Open Mart Improvement Plan

8/3/2019 Open Mart Improvement Plan

1/22

Open*MartMonroeville, PA Location Improvement Plan

Through data analytics using database software Open*Mart is reinvented. The

Monroeville, PA location is given a new targeting plan, a new advertising scheme, anew layout, and more. This report covers the process that Dr. Yoo is advised tofollow in order to raise the status of his Open*Mart location.

IE 330 Final Project

December 5, 2011

Patrick Clifford

Joe Gigliotti

Brittany Murphy

Michael Tomashefski


2/22

1

Introduction

Retailanalyticsisthein-depthprocessofretailimprovementthroughsmarterandmore

effectivebusinessdecisions.Thesedecisionsaredrivenbytheanalysissdatawhichsupports

possiblechoicesandoptionsforretailcompanies.Thisdataisretrievedthroughstudiesthat

includeanalyzingpastretailtransactionsandthedetailsofeach.Trendscanbeobservedthroughtheretaildataleadingtofuturepredictionsandultimatelyamoreefficientlyrun

business.Retailanalyticscanhelpimplementanentirelynewsystemtoaretailoperation,

completelytransformingthewayabusinessruns.

ProblemDescription

Open*Martisaretailcompanyspecializinginprovidingcustomerswiththeproductstheyneed

whetheritbehomeappliances,groceries,clothing,computers,ormore.Theyfocuson

providingtheircustomerswithspecializedsections,eachfocusingondifferentproducttypes.

WithmultiplelocationsaroundtheU.S.,Open*Martaimstoruneachlocationsattop

efficiency.Dr.Yoo,themanageroftheMonroeville,PAOpen*Martlocation,isinterestedinconductingaretailanalysisofhisstore.Heslookingtoimprovethesalesandincreaseprofits;

allwhilemakingthingsrunmoresmoothly.

Asaretailmanager,Dr.Yooisnotconfidentenoughtoimprovehisstoreonhisown.After

receivingacallfromhisCEO,Dr.Liying,Dr.YooishopingtoprovideDr.Liyingwithadetailed

analysisofanefficientlyrunstore.Dr.YoohashiredDr.Reddy,anemployeeofCustomer

RelationshipManagement(CRM)toprovidehimwithdetailedtransactionanddemographic

data.

Mr.Reddycollectedtransactiondatarelatedtotheprevioustwoyearsofsales.Thisdatawas

dumpedintoadatawarehouse.Thetransactiondatacontainsthefollowinginformation:

CustomerID,ItemType,ItemNumber,VendorID,Week,Day,andUnitsBought.Usingadata

dictionary,eachattributeforeachtransactionisgivenanumberrelatingtoaspecificdefinition.

Inadditiontothetransactiondata,Mr.Reddycontactedhismanager,Mr.David,toassisthim

inthedataanalysis.

Mr.Davidgathereddemographicdata.Thisdataincludesinformationpertainingtothe

customersoftheMonroeville,PAOpen*Martlocation.Detailsofcustomersfamilysize,

income,ethnicity,pets,tvs,ages,children,workhours,occupation,education,andmagazine

subscriptionsareincludedinthedata.Storedsimilarlytothetransactiondata,thedemographic

dataalsocontainsnumbersrelatingtoadatadictionary.

TheproblemathandforDr.YooisastorebelowthequalityDr.Liyingwouldapproveof.Heis

relyingonMr.ReddytoprovidehimwiththenecessaryreporttoimpressDr.Liyingonthe

statusofhisstore.

ProjectObjectives

Dr.Yoois,overall,aimingtoimprovehisstore.Thiscanbeachievedbyimprovingsalesand

improvingefficiency.Improvingsaleswillbeaccomplishedbypullinginmorecustomers.


3/22

2

Throughadvertisingandcouponing,morecustomerswilllearnaboutmoredealsandmore

products.Itisimportanttoknowhowtoadvertisetocustomersbasedontheirspecificneeds.

Customerscanbegroupedbasedonfamilyandtransactioncharacteristics.Family

characteristicsincludefamilysize,ages,ethnicity,income,andmore.Transaction

characteristicsincludeitemsbought,quantityofitemsbought,frequencyofpurchases,items

boughttogether,andmore.Improvingefficiencywillbeachievedbyanalyzingtrendsinpurchases.Byknowingwhathasbeenpurchasedtogetherandwhenithasbeenpurchased,Dr.

