MTAT.03.319BusinessDataAnalytics

Lecture7:CrossSelling&Upselling

(Cross/Up)SELLING

Tips

• Youalreadyknowaboutfollowingtips• “Thecostofacquiringanewcustomerisoftenaround4timesmoreexpensivethanitistoselltoanexistingcustomer.“

• Somethingnew:• Themostsuccessfulbusinesspracticestoachievethisarebyup-selling andcross-selling.

FastFoodSeller

WouldyoulikeEstonianpotatoeswiththat?J

CrossSelling

CrossSelling:AmazonShopping

CrossSelling:Definition

• Cross-sellinvolvesthesaleofmultipleproductsofferedbyasingleproduct/serviceprovidertoaneworexistingcustomer.

UpSelling

TipsForCross/Upselling?

CrossSelling• PeersAlsoBought• Incentives• OnProductCopy• DiscountedSecondBuy• BuildARelationshipAndThenAsk

UpSelling• Sellthebenefitsoftheup-sell• KeepTheUp-SellBelow25%OfTheOriginalOrder

• HighlightYourUp-sell

ReturnofCross/UpSellingStrategy?

• Amazonreportedlyattributesasmuchas35percentofitssalestocross-sellingthroughitsoptionsoneveryproductpage

• “customerswhoboughtthisitemalsobought”and• “frequentlyboughttogether”.

HowtoIncreaserevenuesfromexistingornewcustomers?

Source:https://www.slideshare.net/NYCPredictiveAnalytics/building-a-recommendation-engine-an-example-of-a-product-recommendation-engine

Up-selling

Cross-selling

Quiz:CrossSellingorUp-Selling?

Quiz:CrossSellingorUp-Selling?

Source:https://www.slideshare.net/dmedeiros/crosssellecommerce07

CustomerLifeCycle:

WhereCrosssellingandUpsellingfitsinCLC?

Howtosolvethisproblem?

• Whatproductstorecommendtowhom ?

• Solution:Technicalbasedapproach

• Atwhatstage ofthebrowsingprocesstoshowotheroptions?

• Solution:Psychologicalbasedapproach

• Outofscopeofthiscourse.

Whatproductstorecommendtowhom ? :RecommenderSystems

GoalofaRecommenderSystem:Identifyproductsmostrelevanttotheuser(Eg.Topnoffers).

FriendRecommendations

ProductRecommendations

JobRecommendations

ANaiveunderstandingofRecommenderSystems

Users MatchingItems

QuizWhatareusersandmatchingitemsthefollowingcases:

a.)LinkedIn

b.)Facebook

c.)Amazon

d.)Netflix

(Users: members, Items: jobs)

(Users: members, Items: members)

(Users: members, Items: products, e.g., books)

(Users: members, Items: movies, TV shows)

TypesofRecommendationSystems

• PopularityBasedSystem• Classificationbased• CollaborativeFiltering

• NearestNeighbor(rememberKNNclassificationtechnique?)• MatrixFactorization(wewillnotcover)

Solution1:PopularitybasedRecommenderSystem

Recommenditemsviewed/purchasedbymostpeopleRecommendations:Rankedlistofitemsbytheirpurchasecount

Betterwouldbesomethinglikethis

QuizWhichofthefollowingistrueofapopularitybasedrecommendersystem?

CangeneratePersonalizedRecommendations?

CanuseContext(Eg.timeofday)?

CanuseUserFeatures?

CanuseItemFeatures?

CanusePurchaseHistory?

IsitScalable?

Solution2:ClassificationModelUse features of both products as well as users in order to predict

whether a user will like a product or not.

UserFeatures(Eg.Age,Gender)

ProductFeatures(Eg.cost,quality)

PurchaseHistory

Classifier LikeorNot?

Limitation:Difficulttocollecthighqualityinformationaboutproductsandusers.

QuizWhichofthefollowingistrueofaClassificationmodelbasedrecommendersystem?

CangeneratePersonalizedRecommendations?

CanuseContext(Eg.timeofday)?

CanuseUserFeatures?

CanuseItemFeatures?

CanusePurchaseHistory?

IsitScalable?

Solution3:NearestneighborCollaborativeFiltering

Item-based

Recommenditemsthataresimilartotheitemstheuserbought.

Similarityisbaseduponco-occurenceofpurchases.

“ItemsAandBwerepurchasedbybothusersxandy,sotheyaresimilar.”

User-based

Finduserswhohaveasimilartasteofproductsasthecurrentuser.

Similarityisbaseduponsimilarityinusers’purchasingbehaviour.

“UserxissimilartouserybecausebothpurchaseditemsA,BandC.”

