Ad exchange is a platform of matching advertiser’s ads to publisher’s slots for each audience-impressions.  The massive scale of its operation, the technological challenge and sophistication are unprecedented; and yet the basic matching mechanism is under-designed in comparison to traditional matchmaking. There are so many things that publisher care about which ad will be shown on their sites, not just what’s is the highest bid price; yet, the within current ad exchange design, there is no opportunity for publishers to express their preferences fully.

If ad exchange were a matchmaker, what it would be like?  Imaging publisher as bride and advertisers as potential grooms – our ad-exchange-matchmaker goes to a bride and say,

M:  Let’s put it out there and see who will be the richest man coming here for you

B:  But I am scared of marrying one I do not know”

M:  Well, you can give me a black list and tell me what you do or do not like as filters

B:  Can I pick it myself?

M:  No, I will pick one for you –  based on wealth

Now you get the idea of the frustrations of our publisher/bride.  There need to be a process for publishers to perform data-driven ads evaluation based on their objective and value, not just bid price.  About publisher concerns of ad exchange and RTB,  Brian O’Kelley discussed here on ClickZ.  A better designed matching mechanism should handle publisher’s preference properly, which may ease some of the publishers’ concerns (channel conflict, data leak and brand safety) if not solving them.

Ad Exchange is not like a financial exchange

The “exchange” in the name is suggestive of a financial stock exchange market, and interesting observations can be made based on this analogy.  However, there are some fundamental differences such as the lack of liquidity in ad impression and information asymmetry.  Jerry Neumann has blogged about this topic profusely;  it is still a topic of great interest today, as seen in a recent article by Edward Montes.

In fact, the differences are easy to understand. The harder part is, like Jerry asked,  If the ad exchange aren’t exchanges, what are they?  or I should add, what should they be like?

Publisher’s preference is the missing piece

The analogy with financial exchange (stock and future) is not a good analogy partly because of its inability to fully model advertiser preference. Not all impressions are of the same value to an advertiser and not all advertisers give the same value to an impression. The commodity auction model as embedded in ad exchange does better, because it allows advertisers to bid based on any form of evaluation – a chance for advertiser to fully express its preference over audience, contextual content and publishers’ brand.

Still, there is a problem for the current auction model: after collecting all the bids from advertisers, it takes the highest bidder to be the winner, as if price is the only thing publishers care about.  In reality, not all bids with the same price are of the same value to a publisher.  Publishers care about brand safety and contextual relevancy as well; in fact, the quality of user experience may mean more to publishers than advertisers!  In sum, publishers care about the quality of the ads above and beyond the bid price.  Unfortunately, the current ad exchanges lack the proper mechanism allowing publishers to articulate their full preferences.  This results in lost of market efficiency and lost of opportunities to remove transaction frictions.  This is a design flaw.

Display marketplace is still far from perfectly efficient and this design flaw does not help.  The recent developments of Private Marketplace are piecemeal attempts to overcome this design issue.  Some market movements in late merge and acquisition attempts can be understood from this angle.

Where can we look for design idea on how to handle this issue?  – paid search and game theory!

The quality score framework from paid search

In many ways, paid search is just like ad exchange, with Google plays one of a few “publisher” roles.   In both markets, advertisers are competing for ad-view through auction bidding;  if we equate audience in display to keywords in search, then the bidding processes is quite the same:  search advertisers do extensive keyword research, look at past performance along side of other planning parameters such as time of the day etc. to optimize their bids;  similarly display advertisers look at the audience attributes, the site and page contents, past performance and planning parameters as they perform bid optimization.

The bidding processes in both markets are similar;  the differences lie in the post-bidding ad evaluation.

After all bids are collected, ad exchange today simply select the highest bidder.  In case of paid search, bids are evaluated on price, ad relevancy and many other attributes.  Google has mastered the evaluation process with its Quality Score framework.  This difference in having a Quality Score framework vs not is not a small thing.  As anyone familiar with the history of paid search know, the quality score framework played a pivotal role in shaping the search industry when Google introduced it around the turn of the century.  The post-bidding ad evaluation for display may just be a critical piece of technology and have potentially significant impact on the efficiency and the health of the display market.

The need for a non-trivial post-bidding ad evaluation calls for an extra decision process (and algorithm) to be added, either at ad exchange or at publisher’s site, or both.  In this new model and with this extra component, ad exchange will send the full list of bidding to the publisher instead of picking a winner based on price alone.  It is then up to the publisher to decide which ad will be shown.  With millions of publishers, large and small, this seemingly small change may be a trigger for more inside this industry where technology is already orders of magnitude more complex than paid search.

