Lessons Learned From Analyzing 1,000,000,000 Company Mentions

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Lessons Learned From Analyzing 1,000,000,000 Company Mentions

  1. 1. @mention/mentionapp 17.61% positivementions 5.81% negativementions 76.58% neutralmentions Ahugenumberofmentionsareactuallyprettyneutral.Peopleseekingassistancewitha service,askingquestions,orforadvice.Mostaredaytodaycommunications,notonly praiseorcriticism-aclearsignthatcustomerservicehasgonesocial. "ThePac-ManEffect" 58 majorlanguagesspoken English French Spanish German Portuguese 64.39% 16.20% 11.27% 2.84% 1.24% TOPLANGUAGES 66.67% Twitterrocks.Butyoucanfindyourownchannel. Trymixingafewothersourcestoreachanaudiencethatmostaren'tmessagingto. WHERETHEYCAMEFROM SUNDAY MONDAY TUESDAY WEDNESDAY 11.36% 14.38% 15.68% 15.52% 15.78% 15.06% 12.22% THURSDAY FRIDAY SATURDAY DAYSWITHMOSTMENTIONS It’simportanttogetbacktothem quickly,butdon’tonlytalktothem. 91% ofmentionscomefrom people withfewerthan500followers. 8.58%ofmentionscomefrom influencers INFLUENCERS STARTMONITORINGYOURCOMPANY’SMENTIONSTODAYWITH Thursdaysseethemostmentions. Don’tmissoutoncommunicatingSaturdaysandSundays though.Schedulepoststogetpeopletomentionyouwhenmost othercompaniesaren’tcommunicating. 1,000,000,000 LessonsLearnedFromAnalyzing fromnearly alerts.200,000 companymentionsdeliveredinthelast2years Don’tunderestimatetheimportanceofnon-Englishmentions. 36% areindifferentlanguages.It’simportanttohaveastrategy forthesetoo.

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