Yoowillknowwhatitemstohaveonstockforthefuture.Improvedefficiencycanalsobe

reachedthroughstorelayout.Placingitemsthatarefrequentlypurchasedtogetherneareach

other,customerscanfindtheirdesiredproductsmorequickly.Simplewaystolocatewanted

itemsisimportant;itkeepscustomershappysothattheyaresuretoreturntoDr.Yoosstore

again.

Methodology

Thisprojectrequiresturningalargesetofrawdataintousableinformationbyusingexplicit

dataminingtechniquestogiveastorespecificadviceandrecommendations.Thefirststepthatneededtobedonewastocomprehendthedatabaseschema.Thisschemaneedstocoverall

theinformationthatisincludedforthisproject.Forthistoworkproperlyitalsorequiresthe

useofprimaryandforeignkeysinordertobuildrelationships.Whentheschemaiscompletely

filledout,anERdiagramcanbefabricatedwiththeinformation.TheERdiagramforthisproject

neededtohavemanytablesandrelationshipsthatcompletelycoverthedatabeingused.Itwas

determinedthat7tablesshouldbeusedforthisproject:couponusage,customerinformation,

femaleinformation,maleinformation,itemsinthestore,transaction,andsubscription.Then

wealsoneeded6relationshipsinordertolinkthetables,thisincluded:itemsbought,coupons

used,subscriptions,transactions,malesinhousehold,andfemalesinhousehold.Withallthis

setupthenextstepistosetupthesedatabasesinMicrosoftAccessanduploadthedatafrom

MicrosoftExcel.ThenallthedatatypesandrelationshipsarecompletedsothattheAccessfile

hasalltheinformationanditisallassociatedtogetherlogically.

TheMicrosoftAccessdatabaseallowsustowritedifferentqueriesinordertofindtargetdata.

Thiswasthemainfocusinthenextstepquireswerewrittenthatallowsustolocateuseful

information.Thequeriesthatwedecidedtowriteincluded:whatitemsareboughttogether,

whatitemspeoplewithchildrenbuy,wherepeoplegetthemostcouponsfrom,whatarethe

majorsubscriptionsandwhattheybuy,findingtopcustomersandproducts,andfinallywhatis

themostpopularbrandofsnacks.Thesequiresgiveusinformationthatallowustoeasily

identifywhatitemstoadvertise,howtolayoutthestore,andwhattypeofproductstosell

moreof.

AfterthequeriesarewrittenoutanothermethodtogatherinformationonadatabaseisK

Meansclustering.KMeansclusteringisanadvancealgorithmthatdeterminesthebuying

habitsofcustomersandgroupsthemintosimilarbehaviors.Thisalgorithmwaswrittenin

MicrosoftExcelwithVBAcodingtotakethepurchasinginformationof2productsandgroup

theirbuyersbyhowmuchtheybuy.Thiswasdonefor8pairsofitemstobetterunderstand

customersbuyinghabits.AlongwithKmeansclusteringanothertooltounderstandthe


4/22

3

customersbuyinghabitsisthesimilarityanalysis.Thesimilarityanalysisgivesagood

understandingonwhatitemsarepurchasedbyacertaindemographicofpeople.This

informationcanbeusedtosendoutcouponsandadvertisementstothosedemographicsof

peoplethatbuyaproductthemost.

Thelastthingtodointheprojectwastotakealltheinformationthatwasgatheredinthepreviousstepsandmakedetailedrecommendationsthatcouldbenefitthecompany.These

recommendationsincludeproductplacementwithinthestore,whotoadvertisecertain

productsto,whatproductstobuymoreof,andwhatdealstogiveonitemsboughttogether.

Theserecommendationscouldsavethecompanyalotofmoneyonadvertisingcostsbyonly

selectingatargetdemographicofpeopletopublicizeto.Theserecommendationscanalsolead

tohighercustomerloyaltybysendingdealstofrequentcustomers.

DatabaseDesign

Theinformationthatwasgivenpertainingtothestorestransactionsandcustomerswere

examinedandsplitintoseventablesinordertomaketheinformationeasiertoanalyze.Eachofthetablesnamesandattributescanbeseenbelowinthedatabaseschema.

