This analytics project analyzed a company's sales and marketing dataset to determine which channel has the most potential to maximize growth (Retail, Catalog, or Website). This project answered the following questions: What channel should strategic development be focused on to maximize growth? Do customer segments correlate to a channel? How do we determine synergies between the various sales channels? Does demographic data correlate to a channel and push revenues into the other channels? Are demographics and synergies important to growth of the company?

1. Chris Robinson
Mary Sauer
Adam Schackmuth
James Young
December 11, 2014
Marketing Analytics
Multi-Channel Retailing
1
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*Slides include notes and voiceover

2. Section 1
Overview of Business Case & Marketing Data
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3.  Multi-Channel Retailing Organization
 Analyzed data to determine which channel
has the most potential to maximize growth
› Channels
 Retail
 Catalog
 Website
 Analysis can be used to identify customer
segments to:
› Attract new customers
› Retain the best customers
› Avoid unprofitable customers
Overview-Business Case / Marketing
Data
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4.  Business Case – Questions we focused on:
› What channel should strategic development be
focused on to maximize growth?
› Do customer segments correlate to a channel?
› How do we determine synergies between the
various sales channels?
› Does demographic data correlate to a channel
and push revenues into the other channels?
› Are demographics and synergies important to
growth of the company?
 Product
› Food products purchased during the Christmas
season as gifts
Overview-Business Case / Marketing
Data
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5.  Customers
› Loyal to brand
› Products purchased for gifts
› Wide variety of personal interests
 External Market
› Mail-Order catalog market on decline
› Low cost of e-commerce makes it difficult for
brick-and-mortor stores to compete on price
› A multi-channel approach is necessary in
today’s economy
Overview-Business Case / Marketing
Data
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6. Section 2
Description of Data
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7.  Dataset 9 contains 4 separate files:
› DMEFExtractSummaryV01
› DMEFExtractContactsV01
› DMEFExtractLinesV01
› DMEFExtractOrdersV01
Description of Data
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8.  DMEFExtractSummaryV01 – Summary File
› 101,051 records
› Customer buying activity, demographic, psychographic
and distance to retail store information
› Data summarized by channel & season (Internet, catalog,
retail / Spring, Fall)
› This file contains all of the information used in regressions
and data analysis
› Demographic (10,929 cases)
 Age – (45-54 years old)
 Income - (over $50k, most over $100k)
 Home – (homeowners)
 Dwelling – (single-family home)
 Length Residence – (over 20 years)
 Occupation – (professional/technical,
administrative/management)
› Information can be used to segment & target
Description of Data
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9.  DMEFExtractSummaryV01 – Summary File
› Cleaned data of no responses, 10,929 cases
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Description of Data
Statistics
AgeCode IncCode HomeCode Dwelling LengthRes OccupCd
N Valid 10929 10929 10929 10929 10929 10929
Missing 0 0 0 0 0 0
Mean 4.76 6.54 1.98 1.16 13.83 5.02
Median 5.00 7.00 2.00 1.00 15.00 5.00
Mode 4 9 2 1 20 1
Std. Deviation 1.240 2.198 .134 .588 6.010 4.743

10.  DMEFExtractSummaryV01 – Summary File -
Continued
 Sales Dollars – summarized by channel(retail,
internet, catalog) and season (Fall, Spring) for 2004
– 2007, and Pre-2004
 Internet & Catalog purchases were categorized
into Gift/Non-Gift Purchases
› Retail - minimum ($1), maximum ($2,318)
› Internet - minimum ($18), maximum ($2,518)
› Catalog- minimum ($19), maximum ($2,106)
Description of Data
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11.  DMEFExtractContactsV01 – Marketing
contact records
 3,389,239 records
 Customer contact dates and contact types
(catalog or email)
 Shows data for each month for 2005-2007
 Data shows us:
› Contacts peak in November and December
› 70% of contacts are made via email
Description of Data
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12.  DMEFExtractLinesV01 – Line item detail
 618,661 records
 Order dates, dollar amount, items purchased as
gifts
 Shows data for each month for 2001-2007
 Data shows us:
› ~90% of items are purchased as gifts
Description of Data
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Gift
Frequency Percent Valid Percent
Cumulative
Percent
Valid N 24098 11.2 11.2 11.2
Y 190774 88.8 88.8 100.0
Total 214872 100.0 100.0

