Lies, Damned Lies...

Building the Perfect Display Ad Performance Dashboard, Part I – creating a measurement framework

There is no shortage of pontification available about how to measure your online marketing campaigns: how to integrate social media measurement, landing page optimization, ensuring your site has the right feng shui to deliver optimal conversions, etc. But there is very little writing about the other side of the coin: if you’re the one selling the advertising, on your site, or blog, or whatever, how do you understand and then maximize the revenue that your site earns?

As I’ve covered previously in my Online Advertising 101 series, publishers have a number of tools and techniques available to manage the price that their online ad inventory is sold for. But the use of those tools is guided by data and metrics. And it’s the generation and analysis of this data that is the focus of this series of posts.

In this series, I’ll unpack the key data components that you will need to pull together to create a dashboard that will give you meaningful, actionable information about how your site is generating money – or monetizing, to use the jargon.

We’ll start by taking a high-level look at a framework for analyzing a site’s (or network’s) monetization performance. In subsequent posts, we’ll drill into the topics that we touch on briefly here.

 

Getting the measure of the business

Ultimately, for any business, revenue (or strictly speaking, income or profit) is king. If you’re not generating revenue, you can’t pay the bills (despite what trendy start-ups will tell you). But anyone running a business needs a bit more detail to make decisions that will drive increased revenue.

In the ad-supported publishing business, these decisions fall into a couple of broad buckets:

• How to create more (or more appealing) supply of sellable advertising inventory
• How to monetize the supply more effectively – either by selling more of it, or selling it for a better price, or both

Another way of thinking about these decisions is in a supply/demand framework that is common to almost all businesses: If your product is selling like hot cakes and you can’t mint enough to meet demand, you have a supply problem, and you need to focus on creating more supply. If, on the other hand, you have a lot of unsold stock sitting around in warehouses (real or virtual), you have a demand problem, and you need to think about how to make your products more compelling, or your sales force more effective, or both.

Online publishers usually suffer from both problems at the same time: Part of their inventory supply will be in high demand, and the business will be supply-constrained (it is not easy to mint new ad impressions the way a widget manufacturer can stamp out new widgets). Other parts of the inventory, on the other hand, will be hard to shift, and the business will be demand-constrained – and unlike widgets, unsold ad inventory goes poof! when the clock strikes midnight.

So analysis of an online ad business needs to be based on the following key measures:

• How much inventory was available to sell (the Supply)
• How much inventory was actually sold (the Volume Sold)
• How much the inventory was actually sold for (the Rate)

It’s ultimately these measures (and a few others that can be derived from them) that will tell you whether you’re succeeding or failing in your efforts to monetize your site. But like any reasonably complex business (and online advertising is, at the very least, unreasonably complex), it’s really how you segment the analysis that counts in terms of making decisions.

 

What did we sell, and how did we sell it?

Most businesses would be doing a pretty poor job of analysis if they couldn’t look at business performance broken out by the products they sell. A grocery chain that didn’t know if it was selling more grapes or grape-nuts would not last very long. Online advertising is no exception – in fact, quite the opposite. Because online ad inventory can be packaged so flexibly, it’s essential to answer the question “What did we sell?” in a variety of ways, such as:

• What site areas (or sub-areas) were sold
• What audience/targeting segments were sold
• What day-parts were sold
• What ad unit sizes were sold
• What rich media types were sold

The online ad sales business also has the unusual property that the same supply can (and is) sold through multiple channels at different price points. So it is very important to segment the business based on how the supply was sold, such as:

• Direct vs indirect (e.g. via a network or exchange)
• Reserved vs remnant/discretionary

Depending on the kind of site or network you’re analyzing, different aspects of these what and how dimensions will be more important. For example, if you’re running a site with lots of high-quality editorial content, analyzing sales by content area/topic will be very important; on the other hand, if the site is a community site with lots of undifferentiated content but a loyal user base, audience segments will be more relevant.

 

Bringing it together – the framework

I don’t know about you, but since I am a visual person to start with, and have spent most of the last ten years looking at spreadsheets or data tables of one sort or another, when I think of combining the components that I’ve described above, I think of a table that looks a bit like the following:

This table is really just a visual way of remembering the differences between the measures that we’re interested in (volume, rate etc) and the dimensions that we want to break things out by (the “what” and “how” detail). If you don’t spend as much of your time talking to people about data cubes as I do, these terms may be a little unfamiliar to you, which is why I’m formally introducing them here. (As an aside, I have found that if you authoritatively bandy about terms like “dimensionality” when talking about data, you come across as very wise-sounding.)

In the next posts in this series, I shall dig into these measures and dimensions (and others) in more detail, to allow us to populate the framework above with real numbers. We’ll also be looking at how you can tune the scope of your analysis to ensure that

For now, here’s an example of the kinds of questions that you would be able to answer if you looked at premium vs non-premium ad units as the “what” dimension, and direct vs indirect as the “how” dimension:

 

As this series progresses, I’d love to know what you think of it, as well as topics that you would like me to focus on. So please make use of the comments box below.