Transaction(TransactionID,CustomerID,Week,Day,UnitsBought)

Item(TransactionID,ItemType,ItemNumber,VendorID)

Coupon(TransactionID,CouponValue,CouponOrigin)

Customer(CustomerID,FamilySize,Income,Ethnicity,Dogs,Cats,NumberTVs,Children)

Subscriptions(CustomerID,Cable,Newspaper,BetterH&G,GoodHouse,LadiesHJ,McCalls,

Redbook,ReadersDigest,Cosmopolitan,TVGuide,People,Glamour,Time,Newsweek)

MaleInformation(CustomerID,Age,WorkHours,Occupation,Education)

FemaleInformation(CustomerID,Age,WorkHours,Occuparion,Education)

Inordertosetuptherelationshipsforeachofthetablesgivenabove,anERdiagramwas

constructed.TheERdiagramcanbefoundintheAppendixandshowshowthewholedatabase

isrelated,aswellastheprimarykeysforeachtableandalloftheremainingattributes.

Belowareallofthequeriesthatwereusedinordertoanalyzetheinformationinthedatabase.

Foreachquerythereisashortsummaryofwhatitismeanttoreturn,thecodethatwas

written,andasampleoftheresults.

TypesofItemsBoughtOrganizedbyNumberChildren:

Thisqueryorganizesthetypeofitemboughtalongwithhowmanychildrenthecustomerhas.

Itthengivesthenumberofeachoftheunitsbought.Thisqueryisusedtodeterminehowto

advertisetopeoplewithchildrenandalsotobetterorganizethestore.


5/22

4

SELECTcustomer.children,item.itemtype,count(item.itemtype)ASnumberofitems

FROMcustomer,item,[transaction]

WHEREcustomer.customerid=transaction.customerid

ANDtransaction.transactionid=item.transactionid

GROUPBYcustomer.children,item.itemtype;

Image1

WheretheCouponsCameFrom:

Thistableshowsthelocationwhereeachofthecouponsusedcamefrom.Thisquerywasused

toplacecouponsinlocationsthattheywillbeusedandseenthemost.

SELECTcouponorigin,count(couponorigin)ASnumber_used

FROMcoupon

WHEREcouponorigin>18

GROUPBYcouponorigin

ORDERBYcount(couponorigin)DESC;


6/22

5

Image2

NumberofEachSubscriptionsthattheCustomershave:

ThisshowsanexampleofthenumberofsubscriptionsthecustomershaveforBetterhome&

Gardens.Thiscodewasrepeatedforeachofthetypesofsubscriptions.Thisquerywasusedto

determinewhatarethemostpopularmagazinesothatcouponsandadvertisementscanbe

usedmoreefficiently.

SELECTcount(betterhg)ASBetter_home_garden

FROMsubsciption

WHEREbetterhg="yes";

Image3

NumberofEachoftheItemsBought:

Thisquerytellsthetopandbottomnumberofunitssold.Thiscanbeanalyzedtodetermine

placementinthestorealongwithhowtoadvertisetheitems.

SELECTItem.ItemType,SUM(Transaction.UnitsBought)ASTotalUnits

FROMItem,[Transaction]

WHEREItem.TransactionID=Transaction.TransactionID


7/22

6

GROUPBYItem.ItemType

ORDERBYSUM(Transaction.UnitsBought)DESC;

Image4

TopCustomers:

Thisquerytellsthetopcustomerbyhowmanyunitstheybought.Thisinformationisusefulto

sendspecialpromotionstothesepeopleinordertokeepthemloyaltothecompany.

SELECTTOP10Sum(Transaction.UnitsBought)ASTotalUnits,Customer.CustomerID

FROMCustomer,[Transaction]

WHERECustomer.CustomerID=Transaction.CustomerID

GROUPBYCustomer.CustomerID

ORDERBYSUM(Transaction.UnitsBought)DESC;


8/22

7

Image5

ItemsBoughtbyTVOwners:

Thisquerytellswhatunitsareboughtbypeoplewhoowntelevisions.Thisinformationisuseful

indeterminingwhatitemstoadvertiseontelevision.

