di&a slides: descriptive, prescriptive, and predictive analytics

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March 2, 2017Descriptive, Prescriptive and Predictive Analytics

PollingQuestions

§ What typeofstatisticalanalysesdoyouuseorplantouse(canchoosemultipleanswers)?− Descriptive− Predictive− Prescriptive− Idon’tuseanyofthese− Idon’tknowthedifferencebetweenthese

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PollingQuestions

§ What typeofstatisticalanalysesdoyouuseorplantouse(canchoosemultipleanswers)?− Descriptive− Predictive− Prescriptive− Idon’tuseanyofthese− Idon’tknowthedifferencebetweenthese

§ Howfrequentlydoyouusestatisticalanalysesinyourwork?− Idon’tcurrentlydoanytypeofstatisticalanalysis− Lessthanonceaweek− Onceorafewtimesaweek− Atleastonceaday


TopicsForToday’sWebinar


§ Overviewofstatisticalanalysisprocess− Formingahypothesis

− Identifyingappropriatesources

− Proving/Disprovingthehypothesis

§ Typesofdataanalysis− Descriptivedataanalytics

− Predictivedataanalytics

− Prescriptivedataanalytics

§ Howthesetypescomparewithintheanalyticenvironment

§ Keytakeawaysandsuggestedresources

Combine?

Descriptive

Predictive

Prescriptive

TheProcessofStatisticalAnalysis


FormHypotheses

• Null:Nothingspecial

• Alternative:Somethingunique,anactionablefinding,etc.

IdentifyDataSource

• Don’tgooverboard!

• Collectyourown,OR

• Usesecondarydata

Prove/DisproveHypothesis

• IsTypeIorTypeIIerrorworse?

• Chooseconfidencelevel

• Reject/notrejectnull

Whenwehaveresourceconstraints,StatisticalAnalysisenablesustomakequantitativeinferencesbasedonanamountofinformationwecananalyze(asample).

Step1:FormingaHypothesis

§ Instatisticalanalysis,wehavetwohypotheses:− Nullhypothesis:Claimsthatanyirregularitiesinthesamplearedue

tochance− Alternativehypothesis:Claimsthatirregularitiesinthesamplearedue

tonon-randomcauses(andwouldthereforereflectthepopulation)§ Whatareyoureallylookingtodiscover/prove?− Experiment1:

§ Null:Thereisnodifferenceintheamountsoldwhencomparingsalespeoplewhodidanddidnotreceivetraining.

§ Alternative:Thereisadifferenceintheamountsoldwhencomparingsalespeoplewhodidanddidnotreceivetraining.

− Experiment2:§ Null:Thesalespeoplewhoreceivedtrainingdonotsellmoreonaveragethanthesalespeoplewhodidnotreceivetraining.

§ Alternative:Salespeoplewhoreceivedthetrainingsellmoreonaveragethanthosewhodidnotreceivethetraining.


Step1

Step2:IdentifyingAppropriateSources

§ Remember,youdon’tneedBigDataforeverydecision!§ Sometimes,knowingwhatdatayoudon’t needisjustasimportantasknowingwhatyoudo need.Keepyourenddecisioninmind.

§ Potentialsourcesofdata:− Primarydata− collectnewdata

§ Whotoinclude:Randomsample,stratifiedrandomsample,etc.§ Howmanytoinclude:Samplesizecalculatorsonline(free)§ Determinethelevelofmeasurementneededforyourdesiredanalysis:categorical,ordinal,interval,rational

§ Asnecessary,designacontrolgroup− Secondarydata− utilizeexistingdata

§ Censusrecords,syndicateddata,governmentdata,etc.

§ Consideryourdataneeds,datacleanliness,cost,etc.,whendeterminingappropriatesources.


Step2

Step3:Proving/DisprovingtheHypothesis

§ Establishaconfidencelevelpriortoanalysis.§ Confidencelevels:

1. Determinehowsignificantadifference/irregularitymustbeforyoutoprove/disproveyouralternativehypothesis.

2. Determinehowconfidentyoucanbeinyourdecision.

§ Evenwithahighconfidencelevel,youaren’talwaysright:− TypeIerror:Yourejectthenullhypothesisbutshouldn’thave.− TypeIIerror:Youdonotrejectthenullhypothesisbutshouldhave.− Howtodecreasethelikelihoodoftheseerrors:changetheconfidencelevel,increase

samplesize(beawareofeffectsize),etc.