Nicely executed retargeting opt-out (for a change)

Retargeting (sometimes called remessaging or remarketing) has taken off in a big way, recently – Google introduced the feature into AdWords earlier this year, and a host of other players are in the game. Consequently, the interwebs now abound with commentary on the rather spooky nature of the technology, with people being “followed around” the Internet by ads for things they were either searching for, or were looking at on e-commerce websites.

It is true that most retargeting implementations are a bit clunky, and I have been on the receiving end of plenty of them myself. Their most irritating aspect seems to be that the time window for perceived relevance of the retargeted ads seem to be ridiculously long. It’s somehow almost more irritating to be deluged by ads for that miscellaneous widget site that you once visited a few weeks ago (even though you have since satisfied your need for widgets elsewhere) than it is to be served non-targeted (or more broadly targeted) ads.

Such ads are made more bearable by a robust opt-out capability; many ad networks have adopted the IAB’s self-regulatory program, which calls for the advertiser to make it possible to opt out of these kinds of ads, which is to say, stop receiving them; stopping the data collection is a more difficult matter.

So today I want to give a little love to TellApart, not because their retargeting implementation is especially subtle or innovative, but simply because they provide a nice opt-out implementation. Last week I spent a little time looking for a desk for my daughter (who currently occupies our dining table with her homework). So since then I have been served retargeted ads on behalf of the site I visited (www.childrensdesks.com) on various sites. Here’s one from Business Insider:

The nice thing about the ad is it has a little “x” icon in the top right (which actually makes a little more sense than the IAB’s suggested “Advertising Option Icon”, which is a bit cryptic). Clicking it gives me this:

The ability to opt out right in the ad unit is nice, and makes me feel more well-disposed to the advertiser and the site that the ad is running on. Clicking through the “Learn More About These Ads” link at the bottom takes me to TellApart’s website with a little more information and the same option to opt out – though no option to opt out of certain categories of ads, or groups of advertisers.If more retargeting networks provided simpler opt-out capabilities like these, it might help to make these ads seem like less of a scary proposition.

Does Display help Search? Or does Search help Display?

One of the topics that we didn’t get quite enough time to cover in detail in my face-off with Avinash Kaushik at last week’s eMetrics Summit (of which more in another post) was the thorny issue of conversion attribution. When I asked Avinash about it, he made the sensible point that trying to correctly “attribute” a conversion to a mix of the interactions that preceded it ends up being a very subjective process, and that adopting a more experimental approach – tweaking aspects of a campaign and seeing which tweaks result in higher conversion rates – is more sound.

I asked the question in part because conversion attribution is conspicuously absent from Google Analytics – a fact which raises an interesting question about whether it’s in Google’s interest to include a feature like this, since it may stand to lose more than it gains by doing so (since the effective ROI of search will almost certainly go down when other channels are mixed into an attribution model).

Our own Atlas Institute is quite vocal on this topic, and has published a number of white papers such as this one [PDF] about the consideration/conversion funnel, and this one [PDF], on which channels are winners and losers in the new world of Engagement Mapping (our term for multi-channel conversion attribution).

The Atlas Institute has also opined about how adding display to a search campaign can raise the effectiveness of that campaign by 22% compared to search alone – in other words, how display helps search to be better.

However, a recent study from iProspect throws some new light on this discussion. The study – a survey of 1,575 web consumers – attempted to discover how people respond to display advertising. And one of the most interesting findings from the study is that, whilst 31% of users claim to have clicked on a display ad in the last 6 months, almost as many – 27% – claimed that they responded to the ad by searching for that product or brand:

This raises the interesting idea that search can actually help display be better, by providing a response mechanism that differs from the traditional ad click behavior that we expect. Of course, this still doesn’t mean that search should get 100% of the credit for a conversion in this kind of scenario – in fact, it makes a stronger case for “view-through” attribution of display campaigns – something that ad networks (like, er, our own Microsoft Media Network) are keen to encourage people to do, to make performance-based campaigns look better.

All this really means that, of course, it’s not a case of display vs. search, but display and search (and a whole lot of other ways of reaching consumers). Whether you take the view that it’s your display campaign that helps your search to be more effective, or your search keywords that help your display campaign to drive more response, multi-channel online marketing – and the complexity that goes with measuring it – looks set for the big time. And by “big time”, I mean the army of small advertisers currently using systems like Google’s AdWords, or our own adCenter. So maybe we’ll see multi-channel conversion attribution in Google Analytics before long.

PubMatic kicks us when we’re down (but gently)

As if we weren’t all feeling gloomy enough already, PubMatic has just released its Q4 2008 Ad Price Index report, which makes for sobering reading. For those of you not familiar with PubMatic, they provide “multi-network optimization” for publishers who are looking to maximize the yield on their remnant ad inventory (i.e. the inventory the publisher can’t sell themselves).

Rather than manually dealing with a handful of networks directly, the publisher hands their inventory over to PubMatic who ensures that the most profitable ad is shown, whether it comes from Google Adsense, BlueLithium, AdBrite, or another network. Since a key part of what PubMatic does is measure the CPMs for online ads, they have access to lots of ad price data, and every quarter they roll this data up into a report (available here as a PDF).

