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Re: Frequency Capping

From: Tamir Israel <tisrael@cippic.ca>
Date: Fri, 13 Jul 2012 14:57:49 -0400
Message-ID: <50006FAD.5090300@cippic.ca>
To: Jonathan Mayer <jmayer@stanford.edu>
CC: Brian O'Kelley <bokelley@appnexus.com>, Chris Mejia <chris.mejia@iab.net>, David Wainberg - NAI <david@networkadvertising.org>, W3C DNT Working Group Mailing List <public-tracking@w3.org>, Brendan Riordan-Butterworth <Brendan@iab.net>, Mike Zaneis <mike@iab.net>, Peter Cranstone <peter.cranstone@gmail.com>, Alan Chapell <achapell@chapellassociates.com>
On 7/13/2012 2:32 PM, Peter Cranstone wrote:
> Does the current 'unique identification' method contain personally 
> identifiable information? Or is it 'just a count?'
>
> Peter

Seems to be the case:

On 7/12/2012 10:04 PM, Jonathan Mayer wrote:
> On Thursday, July 12, 2012 at 4:26 PM, Brian O'Kelley wrote:
>> Jonathan,
>>
>> I think your points below are valid - we could reorder campaign 
>> selection such that we check frequency caps only for the limited set 
>> of campaigns that sort to the top on expected value. It would be very 
>> expensive, but it would be technically possible.
> Could you say a bit more about the potential costs of implementing a 
> different approach to frequency capping?
 > I'm curious -- would this be a one-time re-engineering cost or an 
ongoing cost?
>> However, one of the core tenets of performance advertising is the use 
>> of frequency data to predict response. It's part of the holy trinity 
>> (creative, placement, frequency) that dictates the success of direct 
>> response advertising. For performance advertising to work we need to 
>> lookup frequency before we figure out the price, and that can't 
>> happen the reordered algorithm you suggest.
> Ok, so how about this algorithm:
>
> 1) Begin with the set of all campaigns.
>
> 2) Filter by targeting criteria.
>
> 3) Assign an expected revenue to each campaign, without knowing actual 
> impression frequency counts.  An implementation might use a trivial 
> heuristic (e.g. assume no impressions or average impression frequency) 
> or something more sophisticated (e.g. use a probability distribution 
> of impression frequency).
>
> 4) Select the n campaigns with greatest expected revenue.
>
> 5) Filter by frequency capping.
>
> 6) Assign a new expected revenue to each campaign using actual 
> impression frequency counts.
>
> 7) Select the campaign with greatest expected revenue.
>
> Or, even better, maximize expected revenue across the n candidate 
> campaigns:
>
> 1) Begin with the set of all campaigns.
>
> 2) Filter by targeting criteria.
>
> 3) Construct a portfolio of n campaigns that maximizes portfolio-wide 
> expected revenue, that is, the greatest individual campaign revenue. 
> An implementation might use trivial heuristics (e.g. choose n/3 ads 
> assuming no impressions, n/3 ads assuming average impressions, and n/3 
> ads assuming loads of impressions) or something more sophisticated 
> (e.g. use a probability distribution of impression frequencies).
>
> 4) Filter by frequency capping.
>
> 5) Assign a new expected revenue to each campaign using actual 
> impression frequency counts.
>
> 6) Select the campaign with greatest expected revenue.
>> Note that from a privacy perspective, you should be very pleased that 
>> our optimization algorithm doesn't use any historical user data or 
>> behavioral segmentation (though our clients could explicitly target 
>> their own behavioral data outside the algorithm).
> With the caveat that other participants assuredly disagree: I have no 
> objection to behavioral targeting.  I and other researchers have, in 
> fact, worked to develop practical, privacy-preserving approaches (see 
> http://33bits.org/2012/06/11/tracking-not-required-behavioral-targeting/). 
>  My concern is the collection of a user's browsing history by an 
> organization he or she has no relationship with.  It seems totally 
> backwards to me to get rid of a practice that might have some marginal 
> economic value (behavioral targeting) and keep the practice that 
> imposes serious privacy risks (ID cookies and equivalents).
>> I'd be happy to answer any other questions - this is a personal 
>> passion of mine!
>>
>> Brian
>>
>>
>> From: Chris Mejia <chris.mejia@iab.net <mailto:chris.mejia@iab.net>>
>> To: Jonathan Mayer <jmayer@stanford.edu 
>> <mailto:jmayer@stanford.edu>>, David Wainberg - NAI 
>> <david@networkadvertising.org <mailto:david@networkadvertising.org>>
>> Cc: W3C DNT Working Group Mailing List <public-tracking@w3.org 
>> <mailto:public-tracking@w3.org>>, Brendan Riordan-Butterworth 
>> <Brendan@iab.net <mailto:Brendan@iab.net>>, Mike Zaneis <mike@iab.net 
>> <mailto:mike@iab.net>>, Brian O'Kelley <bokelley@appnexus.com 
>> <mailto:bokelley@appnexus.com>>