SELECTitem.itemtype,count(transaction.unitsbought)ASnumber_units_bought

FROMitem,subsciption,[transaction]

WHEREsubsciption.customerid=transaction.customerid


ANDsubsciption.cable="yes"

GROUPBYitem.itemtype;


9/22

8

Image6

ItemsBoughtbyPeopleWhoHavetheTop3Subscriptions:

ThefirstqueryisthetypesofitemsboughtbypeoplewhohaveasubscriptionforBetter

Homes&Gardens.Betterhomeandgardenswasdeterminedtobethe3rdmostpopular

subscriptionsoknowingwhichitemspeoplewhohadthissubscriptionboughtcanhelpdeterminewhatitemstoadvertise.

SELECTitem.itemtype,count(item.itemtype)ASNumber_of_units




ANDsubsciption.betterhg="yes"

GROUPBYitem.itemtype

ORDERBYcount(item.itemtype);

Image7


10/22

9

ItemsBoughtbyPeopleWhoHaveaSubscriptiontoReadersDigest:

ThenextqueryisthetypesofitemsboughtbypeoplewhohaveasubscriptiontoReaders

Digest.ReadersDigestwasthe2ndmostpopularsubscription,soknowingwhichitemspeople

whohadthissubscriptionboughtcanhelpdeterminewhatitemstoadvertise.





ANDsubsciption.readersdigest="yes"



Image8

ItemsBoughtbyPeopleWhoHaveaSubscriptiontotheNewspaper:

Thisqueryisforpeoplewhohaveasubscriptiontothenewspaper.Thenewspaperhadthe

mostsubscriptionsofanyothermagazinesoknowingwhichitemspeoplewhohadthis

subscriptionboughtcandeterminewhatitemstoadvertise.Alsobecausethenewspaperis

circulatedinlocalareasitisthemosteffectivewaytoadvertisetosubscriptionholders.


11/22

10





ANDsubsciption.newspaper="yes"



Image9

ItemsBoughtTogether:

Thisqueryshowswhatitemsareboughttogetherbythecustomers.Thisinformationisuseful

todetermineanyspecialdealstoplaceonitemsalongwithhowtoplacetheitemsinthestore.

SELECTTransaction.Week,Item.ItemType,SUM(Transaction.UnitsBought)asItem_Bought

FROM[Transaction],Item

WHERETransaction.TransactionID=Item.TransactionID

GROUPBYTransaction.Week,Item.ItemType


12/22

11

ORDERBYtransaction.week;

Image10

NumberofUnitsSoldof17byEachVendor:

Thisquerybreaksdownhowmucheachvendorsellsofitemnumber17.Thisisusefulinorder

toseewhichvendorhasthemostpopularproductinordertobuymorefromthemandless

fromunpopulartypes.

Selectitem.vendorid,count(transaction.unitsbought)ASunits_bought

FROMitem,transaction

WHEREitem.itemtype=17

ANDitem.transactionid=transaction.transactionid

GROUPBYitem.vendorid

ORDERBYcount(transaction.unitsbought)DESC;


13/22

12

Image11

Analytics

K-meansclusteringwasusedinordertogroupcustomerstogetherbasedontheproductsthat

theybuy.Thetopfouritemsthatcustomersbuyandthebottomtwoitemsthatcustomersbuy

werecomparedusingk-meansclustering.First,thenumberofunitsboughtbyeachcustomer

wasusedtoconstructalistofeachcustomerandhowmanyofeachofthetwoitemsthey

bought.Next,thethreecolumnsofinformation,customerIDandthenumberofeachitem

boughtbythatindividual,wasputintoanExcelfilethatalreadycontainedtheVisualBasiccode

fork-meansclustering.TheVBAcodewasalteredforeachindividualsituation.

Thenumberofclusterswaseither3or4,andthenumberofdatapointsforeachsituationwas

different.Afterthecodewasproperlyaltered,itwasrunandtheresultsgavewhichcustomer

wasineachclusterandthecentroidoftheclusters.Fromthisinformationaplotcouldbe

constructedmakingiteasytoseewheretheclustersfellonthegraph.Allofthek-means

clusteringplotsthatwereusedintheanalysiscanbeseenintheAppendix.Theplotswerethen

studiedinordertodeterminewhichgroupsofcustomerswouldbebesttousetoperform

similarityanalysis.

