Advertising & Recommendation

Dou Shen Baidu

@沈抖 (weibo)

Outline

Overview

Evolution of Display Ads

Metrics

User & Data

Targeting & Recommendation

Future

Overview

Definition

Advertising

A form of communication used to encourage or persuade an audience to continue or take some new actions.

Computational Advertising

A principled way to find the "best match" between a user in a context and a suitable advertisement.

Recommendation

A particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences.

Ad examples

How about these?

Major players

Players Inventory

• Users / Searcher

Supply

• Sellers / Publishers / Search Engines

Demand

• Buyers / Advertisers / Marketers

Campaign Types Branding (awareness and message reinforcement)

Direct response (ROI/acquisition-focused)

Engine

Traffic

Ad User

Pricing models

CPM = cost per thousand impressions

CPC = cost per click

CPT/CPA = cost per transaction/action

Advertising revenue market share by media

Historical annual revenue trends (U.S.)

Advertising format share

Internet ad revenues by industry category

Internet ad revenues by pricing model

Evolution

Changes

Ad format

Interactive Ads

Display Ads

Text Ads

PC Mobile

Search Web page Game Video App Email Social

Traffic

The evolution of display advertising - IAB

Video from IAB

Direct buying & selling

For advertisers They can only buy media, not audience

They pay the same negotiated CPM for all traffic

Manually

For publishers They charge usually higher for direct buying

But it is hard for them to sell all their inventory

Advertisers & Publisher: negotiate directly for certain amount of impressions at specific slots

Publisher: when an impression starts, retrieve the ad per agreement and display it

Ad network

Ad networks: collect inventory from publishers

Advertisers: buy inventory from ad networks, bidding on segments according to certain criteria

Ad networks: when an impression starts, retrieve all the ads satisfying the given criteria

Ad networks: rank the candidates by GSP: p_i * b_i

Ad networks: once an ad is clicked, charge the advertiser by GSP: p_{i+1} * b_{i+1} / p_i

Ad network

For publishers Sell the inventory to one or more ad networks

Split the revenue with ad networks

For advertisers Buy inventory by specifying segments per media attributes or user attributes

Bid on segment level

For ad networks Create segments, with right granularity, for advertisers’bidding,

Rank ads by maximizing CPM

Consider the balance between publisher, advertiser and users

Ad exchange

Advertisers: talk to DSP for specific campaign goals, by CPM, CPC, or CPA

Exchange: when an impression starts, send bidding requests to proper DSPs

DSP: convert the agreement with advertisers to CPM and send back the bid response

Exchange: collect the bid responses, pick up the highest bid and charge by second-price.

DSP: charge the advertisers once the user take actions after the ad shows according to the agreement

Ad exchange

Goal

Improve the efficiency of the ecosystem

Challenges

System Performance

• High performance and scalable platform to support billions of impressions

Auction Design

• Appropriate ranking, pricing strategies and incentive for all players – Advertisers to bid according to true value;

– Publishers monetize more traffic in spot market

Ad exchange

Challenges:

High efficient Traffic quality control

• Combat with click frauds/spam and invalid traffic.

Expressive bidding language for DSPs

• Cookie mapping, Web page content analysis, audience behavior analysis.

• Providing tools & reports to help DSPs optimize their bidding.

Callout Control

• QPS Control according to DSPs request and performance

Demand side platform (DSP)

Goal: Help advertisers bid on Right Audiences in Right Time and Right Context with Right Price.

Maximization ROI with appropriate number of conversions.

Challenges: Pre-Targeting & Traffic Pre-Filter

Generate audience profiles and segmentation

• Collect and integrate audience data, both first-party and third-party

• Put users into proper segments

Demand side platform (DSP)

Challenges: Performance(conversion rate) estimation.

• Conversion: purchase, download, register or any actions defined by advertisers.

• Hard to collect ground-truth conversion for all of the advertisers.

• CvR Proxy: Visit Depth, Dwelling Time

Adaptive bid optimization.