PubMatic has been doing this for 15 months now, and so far, they’ve yet to deliver any good news:

Given the economic Armageddon that overtook the world, PubMatic’s report that average prices only softened by $0.01 during Q4 actually seems like pretty good news. But then again, Q4 was the holiday season; and compared to Q4 2007, Q4 2008’s numbers look pretty horrendous.

The detail of the report contains some more interesting tidbits: for example, the average CPMs for small sites (fewer than 1 million PV/mo) are the highest, at around $0.60, whilst the average CPMs for large sites languish at around the $0.17 mark.

Before you start predicting the doom of the mainstream media, however, it should be pointed out (as Mike Nolet has done) that there is a sample bias in the PubMatic numbers – whereas a small publisher is wholly dependent on ad networks for all their revenue (lacking the resources to sell their inventory themselves), and so is likely sending all its inventory (including the juiciest stuff on the home page) to PubMatic, a large site will only be sending the inventory that they couldn’t sell themselves – i.e. the bottom-of-the-barrel stuff.

It also turns out that average prices for the largest and smallest publishers have slumped by around 50% in the past year, whilst prices for medium-sized sites have remained more solid:

I’m at something of a loss to explain why this might be – at the high end, it may be because large sites are becoming more efficient at selling their inventory themselves, so it’s only the really cheap stuff that is being passed on to PubMatic; whilst at the bottom end, small publishers are becoming increasingly crowded out by new sites.

What would be immensely useful would be for PubMatic to provide some kind of indication of the proportion of inventory from sites that is being served through them; this would make it easier to understand if changes in average prices through PubMatic are the result of a change in the mix of inventory that is being passed to the company. However, I would be very surprised if PubMatic had access to this kind of data.

One more thing…

Once you’re done reading the Ad Price Report, stick around on the PubMatic site a little longer and download their White Paper entitled “Death to the Ad Network Daisy Chain”. This little document does a nice job of explaining how an impression is passed from one ad network to another, and highlights the surprisingly high proportion of ad calls that are returned ‘unsold’ by networks. The document then goes on to talk about how ad operations folk have to manually set up ‘daisy chains’ of ad networks to try to ensure that the maximum amount of inventory is sold. As the title of the document implies, this is held to be a bad thing.

Because of the nature of the business that PubMatic is in, the recommendation in the document is that publishers use ‘dynamic daisy-chaining’ (which is essentially what PubMatic does, choosing the order of daisy-chaining based on expectations about which network will be likely to monetize an impression most effectively) to solve this problem. At one point the document states (my emphasis):

Due to the volatility of online ad pricing … creating a dynamic “chain” of ad networks, rather than a static one, is the only way for a publisher to ensure that they can get the best price possible for their ad space.

I would respectfully disagree with this statement; another way of achieving this is to use an ad network that is a member of an ad exchange, and which can therefore draw on a larger pool of advertisers than just those with whom it has a direct relationship.

But I don’t disagree with the main sentiment of the PubMatic paper, which is that publishers still struggle with significant inefficiencies in the way they monetize inventory; and I believe we’ll see the kind of multi-network optimization solution that PubMatic offers (also available from Rubicon and AdMeld) become increasingly important as the year wears on.

Yahoo! APT (formerly AMP!) emerges blinking into the sunlight

Well, they said it would launch in Q3, and it has – yesterday Yahoo! unveiled its new ad management platform, called APT, at a razzamatazz-filled event in New York introduced by John Hamm, star of Mad Men (currently the topic of much debate in our house as to its merits).

APT started life as “Project APEX”, which Jerry Yang started to talk about around a year ago as a successor/complement to Panama (Yahoo’s search ad platform, properly known as Yahoo Search Marketing). Yahoo then pre-announced something called AMP! (Ad Management Platform) in March, saying that it would revolutionize the way that ad media was bought and sold, drastically simplifying the selling process for publishers in particular. And yesterday AMP!’s name had changed again, to APT. APT does not appear to stand for anything, but at least they have dropped the exclamation point.

So what is APT? Well, according to Yahoo, it’s “designed to simplify the process of buying and selling ads online while connecting all the market players -- publishers, advertisers, agencies, networks, partners and developers -- from a unified platform to do business more efficiently and effectively”.

However, in its first incarnation, APT is principally a tool for publishers, aiming to make it easier for them to respond to advertiser/agency RFPs, and allowing them to build ad hoc private networks in order to be able to re-sell other publishers’ inventory. This latter capability is strongly linked to the initial user base for AMP, which is Yahoo’s Newspaper Consortium [PDF] members. The Newspaper Consortium is a sort of hybrid advertising and content network. Two members of this network – the San Francisco Chronicle and San Jose Mercury News – are founding customers of APT.

 

APT for publishers

According to the blurb on the APT site, APT will bring the following benefits for publishers:

• Simplified workflow (creative management, campaign management)
• Analytics & yield optimization (inventory management & prediction)
• Increased inventory liquidity through cross-selling abilities and integration with Right Media Exchange
• Extensibility via web services & an API
• Access to Yahoo!’s expertise (creative development, targeting, etc)

The only screenshot available is of the APT dashboard, which looks fairly nice, but doesn’t reveal that much about the functionality of the system (click the image to view it full-size):

There are also some more tidbits in the video which Yahoo release in April during the original AMP announcement.

