Developing predictive models for identifying dormant users and optimizing re-engagement strategies in digital communities
Abstract
User engagement is critical to online community success and sustainability in an ever-changing digital landscape. However, the active user base tends to shrink, as more users remain inactive or dormant after their initial log-in. This paper thus focuses on developing predictive models of detecting sleeper users in digital communities and identifies optimized re-engagement strategies that mitigate user churn. Leverage machine-learning techniques such as random forests, support vector machines (SVM), and logistic regression, among others, to predict dormancy by users at different points of interaction patterns, behavior, and engagement metrics. The paper will also elaborate on the personalization of re-engagement strategies, including targeted notifications, incentive programs, and personalized content for enhancing user retention. This work in this domain should continue by integrating data across various channels, conducting real-time predictive modelling about external factors influencing these communities, and refining such models and strategies.
How to Cite This Article
Preetham Reddy Kaukuntla (2022). Developing predictive models for identifying dormant users and optimizing re-engagement strategies in digital communities . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 3(5), 598-603. DOI: https://doi.org/10.54660/IJMRGE.2022.3.5.598-603
References
- 2. Platform-Specific Factors Platformdesignanduserexperience(UX\playacriticalroleinmaintaininguserengagement. Featuressuchaseaseofnavigation, contentrelevance, andpersonalizationcaninfluenceuserretention. Whenplatformsfailtoproviderelevantcontentorfailtoevolveinlinewithuserpreferences, theyrisklosingactiveusers. Furthermore, thepresenceofnotifications, rewards, orincentivescaneitherdriveordeterengagement. Afailuretoincorporatefeaturesthatfocusonengagementcanresultinahigherdormancyrate.
- 4. External Factors Externaleventslikechangesinthegeneraldigitalenvironmentorcompetitivelandscapewillalsoaffectuseractivity. Usersmaybeincreasinglyattractedtootherplatformsbynewerfeaturespresentedbynewlyestablishedplatforms. Usersmayalsostarttogiveprioritytoout-of-Interneteconomicorsocialactivitiesleadingtolowonlineuserengagement. D. Re-engagement Strategies Basedon Predictive Models Re-engagementstrategiestrytoreactivatedormantusersbyencouragingareturntoaplatformoracommunity. Themaingoalsarepreventingthelossofusersandreducingchurn, whichiscriticalformaintaininglong-termsustainabilityinthedigitalplatform. Predictivemodelscanhelpidentifyanoptimaltimeandapproachwhenre-engagingusersbypersonalizingstrategiesandmakingsuretheyareeffective. Manystudieshavecometoconfirmthatproperactivationofuser-specificre-engagementeffortsenhancestheprobabilityofidleusersreturningtoaplatform. Onespecificexamplehereisutilizingtargetedpushnotificationsorsendingemailcampaignsfromplatformsalikeane-commercewebsiteormobileapplication. International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com600 Fig1: Predictive Model
- 5. Methodology Themethodologyforthisresearchpaperfocusesondevelopingpredictivemodelstoidentifydormantusersindigitalcommunitiesandoptimizingre-engagementstrategies. Theprocessconsistsofseveralphases, includingdatacollection, modeldevelopment, strategydesign, andevaluation. Thissectionoutlinesthespecificmethodsandapproachesusedtoaddresstheresearchobjectivesandquestions.
- 6. Datacollection
- 1. User Activity Data Thefrequencyoflog-ins, sessiontime, interactionsthroughlikes, shares, comments, andconsumptionofcontent-allthesehelptofindoutthelevelofusers'engagementovertime.
- 2. Demographic Information Age, location, deviceusage, andmembershipperiodcanserveascontextualinformationtousers'profilesandtheiroverallengagementpatterns. Itwillhelptoidentifygroupslikelytogodormant.
- 3. Transaction History Forservicesorapplicationsthatallowtransactiondata, suchase-commerceorsubscription-basedservices, thiscanincludepurchasedata, productviews, andcartabandonmentratestorefinetheuserengagementanalysis. B. Feature Engineering Severalfeatureswillbegeneratedfromthedatagatheredforbettermodelperformanceinthisstudy:
- 1. In Engagement Metrics Featureslikeloginsinthelastmonth, averagetimespentpersession, andinteractionrate(comments, likes, etc.\willbecalculatedtomeasurethelevelofengagementofusers.
- 2. Dormancy Indicator Abinaryvariable(1=dormant,0=active\willbecreatedbasedontheinactivityperiodauserwhohasn'tloggedinformorethan30dayswillbelabeledasdormant.
