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Mobile advertising: The pre-click experienceMounia LalmasDirector of Research, Advertising Sciences
Work in collaboration with Ke (Adam) Zhou, Miriam Redi and Andy Haines
UK Internet users
comScore 2015
Facebook Suggested Post Twitter Promoted Tweet Yahoo Sponsored Content
Native advertising on mobile
Why native advertising?
Visually Engaging
Audience Attention
Higher Brand Lift
Social Share
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.
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)
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
Using ad feedbacks as signal of bad ad
Metric of ad pre-click experience
Offensive Feedback Rate (OFR): offensive feedback / impression
highly offensive ads
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
What makes an ad preferred by users? Methodology
● Pair-wise ad preference + reasons● Sample ads with various CTR (whole spectrum)● Comparison within category (vertical)
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)
Engineering ad pre-click features
brand
HISTORICAL FEATURES click-through rate, dwell time, bounce rate …
BRAND
READABILITY
SENTIMENT
AESTHETICS
VISUALS
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)
LayoutReadability (num of sentences, words)Image Composition (Presence of objects, symmetry)
Aesthetic appealColors (H.S.V, Contrast, Pleasure)Textures (GLCM properties)Photographic Quality (JPEG quality, sharpness)
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
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
Visual features
Color DistributionHue, Saturation, Brightness
Rule of Thirds Image Composition and Layout
Emotional Response Pleasure, Arousal,
Dominance
Depth of FieldSharpness contrast
between foreground and background
Objective Quality Sharpness, Noise, JPEG
quality, Contrast Balance, Exposure
Balance
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
DataAround 30K native ads served on iOS and AndroidAd feedback data obtained from Yahoo news stream
ClassifierLogistic Regression as a binary classifier● positive examples: high quantile of ad OFR● negative examples: all others
Evaluation5-fold Cross-validation Metric: AUC (Area Under the ROC Curve)
Predicting a bad pre-click experience
Model performance
Performance per feature:1. product 2. trustworthiness 3. brand 4. aesthetic appeal 5. clarity6. layout
Model performance (AUC)● No historical: 0.77● Historical: 0.70● Both: 0.79
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%
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