FORDHAM UNIVERSITY Graduate School of Business Marketing … · FORDHAM UNIVERSITY Graduate School...

FORDHAM UNIVERSITY

Graduate School of Business

Marketing Decision Models, MKGB 77AA

Professor: Mohammad G. Nejad, Ph.D.

Strategic Analysis of a School Supply Vending Machine Campaign in New York City

Team Members:

Anqi Wang

Dongqi Wang

Hannah Parker

Guillermo Ponte

Sean Scott

Dear CMO,

At Staples, we are committed to helping our customers every day. We want to grow our

businesses, increase our profitability and advance in key strategic initiatives. This has historically

enabled us to stay competitive and create more value for our shareholders. We want to offer our

customers the best products and services. We operate a tailored portfolio with unique

characteristics for each unique location. Also, we want to make our customers’ lives easier,

because we understand that time is very valuable for them. Therefore, we want to not only

enhance their shopping experiences but also seamlessly integrate it with their day to day lives.

Profitable companies of the 21st century will be those that align the needs of their business with

the needs of the world around them. As marketing employees at Staples, we want to focus on

two pillars: Accessibility and Affordability.

Our top priority is to continue to improve service and value. We want to be accessible for our

customers, because we understand how busy they are. On the other hand, Staples understands

that customers have different incomes levels and necessities. Thus, we want to offer a range of

products, perfect for each unique demographic. Our biggest opportunities to be more accessible

is through cost savings in areas such as supply chain, merchandising, store operations and real

estate, marketing, salesforce, business process and IT outsourcing, and customer service.

In 2015, Staples saw sales decrease from the previous year. Now, for 2016, we want to change

this. We want to increase our sales significantly through a new, exciting sales program targeted

to urban areas. We remain focused on optimizing our retail square footage in North America

through store closures and improved productivity. The proposed installation of 250 vending

machines around NYC area will allow us to accomplish these goals.

Our corporate responsibility programs are a critical part of our customer commitment. We

understand that our customers are more aware of the environment, and we want to offer them

new programs to reduce their carbon footprint. We want to introduce a new holistic marketing

campaign where our customers will buy eco-friendly products and recycle their old supplies in a

completely new shopping environment. I invite you to read more about our corporate

responsibility and the marketing campaign on the following pages.

Sincerely,

Staples Marketing Team

Table of Contents

I. Executive Summary…………………………………………………………………. 1

II. Project Flow Chart……………………………………………………………………5

III. Phase 1 - Data Analysis:

a. Summary…………………………………………………………………………. 6

b. Memo to the Manager…………………………………………………………..... 7

c. Analysis Flow Chart……………………………………………………………….8

d. Analysis: Internal………………………………………………………………… 9

e. Analysis: External……………………………………………………………….. 15

IV. Phase 2 - Strategic Analysis:

a. Summary………………………………………………………………………… 21

b. Company Overview……………………………………………………………... 22

c. Market Overview…………………………………………………………….….. 24

d. Opportunity……………………………………………………………………….25

e. Target Customer………………………………………………………………… 29

f. Goals…………………………………………………………………………….. 31

V. Phase 3 - Marketing Metrics

a. Summary…………………………………………………………………………..34

b. Metric Overview (Table) ………………………………………………………... 35

c. Metrics Model (Figure) …………………………………………………………. 38

d. Marketing Dashboard……………………………………………………………. 39

VI. Limitations………………………………………………………………………..….. 40

VII. Works Cited………………………………………………………………………….. 41

VIII. Appendices:

a. Tables and Figures……………………………………………………………….. 42

b. SAS Code……………………………………………………………………….... 61

1

Executive Summary

For the past 30 years Staples Inc. has found success as a trusted supplier of office and

school related products to both businesses and individual customers. Staples’ in-store copying

and faxing services as well as its established B2B division have helped build a large and

expansive customer base. Staples’ main strengths include its large product portfolio and

numerous retail channels which include stores, catalogs, and a website. Despite Staples’ success,

the office supplies industry is currently in decline. The majority of office work shifting online

has threatened paper, pens, and printer ink with obsolescence. Staples is currently fighting to

merge with Office Depot in order to effectively compete with online retailers such as

Amazon. Staples is a heavy hitting retailer, but opportunities for further growth are certainly

available.

Based on marketing intelligence formed from analysis on internal Staples data, external

demographic data, and industry secondary reports, it is recommended that Staples move forward

with a plan to begin offering their products in Staples vending machines. The vending machines

will initially be rolled out in the five boroughs of New York City and will include all green

products and a recycling receptacle. This original strategy will bolster Staples’ multi-channel

approach and help Staples to ultimately reduce costly retail space.

Performing analysis on internal Staples data was the first step in developing the

marketing intelligence project. The dataset included information on 14,448 orders from 10,000

different households. The dataset was first aggregated by customer ID, and detailed number of

orders, sum revenue, and days since last order for each customer. The customers were then

classified as either “High Level”, “Average”, or “Low Level” based on their revenue. Regression

models were then created using the aggregated dataset that modelled revenue. Findings of the

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regression included purchases in quarter 3 are more profitable than any other quarters, purchases

made via web are more profitable than purchase made through other channels, and purchases

made with a credit card are more profitable than purchases made with any other payment method.

The next stage of the internal data analysis involved finding key customer segments to

target. Customers were clustered by number of orders, sum revenue, and days since last

order. The first segment made up three quarters of the dataset and was characterized by their

light use of Staples. The second segment was the smallest of the four, making up less than a

percentage of the dataset. The third segment made up about a fifth of the dataset and customers

in this segment were characterized by their consistent use of Staples. The fourth and final

segment made up about a fifth of the dataset and consisted of slightly more desirable customers

than segment 3.

In order to further assess Staples’ current position in the market, external data was

collected. Census data was obtained that detailed demographic variables for all New York City

zip codes. The goal was to ascertain what New York City looks like in terms of Staples’ target

market. It was found that all five boroughs of New York fall well above national averages in

terms of income, education level, and student enrollment. Additional analysis involved

understanding the geography of the city in terms of demographic characteristics and behavior, in

order to efficiently roll out the campaign. Zip codes were clustered using the variables number

of students enrolled in Pre-K through 12th

grade, median household income, and percent of

population with a Bachelor’s degree or higher. Four clusters were found. The four identified

segment were “Wealthy Families”, “Lower Middle Class Families”, “Downtown Manhattanites”,

and “Upper Middle Class Families.” The second and fourth segments are the most attractive to

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Staples because of their high student enrollment. Further details of the segments can be found in

Figure 37.

Based on secondary reports, it is known that Staples would like to reduce its retail space

and maintain its multi-channel approach to business. Based on internal data analysis it is known

that Staples’ customers prefer to pay with a credit card and prefer to not make purchases in brick

and mortar locations. The vending machine approach will provide a way for Staples to reach its

goals while giving its customers a new and convenient way to purchase office and school

supplies with their credit cards. The vending machines will be distributed in zip codes deemed

desirable by the external data analysis, as well as areas near schools and shopping malls.

The vending machines will first be rolled out in New York City because of its large and

diverse population and its willingness to care for the environment. The recycling receptacle and

inclusion of green products will encourage already environmentally conscious New Yorkers to

use the vending machines to satisfy their office and school supplies needs and will promote

additional eco-friendly behavior from the rest of the population.

In order to measure the effectiveness of the campaign, 11 key marketing metrics will be

formulated and tracked. For profit monitoring, customer deciles, return on marketing investment,

profit margin, payback period, internal rate of return, and break-even point will be track.

Multichannel sales success will be measured through market penetration rates, acquisition costs,

and a sales funnel approach. The awareness of the sustainability campaign will be measured

using customer awareness. Finally, in order to improve the customer experience, Staples will

monitor the firm’s Net Promoter Score.

Implementing the vending machines approach in New York City is a way for Staples to

attain its goals of reducing retail space and maintaining its multiple channels and large product

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portfolio while increasing the share of its customers’ wallets. Should the project be a success in

New York City, it can be scaled to other urban and suburban areas across the country. Solid

marketing intelligence supports this original idea and its future success. The future for Staples,

the Office Supplies Industry, and vending machines is bright.

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Project Flow Chart

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Phase 1

a. Summary

Phase 1 of this Staples marketing intelligence project involved looking at both internal

and external data in order to identify Staples’ strengths, its customer demographics, and

characteristics of a specific market in order to help identify a new opportunity. Analysis of

internal data was performed in SAS. This analysis produced four customer segments that were

clustered on the variables order frequency, revenue, and days since last order. Regression

models were then run on these segments with revenue as the independent variable. The models

communicated that the most profitable customers purchase via the web, with a credit card, and in

quarter 3.

