 
avant|marketer: So, the flow of data in and out of ad serving systems will be done in common sense terms? Basically, advertisers will be able to say to a delivery system, "I'm uploading a set blue banners, and a set of red banners," and then, after the campaign ends, the system will provide figures to the effect that the blue banners performed better than the red banners, or vice versa. Is that the critical leap forward, here?
Hitendra Wadhwa: That's only part of it.
The critical leap forward comes in how this will alter the way in which ad targeting is done.
Once data begins to flow in these terms, advertisers will be able to say to the ad server, through the optimization system, that they are putting in a set of creative with certain attributes - color attributes, text-style attributes (Is the ad a plain text ad or not?) - and not only will the ad server be able to recognize the creative, it will be able to apply the entire aggregated history of that very type of creative - all of those generalized learnings, we've been talking about - to the targeting of that specific creative set.
avant|marketer: So, in effect, real-time optimization processes are going to involve two distinct layers of optimization, one which involves the application of campaign-specific rapid feedback data, and the other which involves the application of aggregated historical data, which will be used to assist in targeting ads from the get-go?
Hitendra Wadhwa: Right. That's absolutely right.
If you really think about it, all optimization efforts ultimately seek to boost performance.
So optimization is ideally done by blending whatever relevant historical data is available, with the rapid feedback provided by the various interactive channels that a campaign is being delivered through.
The more relevant the historical data that is available to the advertiser from the get-go, the less investment the advertiser has to make in the rapid feedback, real-time portion of the optimization process. So, the build-up of this historical data is likely, over time, to bring the costs of campaign optimization down for advertisers, and is also likely to considerably lessen the amount of time it takes for a campaign to reach its optimal performance level.
avant|marketer: What's emerging from what you've said is a picture of ad targeting that is vastly different from the one we have today. Right now, ad targeting is often achieved based on the advertiser leveraging a whole host of disparate pieces of often prohibitively costly data: Data contained on cookies, opted-in user data (typically brought in from the publisher at a substantial premium), and so on.
With the introduction of real-time optimization into the equation, does all of this data become obsolete, from a targeting perspective? And, does real-time optimization improve the cost efficiency of ad targeting then, by eliminating the advertiser's need for using all this costly data?
Hitendra Wadhwa: I think it's fair to say that ad targeting has really, so far, been an unfulfilled dream.
There's been a lot of promise and, to some extent, hype about the one-to-one personalization, targeting, etc., etc. capabilities of our medium. Certainly, a few companies have tried to do some very ambitious things in the ad targeting arena. But, overall, these efforts have received a very lukewarm reception from advertisers and publishers. And the reason for this, I think, is mostly that the lifts generated from these efforts have been too minimal to justify the costs of involvement from either side - publishers or advertisers. So, thus far, ad targeting has failed to become institutionalized as a serious and powerful practice.
What I think real-time optimization will do to move ad targeting forward is allow the advertiser to lay out a road map for the progressive targeting of a campaign that starts from a very basic level, in a way that doesn't involve a whole lot of costly, complicated data going into the equation, but just involves a few simple data-driven parameters like time of day and geographical location. Used in conjunction with rapid feedback real-time optimization algorithms and the historial data, this very basic data set alone will allow the advertiser to reap significant boosts in performance - so there will be no longer an initial requirement that the advertiser bring masses of outside targeting data to the table - opt-in data, and so on - in order to do ad targeting, and get results from it.
The other thing that real-time optimization will do to with respect to all of this targeting data is to allow it to be used much more efficiently. After the initial phase of real-time optimization is complete, and the initial performance boost has happened, real-time optimization systems will allow the advertiser to start to incorporate other data streams, like opt-in data, where this is available, and to figure out, just like with the creative, precisely which data streams are leading to a significant boost in campaign performance and which aren’t, and eliminate the use of the data streams that are not adding to boosts in performance.
So, I would not as much claim that real-time optimization makes the traditional data obsolete, as that it allows the advertiser to incorporate this data at a later stage, allowing the advertiser to then even further improve targeting. Where such additional, more robust data is available - opt-in data, and all of that - real-time optimization will, essentially, make it's use much more efficient, which will, in turn, favorably impact cost, because the advertiser is not paying to incorporate data streams that the optimization process demonstrates are not impacting campaign performance.
avant|marketer: So far, most discussion we've had has focussed on the separate optimization of individual Internet marketing channels - Email, Banners, what have you. The Holy Grail here would seem to be the simultaneous real-time optimization of all the channels that comprise a given Internet media mix. Are we ever going to be seeing total media mix optimization, as such, in which the metric optimized for is overall campaign ROI?
Hitendra Wadhwa: It's important in looking at this question that we differentiate between two different types of media, if you will. One of these involves the marketers interaction with customers in channels and on media that the marketer owns - house file email lists, their own web site and in-house banner inventory, and so on. Here we're mostly talking about retention channels. The other involves interactions with customers through media that the advertiser has bought on partner sites, third party email lists, etc.
In the first case, where you're talking about the advertiser's own real estate, we see the advertiser's ability through real-time optimization to simultaneously optimize their campaigns across all of their internal customer touch points, if you will, as something that will soon be a reality, and something that is coming very quickly.
In the second case, when you're talking about bought third-party media, where the situation is that I want to establish a media mix that involves essentially buying customers by paying money to third-party media properties, whether those properties include a Yahoo!, a New York Times Digital, or an eUniverse (for email delivery), and try to do an integrated optimization across those properties, I see it as being less practical for real-time optimization to happen at the advertiser level.
I think in these cases, it is going to prove far more practical and powerful that real-time optimization happen from within the media outlets themselves. This is something that I think will be an attractive and valuable option for particularly the media outlets that have a lot of scale, a lot of advertisers, and a lot of variation in the types media channels that they offer - a New York Times Digital, for instance.
In these cases, you can imagine then, that the real-time optimization tools are likely to influence how the media outlets actually package their media for their advertising customers, and will allow large media outlets to more significantly drive financial value for their advertisers, by enabling the advertiser to start with a media mix that is spread out broadly across the various types of media that they offer, but then, through rapid feedback real-time optimization, allow the advertiser to home in on the sweet spot of media that is providing the highest ROI in their specific case. So what happens is that media outlets begin to segment their media properties not as they do now, in terms of say, customer profiles or demographics, but rather in terms of the media itself.
avant|marketer: Paramark claims PILOT can real-time optimize for any metric. However, at the outset, you were quick to link real-time optimization to direct response. Yet, firms such as Dynamic Logic are taking big strides toward make the branding impact of Internet Ad campaigns measurable, in an automated fashion. Are we going to see real-time optimization systems then, begin to optimize campaigns based on the real-time branding feedback delivered by say, Dynamic Logic's AdIndex system?
Hitendra Wadhwa: We have actually already been approached by a worldwide brand in the consumer packaged goods arena that has asked Paramark to develop a branding optimization solution along the lines of what you're alluding to. When the solution is done, the vision is that the advertiser will be able to identify what is the optimal story-boarding strategy in terms of what sequence and what frequency which ads should be shown to which segments of customers, in order to maximize the impact on the specific branding metrics that are important to the advertiser.
But, to do this is a big leap from the direct response scenarios that we have been discussing. Because, when it comes to Branding, what one is trying to do is to gradually shift the psychological state of a consumer through multiple advertising impressions, over a very extended period of time. This contrasts significantly with the impulse-oriented nature of direct response marketing, where what we look at often has to do with cause and effect connections happening over the matter of a few seconds. So to do branding optimization in real-time does require some changes in the approach one takes to real-time optimization. And these are changes we're currently conducting research into.
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