Of the announced functionality, the ‘cross-selling’ capabilities in APT are some of the most interesting, representing a twist on the network model. Rather than running a traditional network where the publisher members give a certain portion of their inventory to a centrally managed hub, with APT, publishers can buy and sell inventory directly to and from one another. This will likely mean that publishers can get a better price for that inventory than if they sold it at remnant prices. If APT does a good job of this, it will make publishers’ lives much easier, and deliver a much-needed fillip to their revenues.

SImilarly, APT – by integrating with Yahoo’s Right Media Exchange (RMX) – could attract networks to work with Yahoo/RMX by offering a new pool of inventory that those networks could resell.

 

What about advertisers and agencies?

Where APT’s value is less clear to me is as a tool for advertisers and agencies. APT will provide a one-stop-shop for buying inventory on Yahoo’s properties and those of its publisher partners (i.e. the Newspaper Consortium folks) – and, given Yahoo’s strength in behavioral targeting, should be able to offer innovative inventory packages and highly targeted buying. But do advertisers and agencies need another interface for buying ads? These organizations would prefer to buy their media through the third-party ad server solutions (DoubleClick and Atlas, mostly) that they already use. Already they face fragmentation in their buying systems for search, contextual, display and rich media advertising – another tool may add to the pain, not ease it.

This may seem like a biased comment (since Atlas is now part of Microsoft), but in fact Microsoft faces the same kinds of challenge as we evolve our advertising platform. Our adCenter product offers a web UI for buying advertising (mostly search and contextual, with display coming), but larger advertisers prefer to interact with it indirectly through its APIs. And this – rather than a ‘one-stop-shop’ buying UI – is how I think APT will end up interacting with advertisers and agencies. Sensibly, Yahoo seems to have appreciated this, since a big part of its APT story is its extensibility through third-party integration.

in conclusion, if APT can create a more transparent network buying experience, and at the same time enable publishers to gain more control over how their inventory is sold, then it will definitely be a Good Thing for the industry – and for Yahoo, as it looks to position itself against Google and Microsoft. It’s going to be interesting to see how APT develops.

Online Ad Business 101, Part III - Ad Networks

So far in my nascent Online Ad Business 101 series, I've covered the overall advertising value chain, and looked at a superficial level at how an ad 'call' is actually handled. This installment brings together themes from those two first posts, by taking a look at ad networks.

As I have mentioned before, ad networks are in the media representation business. Even the biggest publishers don't typically have the resources to sell every last scrap of their available inventory day in, day out, so they hand over a portion of their inventory - the remnant inventory - to ad networks. Small publishers, on the other hand, have no resources of their own to sell their inventory, so they have to go to the market via networks. The networks aggregate all the inventory that they have available and then sell this inventory to advertisers.

Ad networks make money by selling the inventory for a higher price than they buy it. They can achieve this in a number of ways, which I shall list in broad order of sophistication/difficulty (with the easiest first):

• Simple arbitrage: The network buys from the publisher at a rock-bottom price (because the publisher would literally make nothing from the inventory otherwise) and sells the inventory on in larger aggregated blocks at a slightly higher price. The "value add" is small - the network is simply allowing the advertiser to soak up some remaining part of their budget without having to go to lots of individual publishers.
• Vertical aggregation: The network buys lots of small parcels of inventory in specific verticals (e.g. travel). It then aggregates the inventory for sale according to these segments, enabling it to charge a bit more. The advertiser is able to extend the reach of their campaign in a target audience without having to deal with lots of publishers.
• Price model arbitrage: The network buys inventory on a CPM (cost-per-thousand impressions) basis, providing the publishers with a nice, reliable revenue stream. But it sells the inventory on a CPC (cost-per-click) or CPA (cost-per-acquisition) basis, reducing the risk of the inventory for advertisers (who are only paying for success), and absorbing the associated risk itself. The network makes money on the difference between the CPM it pays publishers and the "effective CPM" (eCPM) it charges advertisers.
• Platform specialization: Advertising on emerging-media platforms such as video and mobile still requires quite a lot of specialized technology, forcing Rich Media vendors to build close relationships with the publishers that they deal with. Over time, many of the vendors in this space have gone the extra mile for their advertiser customers and turned themselves into networks, making it easier for advertisers to buy ads in these new formats across a range of publishers.
• Behavioral targeting: The network buys inventory from publishers, and when the ad call is passed over to the network, it drops a third-party cookie. By doing this across all its publisher clients, the network can build up a profile of users by cookie ID - knowing, for example, that cookie ID XYZ123 has visited ten sites about watersports in the past week. The network can then use this information to add value to the inventory it's reselling, enabling advertisers to buy "active surfer dudes" and the like.

 

Can you give me some examples?

Sure. Here are some examples of ad networks which (roughly) map to the types above. In practice, of course, most ad networks employ a combination of the above techniques to maximize the margin on the media they represent.