External zip code data was also analyzed in SAS in an effort to get a well-rounded

understanding of Staples’ environment. In order to focus this project, New York City was chosen

as the target location. New York City ZIP codes were clustered based on income, student

enrollment and educational attainment. Four distinct clusters of ZIP codes were identified – they

vary significantly by income and education. Two clusters are concentrated in Manhattan and can

be described by high income, high education, and moderate to below average student enrollment.

The other clusters are more heavily focused in Brooklyn, the Bronx, Queens, and Staten Island.

These remaining groups are characterized by lower income but a higher enrollment rate for

students in secondary school.

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b. Memo to Manager

Based on internal dataset analysis, our team has several suggestions to the manager.

1. Staple needs a creative slogan to make customers have a positive image of brand;

2. Cooperate with Credit Card Banks, providing credits, gifts or cash back if customers use

credit cards to pay orders, especially in off-seasons (Quarter 1, Quarter 2, and Quarter 4);

3. Provide marketing promotion such as seasonal discount, quantity discount, or gross

promotion to simulate customers to purchase more and then increase sum revenue, especially

in off-seasons (Quarter 1, Quarter 2, and Quarter 4).

External analysis has also provided several key insights:

4. New York City has a much higher average income and education rate than the national

average

5. Manhattan is characterized by extremely high income – these areas will value higher-end

products

6. The remaining boroughs have lower incomes on average, but much higher enrollment

rates – these areas are more family oriented

7. Overall, a larger proportion of NYC is characterized by lower income and larger student

enrollment – this may be a more lucrative area for Staples to target any new marketing

campaigns

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c. Analysis Flow Chart

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d. Analysis Procedure – Internal Data Analysis

In order to understand Staples’ current customer base, a twelve year internal dataset was

analyzed (through April 30, 2009). The dataset includes variables such as: order source, quantity

of items purchased, returns, payment information, and purchaser ZIP code.

There is one record per order, with multiple orders per household. Orders within the same

household are indicated with matching Household-ID numbers (one number per unique

household). The dataset contains 14,448 order recorders from 10,000 unique households.

The purpose for analyzing the internal dataset is to find what factors influence revenue

for each customer order. This objective will be met by running regression models to predict

revenue for each order and segmenting customers based on their number of orders, revenue, and

purchase recency. A marketing campaign will then be designed to help Staples appeal to more

customers and thus increase revenue. The analysis includes two main parts: dataset modification/

aggregation and data analysis.

1. Dataset Modification and Data Aggregation

For dataset modification, real revenue was calculated for 14,448 orders, as the gross

product revenue before and after the date “01/25/2007”. A new variable, “Quarter”, was created

based on the month of each revenue. Next, payment category was recoded into specific payment

methods. Total item quantity was also calculated for each order. Next, items were coded based

on order methods; orders could be coded as one of the following: catalog, web, or credit card.

Data modification also required creating a new variable to indicate if the order was in Quarter 3

(1 is Yes, 0 is No).

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The dataset was then aggregated by customer ID. This left 10,000 customer records. For

each customer ID, new variables were created based on this aggregation: number of orders, sum

revenue, and days since last order (“04/30/2009” was set as the current day). After that,

customers were defined as either “High level customer”, “Average level customer”, or “Low

level customer” based on their sum revenue compared to mean and standard deviation of total

revenue.

If sum revenue of one customer is greater than [mean + standard deviation], we defined

that customer as “High level customer”. If sum revenue of one customer is lower than [mean –

standard deviation], we defined that customer as “Low level customer”. If sum revenue of a

customer is between [mean – standard deviation] and [mean + standard deviation], we defined

that customer as “Average level customer”.

2. Data Analysis on Modified Dataset

The first step in analyzing the modified dataset was calculating sum revenue of each

Quarter and each Payment Method. This would provide insights into which quarters generate the

most revenue. Figure 1 and Figure 2 indicate the total revenue and the percentage of total

revenue for each Quarter.

These two graphics show that people purchase most in Quarter 3, which contributed 82%

of total revenue. This is most likely due to school starting in late summer and the high demand

for school supplies during this quarter.

Next, payment method was analyzed. We wanted to know which Payment Method people

most frequently used. Figure 3 and Figure 4 indicate the total revenue and percentage of total

revenue for each Payment Method.

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Based on these Figures, most people purchase from Staples by Credit Card, since nearly

90% of total revenue was generated by this payment source. The next phase of analysis

involved looking into order channel. According to the dataset, there are three ways: Catalog

Order, Web Order, and Other. Figure 5 and Figure 6 show total revenue and percentage of total

revenue on each Order Indicator.

These two Figures illustrate that nearly 1/3 of total revenue was created by Web Orders

while nearly 2/3 total revenue was created by Catalog Orders. Catalog and Web are the two main

ways orders are placed. Catalog is still the most popular indicator, even though people are

increasingly purchasing online.

Finally, a multiple linear regression model was built to predict real revenue. Real

Revenue was chosen as the independent variable, and Quantity, Web Order, Credit Card, and

Quarter 3 were selected as dependent variables. Figure 7 shows the result of multiple linear

regression models.

According to the analysis results, the p-value is less than 0.0001, so the multiple

regression model results are significant. Each p-value for the independent variable is less than

0.05, which means all independent variables have an influence on the independent variable. The

model is shown below:

Real Revenue = 10.80037 + (16.63232*Quantity) + (2.80396*Web Order) – (1.84099*Credit

Card) + (5.57690*Quarter 3)

This model exhibits that if one customer purchases one additional item, he will generate

16.63232 more for real revenue; if a customer places an order by web, he will generate 2.80396

more for real revenue; if a customer pays by credit card, his revenue will reduce by 1.84099.

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Additionally, if a customer purchases in Quarter 3, he will generate $5.58 more in revenue than

any other quarter. According to the t-value, Quantity has the most influence on the real revenue,

while credit card has the least effect on the real revenue.

The adjusted R square is 0.5678, which means that 56.78% of cases could be explained

by this regression model. While the R-squared is sufficient, other factors may be influencing real

revenue.

3. Data Analysis on Aggregated Dataset

For data analysis on the aggregated dataset, the objective is to segment Staples customers

based on order frequency, sum revenue and days since last order. The objective is to find out

unique features for each segment and to find the most valuable customers for Staples. After

several attempts, Staples customers were grouped into 5 clusters. Figure 8 shows the initial seed

centers for the 5 clusters. The three variables have been standardized, so that they do not have

unequal influence on the clusters. Figure 9 indicates the mean values for each variable for each

cluster. The mean numbers in the table are still standardized, and Figure 10 shows descriptive

statistics for each Cluster. There was only one customer placed into Cluster 5. This most likely

meant it was an outlier, so the cluster was ignored. The summaries of the clusters are listed

below.

Cluster 1: There are 7,401 customers in Cluster 1, and these customers had the following

characteristics:

1. On average they had the least number of orders (1.08); many of them only purchased once.

2. On average, they purchased the least amount in terms of revenue ($47.98);

3. The average days since last order are 17,478.

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Cluster 1 represents Staples’ “light users”. This group is important because of its size.

Staples must find a way to get them to purchase again.

Cluster 2: 63 customers with the following characteristics:

1. They purchased frequently at Staples (average of 7.57 times);

2. Their average revenue is relatively high ($593.40)

Customers in Cluster 2 can be defined as “heavy users” at Staples. They make purchases

at Staples frequently and buy much more than customers in other clusters. However, the number

of customers in Cluster 2 is low. Staples should aim to transition customers into Cluster 2.

Cluster 3 & Cluster 4: Customers in Cluster 3 and Cluster 4 had similar buying habits.

They know and like Staples, but only make purchases when they really need school and office

supplies. There is only one customer in Cluster 5, so this individual was moved into cluster 2.

That customer is also a heavy user.

After identifying clusters, sum revenue generated by each segment and by each customer

level was calculated. Figure 11 and Figure 12 show the sum revenue and percentage of each

cluster; Figure 13 and Figure 14 indicate the sum revenue and percentage at a customer level;

Figure 15 shows the number of each customer level, and Figure 16 shows the most popular

Payment Method used by each Cluster.

As seen in these figures, most of the revenue came from Cluster 1 and Cluster 3.

Although customers in Cluster 1 and Cluster 3 are not heavy users for Staples, they have huge

purchasing power. It is important to make sure that these customers have access to a Staples for

when they do need office and school supplies.

