mobile advertising: the preclick experience

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