Determined two courses for the Dominick's transnational database analysis: one performed on a corporate level to facilitate a variety of corporate planning activities; and the other one on a category level to improves sales performance and expand product offerings.
• Extracted one year sales data from 109 Dominick's stores in Chicago district and merged with store demographic data.
• Analysis the data by segmentation analysis (create groups of the stores similar in performance), response analysis (find targetable characteristics of identified groups of stores) and model validation (evaluate performance of the model on a 20% hold-out sample) utilizing SAS
• Explicated the result in 25 pages report, which discussed the evaluation of potential locations for a new store and choice of the stores to test market a new product.
Business Idea Competition: Miao guide
An official account on the largest Chinese Social Media App WeChat. Miaoguide is made for helping Chinese international student find internship or full time job at US job market. This business Idea competition was held by UTD-JSOM Entrepreneurship division
Student’s Alcohol Consumption Data AnalysisDemin Wang
Some of the most important new data to emerge on young adult drinking were collected through a recent nationwide survey, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). According to these data, about 70 percent of young adults or about 19 million people, consumed alcohol in the year preceding the survey.
Short exploratory data analysis focusing on the alcohol variables from the Portuguese school dataset. Our main goal is using Data Mining To Predict School Student Alcohol Consumption and finding the significant factors.
Oracle vs Salesforce.com Case Analysis: Competition on Hosted CRMDemin Wang
Oracle vs Salesforce.com Case Analysis: Competition on Hosted CRM. In order to compete with salesforce.com in On Demand CRM as well as maximize profits from the Siebel acquisition Oracle needs to:
Add on to the hosted CRM services acquired with the acquisition of Siebel
Optimize Siebel’s packaged software line
Our Recommendation:
Develop a more nimble and customizable product
Target small and medium businesses
Offer a competitive price
As part of the OESON Data Science internship program OGTIP Oeson, I completed my first project. The goal of the project was to conduct a statistical analysis of the stock values of three well-known companies using Advanced Excel. I used descriptive statistics to analyze the data, created charts to visualize the trends and built regression models for each company.
Business Idea Competition: Miao guide
An official account on the largest Chinese Social Media App WeChat. Miaoguide is made for helping Chinese international student find internship or full time job at US job market. This business Idea competition was held by UTD-JSOM Entrepreneurship division
Student’s Alcohol Consumption Data AnalysisDemin Wang
Some of the most important new data to emerge on young adult drinking were collected through a recent nationwide survey, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). According to these data, about 70 percent of young adults or about 19 million people, consumed alcohol in the year preceding the survey.
Short exploratory data analysis focusing on the alcohol variables from the Portuguese school dataset. Our main goal is using Data Mining To Predict School Student Alcohol Consumption and finding the significant factors.
Oracle vs Salesforce.com Case Analysis: Competition on Hosted CRMDemin Wang
Oracle vs Salesforce.com Case Analysis: Competition on Hosted CRM. In order to compete with salesforce.com in On Demand CRM as well as maximize profits from the Siebel acquisition Oracle needs to:
Add on to the hosted CRM services acquired with the acquisition of Siebel
Optimize Siebel’s packaged software line
Our Recommendation:
Develop a more nimble and customizable product
Target small and medium businesses
Offer a competitive price
As part of the OESON Data Science internship program OGTIP Oeson, I completed my first project. The goal of the project was to conduct a statistical analysis of the stock values of three well-known companies using Advanced Excel. I used descriptive statistics to analyze the data, created charts to visualize the trends and built regression models for each company.
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
Optimizing Assortments by Focusing on Attribute-Based Demand PatternsG3 Communications
View the full webcast here: http://rtou.ch/2p7g5qg
Learn how to analyze the everyday shopper’s buying behavior using retail data pattern recognition principles and applying those to the average retail environment. Kevin Stadler, President & CEO, and Marsha Shapiro, SVP of Product Management from 4R Systems present a unique approach to consumer patterns and how Assortment Optimization applies. They will cover:
· Retail data pattern recognition guiding principles
· Roadmap to applying consumer pattern principles within the retail environment
· Best uses in retail & key learnings
· Examples and applicability in Assortment Optimization
Retail analytics ASIA workshop_ New perspective on GfK retail analyticsRichard Jo
Shared training presentation for the retail service managers throughout ASIA in 2005. I have developed new mothodology of marketing data analytics for retailers.