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It seems that the top 11.36% of Staples customers (high quality customers) generated

nearly 40% of the revenue. This phenomenon is reasonable because in the real business world 80%

revenue of a company comes from top 20% customers. For Staples, this rule also applies. As

seen in Figure 16, every cluster places the majority of orders with their credit cards. Based on

this figure, perhaps Staples should cooperate with Credit Card companies, providing some

credits, gifts or cash back if customers use credit card to pay the order.

The next step in the analysis involved identifying which variables had relationships with

the sum revenue for each customer. Days Since Last Order and Number of Orders were selected

as the specific variables. Next, the Pearson coefficient was calculated between Sum Revenue and

Days Since Last Order and between Sum Revenue and Number of Orders. Figure 17 and Figure

18 show the results of this correlation analysis.

The p-values of two analyses are both less than 0.0001, indicating that the relationship

between Sum Revenue and Days Since Last Order is significant. The relationship between Sum

Revenue and Number of Order is significant as well. The Pearson coefficient between Sum

Revenue and Days Since Last Order is -0.13064, which means Sum Revenue has a weak

negative relationship with Days Since Last Order, so days since last order could not have much

influence on sum revenue. The Pearson coefficient between Sum Revenue and Number of Orders

is 0.62946, which means Sum Revenue has a strong positive relationship with Number of Orders.

So more orders means more revenue. That’s why it is really necessary to find a way to increases

the purchase rates of the large segments.

From the analysis, several key findings were identified:

1. People purchase the most from Staples in Quarter 3 (July, August, and September);

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2. Most orders were paid by Credit Card;

3. Nearly 2/3 of the orders and the revenue came from catalog orders;

4. In 56.78% cases, revenue for an order could be explained by our regression model, using

the dependent variables quantity, web orders, credit card, and quarter 3;

5. Customers are segmented into 5 clusters, each cluster has unique characteristics;

6. For each customer, Revenue has a strong positive relationship with number of order

placed;

7. 10% of the High level customers generated nearly 40% revenue.

e. Analysis Procedure - External Data Analysis

In order to craft and execute a new marketing campaign for Staples, a target location was

first selected. New York City is one of the largest cities in the world. In effect, it serves as one of

Staples’ largest target markets. In order to understand the 5 boroughs of New York City

demographically, New York City ZIP code data was collected from Census.gov. Variables

extracted from the Census American Fact Finder portal include: (by ZIP code) population, total

number of households, total number of households that are families, number of students enrolled

in Pre-K through 12th

grade, median household income, and various other educational attainment

and income demographics. The primary objective of this external data analysis was to ascertain

what New York City looks like in terms of our target market, on a micro-level by ZIP code.

Overall the data shows that, on average, the 5 boroughs of NYC fall well above the

national average in terms of income, education level, and student enrollment. As seen in Figure

25, the average population for a zip code in NYC is 46,747. Compared to the US average of

7,034, this is extremely high. Moreover, an average ZIP code has 45,830 households and 80% of

those (36,909) are family households. Students enrolled in secondary school (Pre-K through 12th

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grade) is 11,682 in an average ZIP code in NYC, compared to the national average of only 1,374.

Median household income is $65,458.64; the national average is $53,482. 30.06% of residents in

the average NYC ZIP code have at least a Bachelor degree; 21.23% is the national average.

Based on these figures, New York City is an attractive urban area for Staples to launch a

campaign. If a campaign can work in an area as well-suited to the target market as NYC, it can

be scaled to other urban areas.

In order for Staples to effectively launch a marketing campaign, the company first needs

to understand the geography in terms of demographic characteristics and behavior. One analysis

that will benefit Staples is the rate of students in secondary school. Given the firm’s large

product portfolio of school supplies, Staples should understand where to target campaigns geared

toward students. Staples needs to understand how income and education levels affect the number

of students enrolled in secondary school. Figure 29 shows the relationship between the number

of students enrolled in Pre-K through 12th

grade and Median Household Income. As Median

Household Income increases, the number of students enrolled in school decreases. Since Staples

needs to find ZIP codes highly concentrated with students, it may be beneficial to consider

targeting lower-income areas specifically and to offer more value-focused products. Relatedly,

Figure 30 shows that as the poverty level of a ZIP code increases, student enrollment increases.

There is also a negative relationship between student enrollment and percent of the population

with a Bachelors or more 31. As the population becomes more affluent, student enrollment tends

to decrease.

The overall demographic characteristics mentioned previously have proven that this

strategy will be well-piloted in New York City given the large population characterized by high

income and education. However, the data also shows that the relationships between income and

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education are not in Staples’ favor. For example, it is exhibited that as income and education

increase, the number of young students decreases. Ideally, Staples would like to choose areas

where high income and education result in higher levels of student enrollment. Essentially, these

ideal areas will be full of wealthy families.

Now, Staples needs to understand how the ZIP codes can be grouped together according to

these traits. With this, the firm will be able to focus in on two location-based strategies:

1. Find areas that do not fit the norm – ZIP codes characterized by high income, education,

and student enrollment

a. These ZIP codes will be easy to target – Staples can emphasize high-quality

products and charge a premium for sustainability

2. Find areas that may be in sync with the averages, but identify unique strategies that will

encourage these individuals to take action

a. These ZIP codes will be trickier to target – Staples will need to offer competitive

prices that compel price-sensitive families to use these kiosks

Since one large target market for Staples is families with young children who frequently

shop for school supplies, ZIP codes were clustered in SAS using K-Means by three variables:

number of students enrolled in Pre-K through 12th

grade, median household income, and percent

of the population with a Bachelors degree or more. As seen in Figure 32, four distinct clusters

were identified based on student enrollment, income, and education.

Cluster 1, containing 32 NYC ZIP codes, has on average 3,190 students enrolled in Pre-K

through 12th

grade, a median household income of $109,016.81, and 61.45% of the adult

population has a Bachelors degrees or higher. Cluster 2 (the largest clusters with 80 ZIP codes)

has a much higher number of students enrolled in secondary school: 11,583.54 on average. This

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cluster’s median household income is $40,160.88, and 18.10% of its population on average has a

Bachelor degree or higher. Cluster 3 only comprises of 2 ZIP codes – it is characterized by low

student enrollment (1,107.50), extremely high income ($247,778.50) and high educational

attainment (60.77%). Finally, Cluster 4 comprises of 65 ZIP codes. This group on average has

5,906 students enrolled in secondary school, a median income of $69,540.49 and only 28.38% of

its population on average having a Bachelor or more.

Cluster 1 – Wealthy Families

Overall, these descriptive statistics provide insights on the demographics of these clusters

as a whole. It is seen that Cluster 1 has high income, high educational attainment, and above

average student enrollment. Cluster 1 is likely willing to pay a premium for environmentally

friendly products (given that they are financially stable and educated), and they have a decent

amount of students enrolled. These are Staples’ “Wealthy Families”.

Cluster 2 – Lower Middle Class Families

Cluster 2 highly contrasts to Cluster 1. Cluster 2 is characterized by extremely high

student enrollment rates, low income, and low education. Cluster 2 represents our “Lower

Middle Class Families”. They likely look for value when buying school supplies for their

children – they are on a budget and working hard to provide for their families. Offering discounts

will help alleviate some of the risk they associate with purchases.

Cluster 3 - Downtown Manhattanites

Cluster 3 can be classified as outliers. These two ZIP codes have low student enrollment,

extremely high income and tend to be very well-educated. They are geographically situated

downtown in TriBeCa, an area famous for affluent celebrities and executives. This group

represents our “Downtown Manhattanites” who have been successful professionally, but haven’t

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focused on family as much. This cluster will respond better to high-end products that may not be

family related – but more suitable for professional use.

Cluster 4 – Upper Middle Class Families

Finally, Cluster 4 is our middle-of-the-road cluster in all aspects. They have average

student enrollment, average income, and average educational attainment levels. These ZIP codes

contain working professionals who also have families. Cluster 4, our “Upper Middle Class

Families”, will be interested in products for their children that are reasonably priced, but they

also place value on the look and feel. This working class group also may be susceptible to the

idea of sustainable products.

Figure 37 shows geographically where each of these distinct groups of ZIP codes are

located. As described above, each group will require a different marketing strategy aimed at

offering a value proposition that is suitable to their needs.

Based on the results of analysis on internal data, external data, and secondary industry

reports it is recommended that Staples offer their products in vending machines. The vending

machines should first be rolled out in New York city because of NYC’s diverse population,

willingness to care for the environment, and willingness to try new things. The recycling

receptacle and inclusion of green products will encourage already environmentally conscious

New Yorkers to use the vending machines to satisfy their office and school supplies needs and

will promote additional eco-friendly behavior from the rest of the population. Staples currently

fulfills the majority of their customers’ orders through their distribution network. However, the

vending machines will make accessing Staples products even more easy and convenient.

