Section 1.1: The Importance of Customer Retention

Customer loyalty and retention play a crucial role in a business's success, ensuring customers return for more purchases, thereby increasing revenue and acting as advocates for the brand. Retaining existing customers is more cost-effective than acquiring new ones, as loyal customers typically spend more and are more likely to try new products or services. Thus, they are essential for sustained growth, profitability, and a positive brand image.

Section 1.2: Understanding RFM Analysis

RFM analysis is an effective technique that helps businesses understand and categorize customers based on three critical factors: Recency, Frequency, and Monetary value. In simple terms, it allows businesses to identify their most valuable customers and strategize ways to keep them engaged.

• Recency (R): This measures the time since a customer last made a purchase or interacted with the business. More recent customers are often more responsive to marketing initiatives.
• Frequency (F): This indicates how often a customer engages with the business. Frequent purchasers are valuable as they represent consistent revenue.
• Monetary Value (M): This looks at the total amount a customer has spent. Recognizing and rewarding high-spending customers is crucial for profitability.

RFM scores are assigned based on these three factors, typically rated from 1 to 10, with higher scores indicating better recency, frequency, or monetary value. Customers are then segmented into various groups based on these scores, allowing for targeted marketing and retention strategies.

Step 1: Create a Virtual Environment

We will first create a virtual environment to manage our dependencies. Here’s how:

# STEP 1: Open the terminal and install the virtualenv library

pip install virtualenv

# STEP 2: Create a new virtual environment named riskmgt

virtualenv venv

# STEP 3: Activate the virtual environment

venvscriptsactivate

Step 2: Import Libraries

Next, we’ll load the required libraries for our project:

import dash

import pandas as pd

import numpy as np

import pathlib

import dash_table

import logging

import dash_daq as daq

import plotly.graph_objs as go

import plotly.express as px

import plotly.figure_factory as ff

import dash_core_components as dcc

import dash_html_components as html

import dash_bootstrap_components as dbc

from dash.dependencies import Input, Output, State

from datetime import timedelta

import layout

import dataPreprocess

Step 3: Load the Data

For our project, we will utilize the Kaggle dataset for E-Commerce data.

The video above provides a comprehensive tutorial on becoming an IBM Data Analyst, emphasizing the use of data analysis tools that can enhance your skills in this domain.

Implementing RFM Analysis

Having understood RFM analysis, we can now implement it using Python and build our application with Dash.

df['TotalSum'] = df['Quantity'] * df['UnitPrice']

snapshot_date = df['InvoiceDate'].max() + timedelta(days=1)

data_process = df.groupby(['CustomerID']).agg({

'InvoiceDate': lambda x: (snapshot_date - x.max()).days,

'InvoiceNo': 'count',

'TotalSum': 'sum'

})

data_process.rename(columns={

'InvoiceDate': 'Recency',

'InvoiceNo': 'Frequency',

'TotalSum': 'MonetaryValue'

}, inplace=True)

Further details on data distribution, cleaning, and wrangling can be found in our GitHub repository. After processing the data, we’ll apply specific thresholds to create segments.

if df['RFM_Score'] >= 9:

return 'Can’t Lose Them'

elif ((df['RFM_Score'] >= 8) and (df['RFM_Score'] < 9)):

return 'Champions'

elif ((df['RFM_Score'] >= 7) and (df['RFM_Score'] < 8)):

return 'Loyal'

elif ((df['RFM_Score'] >= 6) and (df['RFM_Score'] < 7)):

return 'Potential'

elif ((df['RFM_Score'] >= 5) and (df['RFM_Score'] < 6)):

return 'Promising'

elif ((df['RFM_Score'] >= 4) and (df['RFM_Score'] < 5)):

return 'Needs Attention'

else:

return 'Require Activation'

Now that we have processed the data, we can proceed to build the application.

Building the Frontend

We will include a dropdown list populated with countries from our dataset:

html.H6("Select Country"),

dcc.Dropdown(

id="country-dropdown",

options=[

{"label": i, "value": i} for i in tmp_df[0]['Country'].unique()

],

value='Select...'

),

We can also add other controls, such as radio buttons and DAQ toggle switches. For a complete guide on adding these features, please refer to the full code here.

Interactivity Between Components

The frontend, equipped with various widgets and placeholders, should interact seamlessly as user inputs change, utilizing callbacks and functions defined in Dash.

@app.callback(

Output("heatmap", 'figure'),

[

Input("category-type", "value"),

Input("country-dropdown", "value")

]

)

def update_pieChart(category, country):

# Function implementation

Similar callback functions will create interactivity between all visuals and data generated from RFM analysis.

Run the Application

To launch the application, navigate to your project directory in the terminal and run:

python app.py

You can then view the app in your web browser at http://127.0.0.1:8050/.

The second video tutorial covers how to use the SEC EDGAR API in Python to retrieve SEC filings and financial data, which can further enhance your analytical capabilities.