How Social Media Advertising and Repetitive Marketing Messages Affect the Online Purhasing Behavior?

In the past decade social media advertising has disrupted the marketing and advertising totally. As social media advertising platforms such as Facebook offer easy, effective and relatively cheap services, they have enabled and encouraged the rise of new kinds of companies operating solely online by tapping into the potential of easily reaching the audience and attracting them to their webstores. This has made it possible for up and coming companies with less well-known brands to attract customers and build up their brands. Naturally as the marketing field has been disrupted by the social media advertising, the traditional rules and guidelines of marketing need to be re-evaluated requiring academic research to understand how social media marketing and people’s behavior online differs from more traditional channels. Additionally, the ability to effectively personalize the marketing messages for different audience groups for example based on the previous engagement or other online behavior brings up another layer to the phenomenon. For the purpose of this study, the audience visiting the webstore of the case company is divided based on their previous brand engagement to three groups; fresh audience with no previous engagement, retargeted audience with some engagement for example on social media platforms or website visits and returning audience with previous webstore visits. Fresh and retargeted audience groups ended up to the webstore via Facebook advertisements while returning audience returned to the site without the need of extra marketing activities. With t-tests and ANOVA it was possible to establish differences in behavior between these groups and based on that, regression models were created to further understand the drivers affecting conversion rate and revenue per user. What comes to the reactions to the Facebook advertisements, people with previous brand engagement, i.e. retargeted audience was much more likely to enter the webstore by clicking the advertisement than fresh audience. Additionally, retargeted audience has higher conversion rate and higher revenue per user values as well. As previous research has also found, previous engagement with the brand is indeed the strongest indicator for purchase intention. In addition to that, returning audience i.e. the people who return to the website on their own have the highest conversion rates and revenue per user values out of the audience groups studied. It is likely that this can be explained with the stronger firm-consumer relationship, making this group the most loyal and profitable customers. For the fresh and retargeted audience groups, time spent on the website has positive affect on both conversion rate and revenue per user. So, it seems that when previous engagement with the brand is lower, clicking the Facebook advertisement and spending more time on the website builds up the firm-customer relationship and improves purchase intention

Saliency-based image enhancement

Ph.DDOCTOR OF PHILOSOPH

Arbitrary view action recognition via transfer dictionary learning on synthetic training data

Human action recognition is an important problem in robotic vision. Traditional recognition algorithms usually require the knowledge of view angle, which is not always available in robotic applications such as active vision. In this paper, we propose a new framework to recognize actions with arbitrary views. A main feature of our algorithm is that view-invariance is learned from synthetic 2D and 3D training data using transfer dictionary learning. This guarantees the availability of training data, and removes the hassle of obtaining real world video in specific viewing angles. The result of the process is a dictionary that can project real world 2D video into a view-invariant sparse representation. This facilitates the training of a view-invariant classifier. Experimental results on the IXMAS and N-UCLA datasets show significant improvements over existing algorithms