Data mining on employee turnover

1. Exploratory data miningonEmployeeTurnover Viabilityandbenefitson a “lowinvestment” case Lisbon , August 2009 Paulo Xavier PRPlathXavier@gmail.com

2. ExecutiveSummary Data miningprojects , particularlyon HR issues, tend to beseen as being expensive highriskdue to uncertaintyofbenefitsorreturn onlyviableifsupportedbyhighlevel HRMS or “Data warehouse” and applied to verylargeorganizations. Thefollowing case showedthat a lotcanbedonewithnoneofthoserequirements, withimmediatbenefits, altoughrequiring a goodknowledgeofthesubject (organizationalbehaviour & HR Mgmt) and some knowledgeonthetechnologybehind data miningandanalyticsin general. The Case: A minimum data collection, onanorganizationofaprox 1500 employees, wasenough to identify some ofthekeyfactorsthat stand behindemployeeturnover, exposingspecificareas for immediateintervention, allowinganestimatedreductiononaprox 50% oftheoccurringandundesiredturnover. Increasedinvestmentsandtimecanobviouslyimproveresults. Data mining on Employee turnover data - Paulo Xavier - August 2009

3. Data collectedandanalysisprocedure Onlyconsideredexitsduringonespecificyear (with a particularlyhighnumberofexits). Thiswas a yearwithhighlabourmarketactivity . Appartfrominformationof general interest (Age, Seniority, genderetc) itwasmostly data concerningtheemployeesCompensationand Performance thatwasincluded. Final data to betreatedincluded1580 recordsand34variablesfromwhichonlyhalfproved to berelevant. Severalalgoritmsweretriedbutthesimplestapproachprovedthemostreliableandproducedthemostinterestingresults. Trainingofthemodelwasdoneon a sampleof 70% ofthe data andtestingontheremaining 30%. Fromseveralinitialteststhosevariablesthatprovedirrelevantweregraduallyexcluded. The final version, whoseresults are presented, isnottheonethatproved more accurateinpredictingturnover, Howeverit´stheonethatincludesvariablesofrelevance for futureactions/decisionsand for thatreasonconsideredthe final one. Inthis case, more thenaccuracyinprediction , thecapacity to turnconclusionsinactionabledecisionswasconsideredrelevant. All final conclusions are translatableinspecificactions/decisionsthatinfluenceturnover. Impactisestimatedinbeingable to reduceturnoverinaprox 50% versus previoussituation. Data mining on Employee turnover data - Paulo Xavier - August 2009

4. DecisionTreeandrules Thefollowingdecisiontreeandnext slide withrules are the final resultfromthisexercise. Theyidentifyareaswerecompensationadjustments are necessary to reassurethereductionofturnover. Nonrewardissues are obviouslyalsorelevant to justifyturnover, butinthis case a verylimitedsetofvariableswasenough to obtaintangibleandusefullresults. Minimumexitprobability = 0 Maximum = 1 Undereachrulenumber, thelikelihoodofexit Ex: Rule127lkelihoodofexit = 0,8148 (for 27 observationsinthetestsample) Data mining on Employee turnover data - Paulo Xavier - August 2009

5. Rules Noticerulesnr 127, 59 and 58 Top Performers (Assess = A and B ) are identifiedandscoredonriskofexit. Theinclusionof Performance assessmentresults, allowstheidentificationofthose cases werespecialmeasures for adequateprotectionofhigherperformersisneeded. Rulenumber: 2 [cover=748 (68%)] range.lev.1.evol>=0.015 Rulenumber: 6 [cover=246 (22%)] range.lev.1.evol< 0.015 Senior_< 1.2 Rulenumber: 127 [cover=27 (2%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age< 37.5 Rng.pos.Level.1< 0.635 Assess=A,B Senior_< 5.955 Rulenumber: 28 [cover=20 (2%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age>=37.5 Assess=C,D,N/A Rulenumber: 125 [cover=15 (1%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age< 37.5 Rng.pos.Level.1< 0.635 Assess=C,D Senior_< 2.675 Rulenumber: 59 [cover=14 (1%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age>=37.5 Assess=A,B Senior_< 15.38 Rulenumber: 30 [cover=10 (1%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age< 37.5 Rng.pos.Level.1>=0.635 Rulenumber: 58 [cover=8 (1%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age>=37.5 Assess=A,B Senior_>=15.38 Rulenumber: 126 [cover=7 (1%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age< 37.5 Rng.pos.Level.1< 0.635 Assess=A,B Senior_>=5.955 Rulenumber: 124 [cover=7 (1%)] range.lev.1.evol< 0.015 Senior_>=1.2 Age< 37.5 Rng.pos.Level.1< 0.635 Assess=C,D Senior_>=2.675 Data mining on Employee turnover data - Paulo Xavier - August 2009