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Mobile advertising: The preclick experience

Native advertising is a specific form of online advertising where ads replicate the look and feel of their serving platform. In such context, providing a good user experience with the served ads is crucial to ensure long-term user engagement. This talk present an overview of work aimed at understanding the user preclick experience of ads and building a learning framework to identify ads with low preclick quality.

Work in collaboration with Ke (Adam) Zhou, Miriam Redi and Andy Haines. An version of this work was presented at WWW Montreal, April 2016.

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Mobile advertising: The preclick experience

  1. 1. Mobile advertising: The pre-click experience Mounia Lalmas Director of Research, Advertising Sciences Work in collaboration with Ke (Adam) Zhou, Miriam Redi and Andy Haines
  2. 2. UK Internet users comScore 2015
  3. 3. Facebook Suggested Post Twitter Promoted Tweet Yahoo Sponsored Content Native advertising on mobile
  4. 4. Why native advertising? Visually Engaging Audience Attention Higher Brand Lift Social Share
  5. 5. Bad ads disengage users D. G. Goldstein, R. P. McAfee, and S. Suri. The cost of annoying ads. WWW 2013. A. Goldfarb and C. Tucker. Online display advertising: Targeting and obtrusiveness. Marketing Science 2011.
  6. 6. User interaction with ads The user spends time on the ad site (post-click) The user converts The user clicks on the ad (click) The user hides the ad (pre-click)
  7. 7. The pre-click ad experience How to measure that an ad is bad? What makes an ad bad? How to predict that an ad is bad? The user hides the ad
  8. 8. Using ad feedbacks as signal of bad ad
  9. 9. Metric of ad pre-click experience Offensive Feedback Rate (OFR): offensive feedback / impression highly offensive ads
  10. 10. CTR vs. Offensiveness (OFR) Bad ads attract clicks (clickbaits?) Weak Correlation CTR/OFR • Spearman: 0.155 • Pearson: -0.043 Quantile analysis • High OFR ⇔ various CTR • Higher CTR ⇔ higher OFR
  11. 11. What makes an ad preferred by users? Methodology ● Pair-wise ad preference + reasons ● Sample ads with various CTR (whole spectrum) ● Comparison within category (vertical)
  12. 12. What makes an ad preferred by users? Underlying preference reasons ● Aesthetic appeal > Product, Brand, Trustworthiness > Clarity > Layout ● Vertical differences: ○ personal finance (clarity) ○ beauty and education (product)
  13. 13. Engineering ad pre-click features brand HISTORICAL FEATURES click-through rate, dwell time, bounce rate … BRAND READABILITY SENTIMENT AESTHETICS VISUALS
  14. 14. Engineering ad pre-click features User reasons Engineerable ad copy features Brand Brand (domain pagerank, search term popularity) Product/Service Content (category, adult detector, image objects) Trustworthiness Psychology (sentiment, psychological incentives) Content Coherence (similarity between title and desc) Language Style (formality, punctuation, superlative) Language Usage (spam, hatespeech, click bait) Clarity Readability (Flesch reading ease, num of complex words) Layout Readability (num of sentences, words) Image Composition (Presence of objects, symmetry) Aesthetic appeal Colors (H.S.V, Contrast, Pleasure) Textures (GLCM properties) Photographic Quality (JPEG quality, sharpness)
  15. 15. Sentiment analysis is the detection of attitudes “enduring, affectively colored beliefs, dispositions towards objects or persons” Sentiment features Types of attitudes ● From a set of types like, love, hate, value, desire, etc. ● Or (more commonly) simple weighted polarity: ○ positive, negative, neutral ○ their strength
  16. 16. Language style features F-score: quantify the level of formality, where formality specifically defined as context-independence Feature Description Punctuation # of different punctuation marks, including exclaim point ‘!’, question mark ‘?’ and quotes Start with number whether text starts with number Start with 5W1H whether text starts with “what”, “where”, “when”, “why”, “who” and “how” Contain superlative whether text contains a superlative adverb or adjective # of slang words number of slang words used # of profane words number of profane words used
  17. 17. Visual features Color Distribution Hue, Saturation, Brightness Rule of Thirds Image Composition and Layout Emotional Response Pleasure, Arousal, Dominance Depth of Field Sharpness contrast between foreground and background Objective Quality Sharpness, Noise, JPEG quality, Contrast Balance, Exposure Balance
  18. 18. Feature correlation with OFR Offensive ads tend to: ● start with number ● maintain lower image JPEG quality ● be less formal ● express negative sentiment in the ad title
  19. 19. Data Around 30K native ads served on iOS and Android Ad feedback data obtained from Yahoo news stream Classifier Logistic Regression as a binary classifier ● positive examples: high quantile of ad OFR ● negative examples: all others Evaluation 5-fold Cross-validation Metric: AUC (Area Under the ROC Curve) Predicting a bad pre-click experience
  20. 20. Model performance Performance per feature: 1. product 2. trustworthiness 3. brand 4. aesthetic appeal 5. clarity 6. layout Model performance (AUC) ● No historical: 0.77 ● Historical: 0.70 ● Both: 0.79
  21. 21. A/B testing online evaluation Baseline system (A): Score(ad) = bid * pCTR Pre-click experience System (B) • Eliminate the ad from ad ranking if P(offensive|ad) > • determined by other constraints (e.g. revenue impact) OFR decreases by -17.6%
  22. 22. Take-away messages How to measure the ad pre-click experience? Offensive feedback rate as a metric Metric Features Model A/B testing What makes an ad good? Aesthetic appeal > Product, Brand, Trustworthiness > Clarity > Layout How to model? Mining ad copy features from ad text, image and advertiser + Logistic regression Does it work? Effective in identifying bad pre-click ads

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