• Estimate win-rate curve: f(bid_price, bid_request_information)

• Tuning bid according to relative performance

Budget allocation

• Budget optimization between different channels (PC, mobiles, apps)

Supply side platform (SSP)

Goal: Yield Optimization: maximize publishers’ revenue

• Guaranteed Delivery: pre-saled contract with specific advertisers • Spot market: send traffic to ad exchanges

More control to retain users

• Tradeoff between user experience and revenue • Brand safety: prevent inappropriate ads

Challenges: Determining whether reserved for GD contract per impression.

Set appropriate minimum price:

• spot market’s minimum price depend on GD contracts price and penalty. Choose the right channel for impressions

Growth of RTB

From network to exchange

In network, advertisers bid on segments ( a group of impressions)

In exchange, DSPs on behalf of advertisers, bid on each single impression, which makes more sense since the value of each impression varies

Media buy Audience buy Manual buy Programmatic buy

Landscape of Display Ads

Metrics

Key Metrics

Quantity Impressions, clicks, conversions: CTR, CVR

Unit Payoff Price for publishers

Value – price for advertisers if measureable

Publishers Advertisers

CPM #Impression*price #Impression*(value – price)

CPC #Impression*CTR*price #Impression*CTR * (value – price)

CPA #Impression*CTR*CVR*price #Impression*CTR*CVR*(value – price)

CTR & CVR

Position Creative Relevance Context Time User Info

Click? Loading time

Conversion?

Site ……

Landing page ……

X

CTR vs. CVR

Landing percentage for clicks on different positions

Correlation For features like ad position, the correlation is negative

For others like relevance, the correlation is positive

More for Branding

Awareness and perceptions of those who saw the ad compared to those that did not

Usually with questions such as “name companies with X product” and “do you think Company X is cutting edge?” posed to both exposed and non-exposed groups.

Brand salience Is the company or its products top of mind?

Brand lift What was the net gain from the advertising campaign in terms of raising awareness and improving perceptions?

Brand search lifted?

Active views

Gross Rating Point (GRP)

Survey on metrics in UK

Measure effectiveness of a method

By analyzing user behaviors, we mark a group of users with a tag "finance”

Then, we target these users with a finance ad

Can we evaluate the targeting effectiveness this way:

Why No?

• Maybe the targeted users are more likely to click on any ads

• We need to know how they behave without seeing this ad, which is called selection bias

Measure effectiveness of a method

Di -- user i is targeted or not

Yi1 -- response of user i after seeing the targeted ad

Yi0 -- response of user i without seeing the ads

Right metric:

0 Targeted population

Untargeted population

1

Response difference of two populations with the ad Response difference of two populations without the ads

Attribution

Important Problem Where should a marketer invest?

What targeting strategies work?

Which ads are driving conversions?

Is one channel more valuable than another?

Scenarios Online to offline store

Across multiple screens

Across digital channels

Solutions Post-click or last-click attribution

Post-View Attribution

Fractional Attribution

User & Data

What is a user?

Ids PC iOS Android WP

Passport Id √√ √√ √√ √√

Cookie √√ √√ √√ √√

IP + Agent √√ √√ √√ √√

MAC √ √ √√ √

IMEI/MEID √ √√ √

ICCID/IMSI √ √√ √

Apple UDID √

Apple IDFA √√

Android ID √√

WP UID √√

Google Adid ? ? ? ?