Forinstance,ifaclusterofcustomersisbuyingalotofoneitemandonlyalittlebitofanother

item,thesepeoplecouldbeofferedpromotionsthatwouldgetthemtobuymoreoftheless

boughtitem.Alsothroughk-meansclustering,customerscanbeanalyzedtoseeifthereare

clustersofpeoplethatmightalreadybeinterestedinacertainitemandcouponscouldget

themtobuymoreoftheseitems.


14/22

13

Results

ItemSaleAnalysis

Lookingatthetotalsalesforeachitemoverallandperdayhelpsvisualizeandunderstand

overallsales.

Graph1

Seenhereingraph1thetotalsalesofeachitemoverthetwoyearperiodareshown.This

reiteratesthedatagiveninthequeries.

Graph2

Thegraphshownhere,graph2,showsthetotalsalesofeachitemperweek.ThiswillhelpDr.

Yootoknowwhenthestorewillbebusiestduringtheweek.

0

10002000

3000

4000

5000

6000

7000

8000

9000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

UnitsSold

ItemType

TotalVolumebyItem

0

200

400

600

800

1000

1200

1400

1600

1800

1 2 3 4 5 6 7

UnitsSold

DayofWeek

ItemVolumebyDayofWeek

Item17

Item12

Item8

Item5

Item3


15/22

14

SimilarityAnalysis

SimilarityCoefficientsMatrix

1 2 3 4 5 6 7 8 9 10

1 -

2 0.33 - 3 0.33 0.67 -

4 0.67 0.67 0.33 -

5 0.67 0.33 0.33 0.67 -

6 0.67 0.67 0.67 0.67 0.33 -

7 0.33 0.67 0.67 0.33 0.67 0.33 -

8 0.33 1 0.67 0.67 0.33 0.67 0.67 -

9 0.17 0.5 0.5 0.5 0.5 0.5 0.5 0.5 -

10 0.5 0.17 0.17 0.5 0.83 0.17 0.5 0.17 0.67 -Table1

Thissimilaritycoefficientmatrixshowsthetoptencustomersthatboughtthemostitemswithintheanalyzedtimeframe.Thematchingcoefficientwasusedtoobtainthepercentages

shown.Thesearebasedon6attributesthatwereusedtodeterminethesimilaritiesbetween

the10differentfamilies.

Analyzingthematrix,thefamilieswiththemostsimilarattributesrecommendedthattargeting

otherfamilieswiththesameattributeswouldsellmoreitemsinthestore.Thepeoplethat

shouldbetargetedwhencreatingadvertisementsarefamiliesofatleastthreepeople,with

incomesthatareaverage,under35,000.Bothfamiliesalsodidnotsubscribetothepaper,so

newspaperadswouldntbeaseffectiveasothertypesofadvertisement.Petswerealsonot

presentwiththesefamilies,sospecialsontheanimalsupplieswouldalsonotaffectthese

shoppers.

Similarityanalysisondifferentitems

ThesefivesimilaritycoefficientmatricesweretakenfromspecificclustersintheK-means

clusteringdata.Fourofthemarecomparingthemostboughtitemsinordertoknowthe

attributesforthepeoplethatarebuyingthemostfromthestore.Theattributesthatwere

lookedatincludedfamilysize,income,childrenandcertainsubscriptions.Thelargestnumber

inthematrixgavethetwocustomersthatweremostsimilar.Sincetheyarebuyingthetop

items,theirattributeswereanalyzedandfoundwhomtotargetwithadvertisements.


16/22

15


foritem8and12

1 2 3 4 5

1 -

2 0.57 -

3 0.71 0.57 - 4 0.57 0.71 0.57 -

5 0.57 0.71 0.57 1.00 -Table2

Herecustomer4and5arethemostsimilarsotheywerelookedatclosertotrytogeneralize

whattypeofpersonismostlikelytobuythetwoitems.Thepeoplethataremostlikelytobuy

eggsandcookareafamilyofoneperson,thatdoesntmakemorethan$35,000andhasno

subscriptionstocableorthenewspaper.Thiswillhelpinadvertisingbecauseitisknownthat

forthesetwoitemsthenewspaperandT.V.arenotplacestoadvertisetowards.


foritems8and3

1 2 3 4 5

1 -

2 0.43 -

3 0.43 0.71 -

4 0.43 0.43 0.43 -

5 0.57 0.86 0.57 0.51 -Table3

Inthismatrixitems3and8werecompared.Customer2and5werethemostsimilar,sotheywereanalyzedfurther.Itwasobservedthatafamilyofoneperson,thatdoesntmakemore

than$35,000anddoesntsubscribetothenewspaper,shouldbetargetedforthesetwoitems.