§ Determinewhichtypeoferrorismoredetrimentaltoyourinvestigationandsetupyourstudyaccordingly.


Step3

Step3:Proving/DisprovingtheHypothesis


Training N Mean Std. Deviation

Std. Error Mean

No training 74 102.643 9.95482 1.15722Training 74 106.3889 9.83445 1.14323

QPctQ3

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

95% Confidence Interval of the Difference

Lower Upper

0.029 0.865 -2.303 146 0.023 -3.74595 1.6267 -6.96086 -0.53103

-2.303 145.978 0.023 -3.74595 1.6267 -6.96087 -0.53102

Levene's Test for Equality of Variances

t-test for Equality of Means

F Sig.

§ Confidencelevel=95%

§ Alpha=0.05

100

102

104

106

108

Notraining Training

Percentof3rdQuarterQuotaSoldbyTrainedvs.Untrained

Salespeople


TypesofDataAnalysis

TypesofDataAnalysis


Predictive PrescriptiveDescriptive

• Aimstohelpuncovervaluableinsightfromthedatabeinganalyzed

• Answersthequestion“Whathappened?”

• Helpsforecastbehaviorofpeopleandmarkets

• Answersthequestion“Whatcouldhappen?”

• Suggestsconclusionsoractionsthatmaybetakenbasedontheanalysis

• Answersthequestion“Whatshouldbedone?”

§ Thoughthemostsimpletype,itisusedmostoften.

§ Twotypesofdescriptiveanalysis:1. Measuresofcentraltendency(tellsus

aboutthemiddle)§ Mean− theaverage§ Median− themidpointofthe

responses§ Mode− theresponsewiththehighest

frequency2. Measuresofdispersion

§ Range− themin,themaxandthedistancebetweenthetwo

§ Variance− theaveragedegreetowhicheachofthepointsdifferfromthemean

§ StandardDeviation−themostcommon/standardwayofexpressingthespreadofdata


Customer_ID ItemsPurchased AmountSpent29304 1 1.09$28308 3 44.43$19962 21 218.58$30281 1 73.02$

6.5

2

1

0

1

2

3

4

5

6

7

Mean Median Mode

Mean,MedianandModeAmountsofItemsPurchased

Descriptive DataAnalytics


AnalysisPredictive

§ Somemistakepredictiveanalysistohaveexclusiverelevancetopredictingfuture events.− However,incasessuchassentimentanalysis,existingdata(e.g.,thetextofatweet)isusedtopredictnon-existentdata(whetherthetweetispositiveornegative).

§ Severalofthemodelsthatcanbeusedforpredictiveanalysisare:− Forecasting− Simulation− Regression− Classification− Clustering


Predictive DataAnalytics

Forecasting

§ Forecasting:− Movingaveragetechnique:usethe

meanofpriorperiodstopredictthenext§ Themeanofperiods1−4=period5§ Themeanofperiods2−5=period6

− Exponentialsmoothingtechnique:similar,butmorerecentdatapointsareweightedmoreheavilyduetorelevance

− Regressiontechniques§ Usecautioninforecasting– Thelargertheforecastedtimeperiod,thelessaccuracythereisintheprojections.


$-

$5,000.00

$10,000.00

$15,000.00

$20,000.00

$25,000.00

2006 2008 2010 2012 2014 2016 2018 2020 2022

NetIncomeofStoreCProjected2017-2020

Predictive

Simulation

§ Simulation− Queuingmodels:usedtopredictwaittimeandqueuelength

§ Resultscanbeusedtocreatestaffschedulesinawaythatreducesinefficiencies,etc.− Discreteeventmodel:usedinspecialsituationswhenqueuingcannotbeused

§ Resultscanbeusedtoidentifybottlenecks,etc.− MonteCarlosimulations:usedtoidentifyprobableoutcomesofascenariobasedonmanypossibleoutcomes(usesrandomnumbergenerationandmanyiterationsofthescenario).§ Resultscanbeusedtopredictthelikelihoodofprofitabilitywithinthefirsttwoyears,etc.