√: exists but not accessible ; √√: accessible

Unified Id

Problem of cookies

Different cookies for different browsers

Cross devices

Churn rate

Goal: persistent and cross-device algorithm ID

Persistent: build a hash function from all collected signals to a unique ID

Cross-device: connect devices by detecting low-probability co-occurrence

User Data

Meta data

Name, age, gender, zip, income bracket, profile interests

Activities (events) with a timestamp:

Purchases

Searches, page views, clicks, …

Activities from mobile and wearing devices

Connections

Friends, followers and others

Data Management Platform

Online

Cookie Syncup

Offline & Online Cookie PII Offline

Privacy

Dot-Not-Track (DNT)

Sixty percent of Internet users would use Do Not Track

Opt-out with price: giving up free stuff

Email / Communication

Discounts / Loyalty Rewards

Personalized services, including ads

Temporarily vs. permanently

Targeting / Recommendation

Demographic targeting

Basic targeting approach for display ads TV, magazines, etc. maintain very detailed statistics of their audience

Important indicator of people interest

Common classic dimensions: Age, Gender, Income bracket, Location, …

Challenges: Obtaining Demographic Information User supplied demographic information

• Most reliable – if filled correctly – In some cases 15-20% of users born on 1st of January

• DMP can help, by connecting online and offline • Privacy concerns

Inferred demographic information

• Based on user browsing/querying behavior • Wider reach – virtually every user • Less accurate

Retargeting

Search Retargeting You search “camera”on a search engine

Next when you browse web pages, you see ads about cameras

Visit Retargeting You put shoes into your shopping cart on Amazon. You leave before you checkout;

Next when you browse web pages, you see ads on the shoes

Compare to other Targeting Much more detailed information • The product you searched • The query you issued • Items put in the cart

Behavioral targeting & look-alike

Behavioral targeting Targeting users by their historical behavior:

• Search queries, browsing pages • Clicks, conversions

Models

• Classification / clustering

Look-alike Advertisers find a set of good users & want to get more

Classification problem

• Selected users as positive examples • Randomly selected users as negative examples

Social targeting

Social Network is growing

Rich self-reported information (age, career, location, like…), including daily activities

People are well connected

Easy targeting based on self-reported info

Infer user interest from the social network

What your friends respond to an ad may decide whether you should see it

More factors: recency

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Targeting according to long-term behaviors

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Targeting according to short-term behaviors

More factors: frequency

CTR Prediction

Prediction System

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Binary labels: 1 click, 0 non-click

Queries

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Training Data

Test data

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Model

Learning System

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Challenges in CTR Prediction

Features Constructing and selecting good features (ad, context, user)

Handling discrete features of very different cardinalities

Labels Noisy labels: Fraud clicks

Highly unbalanced: low CTR in display ads

Dynamics CTR changes over time: e.g., seasonal variation

Large Scale & sparse Millions of ads & billions of users & hundreds of billions training samples

Each ad has about 1 click and each user has far less then 1 click on average a day

E & E

Solutions

Ad Click Prediction: a View from the Trenches (KDD 2013)

Useful tricks for memory savings

Methods for assessing and visualizing performance

Practical methods for providing confidence estimates for predicted probabilities

Calibration methods

Methods for automated management of features

Several directions not working well

Dynamic creative optimization (DCO)

What is DCO? Put the right message in front of the right consumer

Using data

Extremely effective at increasing a campaign’s performance (100%+ lift on CTR)

How Break an ad apart into individual pieces, and create different versions of those pieces for different audiences

Each ad uses a template of one to four dynamic elements, and can rotate in different versions of each element.

A recommendation product

Once click, redirect to an

ad page

The ads you may be interested in

Popular ads

The ads relevant to the clicked keyword

Future

The best is yet to come

Data

More signals with mobile and wearing devices

Ad vs. content

Boundary is getting blurred

Assistance to users

To provide guidance for user discover their true need

Facilitate users to complete tasks on mobile

Convergence of online and offline ads

References

MS&E 239: introduction to computational advertising, Andrei Broder, Vanja Josifovski.

How effective is targeted advertising? Ayman Farahat, Michael Bailey. WWW 2012.

IAB internet advertising revenue report, April 2013.

Advertising - Why Human Intuition Still Exceeds Our Best Technology, Brian Burdick, ADKDD 2013.

Real-time Bidding for Online Advertising: Measurement and Analysis, Shuai Yuan, Jun Wang, Xiaoxue Zhao, ADKDD 2013

http://www.adopsinsider.com/

Thanks!