The new vending machine campaign will increase both sales and customer loyalty by

addressing the needs of the targeted segments. This segment can be described as educated

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families who, based on their decision to live in a large city, value convenience, and, according to

our research, care about the environment. The machines will be located in zip codes whose

populations fit our desired profile and in close proximity to the schools the children attend. This

will eliminate the need to travel to one of Staples’ costly brick and mortar locations and give

easy access to school supplies on the way to and from school each day.

Effective implementation of the vending machines will help Staples reach its goals of

improving customer experience, increasing share of wallet, developing its multi-channel

approach, and increasing its large product offering.

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Phase 2

a. Summary

Based on the results of analysis on internal data, external data, and secondary industry

reports it is recommended that Staples offer their products in vending machines. The vending

machines should first be rolled out in New York City because of NYC’s diverse population,

willingness to care for the environment, and willingness to try new things. The recycling

receptacle and inclusion of green products will encourage already environmentally conscious

New Yorkers to use the vending machines to satisfy their office and school supplies needs and

will promote additional eco-friendly behavior from the rest of the population.


addressing the needs of the segments we are targeting. This segment can be described as

educated families who, based on their decision to live in a large city, value convenience, and,

according to our research, care about the environment. The machines will be located in zip

codes whose populations fit our desired profile and in close proximity to the schools the children

attend. This will eliminate the need to travel to one of Staples’ costly brick and mortar locations

and give easy access to school supplies on the way to and from school each day.

Effective implementation of the vending machines will help Staples reach its goals of

improving customer experience, increasing share of wallet, developing its multi-channel

approach, and increasing its large product offering.

Ultimately, it is recommended that Staples roll out the vending machines because they

allow Staples to reduce its retail space and maintain its multi-channel approach and large product

offering. The vending machines should be rolled in the appropriate New York City zip codes

(based on external data analysis) as well as near schools and shopping malls.

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b. Company Overview

Staples, Inc. is a brick and mortar/ online retailer that provides an assortment of office

products to businesses and individual consumers. The company offers a lowest price guarantee

and emphasizes convenience - consumers can purchase in store, online, via a mobile device or

through social apps. Staples has in-store business centers that offer the following: shipping,

copying, scanning, faxing, computer work stations, tech services, and printing, marketing, small

business lending and credit services (“Staples and Office Depot”, 2016).

Staples Business Advantage is the B2B (business-to-business) division of the company.

This division helps business customers make purchases for their company in a curated way with

customer service, competitive pricing, and an e-commerce site designed for B2B purchasing.

Product offerings within this division include: office supplies, facilities cleaning and

maintenance, breakroom snacks, furniture, and printing and marketing services (“Staples and

Office Depot”, 2016).

Staples falls within the office supplies and stationery stores industry. This industry is made

up of retail stores that engage in one or more of the following (“OFFICE SUPPLIES &

STATIONERY STORES INDUSTRY”, 2016):

● Retailing new stationery, school supplies, and office supplies

● Selling a combination of new office equipment, furniture, and supplies

● Selling new office equipment, furniture, and supplies in combination with selling

computers

Being a leading supplier of office supplies across multiple countries, Staples must cater to

the demands of several different customer bases within the industry. In order to do this, Staples

maintains various retail channels: contract businesses, retail stores, catalogs, and the Web. Major

23

strengths for Staples internally include its multi-channel approach, its large product portfolio,

and its strong distribution network. Staples’ main weakness is its dependence on third-party

vendors. Opportunities in the industry include the growth in online retail sales, private label

brand growth, and focusing on cost control in order to increase profits. Overall, threats to the

industry include increased competition in the market overall due to major online retailers such as

Amazon, as well as a decline in paper consumption in offices (Staples, Inc., 2014). As of 2016,

there were 1,450 retail outlets in the United States that fall into this industry. Sales grossed at

$16,254,000,000 ($2,309,000 per establishment) (“OFFICE SUPPLIES & STATIONERY

STORES INDUSTRY”, 2016).

In order to develop a strategy to improve Staples’ bottom lines within a niche target

market, Staples’ strengths should be further examined. Staples has proved to be the best at two

things in the industry:

1. Utilizing multiple retail channels in order to target end-customers in both the B2C and

B2B space

2. Maintaining a large product and services portfolio (Staples, Inc., 2014)

Staples uses multiple channels such as in-store and Web in order to cater to the demands of

its multi-faceted customer base. Staples uses its contract business to target medium to large sized

businesses and offer them special services such as account management, delivery, proprietary

items stocking, and a wide assortment of environmentally friendly products and services. Its

online and retail stores are crafted to satisfy individual consumers on a geographic basis. For

instance, in suburban areas Staples offers large supercenters. In more urban and rural markets,

the firm operates smaller format stores. Staples’ most dominant strategy, therefore, is to leverage

24

its retail channels in order to attract different customer groups with distinct purchasing behaviors

(Staples, Inc., 2014).

By focusing on cost control, Staples can leverage another key opportunity. Currently, the

firm is focusing on considerably reducing its costs. In 2012, Staples set a goal to save $250

million in annual pre-tax savings by 2015. Its plan has been to save in areas like product cost,

store operations, and supply chain issues. In order to achieve this, the firm has started to reduce

retail space in North America by 15% (Staples, Inc., 2014).

c. Market Overview

A major opportunity within the office supplies industry is the vast growth in online retail

stores. Consumers are increasingly beginning to prefer online shopping due to its interactive

function. The U.S. Department of Commerce cited that online retail sales grew 16.1% between

2010 and 2013, while in-store sales grew by only 4.3%. Currently, Staples drives $10 billion in

sales yearly from its online channel (Staples, Inc., 2014).

The digital age has led the office supply industry to become much less powerful than it

once was. With most office work shifting online, paper and printer ink are becoming nearly

obsolete. Staples competes with online and traditional retailers focused in the office supply

industry (Office Depot) or broader mass merchants (Wal-Mart, Target, and Amazon). This has

led to consolidation within the industry (Staples, Inc., 2014).

Currently, Office Depot and Staples are fighting to merge the two companies. The

companies argue that this merger will allow them to be better positioned to serve the “changing

needs of business customers” and to compete against larger more diverse competitors like

amazon. The FTC is trying to stop this merge due to fear of monopolization in the industry.

25

However, with e-Commerce sites such as Amazon expanding so rapidly, the two firms feel a

merger is the only way to compete (“Staples and Office Depot”, 2016).

d. Opportunity

Secondary reports indicate that Staples is looking to reduce retail space and store

operations costs while still maintaining its multi-channel approach and its large product portfolio.

Based on internal and external research combined with an understanding of the current industry

and market environment, these goals can be achieved with the implementation of Staples

vending machines. The project will initially be rolled out in the five boroughs of New York City,

and will include a receptacle to recycle used Staples products. New York City is the United

States’ 6th

greenest city, and the recycling aspect of the new sales channel will encourage eco-

friendly behavior and draw new users to the machines (NerdWallet, 2016). The contents of the

vending machines will include Staples’ “recycled and eco-friendly” product line as well as other

high performing office and school supplies products. The strategic placement of the vending

machines throughout the five boroughs will be based on analysis of ZIP code level demographic

data as well as the spatial distribution of schools and shopping malls. The successful

implementation of the vending machines will enable Staples to eliminate retail space while still

offering multiple purchase channels and a diverse portfolio of products.

New York City is one of the largest cities in the world with rich culture and history. The

population in NYC is around 8,491,079 people. 26% of the total population in NYC is under 18

years of age. There are in total 3,095,931 households and the average household size is 3 people.

For 2012, the total retail sales per capita were $11,067. The median household income across

New York City stands at $53,657, according to 2014 department of numbers. The per capita

income is $33,095 (Census Data).

26

In New York City there are more than 26,000 people living in each square mile. It takes

75,000 trees to print a Sunday edition of the New York Times. New York City has more people

than 39 of the 50 states in the U.S. The borough of Brooklyn on its own would be the fourth

largest city in the United States. Queens would also rank fourth nationally. Manhattan’s daytime

population swells to 3.94 million, with commuters adding a net 1.34 million people

(Bigapple.com).

The New York City public school system is the largest in the world. New York State’s

policy is to provide language access to public services and programs. More than 1.1 million

students are taught in more than 1,700 public schools with a budget of nearly $25 billion. NYC

spends $19,076 each year per student. The public school system is managed by the New York

City Department of Education. On the other hand, there are approximately 900 additional

privately run secular and religious schools in the city (Schools NYC).

