CRM (Customer Relationship Management) Analytics
Customer Segmentation with RFM (Recency, Frequency, Monetary) Analysis
using FLO's
Dataset
GitHub Repository:
Business Problem
FLO, an online shoe store, wants to segment its customers and determine marketing
strategies according to these segments. To this end, the behaviors of the customers will be defined and
groups will be formed according to the clusters in these behaviors.
Predictive Segments
| Frequency |
| 5 |
can't loose them |
loyal customers
|
champions |
| 4 |
at risk |
| 3 |
need attention |
potential loyalists
|
| 2 |
hibernating |
about to sleep |
| 1 |
promising |
new customers |
| |
1 |
2 |
3 |
4 |
5 |
| Recency Score |
Dataset Story
The dataset consists of the information obtained from the past shopping behaviors of customers who made
their last purchases from FLO as OmniChannel (both online and offline shopper) in
2020-2021.
Variables
- master_id: Customer ID
- order_channel: Shopping platform (Android, ios, Desktop, Mobile, Offline)
- last_order_channel: The channel where the most recent purchase was made
- first_order_date: Customer's first order date
- last_order_date: Customer's last order date
- last_order_date_online: Customer's last offline order date
- last_order_date_offline: Customer's last online order date
- order_num_total_ever_online: The total number of orders made by the customer online
- order_num_total_ever_offline: The total number of orders made by the customer offline
- customer_value_total_ever_offline: The total price paid by the customer for offline
orders
- customer_value_total_ever_online: The total price paid by the customer for online
orders
- interested_in_categories_12: List of categories the customer has shopped in the last 12
months
Requirements
CLTV (Customer Lifetime Value) Prediction with BG-NBD and Gamma-Gamma using FLO's
Dataset
GitHub Repository:
Business Problem
FLO wants to set a roadmap for sales and marketing activities.
In order for the company to make a medium-long-term plan, it is necessary to estimate the potential value
that existing customers will provide to the company in the future.
Dataset Story
The dataset consists of the information obtained from the past shopping behaviors of customers who made
their last purchases from FLO as OmniChannel (both online and offline shopper) in 2020-2021.
Variables
- master_id: Customer ID
- order_channel: Shopping platform (Android, ios, Desktop, Mobile, Offline)
- last_order_channel: The channel where the most recent purchase was made
- first_order_date: Customer's first order date
- last_order_date: Customer's last order date
- last_order_date_online: Customer's last offline order date
- last_order_date_offline: Customer's last online order date
- order_num_total_ever_online: The total number of orders made by the customer online
- order_num_total_ever_offline: The total number of orders made by the customer offline
- customer_value_total_ever_offline: The total price paid by the customer for offline orders
- customer_value_total_ever_online: The total price paid by the customer for online orders
- interested_in_categories_12: List of categories the customer has shopped in the last 12 months
Requirements
lifetimes==0.11.3matplotlib==3.7.1pandas==1.5.1