The matching game analogy

With full preferences being taking into account for both advertisers and publishers, ad exchange looks less like a commodity marketplace and more like the matching game.  It will be interesting to look at market efficiency from the perspective of mechanism design in game theory, which is another way of saying operational market process.

Matching advertisers with publishers under a continuous series of Vickrey auctions is our setup for the discussion – the best model I can think of that mimic the matching game setup;  it shouldn’t be too surprising to anyone that matching game is an interesting analogy to ad exchange.  As a game theory abstraction of many practical cases, matching game includes college admission and marriage market.  Let’s take the marriage market as an example.

Using a simplistic description, a marriage market involves a set of Men and a set of Women.  Each man has a preference vector over the set of women (a ranking of women);  similarly each woman has a preference vector over the set of men (a ranking of men).  A matching is an assignment of men to women such that each man is assigned to at most one woman and vice versa.  A matching is unstable if there exist a man-woman pair not currently matched to each other but both prefer match to each other than their current match – the pair as such is called a blocking pair.  When there is no blocking pair exist, a match will be called a stable match.

Clearly, stability of a match is a good quality: a stable match is not vulnerable to any voluntary pairwise rematch (translating into ad exchange language, a stable match is one such that no pair of advertiser – publisher currently not matched to each other have incentive to switch and form a new match).  A matching is male-optimal if no two males have incentive to switch partners. Female-Optimal is defined similarly.  A stable matching that is both male-optimal and female optimal looks like an perfect efficient market; we hope to find a mechanism that lead to the unique stable matching as such – something we can then mimic to implement for a future ad exchange model.

Unfortunately, there is no unique stable matching for matching game in general (in this case, having too many good things may not be a good thing).  There is also no unique optimal matching that is optimal from both men and women’s perspective.  We learned that Male Proposals Deferred Acceptance Algorithm, sort of like the current auction process in ad exchange in which advertisers played the male roles, produce Male-Optimal stable matching.  If we switch the role of men and women, a similar algorithm exists that produces Female-Optimal matching.  The two algorithms/mechanisms lead to two distinctly different results.  You can read more about algorithmic game theory, computational game theory, specifically on matching game and mechanism design if interested.

So, why are we looking into this and what we’ve learned from it?  Below is my translation, or transliteration to be more appropriate, from game theory speak to the ad:tech domain.

We all like to believe that there is an efficient market design for everything, including the exchange marketplace for ads. Our believe is justified for all commodity marketplace by the general equilibrium theory.  Unfortunately, there is no equivalence of a “general equilibrium” or universal optimal stable match for a marriage market, which implies that there is no universal optimal advertiser-publisher matching in ad exchange.  If this is the case, the search for an optimal market mechanism for ad exchange will be a mission impossible.

However, there exist one-sided optimal condition, advertiser-optimal and/or publisher-optimal matching.  It is also easy to find the corresponding mechanisms that lead to those one-sided optimal stable matching.  The auction market as currently implemented in ad exchanges, with the addition of post-bidding evaluation process, is similar to the mechanism leading to advertiser-optimal matching.

The future seems open for all kinds of good mechanism design.  Still, I believe that there is a “naturalness” in the current style auction market.  It is quite natural for the auction process to start from the publisher side, by putting the ad impression on auction, because it is all start with the audience requesting a webpage – a request send to a publisher. It is not easy to imagine how advertiser can set up a “reverse auction” starting from the demand side, within a RTB context. We can never rule out the possibility, and it may work potentially for trading the ad future.

Conclusion:

I am reluctant to draw any conclusions – these are all food for thought and discussion.  I’d love to hear your comments!

What’s wrong with fake product or any counterfeit?  Read on the market for lemon.

Is BS-ing still “not good, but ok” because a lot of people do it (even worse, relying on it for a living)?

I hope we all get serious about BS-ing, it is not a small thing.

I wish I could write a similar post, but feel the task is too big to take.  A systematic review with vision into the future would require much more dedication and effort than I could afford at this point.  However, I do have a couple of thoughts and went ahead to gather some evidence to share.  I’d love to hear your thoughts; please comment and provide your perspectives.