Report_Imports of goods and services Canada(2023).docxmigneshbirdi
Comprehensive Analysis of Imported Goods into Canada in 2023 - Data Acquisition, Analysis, and Visualization
In the project focused on Data Acquisition, Analysis, and Visualization, I undertook an in-depth examination of the goods imported into Canada in the year 2023. The primary objective was to derive valuable insights from the dataset through various statistical and analytical methods.
2nd Dubai Marketing Club (Pharmaceutical Forecasting) by Dr.Samer SaeedMahmoud Bahgat
2nd Dubai Marketing Club (Pharmaceutical Forecasting) by Dr.Samer Saeed
*#Mahmoud_Bahgat*
*#Marketing_Club*
للاشتراك في نادي التسويق بالشرق الاوسط
*If you are a Marketer now*
To Join our whatsapp &Monthly Meeting in Middle East Cities
Send me ur data on Whatsap
00966569654916
*Fill ur data here as speaker or member*
https://lnkd.in/efkTE7T
Join now
*Marketing Club Facebook Page*
https://lnkd.in/gm4c4hD
*Marketing Club Facebook Group*
https://lnkd.in/gX-5au5
*Egyptian Pharmacists Society Facebook Page*
https://lnkd.in/fucnv_5
•••••••••••••••••••••••••••••
*#Mahmoud_Bahgat*
00966568654916
لخدمات التسويق والدعاية والاعلان
*#Legendary_ADLAND*
Complete Marketing Solutions
*www.TheLegendary.info*
•••••••••••••••••••••••••••••
للحصول على اقامة او شركة في اوروبا
*#Legendary_Europe*
Europe Companies & Residency
*www.LegendaryEurope.Net*
•••••••••••••••••••••••••••••
*Contact Bahgat*
M.Bahgat@TheLegendary.Info
■ *Bahgat Facbook Page*
https://lnkd.in/fVAdubA
■ *Bahgat Linkedin*
https://lnkd.in/fvDQXuG
■ *Bahgat Twitter*
https://lnkd.in/fmNC72T
■ *Bahgat YouTube Channel*
https://www.Youtube.com /mahmoud bahgat
■ *Bahgat Instagram*
https://lnkd.in/fmWPXrY
■ *Bahgat SnapChat*
https://lnkd.in/f6GR-mR
•••••••••••••••••••••••••••••
An introduction to SigmaXL's various Graphical tools
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
During the "Data Driven Strategies" course, me and my colleagues have set ourselves the challenge of predicting the probability of upselling through a logistic regression, using a bank dataframe sample, in order to determine an optimal target to maximize net profit from the planned marketing campaign.
Here you can find attached the final presentation of the project!
3PLs are a virtually perfect competitive business model. With highly variable costs to revenue, it is challenging to make a 3PL company thrive. Here is some research we have done with Lean Transit to achieve remarkable progress towards making 3PLs more profitable.
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
Optimizing Assortments by Focusing on Attribute-Based Demand PatternsG3 Communications
View the full webcast here: http://rtou.ch/2p7g5qg
Learn how to analyze the everyday shopper’s buying behavior using retail data pattern recognition principles and applying those to the average retail environment. Kevin Stadler, President & CEO, and Marsha Shapiro, SVP of Product Management from 4R Systems present a unique approach to consumer patterns and how Assortment Optimization applies. They will cover:
· Retail data pattern recognition guiding principles
· Roadmap to applying consumer pattern principles within the retail environment
· Best uses in retail & key learnings
· Examples and applicability in Assortment Optimization
Retail analytics ASIA workshop_ New perspective on GfK retail analyticsRichard Jo
Shared training presentation for the retail service managers throughout ASIA in 2005. I have developed new mothodology of marketing data analytics for retailers.
Report_Imports of goods and services Canada(2023).docxmigneshbirdi
Comprehensive Analysis of Imported Goods into Canada in 2023 - Data Acquisition, Analysis, and Visualization
In the project focused on Data Acquisition, Analysis, and Visualization, I undertook an in-depth examination of the goods imported into Canada in the year 2023. The primary objective was to derive valuable insights from the dataset through various statistical and analytical methods.