Staples needs to take full advantage of this population by offering the products that they

need. Consumers are expected to spend about $68 billion on back-to-school spending this year,

as compared to $75 billion last year (Fortune.com, 2016). This is a 9.3% decrease that is part of

a larger trend brought on by increasing dependence on technology. Including school supplies

staples and placing the vending machines in areas near schools and populations of young

students will help Staples claim a larger share of this market and potentially bring spending on

school supplies back up.

The median family income for NYC is $71,115 in 2014 according NYC.gov. New York

is home to 653,000 households with at least one child under the age of 18. A family of four

could spend in Manhattan an average of $93,500 - which would cover only cover food, transport,

housing, health care, child care and taxes but not vacations, eating out or savings (NY daily

27

news). Total household expenditures in New York City are above the national average and the

education is not the exception.

In addition to being a media and commercial hub, New York City is home to residents

who are ready and willing to care for the environment. Based on the findings of a 2014 poll,

New York City was named the 6th

greenest city in the United States (Pew Research Center,

2015). Factors including willingness to use public transportation, support for restrictions on

pollution, and willingness to recycle contributed to the rankings. Every day in New York City

there are 2.0 pounds of paper/cardboard recycling collected per resident and .21 pounds of

metal/glass recycling collected per resident (New York City Municipal Refuse and Recycling

Statistics, 2015). These are well above the respective daily national averages of .09 and .05

pounds per person (Municipal Solid Waste, 2015). The recycling receptacle and inclusion of

green products will encourage already environmentally conscious New Yorkers to use the

vending machines to satisfy their office and school supplies needs and will promote additional

eco-friendly behavior from the rest of the population.

Staples must take the secondary report research and internal and external data analysis

into account in order for the new vending machines to achieve all the company’s goals. These

goals include reducing its retail space, maintaining its multi-channel approach and large product

portfolio, increasing share of wallet with existing customers, and taking full advantage of New

York’s large and unique market.

We currently fulfill the majority of customers’ orders through our distribution network.

As we expand our assortment, we are increasingly relying on third parties to fulfill orders and

deliver products directly to our customers. However, we want to keep increasing our relationship

28

with the customers, by understanding them better, supporting them with products specific to their

needs, and making it easier to access our products.


addressing the needs of the segments we are targeting. This segment can be described as

educated families who, based on their decision to live in a large city, value convenience, and,

according to our research, care about the environment. The machines will be located in zip

codes whose populations fit our desired profile and in close proximity to the schools the children

attend. This will eliminate the need to travel to one of Staples’ costly brick and mortar locations

and give easy access to school supplies on the way to and from school each day.

Citizens who can afford to are more likely to buy eco-friendly products than those who

cannot. Because the segment we are targeting is in a high income and education bracket, and

because New York is known to have citizens who care about the environment, the recycling

aspect and inclusion of green products will contribute to the success of the Staples Vending

Machines.

Staples is strategically looking for ways to reduce its number of stores while still

retaining customers and increasing customer lifetime value. As the firm looks to cut costs,

vending machines provide an attractive solution given that they are less expensive to operate in

the long term (Success with Self-Serve Kiosks, 2010). Retailers in multiple industries have seen

the benefit of using self-service machines in order to reduce operating costs. In 2010, the market

for these machines was $3.2 billion.

Retail vending machines (or self-service kiosks) provide several key benefits to large

corporations looking to increase sales without the burden of large store-fronts. Among the firms

that have utilized self-service kiosks for product sales are: Macy’s, Best Buy, Proactiv, Benefit

29

Cosmetics, and Nespresso. Macy’s, for example, understood that its customers placed value on

one-stop shopping. As a result, the retailer launched e-Spot, an automated shop offering

consumer electronics with touchscreen technology for handling sales transactions. Brand inside

Macy’s e-Spot include Apple, Beats, Skullcandy, and iHome. Prices range from $24.99 to

599.99. A transaction is completed in less than two minutes. Customers have given very positive

feedback to Macy’s- they enjoy the no-pressure experience that it provides. Today’s shoppers

value ease of use and instant gratification (Zoom Systems, 2016).

Best Buy was the first retailer to launch an automated retail experience in the consumer

electronics market. The firm introduced the Best Buy Express ZoomShop in 2008. The

machines help consumers stay connected by offering products such as digital cameras,

headphones, phone chargers, and other travel gadgets. Best Buy is known to place these

machines in airports (Zoom Systems, 2016).

Macy’s and Best Buy both understood a value proposition its customers would respond to

well: self-service and instant shopping. In an effort to target urban families with school aged

children during the busy back-to-school season as well as off-season, Staples self-service kiosks

is an obvious solution. Not only will these automated machines (placed strategically throughout

the five boroughs of NYC) help Staples reduce the amount of physical stores in the area, but it

will also provide customers with an interesting, convenient new way to shop.

e. Target Customer

The segments we are going to target as a marketing priority are families in the 5 boroughs

of New York City with children enrolled in secondary school. And later we plan to expand the

target market to the households in the whole North American areas.

30

New York City’s above average population, income levels, education levels, and student

enrollment made it a great springboard location for the campaign. According to Staple Reports,

their B2B sales increased by 1.1% in 2015 while total sales declined by 6.4%. Therefore, it is

necessary to focus on acquisition and retention of household customers. Households make up a

large share of all sales to Staples and they spend billions of dollars a year on the products and

services Staples sells. Targeting households with young children will support this effort and

drive sales, growth, and profit for the company. To be specific, we divide our target customers

in New York City into three groups:

Wealthy Families

Households with high income and high educational levels that are located in areas of

above average student enrollment are our primary target customers. Since environmentalism is

by now deeply rooted in the consumer mind-set and public-policy arena, it will make sense to

market Staples school supplies to affluent and well educated families because they are more

likely to embrace the idea of green and sustainable products. They will pay a premium for the

chance to do well and, in many cases, be seen doing well.

Upper Middle Class Families

Families with average income and average educational attainment levels in areas of

average student enrollment are our second target markets. Compared with households with high

income and educational levels, they are not willing to pay extra for the expensive school supplies

but they are still influenced by the green consumerism, which will eventually persuade them to

use the vending machines. They focus more on the practical value than the price of the product.

31

Lower Middle Class Families

Families with low income and low education in areas of extremely high student

enrollment will make up our third targeted segment. They are on a budget and are working hard

to provide for their children, and they amount to large population in New York City. This means

they have high demand for school products for their children. They are price sensitive and are

not that interested in green products. Hence, for this target market, Staples can cooperate with the

school and offer a program targeted at parents and teachers. By giving rewarding points and

discount, Staples can easily win favor of lower middle class families.

Value Proposition

We strongly believe in delivering school products that generate environmental benefits.

As a world-class retailer, Staples will let customers shop however and whenever they want,

whether it is in store, online, on vending machine or on mobile devices. Also, we will provide

our customers with the most sustainable products, improving our offering of recycling and green

services.

f. Goals

Reduce costs and maximize profits

The digital age has led the office supplies industry to become much less powerful than it

once was. With most office work shifting online, paper and printer ink are becoming nearly

obsolete. Although the fact that the company remains profitable, Staples will struggle to survive

in the coming years. Thus, our primary goal in 2016 is to reduce expenses and maximize profit

with existing customers, and acquiring new customers. We aim to grow sales by 5% within the

next 2 years. Also, we strive to obtain 45% of the market share within 2 years. As we gain more

32

customer satisfaction and gradually expand the offerings through vending machines, we expect a

50% market share within 5 years.

Develop multi-channel marketing

While Staples’ brick-and-mortar physical stores are losing money, we aim to shift focus

towards a multichannel approach as to leverage the existing stores and maximize profit. By

launching vending machine without Staples’ space or a new type of smaller stores that engage

customers with interactive kiosks, we are striving to drive more sales to both online and offline

stores.

Green and recycling product

From the environmental perspective, we are devoted to selling more sustainable products

and services. We will continue to improve sourcing, identification and the promotion of greener

products to customers while at the same time offering easy recycling solutions. For example, by

2020, we aim to recycle 100 million paper, ink and toner cartridges each year across all

operations, especially from the vending machine. In sum, our goal is to make more sustainable

business practices happen.

Provide the best customer experience

Customers are of great importance to us. Therefore, we are making efforts to provide our

customers with optimal customized in-store and online experience and help them find the best

deals and the right products quickly. Our objective is to let customers shop however and

whenever they want, and eventually earn customer satisfaction and loyalty to the company.

33

Perceptual Map

On the Basis of our perceptual map, we realize that there is a need for a brand that is not

only easy to buy but also eco-friendly. Thus, the value proposition of our product could give us a

competitive edge over the other brands mentioned above.