The above chart shows search volume indices for several data analytics related keywords over the last six years.  There are many interesting patterns.  The one caught my eyes first is the birth of Google Analytics: Nov 14, 2005.  No only did it cause a huge spike in the search trend for “analytics”, the first day “analytics” surpass “regression”, it become the driving force behind the growth of web analytics and analytics discipline in general.  Today, more than half of all “analytics” searches are associated with “Google Analytics”.  Anyone who writes the history of data analytics will have to study the impact of GA seriously.

I wish I could do a chart on the impact of SAS and SPSS on data analytics in a similar fashion, but unfortunately it is hard to isolate SAS searches for statistics software vs other “SAS” searches.  When limited to the “software” category, it seems that SAS has about twice the volume of SPSS, so I used SPSS instead.

Many years ago, before Google Analytics and the “web analyst” generation, statistical analysis and modeling dominated the business applications of data analytics.  Statistician and their predictive modeling practice were sitting in their ivy tower.  Since the early years of the 21st century, data mining and machine learning became a strong competing discipline to statistics – I remember the many heated debates between statistician and computer scientists about statistical modeling vs data mining.  New jargons came about, such as decision tree, neural network, association rule and sequence mining.  To whomever had the newest, smartest, most math grade, efficient and powerful algorithm went the spoils.

Google Analytics changed everything.  Along with data democratization came the democratization of data intelligence. Who would’ve guessed that today, for a large crowd of (web) analysts, analytics would become near-synonymous with Google Analytics and building dashboard, tracking and reporting the right metrics the holy grail of analytics?  Those statisticians may still inhabit the ivy tower of data analytics, but the world is already owned by others – the people – as democracy would dictate.

No question about it, data analytics is trending up and flourishing as never before.

comments?  Please share your thought here.

Mining twitter data is a huge challenge.  So far I have not been able to see many interesting data/text mining and data analytics around twitter data.  I have been playing the data lately, and here’s a thematic/topic graph I had – a visualization of all tweets of the last eight hours that are related to to “mexico city”:

You can tell that “Swine Flu” still at the center of all topics, whereas earthquake is clustered alone to the side.

Have you seen any interesting twitter analytics (by the way, I do not mean the twitter metrics or counters etc..)?

Jeff Clark of NeoFormix has a great set of application, the best I have found so far.  FlowingData is another one.

I have been wondering, for the last couple of days, why the “deep dive example” piece that I promised hasn’t come out yet. The reason is just getting too complicated: lack of motivation, busy at work, shifting focus on other things … and last but not least: twitter!

To be sure, I do not mean to join the fashion of crediting / blaming twitter for everything, including the latest recession. I just want to point out a plain and simple fact, that I have been looking, reading random things from twitter or related to twitter so much that it EATS up most of my FREE time; and worse yet, I notice some subtle changes in me, a little ADD like symptom; it erodes my concentration, cuts my usual chain of thoughts into short pieces and stires up my urge to surface, to verbalize anything and everything. 

It is a little sad to see how twitter is winning over the world of advertising, PR, News and celebrites; but it is truly scary to hear story about twitter’s invasion to education. 

I need to ban myself from twitter for a little while and see if the damage is irreversable.

In this discussion, I am going to focus on the attribution problem in the presence of interaction effect. 

Here’s the story setup: there are two ad channels, paid search (PS) and display (D).  

Scenario 1)
      When we run both (PS) & (D), we get $40 in revenue.  How should we attribute this $40 to PS and D?

The simple answer is: we do not know – for one thing,  we do not have sufficient data.
What about making the attribution in proportion to each channels’ spending numbers? You can certainly do it, but it is not more justifiable than any others.

Scenario 2)
    when we run (PS) alone we get $20 in revenue;  when we run (PS) & (D) together, we get $40.
    Which channel gets what?

The simple answer is again: we do not know – we do not have enough data.
Again, a common reasoning of this is:  (PS) gets $20 and (D) gets $20 (= $40 – $20).  The logic seems reasonable, but still flawed because there is no consideration of the interaction between the two.  Of course, with the assumption that there is no interaction between the two, this is the conclusion.

Scenario 3)
    when we run (PS) alone we get $20 in revenue; running (D) alone gets $15 in revenue; running both (PS) & (D) the revenue is $40.
    Which channel gets what?

The answer:  we still do not know. However, we can’t blame the lack of data anymore.  It is forcing us to face the intrinsic limitation in the linear additive attribution framework itself.

