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Evaluation of user practice te mobile advertising

Evaluation of user experience in mobile advertising

The proposed research fits into the scientific field of graphics and information technology, more specifically to the field of interactive media and mobile advertising.

TL’DR: The idea is to effectively measure the influence user interface has on spectacle, user engagement and user practice. The concentrate is on enclosed user practice ter mobile content, such spil ads.

Problem description

The number of mobile devices, such spil smartphones, tablets, wearables, e-readers are on the rise, and so are their capabilities (compass, camera, gyroscope etc.), enabling richer activities and interactions. [9] Spil a result, wij have encountered a shift ter ergonomics of software vormgeving and have bot introduced the fresh ways about how human interact with devices using multiplicity of gestures (swipe, tapkast, haul, pinch, wiggle, etc.), multi-touch inputs and also using voice spil an input. The challenge te to vormgeving the most suitable, intuitive user interface and interaction flow for the specific task.

There is no clear methodology or known technics for comparison and measurement of voorstelling of different user interface elements for interaction with the interactive content (ads, applications, etc.). For online advertising there is a need for ordinary, clear and universal definition for metrics, such spil user engagement, user satisfaction and holistic user practice for user interaction with the device while performing tasks spil search for information, social interactions (communication, commenting, sharing etc.), input forms, playing games or interacting with an ad.

User engagement, among other factors, such spil accessibility, spectacle, usability, human factors and vormgeving, has a big influence on user practice for interactions with the system.

Measuring thesis is rather ingewikkeld, it gets complicated at the mere beginning, since the accuracy of modern measurement contraptions for visitor gegevens analysis seems to differ [Three] . And thesis contraptions only tend to suggest raw gegevens te numbers, letting the owners themselves to interpret how users engaged with the content and how good wasgoed their practice.

Even thicker concern is measuring marketing attribution online [Four] [Five] , since the marketeers tend to advertise cross-channel and users also use numerous devices, which makes it unclear wether users behaviour wasgoed driven by an ad and if so, to what extent? By user behaviour wij mean either act that wasgoed performed online (i.e. user subscribed/booked a test drive) or offline (purchased an voorwerp ter a store), which makes it totally unclear wether the straks wasgoed influenced by an online ad or not, making it hard to measure the terugwedstrijd on investment (ROI). [6]

Doctoral dissertation will be focused on evaluation of user engagement and user practice for different types of user interfaces and interactions ter mobile content/ads and their possible influence on ROI. Spil a byproduct wij will define guidelines for production of more efficient content/ads.

Different research methods will be used, spil described below.

Related work:

It is known that setting [Nineteen] greatly impacts information consumption and so does situation [16] , location and time. [17] Stress and feelings also influences how user perceive working with a device. [Legal] With HTML5 standard it is possible to access device sensors, use their capabilities [9] and gather lots of useful information about our user, which can contribute to form better, more advanced and user specific interfaces.

Some studies has shown that ads are often automatically overlooked or unnoticed (so called banner blindness) [21] [22] [23] [45] , are lightly forgotten [24] and can ter some cases even cause harm to the advertised brand or placement, where they show up. [13] [25]

There has bot studies on optimising constrained budget spend te search advertising [26] , measuring ad effectiveness using geo experiments [27] [28] , both held at Google. Researchers at Yahoo, Celtra and Microsoft are testing different methods on how to effectively predict number of ad clicks vanaf impressions (click-through rate, CTR), based on ad’s multimedia features. [29] [31] This can be odd, since the CTR is considered reasonably high, when it is around 1 %. The straks measurements are often used to compute expected revenue, due to different business models, such spil cost vanaf impression or cost vanaf click, etc.

