If you are not paying for it, you are the product: How much do advertisers pay to reach you?
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Online advertising is progressively moving towards a programmatic model in which ads are matched to actual interests of individuals collected as they browse the web. Letting the huge debate around privacy aside, a very important question in this area, for which little is known, is: How much do advertisers pay to reach an individual? In this study, we develop a first of its kind methodology for computing exactly that -- the price paid for a web user by the ad ecosystem -- and we do that in real time. Our approach is based on tapping on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted prices for winning bids paid by advertisers in order to place targeted ads. Our main technical contribution is a method for tallying winning bids even when they are encrypted. We achieve this by training a model using as ground truth prices obtained by running our own "probe" ad-campaigns. We design our methodology through a browser extension and a back-end server that provides it with fresh models for encrypted bids. We validate our methodology using a one year long trace of 1600 mobile users and demonstrate that it can estimate a user's advertising worth with more than 82% accuracy.
![Data-driven economy
• The user data of an IT company
→ contribute to its overall market valuation
• Companies pursue more and more users personal data
• By purchasing them
• By providing free services (Google search, Facebook etc.)
Internet Measurements Conference [IMC'17] - P. Papadopoulos 3](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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If you are not paying for it, you are the product: How much do advertisers pay to reach you?
- 1. If you are not paying for it, you are the product*: How much do advertisers pay to reach you? Panagiotis Papadopoulos FORTH-ICS, Greece & University of Crete, Greece panpap@ics.forth.gr Nicolas Kourtellis Telefonica Research, Spain Pablo Rodriguez Rodriguez Telefonica Alpha, Spain Nikos Laoutaris Data Transparency Lab, Spain *phraseoriginator:AndrewLewis,a.k.a.blue_beetle
- 2. Data-driven economy • The user data of an IT company → contribute to its overall market valuation • Companies pursue more and more users personal data • By purchasing them • By providing free services (Google search, Facebook etc.) Internet Measurements Conference [IMC'17] - P. Papadopoulos 3
- 3. How is all this data converted to money??? ecosystem of digital advertising ($194.6 billion in 2016) Internet Measurements Conference [IMC'17] - P. Papadopoulos 4
- 4. Digital advertising progressively moving towards a programmatic model ads are matched to interests of individuals elaborated user tracking privacy implications Internet Measurements Conference [IMC'17] - P. Papadopoulos 5
- 5. How much do advertisers actually pay to reach you? Internet Measurements Conference [IMC'17] - P. Papadopoulos 6
- 6. The Motivation… Internet Measurements Conference [IMC'17] - P. Papadopoulos 7
- 7. The Background… Internet Measurements Conference [IMC'17] - P. Papadopoulos 8
- 8. Programmatic auctions of RTB Internet Measurements Conference [IMC'17] - P. Papadopoulos 9 Ad Exchange (ADX) Real-time Auction Website on the user’s browser Demand Side Platforms (DSPs) Bid Request (+user info) 0.95 CPM (+impression) Available ad-slot
- 9. 0.95 CPM (+impression) RTB price notification channel Internet Measurements Conference [IMC'17] - P. Papadopoulos 10 Available ad-slot Ad Exchange (ADX) Real-time Auction Website on the user’s browser Demand Side Platforms (DSPs) nURL example: cpp.imp.mpx.mopub.com/imp?ad_domain=amazon.es&ads_creative_id=ID&bidder_id=ID&..&bidder_name=..& charge_price=0.95&country=ESP¤cy=EUR&latency=0.116&mopub_id=ID&pub name=.. A D Impression delivery (i) you won, (ii) here’s the charge price, (iii) the impression is rendered successfully Winning notification
- 10. The Challenge… Internet Measurements Conference [IMC'17] - P. Papadopoulos 11
- 11. Encrypted prices on the rise Internet Measurements Conference [IMC'17] - P. Papadopoulos • Charge prices in nURLs tend to be encrypted Encryption is a regular practice in desktop RTB auctions (~68%) Lower but rapidly increasing in mobile RTB auctions (~30%) Previous work [Olejnik, 2013] assumes encrypted prices follow the same distribution as cleartext. But is that so? Encrypted 12
- 12. Our approach 1. Leverage Real-Time Bidding (RTB) protocol: 1. 74% of programmatically purchased advertising 2. $8.7 billion in 2016 only in US 2. Methodology to calculate at real time the overall value advertisers pay per individual user based on her leaked information. 3. Year-long dataset (2015) of 1600 real users + 2 real probing ad campaigns Internet Measurements Conference [IMC'17] - P. Papadopoulos 13
- 13. Methodology Internet Measurements Conference [IMC'17] - P. Papadopoulos 14
- 14. YourAdValue browser extension Internet Measurements Conference [IMC'17] - P. Papadopoulos 15 Monitors RTB nURLs & collects features: (i) auction-specific metadata and (ii) personal data the user leaks while browsing the web
- 15. Price Modeling Engine (PME) Internet Measurements Conference [IMC'17] - P. Papadopoulos 16
