1. MARKS AND SPENCER
FINAL REPORT
TEAM MEMBERS
SIVARAM CHEMUDUPATI
TANYA DE DIOS
SRIRAM KARUNAMOORTHY
VINOD NARAPURAN
ANDREA RANIERI
DIVYA RAJASRI TADI

2. MARKS AND SPENCER
Executive Summary
The current report is an in-depth analysis of Marks and Spencer’s (M&S)
greeting card sales and stock for the 2013-2014 calendaryear.The report
aims to accomplishtwo goals:(1) identify inefficiencies in greeting card
stock and sales,and (2) find solutions to improve efficiency in failing areas.
Prior to statisticalanalyses a series of descriptive statistics was performed
to summarize sale and stock performance of the five major greeting card
categories:For Her, For Him, Kids, Generic,and Occasions.Both sales
and stock data for all categories were found to be right skewed,suggesting
that only a small percentage of cards were selling at extremely high
volumes,or had extremely large amounts of stock.Of the five categories,
For Her cards had the greatestamount of sales,and was the largest
greeting card segment.Generic cards,however,performed the worst and
was the second largestgreeting card segment.Primary analysis of this
data consisted ofa series of ANOVA tests which examined how card
category and seasonaffected greeting card sales and availablestock.
Stock efficiency was assessedby the average amount of weeks of stock

3. MARKS AND SPENCER
per season.Again, For Her cards outperformed all other cards in sales,
howeverFor Him cards were found to be mostefficiently stocked.Generic
cards were found to perform the worst in both sales and stock efficiency.
Additionally,all card categories showeda decrease in sales and stock
efficiency in the Fall season.In conclusionwe recommend that M&S
considereither reducing or discontinuing ordering Generic cards,and to
reduce all greeting card orders in the Fall Season.

4. MARKS AND SPENCER
CONTENTS
 INTRODUCTION TO M&S
 DATA DESCRIPTION
 COLUMN/VARIABLE DESCRIPTIONS
 VARIABLES USED IN ANOVA
 DATA CLEANING
 NEW VARIABLE CODING
 DATA EXPLORATION
 SALES PERFORMANCE OF EACH RANGE
 WORST PERFORMER DRILLER TO ITEM LEVEL
 VALIDATING CATEGORIES WITH REGRESSION
 EXECUTIVE ANALYSIS
 REFERENCES

5. MARKS AND SPENCER
INTRODUCTION TO M&S
Marks and Spencerplc founded in 1884 by Michael Marks and Thomas
Spencersituated in London is a major British multinational retailer and
specializes inthe selling of clothing, home products and luxury food
products.In 1988,the company took over Brooks Brothers, an American
clothing company and Kings Super Markets,a US food chain.
In 2008,the traffic to personalized card websites doubled and that is when
Marks & Spencer launched its personalizedgreetings cards business
online. This was run by card supplierTigerprint, a division of Hallmark. It
aimed to take on card retailers such as Clinton and WHSmith, and online
specialistssuch as Moonpig,with this launch. It grabbed a slice of the
£10m online greetings card market with an offer comprising 1,200 exclusive
designs sold through website marksandspencerpersonalised.com.

6. MARKS AND SPENCER
DATA DESCRIPTION
The full Marks and Spencergreeting card datasetincluded weekly stock
and sales information for 3,736 unique greeting cards for the 2013-2014
calendaryear. To increase reliability of analyses a the following exclusion
criterion were imposed;(1) cards with less than 30 weeks of non-zero
data, and (2) cards with non-normal sales distributions. Card sales were
considered normalif skewness and kurtosis were between -1.5 and +1.5
(Tachanhnick & Fidell, 1997).This left a set of 1,250 usable cards for
analysis.
13%
30%
7%
24%
26%
GREETING CARD CATEGORIES
For Him
For Her
Kids
Occasions
Generic

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COLUMN/VARIABLE DESCRIPTIONS
DEPTNAME
A high level categoricalvariable (departmentname) which defines the
group that a productfalls under.
SUBDEPTDESC`
This is a category below the departmentand named as sub department.It
is a subsetof a department.
RangeDesc
This is a sub category further below the sub departmentlevel which
describesthe product better.
ItemDesc
This is a levelbelow the range and further classifies the product.
StrokeDesc
This is the final levelof classificationand is unique to each product.

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UKGROSSSGLS
UK GROSS singles (UKGROSSSGLS)are the number of units of stock
that is being sold in a particular week for a particular stroke (Card) across
all the store locations in UK.
UKGROSSVAL
UK Gross value (UKGROSSVAL)is the pound value of the total stock sold
in a particular week for a particular stroke.
Avg_SalePerCard
This variable gives the average numberof units being sold per week for a
specific Stroke.
TOT_Wk_Stock
This is the sum of the total stock (units) available acrossthe supply chain
(warehouse + store + in transit) for a particular stroke in a week.
Avg_Wk_Stock
This is the average stock of the card across the all weeks.
99lower
This is the lower limit of the 99% Confidence Interval for UK gross singles
for a productspecific to a stroke number during a week.
99Upper
This is the upper limit of the 99% Confidence Interval for UK gross singles
for a productspecific to a stroke number during a week.

