Adage- Six Things You Need to Know About Real-Time Bidding


The real-time bidding industry is only three years old. But in that short time the remarkable technical achievement of real-time ad exchanges has revolutionized advertising. So why is there a growing sense that RTB 1.0 is falling short, that promised campaign improvements remain unrealized?

RTB campaigns actually perform well when executed correctly. But many assumptions and hypotheses about RTB 1.0 are simply incorrect. Here are six reasons:

1. Cookies are far shorter lived than thought. The RTB industry structure — separating data from optimization, and lower-purchase-funnel retargeting from upper-purchase-funnel prospecting — assumes that cookies are reasonably long-lived. In reality, the half-life of an average third-party cookie is about three days, and one-third of all cookies last less than an hour. Buyers can err by either focusing on only a population subset with stable cookies or buying lists of cookies that likely won’t be found again. Stale cookie lists degrade effectiveness and efficiency; real-time bidding requires real-time data.

2. Clicks are a poor metric for display advertising. Reusing search metrics for RTB is asking for trouble. We’ve found that the clicker profiles for thousands of advertisers of products ranging from car insurance to plane tickets to men’s pants to groceries are almost exactly the same. In general, the profile of converters is the exact opposite of clickers, with studies showing that single-digit percentages of online users ever click on ads.

3. Prospecting and retargeting are not separate activities. Advertisers have been encouraged to split marketing objectives into upper and lower funnel, a rational practice if cookies were reasonably stable. But over a couple of days a customer can appear as two or more different cookies because of cookie half-life. The advertising that drove the person to the website, the single hardest task in advertising, won’t be properly attributed because cookie churn has broken the causal link. Mistaken attribution causes advertisers to over-invest in retargeting and under-invest in finding new customers.

4. Data are necessary but not sufficient. Along with whom to target (the data or cookie list), it is crucial to know when (purchase funnel management), where (context and placement), how often (frequency) and how much (auction strategy) to pay. Separating the data from the algorithmic optimization and bidding makes the dynamic optimization required in RTB impossible. Comparing the performance of campaigns with these elements integrated to those that combine independent components reveals that integrated campaigns produce a two to seven times lower cost per action (CPA).

5. Data volume matters. RTB still requires petabytes of data, for two reasons. First, data freshness — less data means more dated insights and greater sensitivity to cookie deletion. Second, targeting models are only as good as the amount and quality of data against which they train. Not all look-alike models are created equal. The amount of model training differentiates the world-class from the mediocre.

6. Machines beat people. The data required for optimal display targeting decisions are incredibly large, and impression volume has exploded. The data Quantcast applies to targeting decisions — 10 billion impression auctions each day — is the equivalent of 200 million filing cabinets of paper. Processing and applying that much information is no job for humans and spreadsheets. Use machine-learning techniques to make tactical buying decisions, and let people focus on strategic campaign planning.

An integrated approach to RTB — data, algorithm and bidding — across the entire purchase funnel is essential to creating a coordinated set of targeting tactics. The industry’s not there yet, but each day moves closer to fulfilling the promise of RTB and targeting — from RTB 1.0 to RTB 2.0.



Real-time bidding (RTB) is a relatively new advertising technology that allows online advertising to be purchased and served on the fly. Instead of reserving prepaid advertising space, advertisers bid on each ad impression as it is served. The impression goes to the highest bidder and their ad is served on the page. The closest analogy would be to the stock market: as stocks (online advertising spaces) come up for sale, brokers (advertisers) bid for the stock. Whoever bids the highest price gets that stock (the ad is served). Then the process immediately starts all over again.

How do advertisers decide when to bid on an ad? Real-time bidding (RTB) platforms buy data about users from across the web. The data is usually in the form of behavioral data gathered from tracking cookies. This information is then fed into the real-time bidding platform, giving advertisers insight into who is about to be served the ad.

Here’s a simplistic example of how real-time bidding (RTB) would work in the real world: A user spends a lot of time on financial websites, checking stocks and looking up Morningstar ratings. They arrive on a webpage that uses Real-Time Bidding to serve ads. On the back end, a major financial services provider has specified that they are interested in users that like stocks. A luxury carmaker has also indicated interest in this audience. The RTB system matches these advertisers with the user profile and they bid on the ad.  Whoever has the highest bid wins, and their ad gets served.

Of course, all this happens in the blink of an eye. Advertisers don’t literally sit and bid on individual ads. Like Google AdWords, they set maximum bids and budgets. The user criteria can also be very complex, taking into account everything from very detailed behavioral profiles to conversion data.

The amount of ads sold through RTB is still relatively low percentage of the overall $26 billion US online advertising market. However, a recent study from Forrester predicted that RTB spending will increase 130% from 2010 to $823 million in 2011.



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