- 16. Required input for the PME Internet Measurements Conference [IMC'17] - P. Papadopoulos 17
- 17. The Evaluation… Internet Measurements Conference [IMC'17] - P. Papadopoulos 18
- 18. Evaluating our approach • Offline year-long (2015) dataset D with mobile traffic from 1600 real users • Weblog Ads Analyzer: filter RTB traffic and extract features (auction’s metadata and user data) from nURLs Internet Measurements Conference [IMC'17] - P. Papadopoulos 19 Metric D Time period 12 months Impressions 78,560 IAB category of publishers 18 RTB publishers ∼5.6k/mont h
- 19. Feature extraction Internet Measurements Conference [IMC'17] - P. Papadopoulos 20
- 20. Features that affect prices (1/3) Internet Measurements Conference [IMC'17] - P. Papadopoulos Distribution of charge prices for the 2 most popular mobile OSes. Whiskers: 5th, 10th, 50th, 90th,95th percentiles More Android devices, but iOS-based devices draw higher prices Percentage of RTB traffic for top mobile OSes. 0% 20% 40% 60% 80% 100% 1 2 3 4 5 6 7 8 9 10 1112 RTBshare Month of the year Android iOS Windows Mob Other 0.01 0.1 1 10 100 Android iOS Chargeprice(CPM) Mobile Device OS 21
- 21. Features that affect prices (2/3) Internet Measurements Conference [IMC'17] - P. Papadopoulos Distribution of the charged prices per ad-slot size (sorted by area size). Whiskers: 5th, 10th, 50th, 90th,95th percentiles Larger ad-slot sizes do not necessarily get charged more 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 320x50 468x60 728x90 120x600 300x250 160x600 300x600 Chargeprice(CPM) Ad-slot sizes 22
- 22. Features that affect prices (3/3) • user location affects the median charge prices • during the day median charge prices are of similar range -> early morning hours - noon: more charge prices with increased values • some IABs are more costly than others -> (“Business & Marketing” more expensive than “Science”) Internet Measurements Conference [IMC'17] - P. Papadopoulos 23 See more about features in the paper…
- 23. Price Modeling Engine: ad campaigns Internet Measurements Conference [IMC'17] - P. Papadopoulos 24
- 24. Real probing ad-campaigns • 2 real probing ad-campaigns in 2016 (A1, A2): various experimental setups Internet Measurements Conference [IMC'17] - P. Papadopoulos 25 Metric D A1 (enc) A2 (clr) Time period 12 months 13 days 8 days Impressions 78,560 632,667 318,964 IAB category of publishers 18 16 7 RTB publishers ∼5.6k/mont h ∼0.2k ∼0.3k Filter name Range of values (type) Cities Madrid, Barcelona, Valencia, Seville Time of day 12am-9am, 9am-6pm, 6pm-12am Day of week Weekday, Weekend Type of device Smartphone, Tablet Type of OS iOS, Android Ad-format (smartphone) 320x50, 300x250, 320x480 or 480x320 Ad-format (tablet) 728x90, 300x250, 768x1024 or 1024x768 Ad-exchange MoPub, OpenX, Rubicon, DoubleClick, PulsePoint Content category of publisher all IABs possible
- 25. Comparison of CPM costs for the different IAB categories in our dataset and the 2 probing adcampaigns. Cost per IAB in cleartext and encrypted prices Internet Measurements Conference [IMC'17] - P. Papadopoulos Median encrypted prices always higher than cleartext Time shift: More recent cleartext prices are higher than the ones last year
- 26. How much do advertisers pay to reach you? Internet Measurements Conference [IMC'17] - P. Papadopoulos 27
- 27. Encrypted Vs. Cleartext prices “It’s safe to assume that encrypted prices follow the same distribution with cleartext prices.” price distribution of encrypted prices (A1): → distinctly different → about 1.7x higher median value than cleartext prices (A2) Internet Measurements Conference [IMC'17] - P. Papadopoulos Comparison of price distributions between cleartext and encrypted, for different time periods and datasets (D vs. A1 and A2). 28
- 28. How much do advertisers pay to reach you? • Cumulative cost from encrypted prices: cannot surpass cleartext (still dominant). • some users more costly than others • median user costs 25 CPM (73% of the users cost < 100 CPM) • 2% of users cost 10-100× more to the ad-ecosystem than the average user! Internet Measurements Conference [IMC'17] - P. Papadopoulos Cumulative CPM paid per user in our year-long dataset (2015) 29
- 29. In summary… • Methodology to measure cost of advertisers per individual user: • At real time and on user’s side based on the user’s profile • Leverage ad-auctions and RTB’s price notification channel • Construct a model to estimate encrypted prices using as features auction’s metadata and user’s leaked info. (> 82% accuracy) • Our methodology is tested using a year-long dataset of 1600 real mobile users Internet Measurements Conference [IMC'17] - P. Papadopoulos 30
- 30. Takeaways Encrypted prices are 1.7x higher than cleartext Median user costs 25 CPM per year Taking into account several different factors: (HTTP+HTTPS, management and intermediaries costs, mobile+desktop traffic) → overall user ad-cost in the range of 0.54-6.85€ per year → cheaper than most users think (10s of dollars [1]) [1] Your browsing behavior for a big mac: Economics of personal information online. WWW’13 Internet Measurements Conference [IMC'17] - P. Papadopoulos 31
