As Facebook works feverishly this year to hammer out its business model, it should consider that the three most profitable online media companies in the U.S. (Google, Microsoft and Yahoo!) all rely on advertising revenue for their businesses. Moreover, advertising has furnished a reliable business model for every electronic medium in U.S. history and there is no reason to think that online social networking platforms will be the exception. With this in mind, Facebook should look into different ways to enable advertisers to place rich, engaging content on the site that users could share with their friends. Allowing brands to place shareable rich content on the site could also play into the company’s recently launched virtual currency program (“Facebook Credits”). The company could permit advertisers to “sponsor” Facebook Credits of users who share the sponsor’s ads with their friends. In this way, the company could generate revenues from both advertising and its virtual currency while more effectively monetizing the myriad of social interactions that occur between users on the site.

Currently, Facebook allows only site-served ads and a strictly limited set of formats. Facebook’s rationale behind its decision to limit the creativity of advertisers in this way is that it ensures a sleeker user experience. Moreover, the company clearly believes that the enhanced relevancy of the ads (since they’re served by the site which has access to all user profile data) will make them engaging despite their rudimentary content. However, relevant does not equal engaging. People share content because it’s entertaining. Conversely, people will often fail to notice boring content, even if it’s ‘relevant.’ The narrow range of creative formats Facebook offers allows little room for truly rich engaging user experiences. Thus, in its effort to create less intrusion, Facebook has in fact enabled the placement of boring content that actually clutters the user experience – despite the fact that it’s targeted for relevancy.

In conclusion, as it searches for a profitable business model, Facebook should proceed as follows. First of all, it should look to emulating profitable online media companies in the U.S. market. Second, it should remember that relevant does not equal engaging and users will ignore even the most targeted ads if they’re not entertaining. I have suggested one strategy the company could use to monetize content sharing by allowing advertisers to place rich engaging ads on the site that users could, by sharing the content, use to earn Facebook Credits sponsored by advertisers. However this is just one approach among many other, equally valid, and equal in quantity to the myriad of ways that users interact on Facebook.



Sean Gelles, Manager, Product Planning

After years of being under-utilized, if not completely ignored, behavioral targeting has re-emerged as an attractive technology in the online advertising marketplace. In light of this renewed interest in behavioral targeting, now is a good time to reconsider the technology’s strengths as a marketing strategy and discuss how advertisers and agencies can capitalize on them. Think of it as a refresher on behavioral targeting. Let’s examine what behavioral targeting actually is, the reasons for its slow penetration and the factors precipitating its resurgence.

Behavioral targeting is simply a way that parties on the demand (e.g. advertisers, agencies, technology vendors, etc.) and supply (e.g. ad exchanges, ad networks, publishers, etc.) sides of the online media business can harness data about users’ online behaviors to increase the value of online inventory as marketing media. The behaviors that companies involved in behavioral targeting track generally include browsing Web sites in specific content categories, visiting an advertisers’ Web site, completing or abandoning an online transaction or entering specific keywords into a search engine but can potentially encompass any and everything a user can do online. Typically the data is collected via publisher or third-party cookies.

In the early days, behavioral targeting vendors – such as Audience Science (formerly Revenue Science) and Tacoda (now part of AOL’s Platform-A) – struggled with a number of obstacles as they attempted to sell their solutions. The major obstacle was scalability. Behavioral targeting vendors are basically ad networks comprised of non-guaranteed or remnant inventory across a set of publishers. This means that desired behavioral segments are frequently small, often too small to meet the particular advertisers’ reach goals. As a result, most advertisers have used premium inventory to meet their reach goals and publishers’ user demographics as a proxy identifiers for their target audience. Other challenges included user privacy and brand integrity (advertisers often had no control or knowledge regarding the content adjacent to which their ads were being placed).