Simple arbitrage: Advertising.com
No doubt my description of Advertising.com as a "simple arbitrage" network will generate howls of protest from AOL (Advertising.com's parent company). But one of Advertising.com's main value propositions is the breadth of sites and audience it can deliver. Because Ads.com deals with so many publishers, advertisers can almost always find some inventory that maps onto the audience they're looking for, and are happy to pay a (relatively) modest fee for the privilege.

Simple arbitrage 2: Google Content Network (AdSense)
No discussion of networks would be complete without a mention of Google AdSense. AdSense provides a way for lots of small publishers to make inventory available to the pool of advertisers that use Google Adwords - in addition to their ads appearing next to Google's search results, these ads can also appear on the small publishers' sites; the ads are matched with the sites on a contextual basis (the content of the site is crawled to extract keywords which then stand in for the keywords that advertisers normally bid against for paid search results).

A crucial feature of this system is that the publisher is paid on a cost-per-click basis, so assumes a big chunk of the risk - if no one clicks, the publisher doesn't get paid. Google makes its money on the margin between the cost-per-click they pay the publisher, and the cost-per-click they charge the advertiser. The value proposition lies in connecting lots of small (and large) advertisers to lots of small publishers who are running sites which have a really good content match to the advertiser's offering. In other words, if you manufacture Mongolian nose-flutes, AdSense allows you to get your ads onto all the Mongolian nose-flute fansites out there, with very little effort.

Vertical aggregation: Martha's Circle
Martha's Circle is the (rather winsome) name for the ad network run by Martha Stewart Omnimedia. It's a classic example of a publisher/media owner extending their brand (and saleable audience) by signing up sites in the same sector (in this case, lifestyle) and creating a niche network. For an advertiser wanting to reach thirty-something women with an interest in the home, this kind of network is a no-brainer when building a media plan. Glam.com is another good example, as is Fox Interactive Media.

Price Model Arbitrage: DRIVEpm
DRIVEpm is Microsoft's own advertising network, acquired with the acquisition of aQuantive last year. DRIVEpm styles itself as a "performance" network, meaning that it uses a variety of techniques (amongst them, price model arbitrage) to enable advertisers to buy inventory on a cost-per-performance basis, whilst still paying publishers on a cost-per-thousand basis. Scott Howe, former GM for DRIVEpm and now VP for the Microsoft Advertising business unit, wrote a great article back in 2005 about some of the dynamics in a performance network from the perspective of a media buyer looking to get the best ROI. Well worth a read.

Platform specialization: VideoEgg
VideoEgg is a video advertising network (the clue's in the name, I guess). Its offering is a classic mix of innovative ad unit technology (their latest offering is something called "AdFrames") with a network attached. Another feature of VideoEgg is that it offers advertisers a CPE (cost-per-engagement) model for buying video advertising, performing the same kind of price model arbitrage that DRIVEpm is doing. Their publisher audience is widget & app developers for social media environments such as Facebook and MySpace, ensuring that their value proposition to advertisers is further differentiated (essential as the online video market becomes more crowded).

Behavioral targeting: Tacoda
Tacoda is also part of AOL's Platform A unit, and markets itself as the world's "first" behaviorally-targeted ad network (a hard claim to substantiate, but equally hard to refute). Tacoda tracks behaviors of the visitors to its network of over 4,000 sites and uses this information to associate behavioral profiles with those users. It then sells inventory on these sites on a user-target-group basis, rather than by group of site or content area. These "audience segments" have names like "Family Chef" and "Photo Bug".

 

How does it actually work?

Understanding how ad networks actually serve their ads is essential in understanding how some of the above business models (especially targeting) work. I'll cover two scenarios - a small publisher/small advertiser scenario, and a large publisher/large advertiser scenario.

Small publisher/small advertiser
A small publisher will insert their ad network's ad code directly onto their site - in many cases, this is the only ad code the publisher is using, and is serving 100% of that publisher's ads. On the other side of the fence, the ad network may provide a web UI to enable advertisers to create or upload ads, and (possibly) allow the advertiser to choose which sites (or groups of site) those ads will appear on. The diagram below summarizes this (thanks to Right Media for the advertiser & publisher people icons):

Examples of this kind of system are Google AdSense and the Yahoo Publisher Network (these are often called "self-service" ad networks). The actual ad delivery model is pretty simple - the same ad server (the network ad server) functions as both publisher and advertiser ad server (the ad call path is on the left side of the diagram above).

Large publisher/large advertiser
When it comes to large publishers using ad networks to deliver inventory to large advertisers, things get more complicated. In this scenario, both the publisher and the advertiser will likely have their own ad servers. The publisher will configure its ad server to "hand off" a certain block of inventory to the network, whilst on the advertiser side, the advertiser ad server will be configured to buy a certain portion of a campaign from a network (or networks). So the ad call has to be passed from the publisher to the advertiser via the network:

 

It's the point at which the ad call passes through the network ad server when the network is able to drop a cookie on the user's machine, enabling behavioral tracking and targeting (assuming, of course, that the users don't delete their cookies in the meantime).