- 31. Backup slides Internet Measurements Conference [IMC'17] - P. Papadopoulos 32
- 32. Why does winner pay the second best price? In a first-price auction: participants guess what everyone else is going to bid -> put down a bid that's slightly higher than the next person's In Vickrey auctions: • a type of sealed-bid auction -> Bidders submit bids without knowing the bid of others • The highest bidder wins but the price paid is the second-highest bid. -> gives bidders an incentive to bid their true value. Internet Measurements Conference [IMC'17] - P. Papadopoulos 33
- 33. What if? • In a future of no anonymous contributions… • In a future of no cleartext charge prices… How to obtain features <-> charge prices pairs? → more probing ad campaigns to cover the necessary experimental setups… Internet Measurements Conference [IMC'17] - P. Papadopoulos 34
- 34. Estimation of encrypted prices • Based on the features: ad-slot size, user location, type of device, time of day, day of week, user interests (IAB) • we train a RF to model encrypted charge prices (> 82,3% accuracy – 0.964 AUCROC) • we estimate the total cost paid for each user in our dataset. Internet Measurements Conference [IMC'17] - P. Papadopoulos 35
Editor's Notes
- Good morning to everyone. In this talk I will present you my work that aims to answer the following question: How much do advertisers pay to reach us?
- In today’s data-driven economy, the amount of user data an IT company holds, has a direct contribution to its overall market valuation. Companies rush to collect user data either by purchasing them or by providing useful services for free.
- The simlest question that comes in mind here is ”Where is all this volume of data converted to money? “ and the answer is pretty straighforward: in the personalized advertising which last year had total revenue of 200 billion dollars
- Online advertising is progressively moving towards a more personalized programmatic model, where adslots are being bought in instantaneous ad-auctions and filled with ads that match the interests of the particular user. Of course, these interests are extracted based on the user interests and behavior collected usually through elaborate and sometimes pervasive tracking. Letting the privacy implications aside…
- a very important question that motivates our work is: How much do advertisers actually pay to reach us?
- Given the lack of Transparency on this aspect, our scope is to shed light in the programmatic auctions and increase the awareness of the users regarding the monetary value advertisers pay to get their attention.
- But before I describe our approach, let me first give you some background on how programmatic auctions work:
- The most popular protocol used in ad-auctions is the Real-Time Bidding. In which, whenever the user visits a website that includes an available ad slot, it triggers an impression request to an ADX. The ADX is a real-time marketplace platform that hosts ad-auctions where the higher bidder wins and pays the second best bid. The ADX will then send Bid Requests to its affiliated DSPs along with some user info in order for them to learn who is the user that they are bidding for. The DSPs, are agencies aiming to help advertisers find the proper audience for their impressions and bid accordingly. As a consequence, each DSP will process the user info and will decide if and how much will it bid for the specific ad slot. Then, DSPs will respond back with their bids which are in CPM (or in other words Cost per Thousand of impressions)...
- ...and the ad-exchange will inform the higher bidder about its win. In this study we leverage this particular step of RTB. This is where the winning DSP gets informed about its win along with the price it got charged which in this example is 0.95 euro for a 1000 of impressions. This step happens through the user in order to ensure the Bidder that the impression was indeed rendered on the user side. So by monitoring this part on the user’s browser we can retrieve the charge price of each auctioned slot.
- So obviously to calculate the total cost of a user, one might just sum all the received charge prices.
- However, the challenge here is that more and more charge prices in nURLs tend to be encrypted by ADXs and DSPs for integrity and confidentiality purposes. In particular, encryption is a regular practice in desktop RTB auctions (around 70%) when although lower in mobile RTB auctions, it’s steadily increasing. A previous work assumes that encrypted prices follow the same distribution as cleartext. In our approach we consider this assumption unverified.
- Therefore in this work we leverage the popular RTB protocol to propose a holistic methodology to calculate the overall cost the advertisers pay for an individual user. We implemented our approach in a Chrome browser plugin, which is able to calculate this cost for each individual user at real time. To asses the effectiveness of our approach, we use a year long dataset which includes weblogs of 1600 real users and we performed 2 real ad-campaigns to retrieve the needed ground truth
- This is a high-level overview of our method. as you can see there are 2 main components the remote Price Modeling Engine and the browser plugin namely YourAdValue.