Predictive

QueuingModelExample


Scenario1 Scenario2

Predictive

MonteCarloSimulationExample


Predictive

Regression

§ Regression− generallyspeaking,usedtounderstandthecorrelationofindependentanddependentvariables


§ Typesofregressionmodels:− Logistic:usedforcategoricalvariables(i.e.,willcustomersshopatyourstoreoracompetitor?)

− Linear:usedtoidentifyalinearrelationshipbetweenthedependentvariableandatleastoneindependentvariables(i.e.,dailystorerevenuepredictedbythenumberofcustomersenteringthestore)

− Step-wise:usedtoidentifyarelationshipbetweendependent/independentvariables.Thisisdonebyadding/removingvariablesbasedonhowthosevariablesimpacttheoverallstrengthofthemodel.

Predictive

Classification&Clustering

§ Classification:usedtoassignobjectstooneofseveralcategories− Sentimentanalysisofsocialmediapostings

§ Clustering:anothermethodofforminggroups− Intragroupdifferencesareminimized− Intergroupdifferencesaremaximized− Commonlyusedtocreateandbetterunderstandcustomergroups


Predictive


AnalysisPrescriptive

§ Decisionscanbeformulatedfromdescriptiveandpredictiveanalysis− IfIneedtocutaproductandIknowthatproductCisleastpreferredandleastprofitable,IwillcutproductC.

§ However,prescriptiveanalyticsexplicitlytellyouthedecisionsthatshouldbemade.Thiscanbedoneusingavarietyoftechniques:− Linearprogramming− Integerprogramming− Mixedintegerprogramming− Nonlinearprogramming


Prescriptive DataAnalytics

LinearProgrammingExample


ProductA ProductB ProductC ProductD ProductEQuantitytoOrderProfitperUnit 5$ 3$ 20$ 50$ 200$ TotalProfit -$

ProductA ProductB ProductC ProductD ProductE Used AvailableStorageSpace 0.05 0.5 1 5 10 1000SellingEffort 0.25 5 0.5 2 7 500MinimumOrder 100 15 20 60 5

ProductA ProductB ProductC ProductD ProductEQuantitytoOrder 100 15 490 60 5ProfitperUnit 5$ 3$ 20$ 50$ 200$ TotalProfit 14,345.00$

ProductA ProductB ProductC ProductD ProductE Used AvailableStorageSpace 0.05 0.5 1 5 10 852.5 1000SellingEffort 0.25 5 0.5 2 7 500 500MinimumOrder 100 15 20 60 5

Solution:

Prescriptive

ComparingtheThreeTypesofDataAnalytics

§ Descriptiveanalysisismostcommon.− Bestpracticetoperformdescriptive

analysespriortoprescriptive/predictive§ Understandthatdistribution,variance,skew,etc.,mayexcludecertainmodels

§ Howtoknowwhichtypeofanalysistopursue:− Howmuchtimedoyouhave?− Whatresourcesareavailabletoyou?


− Howaccurateisyourdata?Howaccuratedoyouneedthemodel/analysistobe?

− Howpopular/acceptedisthemodelyouareconsidering?§ Don’tsubscribeto“that’showwe’vealwaysdoneit,”butremembertouseamodelthatstakeholderswillaccept.

KeyTakeawaysandSuggestedResources

§ Gainingmeaningfulinsightsfromdatarequiresplanning,technicalawarenessandconsistency.

§ Statisticalanalysisisn’tareplacementforyourownlogic(don’tgoonstatisticalautopilot).

§ Utilizeavailableresources(blogs,podcasts,articles,webinarsandonlinecourses)tolearnmore.− LookforAPPLIED statisticstopics

§ Bigdataisnotalwaysrequired.

§ Basicunderstandingofthestatisticalanalysisprocessgoesalongway!


Podcast:NotSoStandardDeviationshttps://soundcloud.com/nssd-podcast

Guide:WhenPredictiveModelsFailsearchdatamanagement.techtarget.com/ezine/Business-Information/When-predictive-analytics-models-produce-false-outcomes

Book:StatisticsinPlainEnglishTimothyC.Urdan

ClosingQ&A


Descriptive

Predictive

Prescriptive?

pg 27

Thankyou!SeeyouThursday,April6forournextDIAwebinar,

BuildingaFlexibleandScalableAnalyticsArchitecture

Catchourwebinarrecapnextweekhere:firstsanfranciscopartners.com/blog

JohnLadley@[email protected]

KelleO’Neal@[email protected]

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