34

Phase 3

a. Summary

Phase 3 of this marketing intelligence project involved identifying metrics and

developing a dashboard in order to track the success of this new campaign. Metrics and

dashboard implementation are crucial in evaluating any marketing program. Once established

these metrics must be tracked on a regular basis and provided to management in the form of a

user-friendly dashboard.

Metrics were created based on the marketing team’s key goals: increase profits, promote

multichannel sales, increase sustainability awareness, and improve customer experience. In order

to track profits, the team developed measures to calculate: customer deciles, return on marketing

investment, payback period, profit margin, internal rate of return, and break-even point.

Multichannel sales success will be tracked through market penetration costs, acquisition costs,

and a sales funnel approach. This will inform the team on how well these vending machines are

performing. Sustainability awareness will be measured through customer awareness, and

customer satisfaction will be tracked by Net Promoter Score.

This marketing metrics model is also presented in the form of a flow chart and dashboard

so that management can more clearly visualize the ways in which this campaign will help Staples’

reach its ultimate goal of increasing profits.

35

b. Metric Overview

36

Product Adoption Funnel

37

Customer Deciles

38

c. Marketing Model (Figure)

39

d. Dashboard

40

Limitations

The team acknowledges some limitations and scope to this marketing intelligence project.

Primarily, this analytics project is strictly within the scope of New York City. External research was

conducted specifically with the five boroughs of New York City in mind, and all assumptions and

strategy decisions were based on this research. While it can be assumed that a project successful in one

urban area can be scalable to other urban areas, additional research will need to be conducted to get a

better understanding of these different markets.

Another limitation of this analysis lies in our inability to connect our external market research

with our internal customer dataset. Because external market research is on a Census level, the team is

unable to identify the percentage of individuals that actually belong to Staples’ customer base. A strong

marketing campaign could be developed if it was known which individuals in the target market were

current customers and which Staples was looking to acquire as new customers.

In terms of internal data analysis, revenue, number of orders and recency were the only attributes

measured for each customer. However, customers can interact with and create value for firms in a

variety of ways including customer lifetime value, customer referral value, customer influencer value, as

well as customer knowledge value (Kumar, 2010). Past purchasing behavior is only a small portion of

predicting future behavior. Future customer behavior will also be influenced by things such as a

customer’s number of connections to other customers, emotional valence of the customer’s reviews, and

willingness to recommend. Additional variables were building a more accurate model for customer

behavior.

41

Works Cited

OFFICE SUPPLIES & STATIONERY STORES INDUSTRY (NAICS 45321). (2016). World

Industry & Market Outlook Report, 1-166.

Staples and Office Depot Issue Open Letter to Customers. (March 18, 2016).

http://investor.staples.com/phoenix.zhtml?c=96244&p=irol-newsArticle&ID=2149502

Q4 2015 Finances. http://finance.yahoo.com/news/staples-inc-announces-fourth-quarter-

110000503.html

Staples Performance Summary 2012-2014.

http://www.staples.com/sbd/cre/marketing/about_us/documents/globalperfsummary-2015.pdf

Will 2016 Be Staples, Inc.’s Worst Year Yet? (January 19, 2016).

http://www.fool.com/investing/general/2016/01/19/will-2016-be-stapless-bestworst-year-

yet.aspx

Staples, Inc. SWOT Analysis. (2014). Staples, Inc. SWOT Analysis, 1-8

Zoom Systems, 2016. http://www.zoomsystems.com/our-clients/best-buy

Success with Self-Serve Kiosks. (2010). Specialty Retail Report.

http://specialtyretail.com/issue/2010/01/retail-products/success-with-self-serve-kiosks/



http://finance.yahoo.com/news/staples-inc-announces-fourth-quarter-%09%09110000503.html

http://finance.yahoo.com/news/staples-inc-announces-fourth-quarter-%09%09110000503.html



http://www.zoomsystems.com/our-clients/best-buy

http://www.zoomsystems.com/our-clients/best-buy

http://specialtyretail.com/issue/2010/01/retail-products/success-with-self-serve-kiosks/

42

Appendix A: Tables and Figures

Figure 1

Figure 2

43

Figure 3

Figure 4

44

Figure 5

Figure 6

45

Figure 7 Multiple Linear Regression Model on Real Revenue

Figure 8 K-means Cluster Analysis Initial Seed Centers

46

Figure 9 Means of K-mean Cluster

Figure 10 Descriptive Statistics for each Cluster

Figure 11

47

Figure 12

Figure 13

48

Figure 14

Figure 15 Numbers for each Customer Level

49

Figure 16

Figure 17: Correlation between Sum Revenue and Days since Last Order

50

Figure 18: Correlation between Sum Revenue and Number of Order

Figure 19

51

Figure 20

Figure 21

Figure 22

52

Figure 23

Figure 24

Figure 25

NYC Census Data: Descriptive Statistics

53

Figure 26

NYC Census Data: Count of Students Enrolled in Pre-K through 12th

Grade by ZIP code

Figure 27

NYC Census Data: Count of Median Household Income Range by ZIP Code

54

Figure 28

NYC Census Data: Count of Population with a Bachelor or More by ZIP Code

Figure 29

NYC Census Data: How Does Median Household Income Affect Secondary School

Enrollment by ZIP Code

55

Figure 30

NYC Census Data: How Does the Count of People Below the Poverty Level Affect Secondary School

Enrollment by ZIP Code?

Figure 31

NYC Census Data: How Does a ZIP Code’s Percent of People with a Bachelors or More Affect

Secondary School Enrollment by ZIP Code?

56

Figure 32

K-Means Cluster Analysis: Final Cluster Means

Figure 33

K-Means Cluster Analysis: Clusters Broken Down by Median Income and Student

Enrollment in Pre-K through 12

57

Figure 34

Distribution of Median Income by Cluster

Figure 35

Distribution of Secondary School Enrollment by Cluster

58

Figure 36

Distribution of Educational Attainment by Cluster

59

Figure 37

60

Appendix B: SAS Code

I. Internal Analysis

/********** internal data analysis ***************/

/* Generated Code (IMPORT) */

/* Generated Code (IMPORT) */

/* Source File: Data Set 8 DMEF0509-2 - EXCEL.xlsx */

/* Source Path: /folders/myfolders/Mylib */

/* Code generated on: Thursday, April 14, 2016 18:30:00PM */

/* Import dataset named Import */;

PROC IMPORT DATAFILE="C:\SAS\Data Set 8 DMEF0509-2 - EXCEL.xlsx" dbms=xlsx

OUT=IMPORT REPLACE;

RUN;

/* DATA Part */;

/* Create and alter new datasets */;

/* Create a new dataset named MODIFY from IMPORT */;

DATA MODIFY;

SET IMPORT;

/* Create a new variable named Mouthnumber indicating the month of each order, then change data

format to mmddyy10., and calculate the RealRevenue for each order by time "01/25/2007" */;

MonthNumber=month(InputDate);

FORMAT InputDate mmddyy10.;

IF InputDate ge input("01/25/2007",mmddyy10.) THEN

RealRevenue=GrossProductRevenueAmount+ShippingHandling+SalesTax;

61

ELSE

RealRevenue=GrossProductRevenueAmount-CancelAmount-ReturnedAmount+CouponAmount-

AdditionalChargesAmount;

/* Create a new variable named Quarter based on Monthnumber */;

IF MonthNumber IN(1,2,3) THEN Quarter=4;

ELSE IF MonthNumber IN(4,5,6) THEN Quarter=1;

ELSE IF MonthNumber IN(7,8,9) THEN Quarter=2;

ELSE Quarter=3;

/* Create a new variable named PaymentThod based on PaymentCategoryCode */;

IF PaymentCategoryCode=1 THEN PaymentMethod="Cash";

ELSE IF PaymentCategoryCode=2 THEN PaymentMethod="Credit Card";

ELSE IF PaymentCategoryCode=3 THEN PaymentMethod="Debit Card";

ELSE PaymentMethod="Coupon/GiftCard";

/* Create a new variable named Quantity indicating purchase quantity of each order */;

IF CatalogItemIndicator="Y" THEN Quantity=CatalogItemQuantity;

ELSE Quantity=WebitemQuantity;

/* Create a new variable named CatalogOrder indicating if the order was Catalog order. 1 is Yes; 0 is

No */;

IF CatalogItemIndicator="Y" THEN CatalogOrder=1;

ELSE CatalogOrder=0;

62

/* Create a new variable named WebOrder indicating if the order was Web order. 1 is Yes; 0 is No

*/;

IF WebItemIndicator="Y" THEN WebOrder=1;

