Thoughts on Facebook Monetization

I’ve been following the Facebook Monetization quest for a while now, and thought I’d throw up a couple of thoughts on that topic just to get them out there and see what people think.

First, let’s distinguish clearly between audience data and audience inventory (impressions). These two items often come bundled together and are thus often confused to be one and the same. That’s incorrect.

Facebook is a clear example where these two items are linked. They have a ton of both. The role of data is to provide insight into the user. This information is then used to decide a) whether you want to buy impressions on this person, and b) what creative you’re going to show them. Inventory is then in some ways just another opportunity to use data to accomplish some meaningful market objective.

Facebook’s data is great. They have a lot of data about me, and I’m guessing a lot of you as well. Unfortunately, their inventory just blows. In any advertising inventory that is embedded alongside content, one of the fundamental barriers that an ad needs to overcome is the drawing power of the content alongside which it’s presented. Because the content is so personalized and interesting to me, the effectiveness of traditional ads that are presented alongside can be very limited. (For the sake of simplicity, I’m going to cleave away all the ‘social advertising’ tactics that have become en vogue recently – that’s a totally separate post. Here, I’m just talking about what to do with the nuts and bolts impression inventory.)

All of this is really not a surprise. In many ways, we’ve seen this movie before with the various online email providers. Think about it. Lots of data (people logged in, registered, whatever) combined with oceans of impression inventory that just doesn’t seem to perform all that well, particularly if you’re using (admittedly shite) metrics like click through.

So then there are two tasks to accomplish.

1. Figure out how to best monetize the low-performing inventory that’s actually on facebook
2. Figure out how best to monetize all that beautiful data.

With regards to the second task, one potential method would be to buy oceans of crap inventory at, say, a $0.50 CPM, add data, and resell it at a $5.00 CPM or higher, keeping the spread, or value creation, or whatever you want to call it.

With that goal in mind, the strategy and tactics become very interesting. Look at this situation through the lens of the emerging trends in DSPs. Where does Facebook, as data provider, fit? Do they monetize by injecting their data into other DSPs directly? Do they aggregate other inventory, add their data, and sell the finished product to DSPs? Or perhaps, they form a DSP of their own with the Facebook data at the core?

I definitely have my own opinions here, but would love to hear what others think…

Have a good weekend everyone!

Easy as 1, 2, 3

So, the people I work with are probably sick of hearing me talk about the three levels of display optimization. For posterity, I’ll write them down here.

True optimization of display advertising spend must address three different, but related questions. I have dubbed them the three levels of display optimization:

Level 1. Bid Optimization – how much is this impression worth? (Equivalenty, how much should I bid?)
Level 2. Creative Optimization – now that I’ve purchased this impression, which creative variation will yield the most value?
Level 3. Landing Page Optimization – now that someone has clicked, what is the most effective sequence of pages/images/offers/whatever to seal the deal and generate revenue?

For reasons I’ve already gone through in a previous post, I believe levels 1 and 2 need to be part of the same operational stack.

Ideally, level 3 would also be part of the same stack, but I can see why advertisers would be loath to give up control over their landing pages to a third party. (Though it does happen occasionally.) My guess is that level 3 will most likely be loosely integrated, rather than optimized from within the same algorithm. This integration will probably occur by passing the “creative signature” – background color 6, with message 8, image 4, and call-to-action 2 – on the click URL. While the advertiser (and his or her landing page optimization algorithm) would almost certainly prefer the passing of this as well as the underlying targeting data, the passing of the latter might be complicated by the accompanying privacy-related conniptions of the FTC. (And don’t forget that conversion information must be passed back to both level 1 and 2, or an integrated level 1/2, to close the feedback loop(s).)

It’s interesting to note that most of the DSP-related buzz has centered around the denizens of Level 1, both the independents and the feverish efforts of the various agency holding companies to roll their own. What isn’t as well documented, though they’re starting to talk about it, are that the current residents of Level 2 and 3 are also starting to make noise on their own.