Itisrealizedthatitwouldbeagoodideatogroupalltheitemstogetherwhenadvertising

becausesimilarpeoplebuyallthree.


foritems17and3

1 2 3 4 5

1 -

2 0.43 -

3 0.57 0.57 -

4 0.43 0.43 0.57 -

5 0.43 0.43 0.86 0.71 -Table4


17/22

16

Inthismatrix,families5and4weremostsimilar.Theirattributeswerenotallthesame

however,theydidsharetheattributeofchildrenundertheageof11.Whentryingtosellmore

snacksandbutter,itisrecommendedtotargetfamilieswithkids.Itisnotedthatthefamilies

havecable,sousingcableadvertisementswouldbeefficienttotargetthem.

SimilarityCoefficientsMatrixforitems17and12

1 2 3 4 5

1 -

2 0.57 -

3 0.57 0.43 -

4 0.57 0.71 0.43 -

5 0.57 0.71 0.71 0.43 -Table5

Thissimilaritymatrixdidnotreallydoagoodjobintellingwhomtotarget.Families2,4and5

arelookedattoseewhattheyhadincommon.Itwasfoundthatwhenadvertisingforsnacks

andeggs,newspaperadswouldnotbeveryeffective.Thisisduetothefactthatnoonethat

boughttheseitemssubscribestothenewspaper.


foritems17and15

1 2 3 4 5

1 -

2 0.57 -

3 0.57 0.14 -

4 0.29 0.43 0.43 - 5 0.57 0.43 0.43 0.71 -

Table6

Thisanalysisgavethesimilaritiesbetweenatopsellingitem,snacks,andalowersellingitem,

pizza.Thetwofamiliesthathadthesameincomeunder$35,000bothhadanewspaper

subscription;thissuggestsanewspaperadwouldbeaneffectivechoice.


18/22

17

TimeSeries

Timeseriesgraphsshowavisualrepresentationoftheamountofeachproductboughtper

weekoverthetwoyearperiod.

Graph3

Thisgraph3showsthetimeseriesforallitemsoverthetwoyearperiod.Thisisextremely

difficulttoread,however,fromthisdatainexcel,thedataforanyitemcanbepulledand

placedintoanindividualgraph.

Graph4showsthesalesofthetop5itemswhilegraph5showsthesalesofthebottom5items.

Graph4

0

50

100

150

200

250

300

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97101

1

2

3

4

5

6

7

8

9

0

50

100

150

200

250

300

159

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

101

Buer

Cereal

Cook

Eggs

Snack


19/22

18

Graph5

Fromthesegraphsitiseasytoseethedifferenceinsaleswhilealsonotinghighsellingweeks

andlowsellingweeks.

Thisdataistakenfromquerieslookingattheweeks,theitems,andthenumberofeachitem

perweek.Theimportanceofthisdatacantranslateintomanyareasofthedataanalysis.Time

seriesallowDr.Yootoestimatesalesovertheyear.Thisleadstoorderingandstockingnumbers.Cuttingdownonextraproductsorderedcansavemoney,likewise,notordering

enoughproductscancausedisappointedcustomersanddeclinesinsales.Trendsofpurchases

allowDr.Yootobefullypreparedeachyear.

Otherhelpfulgraphswouldbetorelatehighsellingitemswithlowsellingitems.Thiswillshow

weeksthathighsellingitemspeakandlowsellingitemsdrop.