13.  DMEFExtractOrdersV01 – Order/trip
information
 241,366 records
 Order date, purchasing channel, payment
method
 Shows data for each month for 2001-2007
 Data shows us:
› Preferred purchasing channel is in-store; phone
second
› Preferred payment method is a bank card; cash
second
Description of Data
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14.  DMEFExtractOrdersV01
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Description of Data
OrderMethod
Frequency Percent Valid Percent
Cumulative
Percent
Valid I 54484 22.6 22.6 22.6
M 5315 2.2 2.2 24.8
P 72483 30.0 30.0 54.8
ST 109084 45.2 45.2 100.0
Total 241366 100.0 100.0
PaymentType
Frequency Percent Valid Percent
Cumulative
Percent
Valid BC 187707 77.8 77.8 77.8
CA 41181 17.1 17.1 94.8
CK 7684 3.2 3.2 98.0
GC 422 .2 .2 98.2
HA 2229 .9 .9 99.1
NV 1687 .7 .7 99.8
PC 456 .2 .2 100.0
Total 241366 100.0 100.0

15. Section 3
Model Statement
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16. Type of Model:
 Multinomial Logistic Regression
› Best suited for modeling consumer choice
16
Model Statement
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17.  Specification of Model
› (FirstChannel = Cat) = -.447 - .002StoreDist -
1.6049(AgeCode=1) - 1.005(AgeCode=2) -
.982(AgeCode=3) - .812(AgeCode=4) -
.605(AgeCode=6) + 2.719(FirstMonth=Dec) +
.680(FirstMonth=Feb) + 1.207(FirstMonth=Jan) +
.617(FirstMonth=Jun) + .593(FirstMonth=Mar) +
1.690(FirstMonth=Nov) - .575(IncCode=1) -
.639(IncCode=2) - .591(IncCode=3) -
.341(IncCode=4) - .608(IncCode=5) -
.445(IncCode=6) - .294(IncCode=7) -
.371(IncCode=8)
Model Statement
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18.  Specification of Model
› (FirstChannel = Int) = - 1.923 + .002StoreDist +
1.871(AgeCode=2) + 1.366(AgeCode=3) +
1.173(AgeCode=4) + 1.024(AgeCode=5) +
.657(FirstMonth=Apr) + 2.332(FirstMonth=Dec) +
.628(FirstMonth=Feb) + .934(FirstMonth=Jan) -
.654(FirstMonth=Jul) + .775(FirstMonth=May) +
1.138(FirstMonth=Nov) - .636(IncCode=1) -
.616(IncCode=3) - .595(IncCode=4) -
.738(IncCode=5) - .445(IncCode=6) -
.361(IncCode=7) - .501(Email=No)
Model Statement
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19. Discussion of Model Specification:
 Dependent Variable
› First Channel
 Independent Variables
› Store Distance
› Customer Age
› First Month of Contact
› Income Level
› Email
Model Statement
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20.  Data Transformations
› Recode FirstYYMM to get FirstMonth
› Experimented with creating interaction
variables, but none were significant
 Hypotheses
1. Customers who came through the internet site
would be younger than those who came
through other channels.
2. Customers who lived farther from retail
locations would be more likely to choose the
catalog or internet channels.
Model Statement
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21. Section 4
Interpretation of Findings
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22.  Set the stage:
› Dependent Variable:
 FirstChannel – First time users preference for order
 RET – Retail store order
 CAT – Catalog order
 INT – Internet Order
› Independent Variables:
 StoreDist = Distance to nearest retail location
 AgeCode = Codes (1-7) for grouped ages
 IncCode = Codes (1-9) for group income brackets
 Email = Y (yes) or N (no)
 FirstMonth = derived from FirstYYMM the MM part (Jan (01) – Dec (12))
 Goals - Understanding the customers’ first purchase may lead to:
› Understanding how to market to these customers allowing the
company to increase profits and market growth.
› Additionally, building customer loyalty by segmenting these
customers and their buying channels,
Section 4: The Findings…
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23.  Independent variable(s) impact on the dependent
› StoreDist
 Further away distance more likely to use Internet as first order
purchase
 Shorter distance to retail location increases chance of first time
purchase as Retail channel.
 Segmenting these customers within retail locations and marketing/advertising with
store coupons and flyers;
 using Internet marketing/advertising to those not within reach of the retail
locations; and
 further segmenting non-internet using customers by use of catalog would make
the most sense
› AgeCodes
 All fell within the 5% significance level.
 Age groups from 18-24 years old and 65-74 years old have less
effect on the dependent variable.