>> Subject: Re: Frequency Capping
>>
>> Jonathan,
>>
>> Frequency capping (f-capping) is usually a contractual obligation for 
>> the party responsible for delivering the ad (an ad-netork, a 
>> publisher, and exchange, etc.) and is almost always required by the 
>> advertiser in insertion orders (the insertion order or "IO" is the 
>> contract between the parties).  It looks like your assumption below 
>> is that f-capping is (only) a 'tactic' to increase ROI for 
>> performance campaigns.  While this is sometimes true (yet mostly 
>> not), it's actually rarely the real motivation of doing f-capping. 
>>  The requirement for f-capping the delivery of a campaign to users is 
>> generally contractually obligated by the advertiser, for several good 
>> reasons, but most importantly for not annoying the user with multiple 
>> servings of the same ad creative, over and over again in one time 
>> frame (i.e. in a 24-hour time period).
>>
>> As f-capping is generally contractually obligated, it's not up to the 
>> deliverer of the ad to CHOOSE which campaigns to f-cap— it's a 
>> REQUIREMENT to f-cap all campaigns where contractually obligated to 
>> do so.  F-capping has happened in television advertising for many 
>> years— imagine how annoying it is when the same tv ad spot plays over 
>> and over again (in fact this happens, and I'm sure we all find it 
>> annoying).
>>
>> To sum up, while f-capping can sometimes increase ROI for advertisers 
>> (it's not necessarily always true), it is most often contractually 
>> obligated (per the Insertion Order).  The primary motivation for 
>> f-capping is to not annoy the user with repeated serving of the same 
>> ad creative during a time period.  In my experience, the vast 
>> majority of f-capping is  set at 1:24 or 2:24, etc. (restricting the 
>> showing of a particular ad creative, 1 time in 24-hours, or 2-times 
>> in 24-hours).
>>
>> I hope this helps clarify the motivation for f-capping and leads to 
>> mutual appreciation for the need.
>>
>> Kind Regards,
>>
>> Chris
>>
>>
>> Chris Mejia | Digital Supply Chain Solutions | Ad Technology Group | 
>> Interactive Advertising Bureau - IAB
>>
>>
>>
>> From: Jonathan Mayer <jmayer@stanford.edu <mailto:jmayer@stanford.edu>>
>> Date: Tue, 10 Jul 2012 14:26:12 -0700
>> To: David Wainberg - NAI <david@networkadvertising.org 
>> <mailto:david@networkadvertising.org>>
>> Cc: W3C DNT Working Group Mailing List <public-tracking@w3.org 
>> <mailto:public-tracking@w3.org>>
>> Subject: Re: Frequency Capping
>> Resent-From: W3C DNT Working Group Mailing List 
>> <public-tracking@w3.org <mailto:public-tracking@w3.org>>
>> Resent-Date: Tue, 10 Jul 2012 21:26:46 +0000
>>
>> I'd sure like to hear more from advertising industry participants 
>> about how frequency capping integrates into advertisement selection. 
>>  The AppNexus approach, if I read correctly, goes roughly as follows:
>>
>> 1) Begin with the set of all campaigns.
>>
>> 2) Filter by targeting criteria.
>>
>> 3) Filter by frequency capping.
>>
>> 4) Assign an expected revenue to each campaign.
>>
>> 5) Select the campaign with greatest expected revenue.
>>
>> The approach includes testing the frequency cap of every campaign 
>> that matches targeting criteria.  What about, instead, only testing 
>> the cap for a subset of those campaigns:
>>
>> 1) Begin with the set of all campaigns.
>>
>> 2) Filter by targeting criteria.
>>
>> 3) Assign an expected revenue to each campaign.
>>
>> 4) Select the n campaigns with greatest expected revenue.
>>
>> 5) Filter by frequency capping.
>>
>> 6) Select the campaign with greatest expected revenue.
>>
>> Some relevant empirical questions include: How often are the highest 
>> revenue campaigns frequency capped?  How well can an ad company 
>> predict which high-revenue campaigns will and won't be frequency capped?
>>
>> Jonathan
>>
>> On Monday, July 9, 2012 at 11:34 AM, David Wainberg wrote:
>>
>>> Hi All,
>>>
>>> In case you haven't seen it already, I recommend Prof. Felten's 
>>> excellent blog on "Privacy by Design: Frequency Capping." Please 
>>> also read Brian O'Kelley's post in the comment section explaining 
>>> what he sees as the technical hurdles for these alternative 
>>> frequency capping methods. (I may be wrong, but I think Brian is a 
>>> former student of Prof. Felten.) This kind of detailed technical 
>>> discussion of these proposals seems very helpful. First, it helps us 
>>> set reasonable expectations on all sides. Second, and more 
>>> interesting to me, is that maybe we can have more discussion and 
>>> collaboration on bringing these sorts of things to production.
>>>
>>> http://techatftc.wordpress.com/2012/07/03/privacy-by-design-frequency-capping/
>>>
>>> -David
>>
>
Received on Friday, 13 July 2012 18:58:40 UTC

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