Level 2 Creative Optimizers include startups like Teracent, Tumri, Dapper, and a host of others, as well as the incumbent rich media players like PointRoll, Eyeblaster, etc, who have added (or are adding) optimization capabilities to their impressive frontend razzle-dazzle.

Level 3 LPO’s include a bunch of companies that I haven’t paid as much attention to, though I’ve been told they’re fairly numerous, especially if you count the site-side analytics guys. I think both Adchemy and Aggregate Knowledge started out here. (Someone confirm?)

Anyway, if you look closely at the good folks in Level 2 and 3, you’ll see that many of these companies are already well on their way toward moving into adjacent slots. Start reading the recent press for many of the companies named above, and you’ll see what I mean.

So who’s going to get the full stack first? Well, barring acquisition, I suppose the right question to ask is: which is hardest to build? Personally, I think it’s Level 2 and 3. (And, btw, have you noticed how similar Level 2 and 3 become if you think about landing pages as just another sequence of full-screen ads?)

That BlueKai thing

So, someone mentioned the topic of BlueKai today, and it reminded me that I wanted to pose the following question:

Are the companies that are using BlueKai to re-sell their targeting data maximizing shareholder value?

My gut says no.

Supposedly, it’s not well known who sells their data to BlueKai, but anyone with Firebug can sniff that out. Ebay definitely does, and I’m certain that I’ve seen in a net trace on expedia in the past as well. (though curiously not tonight.. hmm)

Anyway, my gut says that the folks who are selling this data would earn more revenue by using the first few retargeted impressions for their own advertising and then selling the rest to someone else. (I’ve seen numbers that basically prove this fact for at least one advertiser.)

As it has been described to me, what happens today is something different: If advertiser wants to bid on the first few retargeted impressions, they may be bidding against the very person that they sold their data to – increasing demand, and raising the price (and reducing the ROI) for both of them.

Have I been misinformed? If there are any folks with first hand experience in this sort of execution, please drop a note!

The DSP and why bid optimization is only half the battle

So this is my first work-related post. Apologies to those who aren’t interested in online marketing.

There’s been a lot of back and forth in the media lately about the imminent arrival of so-called Demand Side Platforms, or DSPs. (If you aren’t familiar, the gist is that DSPs will maximize online advertising ROI through sophisticated bid optimization, combined with features such as third-party data integration, real-time bidding, global frequency management, etc.)

The one thing that I haven’t seen discussed explicitly yet is the following:

The bid optimization found in DSPs needs creative optimization to function correctly. It sounds counter-intuitive, but is actually straightforward if you follow a simple line of reasoning.

Many studies have shown that variations in creative can affect the performance of intent-to-purchase (or whatever your metric du jour) by as much as 10x.

If you accept the premise that performance is tied to value, then this means that the right creative can improve the average value of an impression by as much as 10x, or vice versa. (Or 2x, or 3x – whatever.)

But that doesn’t mean that every impression’s value will improve by 10x if paired with the right creative – just the average. Some will improve by 5x, others will improve by 15x. A range of values will emerge around the mean, and the true value of each impression must be determined separately.

What that means for bid optimization then is that for a bid to be truly optimized, it has to be made with a view or prediction of how that impression will perform, when paired with an optimized creative.

For example:

Impressions #232334342: Male, 34, San Francisco, with interests in football, cars, and looking for a first mortgage. Good credit. College graduate.

Bid #1: $3.00 eCPM -based upon average value of all past creative variations

Bid #2: $5.00 eCPM – based upon the prediction that ad variant 6, with background color 8, message 4, and call-to-action 10 is expected to be worth a $5.00 eCPM when paried with this particular impression.

Who do you think wins? Clearly the bidder with better information, Bidder 2.

Bidder 1 on the other hand – because he’s constantly bidding the average – will overbid the less valuable impressions, and underbid the more valuable impressions. Lack of information yields poorly optimized bid, leads to lack of performance, value, and ROI.