- 3. Recency, Frequency, and Monetary(RFM\Features Forplatformswithtransactionaldata, RFManalysiswillbeusedtoevaluatehowrecentlyandfrequentlyusershaveinteractedwiththeplatform, aswellastheirmonetarycontribution(ifapplicable\. Thiscanhelpidentifyuserswhohavedriftedawayfromtheplatformdespiteearlierhighengagement. Fig2: RFMAnalysis
- 4. Behavioral Trends Behavioraldatatrendssuchastherateofchangeinuseractivityovertime(forexample, decreasingactivityoverthepastthreemonths\willbeconstructedtofocusonuserswhoaremostlikelytogodormant.
- 7. Analysis Theanalysispartofthestudylooksatthefindingsofthe International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com601predictivemodelsusedtodetectdormantuserswithindigitalcommunitiesandtheperformanceofthere-engagementtacticsthathavebeenrefinedbasedonthesemodels. Itisherethattheresearchinterpretstheoutcomefromthemodelassessmentandthere-engagementactivityforinsightsintothesuccessofthepredictivemodelingprocessandfurtherimpactonuserengagement. Predictive Model Results Themodelsbuiltinthisworkattemptedtoclassifyusersasdormantoractive, usingamultitudeoffeaturesfromtheirhistoricalbehavioraldata. Thesubsequentdiscussionfocusesontheoutcomeswhenmachinelearningmethodssuchaslogisticregression, randomforests, supportvectormachines(SVM\, and K-meansclusteringwereusedtoclassifyusersbasedontheirprobabilityofbeingdormant.
- 8. Re-engagement Strategies Oncethepredictivemodelsweredevelopedandvalidated, thenextstepwastoimplementre-engagementstrategies. Thesestrategiesweretailoredtothespecificcharacteristicsofusersidentifiedasdormantbythemodels. Below, weanalyzetheeffectivenessofvariousre-engagementapproaches.
- 9. Targeted Notifications Personalizednotificationsweresenttouserswhowerepredictedtobeatriskofdormancy. Thesenotificationsincludedremindersofnewcontent, updatesontheplatform, oractivitieswhichusershadpreviouslyengagedwith.9.1 Success Targetednotificationsresultedina30%higherre-engagementratecomparedtogeneralnotificationsthataredistributedtoallusers. Personalizedcontentthatwastargetedtouserinterestsandbehaviorhelpedcaptureattentionandre-engageuserswhowerebeginningtoshowsignsofdisengagement.9.2 Challenges Onedifficultywaswithtiming. Usersidentifiedasinactiveintheshortterm(e. g., withinthepast30days\respondedmorepositivelytonotificationsthanthoseinactiveforlongerperiods. Notificationssenttouserswithlongperiodsofdormancy(e. g.,60+days\hadfarfewerresponses.
- 10. Results Theresultssectionofthisstudydealswiththeoutputsresultingfromthepredictivemodelscreatedtodetectinactiveusersinvirtualcommunitiesandthere-engagementstrategyappliedfollowingthemodels. Fromtheevaluationofthemodelsandthere-engagementprocesses, insightsaregatheredtodeterminehowwellthemodelsperformedandhoweffectivethestrategieswereinboostingre-engagementamongtheusers. Predictive Model Outcomes Thispaperhasdevelopedpredictivemodelsfortheearlyidentificationofdormantusersfromuseractivitydata. Variouskeymetricslikeaccuracy, precision, recall, F1-score, and Area Underthe ROCCurvewereusedtoassesstheperformanceofthemodels. Ithelpeddeterminewhetherthemodelscouldeffectivelydistinguishdormantusersfromactiveusers. Performanceof Support Vector Machines(SVM\The SVMmodelpresentedasimilarperformanceascomparedtotherandomforestmodel, with83%accuracy. Still, themajorstrengthof SVMwashowitcouldbeefficientconcerninghigh-dimensionalspace. Accuracy:83%Precision:80%Recall:85%F1-Score:0.82AUC:0.87 Whereas SVMprovedtobeafairlysolidperformer, itproducedlessprecisioncomparedtothatachievedwiththerandomforestsapproach. Itcouldindicatethatitmightbetooaccuratetocaptureonlythedormantuserswhileprobablyincorrectlylabelingalotofactiveusersasinactiveusers(falsepositives\. Yetitservedwellasaclassifieringeneralforthisparticulardataset. Fig3: Support Vector Machines Performanceof K-Means Clustering K-meansclusteringwasusedtoclustertheusersintodifferentgroupsbasedontheirbehaviorwithoutusinglabeleddata. Althoughnotadirectpredictorofdormancy, itdidprovidevaluableinsightsintouserbehaviorpatterns, anditrevealedseveraldistinctusersegments, someofwhich International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com602wereathighriskofdormancy.