ELSE WebOrder=0;

/* Create a new variable named CreditCard indicating if the order was purchased by a credit card. 1

is Yes; 0 is No */;

IF PaymentMethod="Cred" THEN CreditCard=1;

ELSE CreditCard=0;

/* Create a new variable named Quarter3 indicating if the order was in Quarter3. 1 is Yes; 0 is No */;

IF Quarter=3 THEN Quarter3=1;

ELSE Quarter3=0;

/* Create a new variable named OrderIndicator indicating by which indicator the order placed */;

IF CatalogItemIndicator="N" AND WebItemIndicator="N" THEN OrderIndicator="Others";

ELSE IF CatalogItemIndicator="Y" THEN OrderIndicator="Catalog";

ELSE OrderIndicator="Web";

RUN;

/* Create a new dataset named AGGREGATE from MODIFY, keep ID, Inputdate, RealRevenue and

PaymentMethod variables, and sort by ID */;

/* Sort the dataset MODIFY by ID and Inputedate */;

PROC SORT DATA=MODIFY;

BY ID Inputdate;

63

DATA AGGREGATE;

SET MODIFY(KEEP=ID Inputdate RealRevenue PaymentMethod);

BY ID;

/* Aggregate the number of order by each Customer ID */;

DO;

IF FIRST.ID THEN NumberofOrder=1;

ELSE NumberofOrder+1;

END;

/* Aggregate the Revenue by each Customer ID */;

DO;

IF FIRST.ID THEN SumRevenue=RealRevenue;

ELSE SumRevenue+RealRevenue;

END;

IF LAST.ID;

DROP RealRevenue;

/* ADD the last day "04/30/2009" */;

LASTDAY=Input("04/30/2009", mmddyy10.);

FORMAT LASTDAY mmddyy10.;

/* Calculate the days since last order for each customer */;

DaysSinceLastOrder=LASTDAY-InputDate;

RENAME PaymentMethod=LastPaymentMethod;

RUN;

64

/* Alter the dataset AGGREGATE, add new variables indicates Mean and Standard Deviation of

Revenue, add X1=Mean+STD; X2=Mean-STD, and then define each customer */;

DATA AGGREGATE;

SET AGGREGATE;

MERGEVAL=1;

RUN;

PROC MEANS DATA=AGGREGATE NOPRINT;

VAR SumRevenue;

OUTPUT OUT=MEANS1;

RUN;

DATA STDDEV1;

SET MEANS1;

IF _STAT_='STD';

MERGEVAL=1;

KEEP SumRevenue MERGEVAL;

RENAME SumRevenue = STD_SumRevenue;

RUN;

DATA MEANS1;

SET MEANS1;

IF _STAT_='MEAN';

MERGEVAL=1;

KEEP SumRevenue MERGEVAL;

RENAME SumRevenue = MEAN_SumRevenue;

65

DATA AGGREGATE;

MERGE AGGREGATE MEANS1 STDDEV1;

BY MERGEVAL;

DROP MERGEVAL;

RUN;

DATA AGGREGATE;

SET AGGREGATE;

X1 = Mean_SumRevenue+STD_SumRevenue;

X2 = Mean_SumRevenue-STD_SumRevenue;

IF SumRevenue gt X1 THEN Customertype = "High quality customers";

Else IF SumRevenue lt X2 THEN Customertype = "Low quality customers";

Else Customertype = "Avg quality customers";

DROP X1 X2;

Run;

/* Alter the dataset AGGREGATE, order data sequence by Sumrevenue, and then slice customers

into Deciles by SumRevenue */;

PROC SORT DATA=AGGREGATE;

BY DESCENDING SumRevenue;

DATA AGGREGATE;

SET AGGREGATE;

BY DESCENDING SumRevenue;

Number=_N_;

IF 1 LE Number LE 1000 THEN CustomerDecilesbySumRevenue="0-10% ";

66

ELSE IF 1001 LE Number LE 2000 THEN CustomerDecilesbySumRevenue="10-20%";








ELSE CustomerDecilesbySumRevenue="90-100%";

RUN;

/* Create a new dataset Named DECILES from dataset AGGREGATE to calculate sum revenue for

each decile and percentage of sum revenue by total revenue for each decile */;

DATA DECILES;

SET AGGREGATE(KEEP=ID SumRevenue CustomerDecilesbySumRevenue);

BY CustomerDecilesbySumRevenue DESCENDING SumRevenue;

IF FIRST.CustomerDecilesbySumRevenue THEN SumDecileRevenue=SumRevenue;

ELSE SumDecileRevenue+SumRevenue;

IF LAST.CustomerDecilesbySumRevenue;

MERGEVAL=1;

IF FIRST.MERGEVAL THEN TotalRevenue=SumDecileRevenue;

ELSE TotalRevenue+SumDecileRevenue;

RUN;

DATA TOTAL1;

67

SET DECILES(KEEP=TotalRevenue) END=EOF;

MERGEVAL=1;

BY TotalRevenue;

RENAME TotalRevenue=Total_Revenue;

IF EOF;

RUN;

DATA DECILES;

MERGE DECILES TOTAL1;

BY MERGEVAL;

DROP MERGEVAL ID SumRevenue TotalRevenue;

PercentageofTotalRevenue=SumDecileRevenue/Total_Revenue;

FORMAT PercentageofTotalRevenue percent8.5;

DROP Total_Revenue;

RUN;

PROC PRINT DATA=DECILES;

RUN;

/* PROC part */;

/* PROC part for dataset MODIFY */;

/* Calculate Sum of RealRevenue on each Quarter */;

DATA MODIFY;

SET MODIFY;

TITLE "Sum RealRevenue by Quarter";


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BY Quarter;

PROC MEANS SUM DATA=MODIFY;

VAR RealRevenue;

CLASS Quarter;

RUN;

/* Calculate Sum of RealRevenue on each PaymentMethod */;

TITLE "Sum RealRevenue by PaymentMethod";


BY PaymentMethod;


VAR RealRevenue;

CLASS PaymentMethod;

RUN;

/* Calculate Sum of RealRevenue on each OrderIndicator */;

TITLE "Sum RealRevenue by OrderIndicator";


BY OrderIndicator;


VAR RealRevenue;

CLASS OrderIndicator;

RUN;

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/* Multiple Linear Regression Model on each customer order (RealRevenue is dependent variable;

Quantity, CatelogOrder, WebOrder and CreditCard are independent variables) */;

TITLE "Multiple Linear Regression Model on RealRevenue";

PROC REG PLOTS(MAXPOINTS=20000);

MODEL RealRevenue = Quantity WebOrder CreditCard Quarter3;

RUN;

/* PROC part for dataset AGGREGATE */;

/* K-Means Clustering */;

DATA AGGREGATE;

SET AGGREGATE;

PROC STDIZE DATA=AGGREGATE OUT=STANDARD METHOD=STD;

VAR NumberofOrder SumRevenue DaysSinceLastOrder;

RUN;

DATA AGGREGATE;

SET AGGREGATE;

PROC FASTCLUS DATA=STANDARD OUT=CLUSTER

MAXCLUSTERS=5 MAXITER=100;


RUN;

/* Merge Dataset AGGREGATE and Dateset CLUSTER by ID */;


BY ID;

RUN;

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PROC SORT DATA=CLUSTER;

BY ID;

RUN;

DATA AGGREGATE;

MERGE AGGREGATE CLUSTER(KEEP=ID CLUSTER);

BY ID;

RUN;

/* Descriptive Statistics on each Cluster */;


BY Cluster;

TITLE "Descriptive Statistics on each Cluster";

PROC MEANS MEAN N MAX MIN DATA=AGGREGATE;


CLASS Cluster;

RUN;

/* Histogram and Scatter Diagram of SumRevenue and DaysSinceLastOrder */;

TITLE "Histogram and Scatter Diagram of SumRevenue and DaysSinceLastOrder";

DATA AGGREGATE;

SET AGGREGATE;

ODS Graphics ON;

PROC CORR DATA=AGGREGATE NOMISS

PLOTS(MAXPOINTS=20000)=MATRIX(HISTOGRAM);

VAR SumRevenue DaysSinceLastOrder;

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RUN;

ODS Graphics OFF;

RUN;

/* Histogram and Scatter Diagram of SumRevenue and NumberofOrder */;

TITLE "Histogram and Scatter Diagram of SumRevenue and NumberofOrder";

DATA AGGREGATE;

SET AGGREGATE;

ODS Graphics ON;

RUN;

PROC CORR DATA=AGGREGATE NOMISS

PLOTS(MAXPOINTS=20000)=MATRIX(HISTOGRAM);

