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Since the creation of advertising in general,different media channels have been in use to deliver advertising messages.One of the most advanced media channels for video advertising is You Tube – the social media platform which is the second most-requested website in the world.Therefore,understanding how to run efficient ads on You Tube is on the fierce of modern marketing as a business and scientific domain.Hence,this thesis examines the efficiency of You Tube video ads as a marketing media instrument.It considers key characteristics of video ads and their influence on users’ attitude towards video ads.Furthermore,this thesis is among first to uncover the relationships between ad characteristics,user experience and attitude towards video ads.Basing on theoretical background and models of Advertising value,primarily Ducoffe model and mediation models,the research model was created.Research employs cross sectional survey analysis.Data was refined and examined,explanatory factor analysis and confirmatory factor analysis were implemented.Moreover,Structural Equation Modelling technique was implemented to prove hypotheses and show mediation in the model.Conclusions to this thesis outline three factors that affect attitude towards video advertisement on You Tube,being informativeness,entertainment and obtrusiveness.Moreover,this thesis has proved that it is User Satisfaction that mediates the efficiency of You Tube video ads（represented by attitude towards video ads）,while direct impact of video characteristics remained still valid.User Engagement was not proved to be the mediator and did not affect attitude towards video ads at all.We defined a range of recommendations for academics and marketers to sharpen their expertise and mastery of You Tube video marketing,outlining our recommendations by each factor that has any influence on attitude towards video ads.Furthermore,at the end of the study the new research framework was proposed for further studies in the area.That framework contains SEM model that can be used for further studies with larger data sets.