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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
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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.
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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
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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.
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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;
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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
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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;
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Image5
ItemsBoughtbyTVOwners:
Thisquerytellswhatunitsareboughtbypeoplewhoowntelevisions.Thisinformationisuseful
indeterminingwhatitemstoadvertiseontelevision.
SELECTitem.itemtype,count(transaction.unitsbought)ASnumber_units_bought
FROMitem,subsciption,[transaction]
WHEREsubsciption.customerid=transaction.customerid
ANDtransaction.transactionid=item.transactionid
ANDsubsciption.cable="yes"
GROUPBYitem.itemtype;
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Image6
ItemsBoughtbyPeopleWhoHavetheTop3Subscriptions:
ThefirstqueryisthetypesofitemsboughtbypeoplewhohaveasubscriptionforBetter
Homes&Gardens.Betterhomeandgardenswasdeterminedtobethe3rdmostpopular
subscriptionsoknowingwhichitemspeoplewhohadthissubscriptionboughtcanhelpdeterminewhatitemstoadvertise.
SELECTitem.itemtype,count(item.itemtype)ASNumber_of_units
FROMitem,subsciption,[transaction]
WHEREsubsciption.customerid=transaction.customerid
ANDtransaction.transactionid=item.transactionid
ANDsubsciption.betterhg="yes"
GROUPBYitem.itemtype
ORDERBYcount(item.itemtype);
Image7
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ItemsBoughtbyPeopleWhoHaveaSubscriptiontoReadersDigest:
ThenextqueryisthetypesofitemsboughtbypeoplewhohaveasubscriptiontoReaders
Digest.ReadersDigestwasthe2ndmostpopularsubscription,soknowingwhichitemspeople
whohadthissubscriptionboughtcanhelpdeterminewhatitemstoadvertise.
SELECTitem.itemtype,count(item.itemtype)ASNumber_of_units
FROMitem,subsciption,[transaction]
WHEREsubsciption.customerid=transaction.customerid
ANDtransaction.transactionid=item.transactionid
ANDsubsciption.readersdigest="yes"
GROUPBYitem.itemtype
ORDERBYcount(item.itemtype);
Image8
ItemsBoughtbyPeopleWhoHaveaSubscriptiontotheNewspaper:
Thisqueryisforpeoplewhohaveasubscriptiontothenewspaper.Thenewspaperhadthe
mostsubscriptionsofanyothermagazinesoknowingwhichitemspeoplewhohadthis
subscriptionboughtcandeterminewhatitemstoadvertise.Alsobecausethenewspaperis
circulatedinlocalareasitisthemosteffectivewaytoadvertisetosubscriptionholders.
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SELECTitem.itemtype,count(item.itemtype)ASNumber_of_units
FROMitem,subsciption,[transaction]
WHEREsubsciption.customerid=transaction.customerid
ANDtransaction.transactionid=item.transactionid
ANDsubsciption.newspaper="yes"
GROUPBYitem.itemtype
ORDERBYcount(item.itemtype);
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
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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;
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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.
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Results
ItemSaleAnalysis
Lookingatthetotalsalesforeachitemoverallandperdayhelpsvisualizeandunderstand
overallsales.
Graph1
Seenhereingraph1thetotalsalesofeachitemoverthetwoyearperiodareshown.This
reiteratesthedatagiveninthequeries.
Graph2
Thegraphshownhere,graph2,showsthetotalsalesofeachitemperweek.ThiswillhelpDr.
Yootoknowwhenthestorewillbebusiestduringtheweek.
0
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
UnitsSold
ItemType
TotalVolumebyItem
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Item12
Item8
Item5
Item3
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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.
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SimilarityCoefficientsMatrix
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.
SimilarityCoefficientsMatrix
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.
SimilarityCoefficientsMatrix
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
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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.
SimilarityCoefficientsMatrix
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.
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TimeSeries
Timeseriesgraphsshowavisualrepresentationoftheamountofeachproductboughtper
weekoverthetwoyearperiod.
Graph3
Thisgraph3showsthetimeseriesforallitemsoverthetwoyearperiod.Thisisextremely
difficulttoread,however,fromthisdatainexcel,thedataforanyitemcanbepulledand
placedintoanindividualgraph.
Graph4showsthesalesofthetop5itemswhilegraph5showsthesalesofthebottom5items.
Graph4
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Cereal
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Snack
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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
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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
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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.
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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.