MARKS AND SPENCER

FINAL REPORT

TEAM MEMBERS

SIVARAM CHEMUDUPATITANYA DE DIOS

SRIRAM KARUNAMOORTHYVINOD NARAPURAN

ANDREA RANIERIDIVYA RAJASRI TADI

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 calendar year. The report aims to accomplish two goals: (1) identify inefficiencies in greeting card stock and sales, and (2) find solutions to improve efficiency in failing areas. Prior to statistical analyses 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 greatest amount of sales, and was the largest greeting card segment. Generic cards, however, performed the worst and was the second largest greeting card segment. Primary analysis of this data consisted of a series of ANOVA tests which examined how card category and season affected greeting card sales and available stock. Stock efficiency was assessed by the average amount of weeks of stock per season. Again, For Her cards outperformed all other cards in sales, however For Him cards were found to be most efficiently stocked. Generic cards were found to perform the worst in both sales and stock efficiency. Additionally, all card categories showed a decrease in sales and stock efficiency in the Fall season. In conclusion we recommend

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that M&S consider either reducing or discontinuing ordering Generic cards, and to reduce all greeting card orders in the Fall Season.

CONTENTS

INTRODUCTION TO M&S

DATA DESCRIPTION

COLUMN/VARIABLE DESCRIPTIONS

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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

INTRODUCTION TO M&S

Marks and Spencer plc founded in 1884 by Michael Marks and Thomas Spencer situated in London is a major British multinational retailer and specializes in the selling of clothing, home products and luxury food products. In 1988, the company

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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 personalized greetings cards business online. This was run by card supplier Tigerprint, a division of Hallmark. It aimed to take on card retailers such as Clinton and WHSmith, and online specialists such 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.

DATA DESCRIPTION

The full Marks and Spencer greeting card dataset included weekly stock and sales information for 3,736 unique greeting cards for the 2013-2014 calendar year. 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 normal if skewness and kurtosis were between -1.5 and +1.5 (Tachanhnick

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& 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

COLUMN/VARIABLE DESCRIPTIONS

DEPTNAME A high level categorical variable (department name) which defines the group that a product falls under.SUBDEPTDESC`This is a category below the department and named as sub department. It is a subset of a department.RangeDescThis is a sub category further below the sub department level which describes the product better.ItemDescThis is a level below the range and further classifies the product.

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StrokeDescThis is the final level of classification and is unique to each product.

UKGROSSSGLSUK 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.UKGROSSVALUK Gross value (UKGROSSVAL) is the pound value of the total stock sold in a particular week for a particular stroke.Avg_SalePerCardThis variable gives the average number of units being sold per week for a specific Stroke.TOT_Wk_StockThis is the sum of the total stock (units) available across the supply chain (warehouse + store + in transit) for a particular stroke in a week.Avg_Wk_StockThis is the average stock of the card across the all weeks.

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99lowerThis is the lower limit of the 99% Confidence Interval for UK gross singles for a product specific to a stroke number during a week.99UpperThis is the upper limit of the 99% Confidence Interval for UK gross singles for a product specific to a stroke number during a week.BestCaseThis 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.WorstCaseThis 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 namesUKGROSSSGLS = UK Gross Singles:Sales (singles = count of product sold) across all channels (store, web, mobile/tablets, teleoperators) across the UK. Does not account for deductions in taxes.

UKGROSSVAL = UK Gross Value:Value of the sales (so if 3 cards are sold and each costs $3, then this column has $9)

UKADVISEDSGLS = UK Advised Singles:Count of a product that has been ordered to the supplier/vendor

UKADVISEDVAL = UK Advised Value:

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Value of ordered products

DATA CLEANING• Removed cards (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) 3 if summer (6, 7, 8) 4 if fall (9, 10, 11)

RANGEDESC and sub-ITEMDESC For Her

Female General Female Rels

For Him Male General Male Rels

Kids

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Kids Activity Kids Age Kids General

Generic Age Blanks Euro Cards Humour Multipacks

Occasions Anniversary Baby ENGAGEMENT Greetings Invites&Announ Wedding

VARIABLES USED IN ANOVA

Winter_SglsThis 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_WinterThis gives the average sales for a specific card during winter season. Similarly Avg_Spring, Avg_Summer and Avg_Fall gives average sales for respective seasons.StAvg_WinThis is the average stock available for a particular card during winter season. Similarly StAvg_Spr, StAvg_Sum and StAvg_Fall gives average stock for respective seasons.AvgWksWint

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This indicates the average number of weeks that is required to sell the entire stock of a particular card in winter. Similarly AvgWksSpr, AvgWksSum and AvgWksFall indicate the average number of weeks to sell the entire stock for respective seasons.

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 quickest each card 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 accordingly as avoid an understocked situation.

As seen in Figure 1, understocking does not seem to be an issue, however some cards are well overstocked.

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SALES PERFORMANCE OF EACH RANGE

The below Bar chart clearly highlights the variation in average sales across different ranges in the everyday cards sub-department. The best performer in this category is “For Her” with an average sales of more than 2200 cards per week followed by “For him” with sales of above 1600 per week. The worst performer of all is the “Generic” category which has sales of around 1200 cards per week.

WORST PERFORMER DRILLER TO ITEM LEVEL

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We then drilled further down to check what item classification products 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.

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.

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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 selection of weekly sales per Card Type and Season. This served as a way of validating the use of the categories of Card Type and Season in predicting sales of a card using similar cards. Observations of weekly sales were first grouped by Card Type nested under Season. Each observation of card 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 each Card Type in a specific Season. The average of card sales under each Card Type and Season was 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 card sales of

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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.