred payment method of customer
Gender: Gender of customer
HourSpendOnApp: Number of hours spend on mobile application or website
NumberOfDeviceRegistered: Total number of deceives is registered on particular customer
PreferedOrderCat: Preferred order category of customer in last month
SatisfactionScore: Satisfactory score of customer on service
MaritalStatus: Marital status of customer
NumberOfAddress:Total number of added added on
particular customer: Complain Any complaint has been raised in last month
OrderAmountHikeFromlastYear: Percentage increases in order from last year
CouponUsed: Total number of coupon has been used in last month
OrderCount: Total number of orders has been places in last month
DaySinceLastOrder: Day Since last order by customer
CashbackAmount: Average cashback in last month
steps:_ this project was performed usong power bi tool, first i performed some data cleaning and validation steps as -removing duplicates -handling outliers -filling null values all these steps peformed in power query editor, a great tool to manipulate your data! then getting this data into the data model and used the suitable visulaizatons, through this step i created measures with DAX language to help me through calculations these measures were: -total number of customers: #of customers = COUNT('E Commerce Dataset'[CustomerID])
-number of churns: number of churns = CALCULATE ( COUNT ( 'E Commerce Dataset'[Churn] ),FILTER ( 'E Commerce Dataset', [Churn] ="1" ))
-churn rate: churn rate = ('E Commerce Dataset'[number of churns]/'E Commerce Dataset'[#of customers])*100
-maximum number of days since last order: max days since last order = MAX('E Commerce Dataset'[DaySinceLastOrder])
.after finishing the previous steps i started to gnerate the required dashboards:_
#EDA dashboard
Explanation:
#what does these dashboards tell us?
##well, lets start with the first one:_
-total number of customers = 17k
-total number of churned customers = 2844
-total number of not churned customers = 14k
-the majority of customers are male and married customers.
-customers prefer logging on with pc more than the mobile phone.
-debit card is the preferd payment methods for our customers.
-the most demanding products are phones.
-number of customers by each tenure.
#Recommendations based on the information above:_
.as we have the majority of our customers from male customers, we have to provide more male related products on our online store to keep the current male customers and attract more customers.
.as the prefered login device is phones not PCs we have to make sure that the user experience with PCs not that bad and modify it if needed.
.facilitate other payment methods.
#days since last order and gender infographics
-the maximum days since last order = 46 days.
-total number of males and females.
-prefered items for both genders.
-prefed payment methods for both genders.
-number of customers and the days since last order they placed.
-total number of orders for both genders and as mentioned above that we have more male customers than females, but the female customers we have are placing orders close to the number of male orders.
-prefered login device for both genders.
#Churn indicators
Explanation
-total number of customers by city tier.
-total number of churned and non churned customers.
-average tenure for all customers.
-churn rate.
-number of customers by satisfaction score, satisfaction score if very important metric to track it is an indicator for the customers tending to churn.
-churn rate by number of addresses, we can notice that more number of addresses the more the customer tend to churn hence this is also a very important metric to track.
#Complains
Explanation
-total number of complains
-number of customers by each order hike
-number of customers by the number of times of using coupons
-number of complains for each warehouse to home distance