Home Analysts Yield Optimizers Poised To Migrate To Exchange Model Says ThinkEquity’s Morrison and Coolbrith

Yield Optimizers Poised To Migrate To Exchange Model Says ThinkEquity’s Morrison and Coolbrith


ThinkEquity Partners LogoBill Morrison and Robert Coolbrith are equity analysts at ThinkEquity Partners and recently authored a report entitled, “The Opportunity In Non-Premium Display Advertising.”

AdExchanger.com: Are data exchanges the key to unlocking value in social media? Who’s getting it right in the data exchange space?

BM and RB: We’ll defer the second part of your question until a later date. As investment research analysts, it’s hard for us to identify “winners” without the help of industry participants, who provide us with invaluable insight and opinions. In our recent conversations with networks and exchanges, when we asked about the most important new companies in the space, the data exchanges invariably came up. However, it still seems to be pretty early in the development of the data exchange model, and we didn’t sense any clear consensus from industry participants on who is taking the lead.

While data exchanges are certainly “hot,” it’s an open question as to whether an auction-based marketplace is the “right” paradigm for monetization of proprietary consumer insight. People have pointed out the similarities between the data exchanges and the offline list marketing/direct mail model, and we think it’s a fair comparison. In both cases, the buyer pays up front for the data and then leverages the data to market to consumers on an individual basis. But that’s complicated in the online world by the matter of actually reaching the identified consumer, which can be a bit like finding a needle in a haystack. An alternative approach might be to realize that the haystack is full of needles, but of varying types. So, allow the inventory to dictate the targeting, not the other way around.

Regardless of how the mechanics play out, we think third-party data exchanges will be important for the monetization of all display media, but will be particularly important in social media, where the inventory doesn’t typically contextualize well, the platforms are rich with demographic, behavioral, and social data, and current monetization levels make targeting a real priority. One thing we’re currently wondering about is which could be the bigger opportunity for social media: the inventory (where value should be enhanced through the use of both first- and third-party data) or the first-party data that can be directly monetized through data exchanges to enhance the value of display inventory across the Web. The data exchanges, by providing a transparent pricing mechanism for behavioral, demographic, and social data, should help answer that question.

How do you see large publishers evolving as media trading achieves scale?

The very largest publishers will be deeply involved in media trading, both as the sponsors of the key liquidity platforms, and, in all likelihood, as traders in their own right. Regarding a broader set of “large publishers,” including the leading vertical publishers, social networks and smaller portals, there are likely to be a lot of changes in terms of how they manage O&O inventory. Direct sales organization will remain relevant for sponsorships and custom integrations, but sales of banners, IMUs, etc. are likely to become more automated, data-driven and audience-centric, whether that inventory is sold through forward/futures markets, co-selling arrangements with partners, or non-premium spot markets. In terms of incremental reach extension, larger publishers will likely find varying degrees of success via curatorial vertical network strategies and via data-driven media trading.

Could agencies disintermediate ad networks or visa versa?

It seems that many traditional media agencies are deeply envious of the margin profiles of their ad network peers, and are intent on putting the ad networks out of business as a matter of principle. Whether or not they’ll succeed is another matter entirely. Agency and network business models appear to be rapidly converging as the agencies build out private networks and adopt media trading, and as the networks gradually abdicate their role in inventory aggregation to the ad exchanges and yield optimization platforms. Agencies have the advantage of adjacency to the client, the client’s data, and the client’s budget, but it remains to be seen if they can successfully adapt to a technology-driven business model—although third parties like MediaMath and Invite Media should be helpful to them in doing so. When it comes to media trading, leading technology-oriented ad networks have significant first-mover advantage—they’ve been dynamically pricing and optimizing ad inventory for years within their own private “markets”. The ultimate winners and losers should be determined by who boasts the best performance on behalf of clients, but there appear to be a number of complicating factors at work.

How do you define premium inventory?

We define premium in the way that Yahoo! and other large publishers traditionally have, as inventory sold with specific guarantees as to placement, timeframe, volume, etc. It is unfortunately a confusing nomenclature, and Yahoo! now makes use of alternate terminology such as “guaranteed” and “Class I”. The distinction between premium and non-premium can, however, be a bit fluid? Run-of-site behavioral targeting sold on the Wall Street Journal, premium or non-premium? Probably premium. Run-of-site demographic targeting across Fox Interactive Media properties? Probably non-premium. These fine distinctions are part of the reason we point to the emergence of a “secondary premium” inventory category encompassing co-selling arrangements, inventory sold through automated sales platforms and forward markets like Yahoo! APT, etc. In formulating our industry estimates, however, our general rule of thumb is that directly-sold inventory counts as premium, inventory sold by third parties as non-premium.