2nd Dubai Marketing Club (Pharmaceutical Forecasting) by Dr.Samer SaeedMahmoud Bahgat
2nd Dubai Marketing Club (Pharmaceutical Forecasting) by Dr.Samer Saeed
*#Mahmoud_Bahgat*
*#Marketing_Club*
للاشتراك في نادي التسويق بالشرق الاوسط
*If you are a Marketer now*
To Join our whatsapp &Monthly Meeting in Middle East Cities
Send me ur data on Whatsap
00966569654916
*Fill ur data here as speaker or member*
https://lnkd.in/efkTE7T
Join now
*Marketing Club Facebook Page*
https://lnkd.in/gm4c4hD
*Marketing Club Facebook Group*
https://lnkd.in/gX-5au5
*Egyptian Pharmacists Society Facebook Page*
https://lnkd.in/fucnv_5
•••••••••••••••••••••••••••••
*#Mahmoud_Bahgat*
00966568654916
لخدمات التسويق والدعاية والاعلان
*#Legendary_ADLAND*
Complete Marketing Solutions
*www.TheLegendary.info*
•••••••••••••••••••••••••••••
للحصول على اقامة او شركة في اوروبا
*#Legendary_Europe*
Europe Companies & Residency
*www.LegendaryEurope.Net*
•••••••••••••••••••••••••••••
*Contact Bahgat*
M.Bahgat@TheLegendary.Info
■ *Bahgat Facbook Page*
https://lnkd.in/fVAdubA
■ *Bahgat Linkedin*
https://lnkd.in/fvDQXuG
■ *Bahgat Twitter*
https://lnkd.in/fmNC72T
■ *Bahgat YouTube Channel*
https://www.Youtube.com /mahmoud bahgat
■ *Bahgat Instagram*
https://lnkd.in/fmWPXrY
■ *Bahgat SnapChat*
https://lnkd.in/f6GR-mR
•••••••••••••••••••••••••••••
An introduction to SigmaXL's various Graphical tools
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
During the "Data Driven Strategies" course, me and my colleagues have set ourselves the challenge of predicting the probability of upselling through a logistic regression, using a bank dataframe sample, in order to determine an optimal target to maximize net profit from the planned marketing campaign.
Here you can find attached the final presentation of the project!
3PLs are a virtually perfect competitive business model. With highly variable costs to revenue, it is challenging to make a 3PL company thrive. Here is some research we have done with Lean Transit to achieve remarkable progress towards making 3PLs more profitable.
Taurus Zodiac Sign_ Personality Traits and Sign Dates.pptxmy Pandit
Explore the world of the Taurus zodiac sign. Learn about their stability, determination, and appreciation for beauty. Discover how Taureans' grounded nature and hardworking mindset define their unique personality.
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
As a business owner in Delaware, staying on top of your tax obligations is paramount, especially with the annual deadline for Delaware Franchise Tax looming on March 1. One such obligation is the annual Delaware Franchise Tax, which serves as a crucial requirement for maintaining your company’s legal standing within the state. While the prospect of handling tax matters may seem daunting, rest assured that the process can be straightforward with the right guidance. In this comprehensive guide, we’ll walk you through the steps of filing your Delaware Franchise Tax and provide insights to help you navigate the process effectively.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
According to TechSci Research report, “India Orthopedic Devices Market -Industry Size, Share, Trends, Competition Forecast & Opportunities, 2030”, the India Orthopedic Devices Market stood at USD 1,280.54 Million in 2024 and is anticipated to grow with a CAGR of 7.84% in the forecast period, 2026-2030F. The India Orthopedic Devices Market is being driven by several factors. The most prominent ones include an increase in the elderly population, who are more prone to orthopedic conditions such as osteoporosis and arthritis. Moreover, the rise in sports injuries and road accidents are also contributing to the demand for orthopedic devices. Advances in technology and the introduction of innovative implants and prosthetics have further propelled the market growth. Additionally, government initiatives aimed at improving healthcare infrastructure and the increasing prevalence of lifestyle diseases have led to an upward trend in orthopedic surgeries, thereby fueling the market demand for these devices.