- The browser extension is responsible for monitoring the user’s RTB traffic and extracting the charge prices of the delivered advertisements at real time. For the cleartext prices the aggregation of the total is straightforward. For the encrypted charge prices, it has to follow a more complex process. It extracts features from the browsing activity which include user’s leaked information and auction’s metadata. Based on those, it applies a decision tree to estimate the value of these encrypted prices.
- This decision tree describes the encrypted prices based on specific features and is derived asynchronously from our remote Price Modeling Engine.
- To create this decision tree, PME requires two types of input: 1) a sample of cleartext prices with their associated features which is acquired by anonymous contributions of users. and 2) ground truth data to assess the difference between cleartext and encrypted prices and this data is acquired through probing ad-campaigns.
- After presenting the design of our approach let me present its evaluation
- First we bootstrap PME with real data from a year long dataset containing weblogs from around 1600 real mobile users. To analyse this large dataset we built a tool to extract RTB prices and features, which include user location, user interests, time of visit, ad slot size, user mobility and many more.
- Let me remind you that the extracted features are a required input for PME to model the encrypted values
- One of the most interesting features we used was the type of mobile device the user has. On the left figure you can see the popularity of the different device types in our dataset. And on the right the distribution of the charge prices for the 2 most popular ones. As we see although Android-based devices are more popular, advertisements in Apple devices were more expensive.
- Another important feature is the ad slot size, and here we see the charge prices the most popular of them had. Surprisingly we see that the size of the ad slot is not linear with the price. So it doesn’t mean per se that the bigger the ad-slot the more expensive it is.
- Other interesting findings that you can see in the paper include that the user’s location (at city level) affects the median charge price (2) In addition, during the day, median charge prices are of similar range, however in early morning hours till noon we see more increased charge prices. (3) Finally as expected, advertisers pay more for particular categories of user interests.
- All these extracted features are used as input in our probing ad campaigns
- In the table, you can see the basic filters we used. Specifically, we ran 2 controlled ad campaigns in Spain testing 144 experimental setups. In the first campaign we collaborate specifically with ADXs that use encrypted prices and in the second, with one that uses only cleartext prices.
- in this figure, we see a comparison of the IAB categories of the RTB impressions we took from (i) the set of encrypted prices from the 1st ad-campaign, (ii) the set of cleartext prices from the 2nd ad-campaign, and (iii) a 2 months subset of our dataset. As we see: the median prices are always higher in case of encrypted prices, compared to the cleartext. there is a timeshift where we see that although the median cleartext prices are usually in the same order of magnitude, they are higher in the case of the more recent ad-campaign contrary to 2 month dataset of last year
- So after fine-tuning our prediction model its time to respond to our motivating question
- As we said previous related work assumed that encrypted prices follow the same distribution with cleartext. To verify this assumption, we plot the distributions for both of them and as we see this assumption cannot be confirmed! As you can see in red and orange lines, the price distribution of encrypted prices from 1st ad-campaign is of higher median value (around 1.7x higher) than cleartext prices of the 2nd ad-campaign.
- However although the values of encrypted prices are higher than the ones of the cleartext prices, we see here that the cumulative cost from the encrypted prices is not surpassing the one of cleartext prices, since the latter is still the dominant price delivery mechanism in mobile RTB. We also see that some users are more costly than others. Specifically, the median user costs 25 CPM across the year, when on the other hand, there is a small 2% of users, for whom the advertising ecosystem spent 10-100× more
- In summary, we propose a methodology to measure at real time the cost advertisers pay to reach a user even when the prices are encrypted. We leverage programmatic instantaneous auctions and specifically the RTB’s price notification channel We built a model to estimate encrypted prices based on features extracted by the the user’s leaked info. We test our methodology using a year-long dataset of 1600 mobile users
- Before I conclude let me give you some interesting Takeaways. The results of this study show that encrypted prices are in general higher than cleartext. And that a median user cost around 25 CPM per year. So considering the portion of the total user traffic we have in our dataset and we estimated that the overall cost of the average user is in the range of 0.5 to 7 euros, quite lower than what most users think according to a previous user survey.
- In first-price auctions participants are trying to first guess what everyone else is going to bid and then put down a bid that's slightly higher than the next person's so they don’t bid their true value. The type of auctions performed in programmatic ad auctions is a generalized type of vickrey auctions where all bids are sealed and no bidder can know the bids of others. So by having winners pay the second best bid it gives bidders an incentive to bid their true value.
- So based on the most important features we analyzed, we train a Random Forests to model encrypted charge prices and finally estimate the total cost advertisers paid for each individual user in the our dataset.