Of course, hybrids of the two models above also exist: large publishers will sometimes hand over some of their inventory to a self-serve network, in particular, in which case the publisher's ad server calls the network ad server, which serves the ad itself.

This picture also becomes more complicated when you consider that many ad networks will pass the ad request on to another ad network if they themselves can't fulfil it (or fulfil it economically). So, for example, a targeted ad network may receive an ad call from a user it has no information about. Rather than serve an ad for that user at a low cost (and thereby preventing that ad impression from being served to another user at a higher cost), the ad network passes the ad call on to a "value" (read: cheap) network. So in the picture above you can have two, or even three or four, ad networks passing the ad call around like a hot potato.

This game of pass-the-parcel isn't really very good for the user, who has to wait a long time to see the ad (which really hurts the advertiser most, since a slow-loading ad might as well not render at all); and it's also not great from a security point of view, because the publisher is ceding control of a portion of their site's screen real-estate to an unknown network and an even more unknown advertiser. Which is why ad exchanges are emerging which provide a centralized clearing-house for inventory, thus dispensing with the round-robin approach described above.

Online Advertising Business 101 - Index of all posts

Online Advertising Business 101, Part I - The Online Advertising Value Chain

When you spend as much time as I do examining the workings of the online ad industry, it's easy to forget that, to many people, it really is pretty opaque. Not only is it characterized by some of the most complex and scalable technology in the world, but it also has its own, pretty unique, economic model to boot.

I've lost track of the number of times I've been asked by people, even super-smart colleagues from within Microsoft, "so, how does the online ad industry actually work?" So I thought I would attempt to provide a bit of a primer through the medium of this blog. Who knows, maybe someone will read it and offer me a book deal ;-)

In this first installment, I'm going to take a look at what I call the online advertising value chain:

This is a simplistic view of the industry, but it does enable us to understand where the key players sit; on the demand side of the value chain, there are advertisers, and their agencies; and on the supply side, publishers, and ad networks (and/or ad exchanges).

 

What's the product?

Before I get onto the content of the boxes in the above diagram, though, we should be clear about what's in the arrows; that is, what's traded in this market? What's the actual product, here?

The answer is advertising inventory. There are no very good definitions of advertising inventory out their on the Internet (Dave Chaffey offers one of the better ones), so I offer my own definition:

Advertising inventory is the supply of opportunities to display advertising in a particular medium.

Most people would use the term "ad impression" instead of "opportunity to display" - the reason I haven't is because I don't like to offer a definition of a term which contains another term that you may need to go and look up. The most common definition of ad impression is this:

An ad impression is a single viewing of a single ad by a single individual.

(Another reason I didn't use it is because it fails to capture the increasing complexity in ad inventory as online advertising evolves. For example, if you're serving video ads, and the user watched half of your 30-second pre-roll ad, was that an ad impression?)

In our value chain above, it's the Publishers who are the creators of advertising inventory. By building websites or software apps or video games or e-mails which are seen by lots of people, and inserting ads into these environments, publishers create a constant stream of ad inventory which, of course, they are looking to sell to advertisers. Agencies and Networks merely help the process along.

Online ad inventory is a very interesting type of good (to use the economics term). It has an incredibly short shelf life (measured in milliseconds as a page loads), but its supply is only indirectly under the control of publishers; external factors (such as a very newsworthy event) can dramatically impact the amount of inventory that a publisher has to offer. As a result, inventory prediction is a major task for publishers; I'll be returning to this topic in a future installment.

Calculating ad inventory

Another useful way of understanding ad inventory is to look at a simple example of how it's calculated. Imagine a pretty straightforward website (this blog, for example), showing pretty simple ads, with no fancy auto-refresh stuff going on (i.e. once a page is loaded, the ads don't change, so for each page impression, you get one batch of ads). How much ad inventory is created?

The answer to this is dependent on two variables - the number of page impressions on the site, and the average number of ads per page. So, for example, if my blog generated a million page impressions per month (I wish), and had an average of 5 ads per page, then the total ad inventory (if you're just using a simple ad impression model) is 5 x 1m = 5m ad impressions per month.

 

The Players

Now that we understand what's being traded, let's take a brief look at the major players in the value chain, and then I'll let you get back to whatever it was you were doing before you started reading this post.

 

The Publisher

We've already covered this guy. He's the one with the site, or the game, or the mobile portal, who is creating ad inventory and wants to sell it to advertisers to provide income for his business. Publishers are interested in maximizing revenues, but also at minimizing risk - they hate to have unsold inventory (that is, ad space with no ads in it) so they employ a number of tactics to ensure that at least something gets shown in an ad unit that they can get a little money for.

Larger publishers have their own sales teams who maintain direct relationships with advertisers and their agencies, cutting deals for big blocks of advertising inventory over expensive lunches in chic Greenwich Village restaurants. But this model only works for big publishers selling to big advertisers. Small publishers can't afford to maintain their own sales force, and even if they did, they'd never get through the doors of Ford, or CapitalOne, because they don't have enough inventory to be of interest on their own account. So these guys sell their ad inventory through Ad Networks.