Graph6

Ashisdatasupplyincreaseshecanviewtrendsinweeksduringtheyearwhenoneitemis

alwaysparticularlyhigh,aspike,yearafteryear.Thiswillallowhimtopairthisitemwith

0

50

100

150

200

250

300

159

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

101

Cleansers

Nuts

Pill

Pizza

So

0

50

100

150

200

250

300

159

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

101

Buer

Eggs

Nuts

Bacon

Pizza


20/22

19

anotheritemthathasaparticularlylowsaleduringthatweek.Withcouponsdiscountinglower

salesitemswithregularlypricedhighersalesitems,thesaleswillincreaseforthoselower

items.Anexamplecouldbetakenfromthedatashowinthetablebelow.Thistableshowsthe

timeseriesforbutter,eggs,nuts,bacon,andpizza.Butterandeggsareviewedtohavespikes

over150afewtimesoverthistwoyearperiod.Withsuchahighsalerate,itislikelythatthis

trendappearseveryyear.Lookingatweek22ingraph6,butterspikesto250salesinoneweek.Withsomanypurchases,itwouldbewisetomanufactureacouponthatoffersadealwhena

customerpurchasesbutter;theygetnutsatadiscountedprice.Thesenutsarethelowest

sellingitemduringthatweek22.Thesalesofnutsshouldincrease,therefore,increasingDr.

Yoosprofits.

Recommendations

Afterthoroughlyanalyzingthedatathatwassupplied,recommendationswereplannedoutto

helpimproveOpen*Martsbusiness.Oneofthequeriesthatwaswrittengavethenumberof

eachproductthatwasboughtbycustomersthathaveTVs.Fromtheresultsofthisqueryitwas

determinedthatforthethreetopitemsfromthislisttheyshouldbeadvertisedonTV.Theseitemsincludeitem17(snacks),item5(cereal),anditem12(eggs).

AquerycomparingwhichproductscustomersthatsubscribetoBetterHomeandGarden

boughtwasruninordertodeterminewhichitemwouldbebesttoadvertiseinthismagazine.It

wasfoundthatitem17(snacks),item12(eggs),anditem8(cook)wouldallbenefitfrombeing

advertisedinBetterHomeandGarden .Thiswouldfurtherenticethepeoplethatbuythese

itemtocometoOpen*Marttobuythem.

SimilarquerieswerewrittenforReadersDigestandtheNewspaper.Bothoftheseresultedin

therecommendationtoadvertiseitem17(snacks),item5(cereal),anditem12(eggs)inthe

givensubscriptions.ByanalyzingthevendorsthatsupplythestoresitemsitwasfoundthatOpen*Martshouldcontinuetobuyitem17(snacks)fromvendor28400,41200,and17423.

Thesethreevendorsprovidethebrandsofitemsthatsellbest.

Itwasdeterminedthatthetop3customershavethefollowingthreeIDnumbers15538702,

15514612,and15104398.SincethesethreecustomersarethemostloyaltoOpen*Martand

buythemostitems,couponsshouldbesenttothemforacertainpercentageofftheirnext

purchase.Thiswouldbeagoodwaytopromotecustomerloyaltyandrewardthestoresbest

supporters.

Bylookingintowheremostofthecouponsusedoriginate,thebestwayofprovidingcoupons

wasfound.Open*MartshouldputmorecouponsintheSundaySupplementVendor,theNewspaperAdStore,andin-packwithotherpurchases.Itwasalsofoundthathouseholdsthat

havechildrenover18buythemostfromOpen*Mart.Duetothisfinding,itwouldbebeneficial

tosendthesefamiliescouponbookletssothattheykeepcomingbacktoOpen*Marttospend

theirmoney.

Thesetupofthestorecanbeveryhelpfulinpromotingtheitemsthatpeoplenormallydont

buy.Open*Martshouldstrategicallyplaceitslowestsellingitemsinthefrontofthestore


21/22

20

wherepeopleconstantlywalkinandout.Similarly,thetopsellingitemsshouldbeplacedinthe

backofthestoresothatcustomershavetowalkbyalltheotheritemsandadvertisementsin

ordertogettowhattheycamefor.Thiswillinfluencepatronstobuyextraitemswhenthey

comeintoOpen*Martwhichinturnwillsellmoreproducts.

Therecommendationsthatsufficedfromthesimilarityanalysiscouldbesummedupwiththesefewgeneralizations.Advertisetopeoplethathavelowerincomesalaries,andsmallfamilies.

Alsohavemoreadvertisementsonbillboards,becausenoteveryonesubscribestocable,

newspaper,ormagazines.Familieswithkidsaremoreinclinedtobuysnacks;thiscouldbeused

toadvertiseotherproductsthatmightnotbesellingaswell.Byputtingacoupononcertain

snackitemsitcouldhelpboostsales.