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In your report, you suggest companies like Vizu will aid in providing analytics and support for brand awareness campaigns and the long-rumored brand dollars. Do you think agency compensation structures have kept media budgets tilted toward traditional media too? Hence, the slowness in moving branding budgets online?

We’re not sure that compensation plans vary all that significantly between online and offline media. However, there is clearly a large opportunity cost for sales people when they focus on selling digital brand campaigns to their clients. Executing an online media campaign is far more complex, time consuming, and expensive for brand marketers and agencies than it is to execute a campaign in television, for instance. Publicis/Denuo recently completed a study and found that the cost of executing online campaigns (excluding creative development) represented approximately 15-20% of the media spend on average, compared to offline execution costs on the order of 2-3% of media spend. That is an astounding cost differential, if you think about it, and one of the major reasons that only 3-4% of brand dollars have migrated to the Internet to date (compared to ~15% for direct response). There are a number of other “pain points” within the online advertising ecosystem that are slowing the offline-to-online migration of brand dollars, including a lack of standards for comparing the efficacy of brand campaigns across online and offline media, too many proprietary formats in emerging media categories like video, sub-optimal inventory allocation decisions by publishers, inventory forecasting challenges that often lead to over or under delivery of campaign goals, etc. The good news is there are lots of companies that are trying to create solutions that alleviate these “pain points,” and we remain optimistic that online will continue to take share from offline brand budgets.

How will demand-side optimization affect pricing and performance of online media?

We view any limitation on buyers’ performing their own allocation decisions (and iterative refinements) as an economic externality in the online display ad market. While we don’t think this argument has been generalized, externalities in auction-based sales processes tend to impose a tax on the seller. An unfair auction process provides bidders with an incentive to offer less than their best price. While we wouldn’t qualify the display ad market as “unfair,” buyers have had very limited ability to perform real-time inventory allocation decisions prior to the advent of now-emerging real-time-bidded ad exchanges. As transparency to buyers increases, prices in general should increase. As the options available to media buyers in terms of inventory allocation increase (e.g., agencies may direct impressions to the client account of their choosing, or re-vend the inventory in a spot advertising exchange), prices should increase. So, we view demand-side optimization as a significant catalyst for both pricing and performance.

Your coverage of the yield optimization companies such as Rubicon Project, AdMeld, Pubmatic, YieldBuild and Yieldex in your recent report was extensive. What initially drew you to the yield innovators? Are they the new ad networks and/or exchanges of the future?

When we became aware of the yield optimization platforms in late 2007, we were initially skeptical. The Right Media Exchange had already been acquired by Yahoo!, DoubleClick (and its advertising exchange) had already been acquired by Google, and AdECN had already been acquired by Microsoft. Why would the world need yield optimizers if it already had dynamically-bidded ad exchanges (on their way to real-time-bidded models)? The dynamic default management approach typical of the yield optimizers should offer superior yield and efficiency versus static default management by human ad operations personnel in most (but not all) cases, but seems unlikely to outperform a liquid ad exchange.

The problem of yield optimization is not trivial, in as much as it involves dynamically allocating inventory to optimize revenue derived from multiple buyers whose pricing varies over time and across variable inventory; complicating matters is that this optimization process is performed utilizing retrospective pricing data. By comparison, in an ad exchange, revenue for each individual advertising impression is maximized by an auction in which each bidder offers their best price (assuming no marketplace externalities). We believe that ad exchanges represent the likely endgame for non-premium display inventory aggregation over a several year time horizon; what we had not earlier appreciated was that publishers required a near-term solution for yield management given ad exchange liquidity constraints and marketplace inertia. Now that many of the yield optimization platforms have built substantive publisher footprints, they appear poised to migrate toward the ad exchange model—real-time APIs for ad network partners appear to be just the first step in that direction.

If you had to pick one company in your recent report that is under the radar but has an exciting opportunity ahead, which one would it be and why?

It’s not in our report, but we think AdExchanger.com is terrific and has a bright future ahead of it.

Good answer. 🙂

If you’d like a copy of Bill and Robert’s report, “The Opportunity In Non-Premium Display Advertising,” email Bill at: – wmorrison at thinkequity dot com.

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