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
Remote sensing and monitoring are changing the mining industry for the better. These are providing innovative solutions to long-standing challenges. Those related to exploration, extraction, and overall environmental management by mining technology companies Odisha. These technologies make use of satellite imaging, aerial photography and sensors to collect data that might be inaccessible or from hazardous locations. With the use of this technology, mining operations are becoming increasingly efficient. Let us gain more insight into the key aspects associated with remote sensing and monitoring when it comes to mining.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
2. Overview of Business Problem
• In the 1990’s and early 2000’s, Dominick’s was a chain of over
100 grocery stores in the Chicago Metropolitan area
• For this evaluation, we are performing a corporate-level as
well as a category-level data analysis
• Corporate Analysis – Relate store sales performance
with known demographics to facilitate corporate
planning activities and test potential locations
• Category Analysis – Relate category sales performance
with known demographics to improve sales
performance and expand product offerings
3. Data Description
Store-level historical data on the sales over more than seven year
period
Customer Count File
Daily sales of stores in 30 product
categories:
• Bakery
• Beer
• Cosmetic
• Dairy
• Meat
• Pharmacy
• Grocery
Store-Specific Demographics
Demographic profiles of stores:
• Age
• Single / Retired / Unemployed
• Mortgage
• Poverty
• Income
• Education
• Household size
• Working woman, etc
• Cheese
• Wine
• Health and Beauty
• Deli
• Fish
• Floral
• Jewelry, etc.
4. Data Preparation
Step 1. The latest year’s sales data was aggregated by Store and
summarized for the year from Customer Count File
Step 2. Demographic variables were added from Store Account File
Resulting data set:
• 1-record per store (94 stores) containing 12-month sales data and
store demographic data
• Sales data on 30 product categories (the ‘Behavior’ variables)
• 43 demographic variables for residents living near the store
5. Approach
1. Segmentation: create groups of the stores similar in their
performance according to certain group of product categories and
dissimilar to the other groups according to the same group of
categories
Method: Non-hierarchical and hierarchical clustering
2. Response Analysis: find targetable characteristics of identified
groups of the stores
Method: Discriminant analysis
3. Model Validation: evaluate performance of the models on a hold-out
sample (20% of the stores)
4. Recommendations and conclusions
6. Dominick’s Data Set
General Data Set
Corporate Analysis
Category Analysis
Data Preparation
Clusters
Hierarchical Clustering and Non-Hierarchical Clustering
Response Analysis
Discriminate Analysis Hold-Out
Group
20%
Model Test
Conclusion and Recommendation
Corporate Analysis Results
Category Analysis Results
Flowchart of the Approach
10. Corporate Analysis - Discriminant Analysis
Confidence Level: 90%
Univariate Test Statistics
F Statistics, Num DF=5, Den DF=79
Variable Total
Standard
Deviation
Pooled
Standard
Deviation
Between
Standard
Deviation
R-Square R-Square
/ (1-RSq)