Fig.Source:http://www.salemmarafi.com/code/collaborative-filtering-with-python/

User– UserCollaborativeFiltering

SimilarUsers

• Considerusersxandywithratingvectorsrx andry• WeneedsimilaritymetricSim(x,y)• CapturetheintuitionthatSim(A,B)>Sim(A,C)

HP1 HP2 HP3 TW SW1 SW2 SW3

A 4 5 1

B 5 5 4

C 2 4 5

D 3 3

Users

Movies

SimilarUsers:Jaccard Similarity

• Jaccard similarity(A,B)=!"Ç!#!"È!#

• Jaccard distance=1- !"Ç!#!"È!#

• Sim(A,B)=1/5;Sim(A,C)=2/4• Sim(A,B)<Sim(A,C):Ignorestheratingvalues

HP1 HP2 HP3 TW SW1 SW2 SW3

A 4 5 1

B 5 5 4

C 2 4 5

D 3 3

Users

Movies

SimilarUsers:CosineSimilarity

• Cosinesimilarity(A,B)=Cos(𝑟%,𝑟&)• -1:dissimilar,0:orthogonal;+1:similar• Sim(A,B)=0.38;Sim(A,C)=0.32

• Sim(A,B)>Sim(A,C):butnotmuch

HP1 HP2 HP3 TW SW1 SW2 SW3

A 4 0 0 5 1 0 0

B 5 5 4 0 0 0 0

C 0 0 0 2 4 5 0

D 0 3 0 0 0 0 3

Users

Movies

NOTE:Fillemptyvaluesby0

Problem:Treatmissingvaluesasnegative

SimilarUsers:CenteredCosine

HP1 HP2 HP3 TW SW1 SW2 SW3

A 4 5 1

B 5 5 4

C 2 4 5 0

D 3 3

Users

MoviesNormalizedratingsbysubtractingtherowmean

Avg. Rat

10/3

14/3

11/3

6/2=3

HP1 HP2 HP3 TW SW1 SW2 SW3

A 2/3 0 0 5/3 -7/3 0 0

B 1/3 1/3 -2/3 0 0 0 0

C -5/3 1/3 4/3 0

D 0 0 0 0 0 0 0

Eachrowaddition=0

Ineachrow,originalvalue– Avg.Rat

Ratingsarecenteredaround0.+:userslikedit- :usersdidnotlikedit

SimilarUsers:CenteredCosine(2)Us

ers

Movies

HP1 HP2 HP3 TW SW1 SW2 SW3

A 2/3 0 0 5/3 -7/3 0 0

B 1/3 1/3 -2/3 0 0 0 0

C -5/3 1/3 4/3 0

D 0 0 0 0 0 0 0

• Sim(A,B)=0.09;Sim(A,C)=-0.56• Sim(A,B)>>Sim(A,C):butnotmuch

• Capturesintuitionbetter• Missingratingstreatedas“average”• Handles“tougherraters”and“easyraters”

Alsoknownaspearson correlation.

RatingPredictions

• Goal:PredictionforuserX anditemi• Whatweneed:

• Let𝑟'betheratingfortheuserX.• LetNbethesetofkusersmostsimilartoX,whohaverateditemi.

• Option1: 𝑟*+ =-.∑ 𝑟0+�0∈3 (Average)

• Option2:𝑟*+ =∑ 456!67�6∈8∑ 456�6∈8

(WeightedAverage)

Foraneighboryin(∈) thesetN

s isthesimilarityoftheuserxanditsneighbory

Item– ItemCollaborativeRating

• ForitemI,findothersimilaritems.• EstimateratingforitemIbasedonratingsforsimilaritems• Canusesomesimilaritymetricsandpredictionfunctionsasinuser-usermodel.

• 𝑟*+ =∑ 47:!5:�:∈8(7:5)∑ 47:�:∈8(7:5)

𝑠+= :similarityofitemsIandj𝑟*+:ratingsofitemi bytheuserxN(i:x):setofitemssimilartoi ,ratedbyuserx.

Item– ItemCollaborativeFiltering

? : Estimatetheratingofmovie1bytheuserC

Ratingsarebetween1to5Emptyboxes:unknownrating

Sim=PearsonCoeff.1) Subtractmeanrating𝑚+ fromeachmoviei.

1) 𝑚- =(1+3+5+5+4)/2=3.62) Row1=(-2.6,0,-0.6,0,0,1.4,0,0,1.4,0,0.4,0)

2) ComputeCosinesimilaritiesbetweenrows

RememberN=2Select2moviessimilarto1andratedbyuser5.

WeightedAverage=(0.41*2+0.59*3)/(0.41*0.59)

=2.6

𝑟*+ =∑ 47:!5:�:∈8(7:5)∑ 47:�:∈8(7:5)

PerformanceMetricforRecommendationSystems

RelevantItemsthatarealsorecommended

IrrelevantItemsthatarerecommended

Relevantitemsthatarenotrecomms

AllRecommendations(madeontrainingdataset)

AllRelevantItems(Allitemsinthetestset)

Precision:MeasureofExactness

Recall:MeasureofCompleteness

NumberofrelevantproductsbeingrecommendednumberItemsbeingrecommended.

#relevantproductsbeingrecommendedtotalnumberrelevantitems.

UsertoUserVsItemtoItem

• UsertoUser• Problem:Sparse:Usershavelimitedinterests(inbuying)

• Item-ItemoutperformsUser-User• UsersaremorecomplexthanItems• ItemshavelimitedgenrethanUsers.• ItemsimilaritymakesmoresensethanUserssimilarity

References

• https://www.youtube.com/watch?v=39vJRxIPSxw• https://www.youtube.com/watch?v=3SlnFQbLQA• GoogleJ