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BestCase
This is the ratio of the “Avg_Wk_Stock”and the “99upper”. This variable
gives the least number of weeks that is required to sell the entire stock
belonging to a particular stoke number.
WorstCase
This is the ratio of the “Avg_Wk_Stock”and the “99lower”. This variable
gives the maximum number of weeks that is required to sell the entire stock
belonging to a particular stoke number.
Variable names
UKGROSSSGLS= UK Gross Singles:
Sales (singles = count of product sold)across all channels (store, web,
mobile/tablets,teleoperators)acrossthe UK. Does not accountfor
deductions in taxes.
UKGROSSVAL = UK Gross Value:
Value of the sales (so if 3 cards are sold and eachcosts $3, then this
column has $9)
UKADVISEDSGLS = UK Advised Singles:
Count of a product that has been ordered to the supplier/vendor

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UKADVISEDVAL= UK AdvisedValue:
Value of ordered products
DATA CLEANING
• Removedcards (STROKENUM)with less than 30 weeks
• Only included cards that sold 50 units or more in a week
NEW VARIABLE CODING
 BirthdayCard
 1 if these strings were in the STROKEDESC:
birthday, bday,cupcake,cake,old, older,balloons,thday,
present
 0 if any of the above but in these ITEMDESC:Wedding,
Engagement,Anniversary.
 1 if ITEMDESC is Age or Kids Age.
 0 if otherwise.
 HeartsCard
 1 if the string “heart” was in the STROKEDESC
 0 if otherwise
 Season
 1 if winter (Weekend.Date is in months 12, 1, 2)
 2 if spring (months 3, 4, 5)

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 3 if summer (6, 7, 8)
 4 if fall (9, 10, 11)
 RANGEDESCand sub-ITEMDESC
 For Her
 Female General
 Female Rels
 For Him
 Male General
 Male Rels
 Kids
 Kids Activity
 Kids Age
 Kids General
 Generic
 Age
 Blanks
 Euro Cards
 Humour
 Multipacks
 Occasions
 Anniversary
 Baby
 ENGAGEMENT
 Greetings

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 Invites&Announ
 Wedding
VARIABLES USED IN ANOVA
Winter_Sgls
This is the number of units sold during winter season (Dec,Jan & Feb) for
a particular card. Similar is the case for the variables Spring_Sgls (Mar, Apr
& May), Summer_Sgls (Jun, Jul & Aug), Fall_Sgls (Sep,Oct & Nov).
AVG_Winter
This gives the average sales for a specific card during winter season.
Similarly Avg_Spring, Avg_Summerand Avg_Fallgives average salesfor
respectiveseasons.
StAvg_Win
This is the average stock available fora particular card during winter
season.Similarly StAvg_Spr,StAvg_Sum and StAvg_Fallgives average
stock for respective seasons.
AvgWksWint
This indicates the averagenumber of weeks that is required to sell the
entire stock of a particular card in winter. Similarly AvgWksSpr,
AvgWksSum and AvgWksFallindicate the average numberof weeks to sell
the entire stock for respective seasons.

13. MARKS AND SPENCER
DATA EXPLORATION: IDENTIFYING INEFFICIENCIES IN
STOCKING
Before analyses were preformed,a series of descriptive analysis were
conducted to identify areas in which greeting cards may be overstocked or
understocked.To do this the mean number of weekly sales for each card
was calculated,and a 99% confidence interval was computed around each
mean. Using the upper bound of this confidence interval, we calculated
how many weeks it would take to run out of stock given the average
amount of weekly stock for that card.The upper bound of the confidence
interval was used because we intended to calculate the quickesteachcard
could sell out. Given that greeting cards have high profit margins, we
assumed the best case scenario (i.e., cards sell quickly),and should be
stocked accordinglyas avoid an understockedsituation.
As seen in Figure 1, understocking does not seem to be an issue, however
some cards are well overstocked.

14. MARKS AND SPENCER
SALES PERFORMANCE OF EACH RANGE
The below Bar chart clearly highlights the variationin average sales across
different ranges in the everyday cards sub-department.The best performer
in this category is “For Her” with an averagesales of more than 2200 cards
per week followed by “For him” with sales of above 1600 perweek.The
worst performerof all is the “Generic” category which has sales of around
1200 cards per week.

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WORST PERFORMER DRILLER TO ITEM LEVEL
We then drilled further down to check what item classificationproducts are
causing this effect. Below bar chart clearly shows that the “EURO Cards”
items show a very low sales of less than 200 cards per week.

16. MARKS AND SPENCER
The above graph shows the frequency for the total number
of cards which take the varying weeks (0-5, 6-10 and so on)
to sell the entire stock. Ex: Frequency 250 of first bar
indicates that number of stroke numbers which has the 0-5
number of weeks required to sell the entire stock.

17. MARKS AND SPENCER
VALIDATING CATEGORIES WITH REGRESSION
The ANOVA provided a basis for predicting card sales as a function of Card
Type and Season. To examine whether sales of cards could predict similar
cards, a linear regression was performed on the average of a random
selectionof weekly sales per Card Type and Season.This served as a way
of validating the use of the categories ofCard Type and Seasonin predicting
sales of a card using similar cards. Observations of weekly sales were first
grouped by Card Typenested underSeason.Eachobservationofcard sales
was then assigned one of two variables randomly,one indicating card sales
to be used as a predictor(random half A), and the other indicating the hold-
out set to be used as the output variable (random half B). This resulted in
two random halves A and B of card sales within eachCard Type in a specific
Season.The average of card sales under each Card Type and Seasonwas
then computed separately for the random halves. A linear regression was
then performed, using average card sales of random half A, Season, and
Card Type to predict average card sales of random half B. Card Type and
Season were dummy-coded before being cast into the regression. Average

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card sales of random half A was a significant predictor of card sales of
random half B, and explained 98% of the variance in the model. (Adjusted
R2 = 0.977, Table xxx.) This indicates that cards of a certain type and sold
within a season are useful in predicting sales of other cards of comparable
features.