Doctoral dissertation: 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 impact user interface has on performance, user engagement and user experience. The focus is on enclosed user experience in mobile content, such as ads.


mobile advertising rich content user interfaces user engagement user experience marketing

Problem description

The number of mobile devices, such as smartphones, tablets, wearables, e-readers are on the rise, and so are their capabilities (compass, camera, gyroscope etc.), enabling richer activities and interactions.[9] As a result, we have encountered a shift in ergonomics of software design and have been introduced the new ways about how human interact with devices using variety of gestures (swipe, tap, drag, pinch, shake, etc.), multi-touch inputs and also using voice as an input. The challenge in to design the most suitable, intuitive user interface and interaction flow for the specific task.

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

User engagement, among other factors, such as accessibility, performance, usability, human factors and design, has a big impact on user experience for interactions with the system.

Measuring these is rather complex, it gets complicated at the mere beginning, since the accuracy of modern measurement tools for visitor data analysis seems to differ [3]. And these tools only tend to offer raw data in numbers, letting the owners themselves to interpret how users engaged with the content and how good was their experience.

Even bigger concern is measuring marketing attribution online [4][5], since the marketeers tend to advertise cross-channel and users also use multiple devices, which makes it unclear wether users behaviour was driven by an ad and if so, to what extent? By user behaviour we mean either action that was performed online (i.e. user subscribed/booked a test drive) or offline (purchased an item in a store), which makes it completely unclear wether the later was influenced by an online ad or not, making it hard to measure the return on investment (ROI).[6]

Doctoral dissertation will be focused on evaluation of user engagement and user experience for different types of user interfaces and interactions in mobile content/ads and their possible impact on ROI. As a byproduct we will define guidelines for production of more efficient content/ads.

Different research methods will be used, as described below.

Related work:

It is known that context [19] 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.[18] 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 ignored or unnoticed (so called banner blindness) [21] [22] [23] [45], are easily forgotten [24] and can in some cases even cause damage to the advertised brand or placement, where they appear.[13][25]

There has been studies on optimising constrained budget spend in 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 per 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 later measurements are often used to compute expected revenue, due to different business models, such as cost per impression or cost per click, etc.

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

  • trust and credibility; familiarity, visual design, aesthetics, shape, content and interaction has great impact on users credibility [43];
  • visual complexity; Youtube research lab for user experience figured out that users prefer simple over complex designs and that they prefer designs they are already familiar with [7];
  • usability and accessibility;[32]
  • aesthetics; [33] [34] [35]
  • content type; researches show that video content greatly impacts users dwell time and in case of product videos, increases confidence in online purchase decisions;[40]
  • emotions.[18]

Numerous studies claims that better technical solutions, as load times, speed and browser performance improves user experience, reduce costs and also increases revenue. [36] [37] [38]

Perhaps one of the most successful and widely known researches made in the field of user interfaces and their impact on interaction, was the Amazon’s online purchase process usability research, where the slight adjustments (commonly referfed to as "The $300 Million Button") made an enormous impact on revenue, increasing it by 45 %.[41] [42]

Research methods and hypothesis

Research methods

HTML5 web standard has been adopted as the de-facto standard for interactive rich media mobile advertising (ads). Its openness makes it easier to extract certain multimedia features from the content, such as text, audio, video, animations, images, buttons, as well as other metadata (aspect-ratio, format, banner size in pixels and kilobytes, etc) and limited information about the user (device type (tablet, smartphone or desktop), platform version and their network information). It also allows the access to sensors and other device capabilities [9]. Image features, such as brightness, saturation, colorfulness, contrast, naturalness and hue, can also be extracted from banner screenshots.

Analysis will be done on real data 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 as a subset of metrics that enables us to better understand user interaction and possibly experience, are listed below:

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

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

  • analysis of patterns that are present in mobile advertisement (user interfaces, their elements and types of interaction; possible improvements in terms of parameterisation, standardisation and evaluation of user experience);
  • analysis of impact of performance (network speed, latency, response times) of served ads;
  • manual content segmentation and automatic data processing (statistical data evaluation);
  • experiment design and analysis, i.e. A/B testing, multivariate statistics,
  • the use of advanced techniques to detect correspondences between gathered data (data mining),
  • machine learning and image processing.


  1. H1. Diverse user interfaces and interactions has different, measurable impact on user experience in 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 experience (in mobile advertisement) based on user's interaction with different user interfaces;
  3. H3. Based on defined metrics (see H2), we can quantitatively evaluate user's experience 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 experience, preferably task and content agnostic. The goals also correspond well with industry needs:

  • model for evaluation of user experience for different types of user interfaces and interactions with mobile ads;
  • improved guidelines for interactive mobile content;
  • understanding the impact of user experience of mobile ads on conversion and marketing attribution.

The research findings will also contribute to faster 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 experience in mobile interaction, with the emphasis on mobile advertising. There are plenty of advertising techniques that users hate [13], all of which we must avoid to prevent damage to advertised brand or placement, where ads appear.

PhD candidate:
Robert Sedovšek, univ. dipl. inž. graf. tehnol.
doc. dr. Aleš Hladnik


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