Number-wise, the interaction effect is a positive $5, $40-($20+$15), which we do not know what portion to be attributed to which channel. The $5 is up for grab for anyone who fight it harder – and usually to nobody’s surprise, it goes to the power that be.

Does this remind anyone of how CEO’s salary is often justified?

What happens when the interaction effect is negative, such as in the following scenario?

Scenario 4)
    when we run (PS) alone we get $20 in revenue; running (D) alone gets $15 in revenue; running both (PS) & (D) the revenue is $30.
    Which channel gets what?
How should the $5 lost distributed?  We do not know. 

What do you think? Do we have any way to justify other than bring out the “fairness” principle?

If the question is not answerable, the logic we use will at most questionable, or plain wrong.

However, all is not lost. Perhaps we should ask ourselves a question: Why do we ask for it in the first place? Is this really what we needed, or just what we wanted? This was the subject of one of my recent post: what you wanted may not be what you needed.

The very first article of the research and measurement section is on MMM. It has some of the clearest discussion of why MMM failed to handle today’s marketing challenge, despite of its decades of success.  But I believe it can be made clearer. One reason is its failure to handle media and campaign interaction, which I think it is not the modeling failure but rather a failure for the purpose of attribution ( I have discussed this extensively in my post: Attribution, what you want may not be what you need).  The interaction between traditional media and digital media however, is of a different nature and it has to do with mixing of both push and pull media.  Push media influence pull media in a way that render many of the modeling assumptions problematic.  

Here’s its summary paragraph:

“ Marketing mix models have served us well for the last several decades. However, the media landscape has changed. The models will have to change and adapt. Until this happens, models that incorporate digital media will need an extra layer of scrutiny. But simultaneously, the advertisers and media companies need to push forward and help bring the time-honored practice of media mix modeling into the digital era.”

The report limit its discussion to MMM, the macro attribution problem.  It did not give a fair discussion of the general attribution problem - no discussion of the recent developments in attribution analytics ( called by many names such as Engagement Mapping, Conversion Attribution, Multicampaign Attribution etc.).  

For those who interested in the attribution analytics challenges, my prior post on the three generations of attribution analytics provide an indepth overview of the field. 

Other related posts: micro and macro attribution and the relationship between attribution and  optimization.

Social Media is superhot these days and naturally I expect it to be a significant topic in Razorfish’s Digital Outlook report; and I was not disappointed

Social Influence Marketing (SIM) is one of the eight trends to watch; social object theory right in the middle of the report, followed by secrets of powering SIM campaigns; the Pulse (tagged as one of the three things every CEO must know) and mobile, both are connected to SIM in some fundamental ways. Most importantly, in the research and measurement section (dearest to my heart), two our of three are about social influence measurement and research; both are excellent.

I am particularly fond of Marc Sanford’s Social Influence Measurement piece.  Marc approaches the topic methodically, providing good conceptual lead-ins as well as rigorous measurement framework. I enjoyed its evenly paced, matter of fact writing style.  Starting from discussion of the two aspects of SIM: sharable contents and people, the Generational Tag technology, Marc made it clear about the importance of separating the values where campaigns touch consumers directly versus the incremental values when the contents passing through viral media, through the power of endorsement.  The methodology part and the technology part of SIM come together nicely in the article.

The Social Influence Research by Andrew Harrison and Marcelo Marer is equally interesting. There are excellent detailed discussion about the challenges that facing the traditional survey and focus group research, and how it can evolve and adapt into a new form of social influence research.

2. Search will not be immune to the impact of the economy

3. Social Influence Marketing™ will go mainstream

4. Online ad networks will contract; open ad exchanges will expand

     with Google’s new interest-based targeting, thing looking to change even more rapidly.

5. This year, mobile will get smarter

6. Research and measurement will enter the digital age

     This is an issue dear to my heart and I have been writing about the importance of Attribution Analytics,  Micro and Macro Attribution many times in recent months; directly from the report:

    ”Due to increased complexity in marketing, established research and measurement conventions are more challenged than ever. For this reason, 2009 will be a year for research reinvention. Current media mix models are falling down; they are based on older research models that assume media channels are by and large independent of one another. As media consumption changes among consumers, and marketers include more digital and disparate channels in the mix, it is more important than ever to develop new media mix models that recognize the intricacies of channel interaction.“

7. “Portable” and “beyond-the-browser” opportunities will create new touchpoints for brands and content owners

8. Going digital will help TV modernize

Read the Razorfish report for details.