autoanalyzer

Auto analyze - visualize marketing data

You can use it by python or use web interface: https://mktanalyze.streamlit.app/

Table of contents

• Library installation
• Usage
  • Set up columns
• Analyze
  • Analyze all for one
  • Or analyze what you need
• Predict Customer Life Time Value
  • Set up predictor
  • Using predictor
  • Take your result
  • Using best predictor you have chosen

I. Library installation

To use marketing analyzer, run this code:

git clone https://github.com/HoangHao1009/autoanalyzer
cd autoanalyzer
pip install -e .

II. Usage

1. Set up columns

from Analyzer import Column, analyze
#For instance: you have a df with columns: 'Customer ID', 'Segment', 'Sales', 'Order Date'

customer = Column.mainColunm(
    df['Customer ID'], df['Segment'], ['Consumer', 'Corporate'], type = 'customer'
)
sale = Column.Sale(df['Sales'])
date = Column.Date(df['Order Date'], '%d/%m/%Y')

2. Analyze

2.1. Analyze all for one

analyze = analyze.AllAnalyze(customer, sale, date)

2.2. Or analyze what you need

#This make for you to custom your data and analyze flexibly
#You can change your customer columns to another segment like:
#customer = Column.mainColunm(df['Customer ID'], df['Segment'], ['Consumer', 'Home-Office'], type = 'customer')
#and see what different by using detailed analysis
basicinfo = analyze.BasicInfo(customer, sale, date)
growth = analyze.Growth(customer, sale, date)
newexisting = analyze.NewExisting(customer, sale, date)
retention = analyze.Retention(customer, sale, date)
cohort = analyze.Cohort(customer, sale, date)
segmentation = analyze.RFMSegmentaion(customer, sale, date)

2.3. Take your results

#all result
analyze.get_full_result()
#all analyze data
analyze.get_analyze_data()
#all visualize chart
analyze.get_all_chart()
#See results by detail analysis
basicinfo.all_data
basicinfo.all_px
#or growth.all_data, newexisting.all_px, ...

3. Predict Customer Life Time Value

3.1. Set up predictor

#take predictor by using allanalyze:
analyze = analyze.AllAnalyze(customer, sale, date)
predictor = analyze.predictor
#Or set up it by using RFMSegmentaion
predictor = analyze.CustomerLTVPredictor(segmentation)

3.2. Using predictor

#Get a hint of how many segment of LTV you would like to set up
predictor.cluster_hint()
#Chose best predictor (machine learning algorithm) of predicting your data
predictor.chose_best_predictor()
#-> params of this:
#remove_outlier_quantile: how many percent of outliner cross to remove it
#cv: the chosing algorithm using Grid/Random Search CV, so you can decide how many cv
#use_randomsearch: use RandomSearchCV or GridSearchCV
#only_modern_model: Use modern models or or even the old models

3.3. Take your result

#info of LTV cluster you've decide
predictor.ltv_cluster_info
#the performance of models you use
predictor.predictor_scores
#best predict
best_estimator

3.4. Using best predictor you have chosen

predictor.run_best_predictor(#put another RFMSegmentaion here to predict)