F Value Pr > F
EDUC 0.1129 0.1102 0.0394 0.1029 0.1147 1.81 0.1200
NOCAR 0.1316 0.1287 0.0453 0.1000 0.1111 1.76 0.1318
INCSIGMA 2323 2264 824.9388 0.1064 0.1190 1.88 0.1070
HSIZE1 0.0829 0.0809 0.0292 0.1045 0.1167 1.84 0.1138
SINHOUSE 0.2173 0.2103 0.0817 0.1194 0.1355 2.14 0.0690
HVAL200 0.1853 0.1758 0.0792 0.1541 0.1822 2.88 0.0194
SINGLE 0.0703 0.0665 0.0306 0.1593 0.1895 2.99 0.0158
NWRKCH17 0.0199 0.0194 0.006933 0.1024 0.1141 1.80 0.1218
TELEPHN 0.0309 0.0293 0.0134 0.1581 0.1879 2.97 0.0166
SHPINDX 0.2482 0.2405 0.0924 0.1168 0.1323 2.09 0.0753
* 17 statistically significant variables in total
11. Corporate Analysis - Discriminant Analysis (Cont.)
Canonical
Correlation
Adjusted
Canonical
Correlation
Approximate
Standard
Error
Squared
Canonical
Correlation
1 0.847077 0.761387 0.030819 0.717540
Multivariate Statistics and F Approximations
S=5 M=15 N=21
Statistic Value F Value Num DF Den DF Pr > F
Wilks' Lambda 0.02426163 1.39 180 223.58 0.0103
Pillai's Trace 2.50666011 1.34 180 240 0.0172
Hotelling-Lawley
Trace
6.07753961 1.44 180 164.86 0.0093
Roy's Greatest
Root
2.54031820 3.39 36 48 <.0001
Means of the
independent
variables are
statistically
different among
segments
Only 2.4% of the
variance in the
discriminant
scores is not
explained by the
differences among
groups of the
stores Ratio between-group SS to
the total SS => Good set of
descriptors
12. Error Count Estimates for CLUSTER
1 3 4 5 6 Total
Rate 0.1429 0.0000 0.0000 0.3333 0.3333 0.1619
Priors 0.1667 0.1667 0.1667 0.1667 0.1667 0.8333
Error Count Estimates for CLUSTER
1 2 3 4 5 6 Total
Rate 0.1818 0.0000 0.0000 0.1667 0.3571 0.1923 0.1497
Priors 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
Corporate Analysis – Classification Results
Original
Dataset
Hold-out
Sample
~ 85% of the stores are classified correctly
~ 84% of the stores are classified correctly
13. Category Analysis: Beer and Wine
Cluster History
Number of
Clusters
Clusters Joined Freq New
Cluster
RMS Std
Dev
Semipartial
R-Square
R-Square Centroid
Distance
Tie
9 CL16 309 8 72804.2 0.0031 .906 197203
8 CL23 CL13 10 93539.6 0.0091 .897 200748
7 CL10 CL31 11 95378.9 0.0085 .888 239550
6 CL7 CL8 21 145510 0.0459 .842 311263
5 CL87 CL11 61 112380 0.0639 .778 318702
4 CL5 CL6 82 170030 0.2099 .568 385452
3 CL4 CL15 85 185394 0.0973 .471 609748
2 CL3 CL9 93 226017 0.3212 .150 696877
1 CL2 304 94 243807 0.1499 .000 1.29E6
Step #1 – Hierarchical Clustering
Conclusion: optimal number of clusters is between 4 and 6
14. Category Analysis: Beer and Wine (Cont.)
Step #2 – Non-Hierarchical Clustering
4 clusters 5 clusters 6 clusters
Pseudo F Statistic 87.53 116.85 131.08
Approximate Expected Over-All R-Squared 0.7692 0.81988 0.85358
Cubic Clustering Criterion -1.336 1.458 2.489
Conclusion: based on the results of both Hierarchical and Non
Hierarchical clustering 6-cluster solution is determined
to be optimal
15. Category Analysis: Beer and Wine (Cont.)
Cluster Summary
Cluster Frequency RMS Std
Deviation
Maximum
Distance
from Seed
to
Observation
Radius
Exceeded
Nearest
Cluster
Distance
Between
Cluster
Centroids
1 35 83267.8 194999 2 268532
2 32 78629.9 206948 1 268532
3 8 131663 250170 2 374603
4 9 82174.1 159203 2 333646
5 9 80329.2 180104 4 377389
6 1 . 0 3 924906
Cluster Means
Cluster BEER WINE
1 144128.421 101864.577
2 326776.212 298713.241
3 493651.738 634093.243
4 649465.774 213912.842
5 955669.947 434505.459
6 383045.800 1552362.060
Cluster #5 is the top seller
of Beer
Cluster #6 is the Top seller
of Wine
Cluster #1 has the lowest
sales of both Beer & Wine
One store in Cluster 6
outlier
16. Discriminant Analysis: Beer and Wine