 The younger aged most likely do not have the income to spend
 The elderly have less impact because they probably do not spend much time on or perhaps
never use the Internet.
Section 4: The Findings… (continued)
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24.  Independent variable(s) impact on the dependent
› IncCode
 Those in the low incomes levels (under 20K), and those in the
higher income level (100K and above), both are above the 5%
tolerance level
 It appears the income range of 30K to 99K has a likely effect
of making a first time purchase on the Internet. You have to
have money to spend money.
› Email:
 For the Catalog, analysis does not show an impact and falls
out of the 5% significance level.
 It is significant for the Internet customer where most likely email
is a way of communication for billing, order receipt, etc.
 With technology advances, many more customers have the ability to
order on the Internet and long as the customer remains receptive to
this channel, it may push down catalog orders.
 Exceptional customer service drives catalog orders, which usually
means the multi-channel company invests in such practice and keeps it
as part of the business model.
Section 4: The Findings… (continued)
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25.  Independent variable(s) impact on the dependent
› FirstMonth
 A few of the months fall out of the 5% significance level for both
Internet and Catalog.
 Summer months of Jun, Jul, and Aug and the month of Oct for Internet
 Creates opportunity for first time purchasers in the holiday months to
use the channel of their preference.
 Catalog has the months of Jan, Nov, and Dec as solid months of
first time purchases.
 Internet has the months most effecting first time purchases as:
Jan, May, Nov, Dec.
 These key months should provide the multi-channel company an
opportunity to build brand loyalty efforts and encourage return
purchases by marketing/advertising to those first time purchasers.
Section 4: The Findings… (continued)
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26. Section 4: The Findings… (continued)
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27. Section 4: The Findings… (continued)
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28. Section 5
Summary and Conclusions
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29.  Multi-Channel Retailing Organization
› Overall Highly Seasonal
› Mail Order/Catalog Holiday Peak 6-8X Higher
› Retail has Smaller Holiday Peak – More Consistent
Throughout Year
 Most Successful Segment
› Middle-aged
› High Income
› Home Owners
 Overall Market
› Explosive Internet Growth
› Stagnating Mail Order & Retail Storefront
Summary
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30.  Multinomial Regression
› Consumer Choice Model
› Heavy Reliance on IBM’s SPSS Tool
› Two Models Developed
 First Time Mail Order Purchases
 First time E-Commerce Purchases
 Two Hypotheses
› Effect of Distance from Retail Store
› Younger Demographics Prefer Internet?
Summary
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31.  Distance from Retail Store
› The Farther Away – Mail Order & Internet Increase
› Conveniently Place Retail Pulls Sales
› Use Model to Locate Retail Stores
 Age & Income Significance – E-Commerce
› Younger has Less Disposable Income
› Older Not Heavy Internet Purchasers
› Model Good for Middle Aged & Middle Income
 Mail Order
› Don’t Like or Don’t Want to Use Internet Channel
Conclusions
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32.  November & December Sales Peak
› Huge for Internet & Mail Order
› Smaller but Still Significant for Retail Storefront
› Use Retail Storefront to Smooth-Out Revenue Flow
 Mail Order & Retail – Down but Not Out
› First Time Buyers – Mail Order Preferred Channel
› Internet Close Behind
› Some Always Prefer Brick-and-Mortar Experience
 Mail Order Preference
› Don’t Like or Don’t Want to Use Internet Channel
Conclusions
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33.  Use Model to Calibrate Retail Presence
› Distance to Store Pulls Revenue
 Use Model to Fine-Tune Going Down-
market
› Higher-Income, Middle-Aged, Homeowners
› Opportunity to go Down-market
 Continue Growing Internet
› Catalog not Going Away – YET
› Convert Mail-Order Buyers to Internet
Opportunities
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34. The End
Thank you!
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