Matthijs Meire
Ghent University
Customer retentionStatisticsDecision support systemMachine learningSupport vector machineProfitability indexCustomer lifetime valueWord of mouthBusinessArtificial intelligenceMarketingRandom forestEconometric modelBest practiceEconomic indicatorData scienceBrand communityCustomer engagementBusiness-to-businessContent (Freudian dream analysis)Face (sociological concept)Context (language use)Sample selectionSales processCustomer advocacyExperiential learningComputer scienceSocial mediaEvent (computing)Customer intelligenceCustomer to customerSentiment analysisText miningAdded valueAnalyticsPredictive analytics
4Publications
3H-index
95Citations
Publications 3
Newest
#1Matthijs MeireH-Index: 3
#2Kelly HewettH-Index: 9
Last. Dirk Van den PoelH-Index: 52
view all 5 authors...
Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms’ increasing use of customer engagement initiatives, surprisingly few st...
37 CitationsSource
#1Matthijs Meire (UGent: Ghent University)H-Index: 3
#2Michel Ballings (UT: University of Tennessee)H-Index: 11
Last. Dirk Van den Poel (UGent: Ghent University)H-Index: 52
view all 3 authors...
Abstract Business-to-business organizations and scholars are becoming increasingly aware of the possibilities social media and predictive analytics offer. Despite the interest in social media, only few have analyzed the impact of social media on the sales process. This paper takes a quantitative view to examine the added value of Facebook data in the customer acquisition process. In order to do so, we devise a customer acquisition decision support system to qualify prospects as potential custome...
29 CitationsSource
#1Matthijs Meire (UGent: Ghent University)H-Index: 3
#2Michel Ballings (UT: University of Tennessee)H-Index: 11
Last. Dirk Van den Poel (UGent: Ghent University)H-Index: 52
view all 3 authors...
Abstract The purpose of this study is to (1) assess the added value of information available before (i.e., leading) and after (i.e., lagging) the focal post's creation time in sentiment analysis of Facebook posts, (2) determine which predictors are most important, and (3) investigate the relationship between top predictors and sentiment. We build a sentiment prediction model, including leading information, lagging information, and traditional post variables. We benchmark Random Forest and Suppor...
31 CitationsSource