User practice when interacting with (mobile) systems tends to be best when it is guided, emotional and clearly encourages user to perform certain tasks. User practice is influenced by the following factors:

  • trust and credibility, familiarity, visual vormgeving, aesthetics, form, content and interaction has good influence on users credibility [43] ,
  • visual complexity, Youtube research laboratorium for user practice figured out that users choose ordinary overheen complicated designs and that they choose designs they are already familiar with [7] ,
  • usability and accessibility, [32]
  • aesthetics, [33][34][35]
  • content type, researches vertoning that movie content greatly impacts users dwell time and te case of product movies, increases confidence ter online purchase decisions, [40]
  • emotions. [Legal]

Numerous studies claims that better technical solutions, spil flow times, speed and browser voorstelling improves user practice, reduce costs and also increases revenue. [36] [37] [38]

Perhaps one of the most successful and widely known researches made te the field of user interfaces and their influence on interaction, wasgoed the Amazon’s online purchase process usability research, where the slight adjustments (commonly referfed to spil “The $300 Million Button”) made an enormous influence on revenue, enhancing it by 45 %. [41] [42]

Research methods and hypothesis

Research methods

HTML5 web standard has bot adopted spil the de-facto standard for interactive rich media mobile advertising (ads). Its openness makes it lighter to samenvatting certain multimedia features from the content, such spil text, audio, movie, animations, pics, buttons, spil well spil other metadata (aspect-ratio, format, banner size te pixels and kilobytes, etc) and limited information about the user (device type (tablet, smartphone or desktop), toneelpodium version and their network information). It also permits the access to sensors and other device capabilities [9] . Picture features, such spil brightness, saturation, colorfulness, tegenstelling, naturalness and hue, can also be extracted from banner screenshots.

Analysis will be done on real gegevens about user behaviour on mobile ads based on real marketing campaigns and also on dummy ads for better comparison. User testings will also be conducted, if necessary.

Some metrics than can be used and categorised spil a subset of metrics that enables us to better understand user interaction and possibly practice, are listed below:

  • number of impressions and clicks,
  • time spent for interaction, this can be misleading since time spent can have different meaning. With purpose specific content (i.e. weather app), lesser time spent with an app is better then with entertainment content (i.e. games, movies, photo galleries, animations, etc.), where longer dwell times are desired aim. This vereiste be taken into account.
  • time needed to achieve certain aim or to finish a task,
  • number of components user interacted with: read articles/comments, movie views, photo views, product views.
  • screen views, unique views,
  • the percentage of uitgang from a screen,
  • behaviour flow,
  • content specific deeds, spil content shares/recommendation, purchase, subscription etc.

Spil part of the thesis, the following research methods can or will be used:

  • analysis of patterns that are present te mobile advertisement (user interfaces, their elements and types of interaction, possible improvements te terms of parameterisation, standardisation and evaluation of user practice),
  • analysis of influence of spectacle (network speed, latency, response times) of served ads,
  • manual content segmentation and automatic gegevens processing (statistical gegevens evaluation),
  • proef vormgeving and analysis, i.e. A/B testing, multivariate statistics,
  • the use of advanced mechanisms to detect correspondences inbetween gathered gegevens (gegevens mining),
  • machine learning and photo processing.

Hypothesis

  1. H1. Diverse user interfaces and interactions has different, measurable influence on user practice ter interactive mobile display ads
  2. H2. Based on test results or analysis of a large dataset of user interactions with mobile ads, it is possible to define metrics for user practice (te mobile advertisement) based on user’s interaction with different user interfaces,
  3. H3. Based on defined metrics (see H2), wij can quantitatively evaluate user’s practice and engagement for mobile advertisement,
  4. H4. It is possible to predict user engagement for interaction with mobile content.

Expected contribution to the science

Broader goals of research are effective understanding of users and their behaviour when interacting with mobile content, evaluation and improvement of their practice, preferably task and content agnostic. The goals also correspond well with industry needs:

  • prototype for evaluation of user practice for different types of user interfaces and interactions with mobile ads,
  • improved guidelines for interactive mobile content,
  • understanding the influence of user practice of mobile ads on conversion and marketing attribution.

The research findings will also contribute to swifter perception and understandings of the content, improve task or problem solving, reduce users frustrations and discomforts when interacting with the mobile devices and overall improve user practice te mobile interaction, with the emphasis on mobile advertising. There are slew of advertising technologies that users hate [13] , all of which wij vereiste avoid to prevent harm to advertised brand or placement, where ads show up.

PhD candidate: Robert Sedovsek, univ. dipl. inz. graf. tehnol. Mentor: doc. dr. Ales Hladnik

Literature

Presently developing Turtl. Formerly worked at Celtra developing application’s front-end. Earned PhD te June 2016, thesis titled “Evaluation of user practice te mobile advertising”.

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