Fromthetimeseriesgraphsseenintheresultssection,thesegraphscanassistwithcouponing

andincreasingitemsales.Dr.Yoocouldimplementasystemthatcreatescouponsforthe

highestandlowestitemsperweek.Asseeningraph6,thehighestandlowestsellingitemscan

bepairedtogetherandmarketedasagroupandmanufactureaweeklyitemoftheweek

coupon.Thiswillkeepcustomersenticedandtocontinueshoppingathisstore.

GroupMembersandRoles

Thebeginningoftheprojecttookalotofbrainstorming.Thisstageoftheprojectwasmainlya

groupdiscussionabouthowwewouldtacklethisassignment.Astheassignmentwentonthe

tasksbecamesplit.Belowisalistofteammembersandtheircontributiontotheteam.

PatrickClifford:Queries,Methodology

JoeGigliotti:SimilarityAnalysis,DataInput

BrittanyMurphy:Planning,TimeSeriesAnalysis,Intro/ProblemDescription/ProjectObjectiveof

Report

MichaelTomashefski:K-MeansClustering,ERDiagram,StoreLayout

InsightsinFurtheringintoFuture

Industrialengineeringandconsultingarecontinuousimprovementtypeofwork.Dr.Yoos

Monroevillelocationhasbeengivenacompleteupdate.Thewaythatheincorporatesboththe

customerandtransactiondataintohowherunshisstorehavebeentransformedintomore

efficientandmorebeneficialwaystowardthecustomersandprofit.However,thereisalwaysa

waytoimproveandwithunlimitedtime,Dr.YoosQuick*Martcouldbeimprovedevenfurther.

InthefutureDr.Yoocouldimplementfurtherandevenmorein-depthdataanalysis.Thiscould

includeexaminingmorethanjustthetop10customers.Inordertoincreasesalesitis

importanttofigureouthowDr.Yoocouldturnhisbottomcustomersintotopcustomers.The

topcustomershavebeenanalyzed,andnow,Dr.Yoounderstandswhattheyareinterestedin

andwhytheyshopatOpen*Mart.Itcouldbehelpfulto,someday,understandeverycustomer

andwhattheylookforwhentheyshopatOpen*Mart.


22/22

21

Throughthetimeanalysischart,itiseasytoseetheitemwiththehighestpeakeachweek.Dr.

Yoocouldkeeprecordsofthetopitemseachweekalongwiththelowestitemseachweek

duringtheyear.Hecouldoffercouponsthatpairthelowestsellingitemtothehighestselling

itemofeachcategory,i.e.food,clothing,etc,toincreasethesaleofthelowestsellingitemfor

thatweek.Analyzingallitemswouldtakemoretimethanthefewthathavebeenanalyzedby

Dr.Yooinhisfirstdataanalysis.

IncreasinginventorycouldbeanotherwayDr.Yoocouldreachouttomorecustomersand

increasesales.Someofhisbottomcustomersmaynotbeinterestedinthecurrentinventory.

LocationexpansioncouldbeanideafortheCEO,Dr.Liyingtolookintoforthefutureofnot

onlytheMonroevillelocation,butallOpen*Martlocationsacrossthenation.

Inadditiontolocationimprovements,Dr.LiyingcouldlookintooverallOpen*Martindustry

improvements.Inorderforindividuallocationstorunefficientlyitisimportantforthe

Open*Martdistributioncenterstoalsorunefficiently.Possibleimprovementsinthe

distributioncenterscouldbeautomation,warehousestorage,truckpackingduringshipments,

operationhours,andmore.Retailworksunderthedominoeffect;keepingthetopofthecompanyefficientkeepsallbranchesundertheOpen*Martnamerunningefficientlyaswell.

Acknowledgements

ThisprojectwasgreatlyenhancedbythehelpfulnessandadviceofManiniMadireddyand

AkshayGhurye.Itwouldnothavebeenpossiblewithouttheirhelp.Theywereagreatresource

ofinformationthroughoutthisproject.

References

Elmasri,Ramez,andShamkantNavathe.FundamentalsofDatabaseSystems.4th.Pearson

AddisonWesley,2003.Print.

Documents

Open Mart Improvement Plan