- 1. Cluster Segmentation Userswhodisplayeddeclininginteractionovertimewereclassifiedashigh-riskclustersindormancy. K-meansrevealedthatuserswhoexperiencedsuddendropsinthefrequencyofinteractionorengagementincontentweremorepronetobecomingdormant.
- 2. Segmentation Insight About20%ofusersintheplatformwereidentifiedasathighrisk, involvingagroupofuserswhohadshownsignificantchangesinbehaviorbuthadnotyetreachedfulldormancy. Thisearlierwarninghelpedtheplatformtotargetre-engagementeffortsbeforefulldisengagement. Futurework Thereareseveralavenuesforfutureresearchanddevelopmentintheareaofidentifyingdormantusersandoptimizingre-engagementstrategies: Incorporationof Multi-Channel Data Inthefuture, modelsmayconsideraggregatingdataacrosschannelsfromemailtosocialmedia, andmobileapplicationsandusesuchinformationtohelpprovidealargerviewofuserengagementthatinformsbetterpredictionsandtargetingfordormantusersacrosschannels. Real-Time Predictive Models Whilethisstudyusedhistoricaluserdatatoidentifydormantusers, real-timepredictivemodelscouldoffermoretimelyinterventions. Bycontinuouslymonitoringuseractivityandadjustingpredictionsonthefly, platformscouldintervenemorequicklyandpreventusersfrombecomingdormantinthefirstplace. Incorporating External Factors Futureresearchcouldexplorehowexternalfactorslikeglobalevents, marketingcampaigns, orchangesinuserdemographicsaffectengagementpatterns. Integratingthesevariablesintopredictivemodelscouldenhancetheirrobustnessandimprovetheaccuracyofre-engagementstrategies. Behavioral Segmentation Furtherworkisneededtoimprovebehavioralsegmentationmodelsthatcandynamicallyadjusttousers'changingpreferencesovertime. Forinstance, incorporatingdeeplearningmodelsorreinforcementlearningtechniquescouldhelppredictlong-termuserbehaviorandtailorstrategiesaccordingly. Gamificationand Engagement Theory Moreresearchisneededtobetterunderstandtherelationshipbetweengamificationanduserengagementindifferenttypesofdigitalcommunities. Byexploringhowdifferentgamemechanics(e. g., challenges, rewards, socialinteraction\influencere-engagement, futurestudiescouldrefinegamificationstrategiesfordifferentusersegments. Conclusion Thestudyaimedtodeveloppredictivemodelsforidentifyingdormantusersindigitalcommunitiesandoptimizingre-engagementstrategiestoenhanceuserretention. Throughtheapplicationofmachinelearningtechniques, includingrandomforests, supportvectormachines(SVM\, andlogisticregression, wesuccessfullydemonstratedthatuserengagementpatternscanbeeffectivelymodeledtopredictdormancy. Theresultsdemonstratethatthemodelcanbeusefulinveryearlyidentificationofat-riskusers, helpingdigitalcommunitiestointerveneinanearlyinterventionmode. Personalizedre-engagementstrategies, liketargetednotifications, incentiveprograms, andpersonalizedcontentrecommendations, werecitedascrucialcomponentsinbetteringuserretentionratesbythestudy. Thisstageofresearchdevelopmentofvariouspredictivemodelsachievedmixedperformances, withthebestmodelperformancebeingshownbythoseusingrandomforestsand SVMsinclassifyingmostdormantusers. Theconclusionindicatedthathighrecallandprecisionperformanceisstillverydifficultformostplatformsthathaveeitherlargeorcomplicatedusersetstoachieve. Futureworkinvolvesafewkeyareasofinvestigation. First, incorporatingmulti-channeldata(forexample, socialmediainteractions, emailengagement, andmobileappusage\wouldgiveamoreholisticviewofuserbehavior, therebyimprovingtheaccuracyofpredictivemodels. Real-timepredictivemodelswouldallowforinterventionopportunitiesbeforeuserscompletelydisengage. Further, moreexternalinfluences, suchasmarketingcampaignsandseasonaltrends, canbeintegratedtocapturealargerrangeofuserbehaviorinfluences. Ultimately, thesynergyofrobustpredictiveanalyticsandtargetedengagementstrategiesissettodrivelong-termuserretentionandsatisfactionwithindigitalcommunities. References
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