Confidence level: 95%
Univariate Test Statistics
F Statistics, Num DF=5, Den DF=79
Variable Total
Standard
Deviation
Pooled
Standard
Deviation
Between
Standard
Deviation
R-Square R-Square
/ (1-RSq)
F Value Pr > F
AGE9 0.0272 0.0261 0.0109 0.1347 0.1557 2.46 0.0400
EDUC 0.1129 0.1051 0.0528 0.1843 0.2259 3.57 0.0058
INCOME 0.2921 0.2793 0.1192 0.1405 0.1635 2.58 0.0324
INCSIGMA 2323 2191 1021 0.1630 0.1948 3.08 0.0137
HSIZEAVG 0.2686 0.2480 0.1303 0.1985 0.2477 3.91 0.0032
HSIZE2 0.0322 0.0298 0.0154 0.1942 0.2410 3.81 0.0038
HSIZE567 0.0325 0.0277 0.0200 0.3176 0.4655 7.35 <.0001
HH3PLUS 0.0844 0.0796 0.0371 0.1628 0.1944 3.07 0.0138
HH4PLUS 0.0650 0.0606 0.0303 0.1833 0.2244 3.55 0.0061
DENSITY 0.001250 0.001192 0.000518 0.1447 0.1692 2.67 0.0277
HVAL150 0.2460 0.2260 0.1217 0.2064 0.2601 4.11 0.0023
HVAL200 0.1853 0.1664 0.0992 0.2417 0.3188 5.04 0.0005
HVALMEAN 47.3071 42.9341 24.4560 0.2254 0.2909 4.60 0.0010
SINGLE 0.0703 0.0664 0.0308 0.1616 0.1927 3.04 0.0145
UNEMP 0.0239 0.0226 0.0103 0.1576 0.1871 2.96 0.0169
WRKWNCH 0.0446 0.0424 0.0187 0.1483 0.1742 2.75 0.0241
TELEPHN 0.0309 0.0287 0.0148 0.1929 0.2389 3.78 0.0041
POVERTY 0.0457 0.0441 0.0175 0.1238 0.1413 2.23 0.0590
Statistically
significant
variables in
discriminating
observations
among groups
17. Discriminant Analysis: Beer and Wine (Cont.)
Canonical
Correlation
Adjusted
Canonical
Correlation
Approximate
Standard
Error
Squared
Canonical
Correlation
1 0.846814 0.751237 0.030868 0.717094
Multivariate Statistics and F Approximations
S=5 M=15 N=21
Statistic Value F Value Num DF Den DF Pr > F
Wilks' Lambda 0.01346418 1.72 180 223.58 <.0001
Pillai's Trace 2.81504177 1.72 180 240 <.0001
Hotelling-Lawley
Trace
7.26639429 1.72 180 164.86 0.0002
Roy's Greatest
Root
2.53474655 3.38 36 48 <.0001
Means of the
independent
variables are
statistically
different among
segments
Only 1.3% of the
variance in the
discriminant
scores is not
explained by the
differences among
groups of the
stores
Good set of descriptors
18. Beer & Wine Category Analysis –
Classification Results
Original
Dataset
Error Count Estimates for CLUSTER
1 2 3 4 5 6 Total
Rate 0.5714 0.6207 0.7143 0.6000 0.8750 1.0000 0.7302
Priors 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
Hold-out
Sample
Error Count Estimates for CLUSTER
1 2 3 4 5 Total
Rate 0.1667 0.3333 0.5000 0.5000 0.5000 0.4000
Priors 0.1667 0.1667 0.1667 0.1667 0.1667 0.8333
~ 27% of the stores are classified correctly
~ 60% of the stores are classified correctly
19. Recommendations
Corporate Level:
• Resource allocation among the stores: perform additional analysis of the stores
in underperforming segments (1 & 6)
• Evaluation of the potential locations for a new store: deploy discriminant
function to predict performance of the stores in different product categories
based on the demographic profiles of their locations
Category Level (Beer & Wine):
• Marketing strategy for a new brand of Beer or Wine: adjust targeting strategy
for a product based on the demographic profile of the location it will be sold
• Choice of the stores to test market a new product: recommend to perform a
market test for Beer in stores of segments 4 & 5 and Wine in segments 3 &6
20. Limitations of the Analysis
Additional data
• Product-level data: assessment of specific product sales in new stores & prediction
of a new product performance that is being considered to be launched
• Customer-specific data: ability to build better predictive models tied to the customer
demographics (scanner data from the loyalty program members’ transactions)
Higher quality analysis at a more granular
level