Mra Project1 - Firoz Afzal

 

PROJECT-MILESTONE 1

 

 

MARKETING & RETAIL ANALYTICS

 Name: Firoz Afzal Batch: DSBA-OCT’21A_G9 Date: 24th July’22

 

CONTENTS 

Agenda & Executive Summary of the data Exploratory Analysis Analysis and Inferences Customer Segmentation using RFM analysis Inferences from RFM Analysis and identified segments

 

AGENDA & EXECUTIVE SUMMARY OF THE DATA Agenda: Understand & underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data and hence recommend customized marketing strategies for different segments of customers. Executve Summary of Daa: Three years data of an automobile part manufacturer has been collected with 2747 entries consist of 20 variables having details of consumers with product & orders details. Problem Saemen: An automobile parts manufacturing company has collected data of transactions for 3 years.

They do not have any in-house data science team, thus they have hired you as their consultant. Your job is to use your magical data science skills to provide them with suitable insights about their data and their customers. Tools Used: Python ,Tableau & KNIME Daa Source: Sales_Data.xlsx

 

DATA DICTIONARY ORDERNUMBER :

Order Number

CUSTOMERNAME :

customer  

QUANTITYORDERED :

Quantity ordered

PHONE :

Phone of the customer  

PRICEEACH :

Price of Each item

ADDRESSLINE1 :

Address of customer  

ORDERLINENUMBER :

order line

CITY :

City of customer  

SALES :

Sales amount

POSTALCODE :

Postal Code of customer  

ORDERDATE :

Order Date

COUNTRY :

Country customer  

DAYS_SINCE_LASTORDER :

Days_ Since_Lastorder

CONTACTLASTNAME :

Contact person customer  

STATUS :

Status of order like Shipped or not

CONTACTFIRSTNAME :

Contact person customer  

PRODUCTLINE :

Product line – CATEGORY

DEALSIZE :

Size of the deal based on Quantity and Item Price

MSRP :

Manufacturer's Suggested Retail Price

 

PRODUCTCODE :

Code of Product

EXPL ORATOR EXPLORA TORY Y ANA ANAL LYSIS AND INFERENCES  

 

DESCRIPTIVE STATISTICS Shape of Daa: The Data set has 2747 Rows & 20 Columns

Daa Descripton: Following were the stats for numeric variables.

No of Duplicaes: Zero duplicate row was observed Key Inferences: No Null Values seen in the dataset        

Sales amount ranges from 482.12 to 14082.8 Order Qty ranges from 6 to 97 Minimum no of days of next order is 42. USA is the country with highest no of customers-928

 

92.5%(2541) of the order has been successfully shipped

 

 

EXPLORA EXPL ORATORY TORY ANALYSIS ANALYSIS AND INFE INFEREN RENCES CES UNIVARIATE UNIV ARIATE ANALYSIS

Price of Each Item

Sales Amount

Inferences:  Variable  Variable Price of Each Item has approx. Normal Distribution but many outliers.   Sales Variable is right Skewed with lots of Outliers.

 

  EXPLORA EXPL ORATORY TORY ANAL ANALYSIS YSIS AND INFERENCES UNIVARIATE UNIV ARIATE ANALYSIS

MSRP

Days_Since_Last_Order 

Inferences:  Data seems not be normally distributed in both the variables but there is almost no outliers in both the variables as well.

 

 

EXPLORA EXPL ORATORY TORY ANALYSIS ANALYSIS AND INFE INFEREN RENCES CES UNIVARIATE UNIV ARIATE ANALYSIS

Inferences:  Classic Cars are the major contribution of Sales.(Approx 40%)   Medium Size deals have worked well in generating sales(Approx 60%)   92% Orders have been shipped successfully successfully..

 

  EXPLORA EXPL ORATORY TORY ANAL ANALYSIS YSIS AND INFERENCES BIVARIATE BIV ARIATE ANALYSIS

Inferences:  Sales are highly correlated with Price of Each Item ( Corr 0.81) MSRP seems to be fairly correlated with Price of Each Item(Corr 0.78)

 

MSRP seems to be fairly correlated with Price of Each Item(Corr 0.78)

 

 

EXPLORA EXPL ORATORY TORY ANALYSIS ANALYSIS AND INFE INFEREN RENCES CES BIVARIATE BIV ARIATE ANALYSIS

Inferences:  USA is the highest contributor of sales followed by Spain & France.

 

  EXPLORA EXPL ORATORY TORY ANAL ANALYSIS YSIS AND INFERENCES BIVARIATE BIV ARIATE ANALYSIS

Inferences:  Classical Cars contribute highest sales among all categories with 33373 ordered quantities.

 

  EXPLORA EXPL ORATORY TORY ANAL ANALYSIS YSIS AND INFERENCES BIVARIATE BIV ARIATE ANALYSIS

Inferences:  Most of the shipped orders were from Medium Size deal.   Highest Disputed Orders is from the Large Deal.

 

 

SALES TREND ANAL ANALYSIS YSIS

Inferences:  Sales is highest in 4 th Quarter for the year 2018 & in 2019.  

 

 

SALES TREND ANAL ANALYSIS YSIS

Inferences:  Sunday has the Highest Sales compared to all Days & Thursday has the least.   Sales is highest in November Month.

 

 

CUSTOMER SEGMENT SEGMENTA ATION USING RFM ANALYSIS ANALYSIS • What is RFM: It stands for Recency Recency,, Frequency & Monetary value. It’s a marketing technique used to quantitatively rank and group customers based on the recency, frequency and monetary total of their recent transactions to identify the best customers and perform targeted marketing campaigns. The system assigns each customer numerical scores based on these factors to provide an objective analysis.

 

 

  • • • • • • • • • • • •

CUSTOMER SEGMENT SEGMENTA ATION USING RFM ANALYSIS ANALYSIS

Parameters Used & Assumption Made:  Following parameters were used.  Customer Name Quantity Order  Price of Each Item Order Date Sales in Amount KNIME Tool Tool is used for RFM Analysis & Consumer Segmentation Assumption Made: Recency is the difference of Order Date from Current Date(6-2020 from order date) Frequency is how order is placed from the customer by capturing variable Days_Since_Last_Order  Monetary Value Value is the produ product ct of Quantity Ordered x Price of Each Item Created 3 bins each for R,F & M with below distribution

• Sum Sales is used for calculating monetary value. • All data has been summarized by customer name.

 

 

CUSTOMER SEGMENT SEGMENTA ATION USING RFM ANALYSIS ANALYSIS

• KNIME Work Flow Image: •  

 

CUSTOMER SEGMENT SEGMENTA ATION USING RFM ANALYSIS ANALYSIS • Output Table Head:

 

INFERENCES FROM RFM ANALYSIS AND IDENTIFIED SEGMENTS • Best Customers: 1. Euro Shopping Channel   2.Souveniers And Things Co.   3.Salzburg Collectable   4.Danish Wholesale Imports   5.La Rochelle Gifts

• Lost Customers  1.Auto Assoc. & Cie.   2.Bavarian Collectables Imports, Co   3.CAF Imports   4.Cambridge Collectables Co.   5.Clover Collections, Co.

Customers on Verge of Churning 1.Saveley & Henriot, Co. Loyal Customers 1.Handji Gifts& Co   2.Saveley & Henriot, Co.   2.Auto Canal Petit   3.Herkku Gifts   3.T 3.Tokyo okyo Collectables, Ltd   4.Marta's Replicas Co.   4.UK Collectables, Ltd.   5.Vida Sport, Ltd   5.Mini Gifts Distributors Ltd.