Advertising & Recommendation. Dou Shen 沈抖 (weibo)

Transcription

1 Advertising & Recommendation Dou Shen 沈抖 (weibo)

2 Outline Overview Evolution of Display Ads Metrics User & Data Targeting & Recommendation Future

3 Overview

4 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.

5 Ad examples

6 How about these?

7 Major players Players Traffic Inventory Users / Searcher Supply Sellers / Publishers / Search Engines Demand Buyers / Advertisers / Marketers Campaign Types User Engine Ad Branding (awareness and message reinforcement) Direct response (ROI/acquisition-focused)

8 Pricing models CPM = cost per thousand impressions CPC = cost per click CPT/CPA = cost per transaction/action

9 Advertising revenue market share by media

10 Historical annual revenue trends (U.S.)

11 Advertising format share

12 Internet ad revenues by industry category

13 Internet ad revenues by pricing model

14 Evolution

15 Changes Ad format Interactive Ads Display Ads Text Ads Search Web page Game Video App Social PC Mobile Traffic

16 The evolution of display advertising - IAB Video from IAB

17 Direct buying & selling 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 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

18 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

19 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

20 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

21 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

22 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

23 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 thirdparty Put users into proper segments

24 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)

25 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

26 Growth of RTB

27 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

28 Landscape of Display Ads

29 Metrics

30 Key Metrics 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) Quantity Impressions, clicks, conversions: CTR, CVR Unit Payoff Price for publishers Value price for advertisers if measureable

31 CTR & CVR Site Position Creative Context Time Relevance User Info Click? Loading time Landing page Conversion? X

32 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

33 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)

34 Survey on metrics in UK

35 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

36 Measure effectiveness of a method Targeted population 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: 1 0 Untargeted population Response difference of two populations with the ad Response difference of two populations without the ads

37 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

38 User & Data

39 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

40 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

41 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

42 Data Management Platform Online Cookie Syncup Offline & Online Cookie PII Offline

43 Privacy Dot-Not-Track (DNT) Sixty percent of Internet users would use Do Not Track Opt-out with price: giving up free stuff / Communication Discounts / Loyalty Rewards Personalized services, including ads Temporarily vs. permanently

44 Targeting / Recommendation

45 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

46 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

47 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

48 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

49 More factors: recency Targeting according to short-term behaviors Targeting according to long-term behaviors

50 More factors: frequency

51 Qyery-ads CTR Prediction q ad ad ad (1) (1) 1 (1) 2 (1) Queries,0,0,1 (1) n q ad ad ad Training Data ( m) ( m) 1 ( m) 2 ( m) n ( m),0,1,0 Binary labels: 1 click, 0 non-click Learning System Model f ( q, ad, w) q ( ad,?),( ad,?) ,( ad,?) Test data n Prediction System di 1, f ( q, adi 1, w) di2, f ( q, adi2, w) din, f ( q, adin, w)

52 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

53 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

54 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.

55 A recommendation product Once click, redirect to an ad page The ads relevant to the clicked keyword The ads you may be interested in Popular ads

56 Future

57 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

58 References MS&E 239: introduction to computational advertising, Andrei Broder, Vanja Josifovski. How effective is targeted advertising? Ayman Farahat, Michael Bailey. WWW IAB internet advertising revenue report, April Advertising - Why Human Intuition Still Exceeds Our Best Technology, Brian Burdick, ADKDD Real-time Bidding for Online Advertising: Measurement and Analysis, Shuai Yuan, Jun Wang, Xiaoxue Zhao, ADKDD

59 Thanks!