Quant v. Audience: The Real Challenge and Opportunity is Using Both

Guest Post by:  Matt Patton Esq. Director of Optimization and R&D at DoublePositive

English: An audience in His Majesty's Theatre ...
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Among the many nuances in the debate about which optimization algorithm represents the best RTB media buying strategy, the most prominent might be the audience retargeting v. audience agnostic approach.  Although exclusive to our business, this dichotomy is conceptually no different than two Wall Street analysts debating about whether a pure qualitative approach is better than a numbers-only quantitative method in purchasing securities.  My engineering training tells me that the only inferences that should ever be drawn as a basis for any decision should originate with the story told by what was actually measured.  But my legal training tells me what I think is the more correct approach, which is, as every law school professor professed so eloquently, “It depends!”

The truth is that measured performance numbers, i.e. impressions, clicks, conversions, and audience data, i.e. categorical audience profiling, both have their own benefits and drawbacks, and will almost always provide you different oscillating returns on your media investment.  On the surface, the only real difference between the two is that one is instinctual while the other is empirical.  With audience data, we infer that a group of users with certain online buying habits will purchase what our clients are selling based on the categorical distance between things previously purchased and what we are currently selling.  With fundamental performance data, we just care about which pieces of inventory have already yielded a statistically significant number of conversions regardless of who actually converted.  Thus it is blatantly clear under the surface that both methods offer the same benefit, which is a calculated a priori probability of a conversion from which we can derive an expected value of an impression, or what my mentor at Advertising.com used to call the “Crown Jewel”, of DR advertising.

The obvious drawback of both approaches is and will always be the cost of learning.  Because of the random nature of Internet advertising, we need to survey the biddable landscape before we can make any type of calculation, qual or quant.  In the quant world, it probably takes more time and thus requires a longer discovery period, while in the audience buying game, those “selective” impressions can get quite expensive when we are still learning which audience represents our sweet spot. Since there is no such thing as a free lunch in this business, both are going to require some upfront cash that will initially skew our calculated CPAs.

Instead of simply accepting this as truth, I want to treat this as an opportunity to thwart the economic black hole of overcoming a campaign’s inertia when starting from rest.  I see opportunity to utilize both approaches as a function of time and campaign maturity in order to get campaigns running more quickly and performing better long term.  I see complex regression analysis and time series plots that will tell us on a campaign by campaign basis of when and how to use audience targeting and when the stats tell us that the audience is worthless.  I see recurring real time cost benefit analysis algorithms telling us that we are paying too much for an audience or that an audience is underpriced.  I see this as a first step towards creating a RTB marketplace where being smarter actually translates to being more successful. For the first time, I see a scientific approach to an age-old art and an artistic approach to centuries-old mathematics.

Will this be accomplished and will it even make a difference? I leave you as a lawyer by saying “it depends”.