One other kind of publisher it's worth calling out here is the search engine - i.e. Google, Yahoo and Microsoft. These search engines are the creators of huge amounts of ad inventory that is sold directly to advertisers and agencies, as well as running significant ad networks (see below).

 

The Ad Network

Ad Networks are essentially outsourced sales houses for publisher inventory. An ad network strikes deals with lots of publishers for their inventory and then aggregates this inventory and sells it on to advertisers and agencies. There are over 300 ad networks in existence today - a breathtakingly large number which is sure to fall soon.

An ad network's value proposition to publishers is that it can sell inventory that the publisher can't sell itself - either because the publisher is small (and so doesn't have its own sales force), or, in the case of larger publishers, the inventory is of too low-value to merit direct selling. This kind of inventory is called remnant inventory.

The network's value to an advertiser is that the advertiser can appear on lots of sites across the Internet (potentially thousands) without having to establish direct relationships with those publishers individually.

At bottom, the Ad Network business model is to buy inventory cheaply and sell it on at a higher price. There are a variety of ways of doing this, some of which I've covered before. One of the most promising is to add value to the ad inventory by adding targeting data (so that the impression can be sold for a higher price). I'll cover this in a future installment.

Networks come in all shapes and sizes. There are 'premium' networks which work with remnant inventory for large publishers; there are vertical networks which focus on a particular industry or technology (such as video); and, at the bottom end, there are contextual networks which provide an auction-based marketplace for selling keyword-based ads on small sites. You may have heard of the #1 network in this space - it's called Google AdSense.

 

The Advertiser

Advertisers also come in all shapes and sizes, of course. The big name advertisers - the folk we've all heard of - will have significant internal marketing departments, and will also likely retain the services of an agency to help them manage their marketing. Their marketing objectives will likely be a mix of brand marketing (raising general awareness) and direct response marketing (getting someone to actually buy something online now).

Smaller online advertisers are almost always focused on direct-response - getting someone to click and buy, or possibly call up. By and large, these folk can't afford to retain an agency to do their marketing for them, so they tend to go straight to certain ad networks or publishers to buy their ads. Again, the #1 in this space is our friend Google, with AdWords (the advertiser-facing side of the AdSense network).

Advertisers are motivated by getting the best ROI on their ad investment; but amongst larger advertisers some other curious motivations creep in, like wanting to make sure that a committed ad budget for a quarter actually gets spent (so that budget isn't cut the following quarter). This drives the behavior of ad agencies, to an extent.

 

The Agency

Last but by no means least, the media agency is an essential intermediary in the advertising value chain. Ad agencies usually do one of two things (or both, such as is the case with our own Avenue A|Razorfish): they create ads (anything from designing an animated banner to filming a 30-second TV ad) - known as the creative business - and they buy the media (i.e. the ad inventory) to display the ads (known as the media business). Whilst the creative side is cooler, the part of ad agencies that is relevant here is the media business.

A media agency, then, is one that buys media on behalf of its advertiser client. The advertiser typically says "I have x million dollars this quarter for online, and this campaign I want to run. Buy me the best media to reach my target audience". It's then the media agency's job to plan a media buy that will deliver the best return for the advertiser.

At the small-business end of the spectrum, the 'media agency' morphs into small SEM (Search Engine Marketing) shops who are good at buying Google AdWords, and maybe have some SEO (Search Engine Optimization) skills to boot to boost a company's natural search rankings.

Media agencies' motivation is driven by getting as much media under their control as possible, since they're paid (particularly at the high-end) with a cut (usually something like 15%) of the advertiser's media budget. They also don't want to under-spend on the budget they've been given, as this can annoy their client (see above).

Media buying is a manual, labor-intensive process right now, and one I'll come back to. Improvements to technology will mean that agencies (especially larger ones) will have to do some pretty fancy footwork to continue to add value for their advertiser clients.

 

That's it for now. in future installments, I shall be looking at the key players in a bit more detail, and looking at some of the interesting economics which underpin the industry. In the meantime, if you have a comment, or something you'd like me to cover, leave a comment.

[Update 6/3/08: A little more info on Ad Networks added]

Online Advertising Business 101 - Index of all posts

The rise of navigational search

There's a very interesting post by Robin Goad over on the Hitwise blog about the change in distribution of search terms on search engines. As more and more people are searching online, two things are happening to the range of search terms, one intuitively obvious, the other somewhat counter-intuitive.

The obvious change is that the total number of different search terms used is going up. If you think of the distribution of search terms as a head/tail curve, this means that the tail is getting longer. The non-obvious change is that the number of terms that make up the top 5, 10 or 20% of all searches (i.e. the most popular terms, or the "head") is going down.

Robin's charts are a little tough to parse, so I've taken a crack at simplifying them a little. Firstly, the chart below shows the change in breadth of search terms in the top 5%, 10% and 20% of search traffic. I've normalized the vertical axis (2005 = 100) to highlight the proportional change. You can see that in the top 5% group, the overall number of search terms fallen by over 80%.