However, despite the challenges, several factors have arisen that are making behavioral targeting a hot topic once again. The most important of these factors are the saturation in premium display inventory and the increasing fragmentation in the online media environment. Premium display has reached a saturation point in terms of supply while increasing media fragmentation and competition with offline channels has placed definitive limits on the price publishers can charge for premium placements. This development has shifted the focus on the supply and demand sides of the online media business to non-premium display inventory. However, non-premium display buying raises the issue of identifying the audience target since publishers can no longer be used as proxies, at least not at scale. This is where behavioral targeting enters the picture. In addition, the launches of behavioral data exchanges (e.g. BlueKai and eXelate) as well as meta ad networks (e.g. MediaMath, [x+1], Varick Media Management) have made behavioral targeting more scalable as user data is now available across networks and networks themselves are now customizable around specific behavioral targets. Meanwhile advocacy groups have been working with major industry players to introduce legislation that would provide opt-out mechanisms for consumers averse to the idea of being tracked online.

Now that behavioral targeting has returned to the spotlight, let’s turn to how advertisers should use the technology for best results. (more…)

One of the biggest obstacles to increasing advertiser investment into online display advertising is that there still is little definitive proof that online branding campaigns have a positive impact on offline sales.  Everyone agrees that online display campaigns can drive sales online and all of the major digital ad campaign management companies have introduced analytical tools, such as Eyeblaster’s Channel Connect for Search, that enable advertisers to measure the impact of the branding elements of their online campaigns on online sales.  The problem is that most consumer purchases still occur offline.  This means that, if online display advertising is ever going to be taken seriously as a branding medium, there needs to be definitive proof that display ads can drive offline sales.

The good news is there are a number of strategies for measuring the impact of online branding campaigns on offline sales.  None of the methods are easy or cheap.  However this research is necessary to increase advertisers’ confidence in online display advertising.  Ultimately, the industry will reach a tipping point after which the value of online advertising for branding will be taken as a given just as is the case for TV.  Until then, the following are the best ways we can prove the value of online display advertising.

Database matching

Description: This technique involves comparing online impressions to offline purchases using a representative panel consisting of consumers whose online behavior and offline purchases are both tracked.

Pros: This is the most rigorous methodology since all the data is electronically recorded.

Cons: It’s expensive.

Vendors: comScore, Dynamic Logic, Nielsen, Platform-A, Yahoo!

Longitudinal surveys

Description: This method involves conducting surveys over time among a representative panel to gauge their purchasing patterns as well as the online ads they’ve seen to determine whether there is an association between the two.

Pros: This is the least expensive methodology.

Cons: It’s less rigorous since it depends on the panelists’ abilities to accurately recall which brands they’ve purchased and, more importantly, which ads they’ve seen.

Vendors: Dynamic Logic, InsightExpress

Geographic testing

Description: This strategy separates different DMAs into control and test groups which are exposed or not exposed to online ads accordingly.  Offline sales in the different DMAs are then compared to determine the impact of exposure or non-exposure to the online ads.

Pros: This is a fairly rigorous methodology.

Cons: It sacrifices reach for the campaign in question, and sales if the ads prove effective,  since certain DMAs are not exposed to online advertising.

Vendors: This is usually done in-house by the advertiser.

Marketing mix modeling

Description: This scheme uses sophisticated statistical analyses such as multivariate regressions on ad serving and offline sales data to estimate the impact of online advertising on offline sales.

Pros: This is a proven method for measuring the impact of TV ads.

Cons: It tends to focus heavily on short-term sales impact and may be less suited for online.

Vendors: MMA, Marketing Evolution, MarketShare Partners

If you’re aware of any advertisers who have completed studies like these, it’s a good idea to share the results and/or the insights.  comScore has done a great job of this recently, publicizing results of some of its studies and even publishing some of its latest research in the last issue of the Journal of Advertising Research*.  Pooling our efforts as an industry is necessary if we’re going to establish once and for all the value of digital as a marketing medium. 

*Fulgoni, G.M., & Mörn, M. P. (2009).  “Whither the Click? How Online Advertising Works.”  Journal of Advertising Research, 49(2), 134-142.

Sean Gelles, Product Planning Manager