VAR SumRevenue NumberofOrder;

RUN;

ODS Graphics OFF;

RUN;

/* Sum Revenue by Customer type */;

TITLE "Sum Revenue by Customer type";

DATA AGGREGATE;

SET AGGREGATE;


BY Customertype;

PROC MEANS SUM DATA=AGGREGATE;

VAR SumRevenue;

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CLASS Customertype;

RUN;

/* Sum Revenue by Cluster */;

TITLE "Sum Revenue by Cluster ";

DATA AGGREGATE;

SET AGGREGATE;


BY Cluster;

PROC MEANS SUM DATA=AGGREGATE MAXDEC=2;

VAR SumRevenue;

CLASS Cluster;

RUN;

/* Export Datasets MODIFY, AGGREGATE and DECILES as excel files to make graphics */;

PROC EXPORT DATA=MODIFY OUTFILE="C:\SAS\Modify.xlsx" DBMS=xlsx Replace;

PROC EXPORT DATA=AGGREGATE OUTFILE="C:\SAS\Aggregate.xlsx" DBMS=xlsx Replace;

PROC EXPORT DATA=DECILES OUTFILE="C:\SAS\Deciles.xlsx" DBMS=xlsx Replace;

RUN;

II. External Analysis

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/********** external data analysis ***********/

/* Source File: nycdata2.xlsx */

/* Source Path: /folders/myfolders/Mylib */

/* Code generated on: Sunday, April 10, 2016 3:13:39 PM */

%web_drop_table(mylib.nycdata);

FILENAME REFFILE "C:\SAS\nycdata2.xlsx" TERMSTR=CR;

PROC IMPORT DATAFILE=REFFILE

DBMS=XLSX

OUT=mylib.nycdata;

GETNAMES=YES;

RUN;

PROC CONTENTS DATA=mylib.nycdata; RUN;

%web_open_table(mylib.nycdata);

/* View data */

DATA mydata;

set mylib.nycdata;

run;

proc print data=mylib.nycdata;

run;

/* new variable showing percent of zipcode with more than a bachelors*/

data mylib.nycdata;

set mylib.nycdata;

pctbachormore= bachelors_or_more/ total_population;

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run;


run;

/* new variable showing percent of zipcode households that are families*/

data mylib.nycdata;

set mylib.nycdata;

pctfamily= total_family_households/ total_population;

run;


run;

/* Histogram of students enrolled in secondary school */

DATA mydata;

set mylib.nycdata;

PROC UNIVARIATE;

Var students_prek_through_12;

Histogram;

RUN;

/* Histogram of Median Income */

DATA mydata;

set mylib.nycdata;

PROC UNIVARIATE;

Var Median_Household_Income;

Histogram;

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RUN;

/* Histogram of Educated Population */

DATA mydata;

set mylib.nycdata;

PROC UNIVARIATE;

Var pctbachormore;

Histogram;

RUN;

/* Basic Descriptive Stats */

DATA mydata;

set mylib.nycdata;

PROC Means N Mean STD Min Max;

VAR TOTAL_POPULATION TOTAL_HOUSEHOLDS TOTAL_FAMILY_HOUSEHOLDS

SINGLE_FATHER_HOUSEHOLDS SINGLE_MOTHER_HOUSEHOLDS

NONFAMILY_HOUSEHOLDS TOTAL_SCHOOL_ENROLLMENT

STUDENTS_PREK_THROUGH_12 INCOME_BELOW_POVERTY_LEVEL

MARRIED_COUPLE_FAMILIES

INCOME_UNDER_10K INCOME_10K_TO_14_9K INCOME_15K_TO_19_9K

INCOME_20K_TO_24_9K INCOME_25K_TO_29_9K

INCOME_30K_TO_34_9K INCOME_35K_TO_39_9K INCOME_40K_TO_44_9K




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INCOME_200K_PLUS MEDIAN_HOUSEHOLD_INCOME HIGH_SCHOOL_DIPLOMA

BACHELORS GRADUATE BACHELORS_OR_MORE pctbachormore pctfamily;

Run;

/* Quartiles */

DATA mydata;

set mylib.nycdata;

PROC MEANS q1 median q3 max;

Var total_family_households TOTAL_SCHOOL_ENROLLMENT

STUDENTS_PREK_THROUGH_12 MEDIAN_HOUSEHOLD_INCOME pctbachormore;

Run;

/* K-Means Clustering */

proc stdize data=mylib.nycdata out=Stand method=std;

var


run;

proc fastclus data=mylib.nycdata out=Clust

maxclusters=4 maxiter=100;

var


run;

/* frequency table showing counties and clusters */

proc freq data=Clust;

tables county*Cluster;

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run;

/* plot clusters */

proc candisc data=Clust out=Can noprint;

class Cluster;

var STUDENTS_PREK_THROUGH_12 MEDIAN_HOUSEHOLD_INCOME pctbachormore;

run;

proc sgplot data=Can;

scatter y=median_household_income x=students_prek_through_12/ group=Cluster;

run;

/***** cluster 1************/

/* descriptives*/

DATA clusters;

set work.Clust;

where

CLUSTER = 1;









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Run;

/* histograms */

DATA clusters;

set work.clust;

where cluster = 1;

PROC UNIVARIATE;

Var STUDENTS_PREK_THROUGH_12;

Histogram;

RUN;

/* confidence intervals */

PROC MEANS DATA = work.clust alpha =.05 CLM;

where cluster = 1;

VAR median_household_income STUDENTS_PREK_THROUGH_12 pctbachormore;

Run;

/******* cluster 2*********/

/* descriptives*/

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DATA clusters;

set work.Clust;

where

CLUSTER = 1;















Run;

/* histograms */

DATA clusters;

set work.clust;

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where cluster = 2;

PROC UNIVARIATE;


Histogram;

RUN;



where cluster = 2;


Run;

/********** cluster 3********/

/* descriptives*/

DATA clusters;

set work.Clust;

where

CLUSTER = 1;







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Run;

/* histograms */

DATA clusters;

set work.clust;

where cluster = 3;

PROC UNIVARIATE;


Histogram;

RUN;



where cluster = 3;


Run;

/******* cluster 4***********/

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/* descriptives*/

DATA clusters;

set work.Clust;

where

CLUSTER = 1;















Run;

/* histograms */

DATA clusters;

set work.clust;

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where cluster = 4;

PROC UNIVARIATE;


Histogram;

RUN;



where cluster = 4;


Run;

/* more plots*/

/* scatter plots */

/* bachormore and students*/

proc sgplot data=work.clust;

reg x=pctbachormore y=STUDENTS_PREK_THROUGH_12/ lineattrs=(color=red thickness=2);

Title "The Relationship Between Education Level and Students in School";

run;

/* poverty and students*/


reg x=INCOME_BELOW_POVERTY_LEVEL

y=STUDENTS_PREK_THROUGH_12/ lineattrs=(color=red thickness=2);

Title " ";

run;

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/* median income and students*/


reg x=INCOME_BELOW_POVERTY_LEVEL

y=STUDENTS_PREK_THROUGH_12/ lineattrs=(color=red thickness=2);

Title " ";

run;

/* students and family households */


reg x=total_family_households y=STUDENTS_PREK_THROUGH_12/ lineattrs=(color=red

thickness=2);

/* box plots for clusters */

PROC SORT DATA = work.clust OUT=MyDataProcessed;

BY Cluster;

proc boxplot data=mydataprocessed;

plot MEDIAN_HOUSEHOLD_INCOME*cluster;

run;


BY Cluster;


plot students_prek_through_12*cluster;

run;


BY Cluster;

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plot pctbachormore*cluster;

run;

/* Hierarchical Clustering --- K MEANS RESULTS ARE MUCH BETTER */

proc cluster data=mylib.nycdata method=centroid ccc pseudo out= tree;

var TOTAL_SCHOOL_ENROLLMENT: STUDENTS_PREK_THROUGH_12:

MEDIAN_HOUSEHOLD_INCOME: pctbachormore:;

copy ZIP: CITY: COUNTY: TOTAL_POPULATION: TOTAL_HOUSEHOLDS:

TOTAL_FAMILY_HOUSEHOLDS:;

run;

proc tree data = tree noprint nclusters=3 out=out;

copy ZIP: CITY: COUNTY: TOTAL_POPULATION: TOTAL_HOUSEHOLDS:

TOTAL_FAMILY_HOUSEHOLDS:;

run;

PROC PRINT DATA=work.out;

run;

FORDHAM UNIVERSITY Graduate School of Business Marketing … · FORDHAM UNIVERSITY Graduate School...

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