This implies that a few very popular search terms are really starting to dominate the traffic. Robin goes a stage further and separates out "navigational" search terms, to produce the following chart. Here the drop-off in diversity is even more marked (note that the buckets in the chart below are different to those in the one above), with an average drop-off of around 80% even up to the 10% point.

What do we mean by navigational search? Searches for sites by their own name; for example, people trying to find the British Airways site by searching on "British Airways".

What this data tells us is that these brand or navigational search terms are starting to crowd the top of the leaderboard for searches, with people using them as proxies for remembering the URL of the site itself.

The reason this is interesting to online marketers is that sites are increasingly having to pay attention to these search terms - and some sites are choosing to buy their own company name as paid search to ensure that visitors click through to their site when they search on their company name. Look again at the search results (linked above) for "British Airways". The first sponsored results is... an ad for British Airways, despite the fact that BA's site is first in the Organic results, just a couple of lines down.

Apart from the fact that having to do this is likely costing BA a fair amount of money (which they could instead be spending on me when I fly to London at the weekend), it's likely to skew BA's picture of how their online marketing mix is really working for them. A lot of users who have been researching online (especially for something like flights) will use a navigational search term to return to the site where they've decided to purchase. Because most analytics tools use a "last-click" attribution model for conversions, BA's reporting on marketing effectiveness is likely to overstate the relative importance of those keywords, when it may really have been other keywords (or other kinds of marketing altogether, such as e-mail) which drove the visitor to the site in the first place.

So what are brand owners to do in this situation? They don't want to drop their navigational search keyword campaigns because they'll lose clicks and business, but on the other hand, buying navigational terms seems like a bit of a tax for these kind of sites, and distorts the numbers. Part of the answer lies in the rules the search engines impose about bidding on other companies' brand names (though this has caused all sorts of misery with Live Search and adCenter), but the true answer lies in a smarter attribution model for the sites involved.

In addition, sites should group branded or navigational search into a separate bucket, and take conversions that are attributed to those terms with a pinch of salt. I would even recommend that these conversions be not included when calculating the overall ROI of paid search, and instead be thought of as part of the cost of more brand-focused marketing activities such as TV. What do you think?

Whither the ad network?

One of the things I was struck by (apart from the fact of how woefully inadequate the New York Hilton is as a major exhibition venue) last week at ad:tech was the staggering number of "Ad networks" touting their wares on the exhibition floor. Of the roughly 300 exhibitors in New York, at least a third included the word "network" in their description. From AdBrite to Zanox, everybody seems to be in the business of "connecting advertisers to publishers". It's clearly the business to be in; but for how much longer?

Buy low, sell high

Ad networks make their money on the margin - by buying inventory cheaply from publishers, and selling that media on at a higher price to advertisers. How successful a network is depends principally on the difference between these two prices. As with any intermediary business, however, there's always the danger that your customers will go straight to your suppliers, putting you out of business, or at least forcing you to trim your margins until the pips squeak (any travel agents reading this, you know what I'm talking about).

So will ad networks get squeezed out? Well, it depends on the network, and how they make their money. At present, one major way that ad networks make money is through simple arbitrage - they exploit the fact that an advertiser cannot easily reach all the publishers where they'd like to place ads, and simply aggregate and repackage inventory before selling it on at a marked-up price. The ad networks are adding a little value here (mainly in the aggregation of inventory), but it's pretty thin.

The trouble with this model is that it's very sensitive to downwards price pressure: because the networks add little value, if someone can offer a similar solution at a better price, the only way a "reach" network (as these networks are known) can compete is on price, cutting into their profits. Plus, the margins that many ad networks charge (up to 70% in some cases) provide a strong incentive for publishers to try to find other ways of selling their inventory.

Fancy a game of risk?

As a result, most ad networks play a smarter arbitrage game: they buy inventory from publishers on a CPM (cost-per-thousand impressions) basis, which is nice and reliable for the publisher (they know for a given volume of traffic what revenues they'll get), and then sell that inventory on on a CPC (cost-per-click) or CPA (cost-per-action) basis to advertisers. The essential feature of this is that the ad network assumes the risk of converting CPC/CPA pricing on the buy-side to CPM pricing on the sell-side - get it wrong, and you'll end up selling inventory for less than you paid for it. But because the network is adding value by assuming this risk, it can justifiably insert a mark-up into the pricing and make a profit from the deal.

This model is more robust from a pricing perspective, but is still vulnerable, because more and more publishers are willing to sell on a CPC basis (e.g. via Adsense) and assume some of the pricing risk themselves, drawing value away from the network, and pushing down the margins. So the networks with the best prospects are those which can add extra value to the inventory as they pass it through to the advertiser.

This "extra value" comes in a number of guises, including:

• Providing access to specialized inventory (e.g. in-stream video or mobile)
• Focusing on specific verticals (e.g. travel)
• Optimizing ad delivery to maximize inventory value
• Adding targeting data to the inventory (demographic or behavioral) to maximize value

At ad:tech, many of the networks (and especially the start-ups) were presenting some spin on one or more of the above themes. All of them, however, are dependent to a greater or lesser extent on scale - the very thing that a budding ad network startup doesn't have.

Size does matter

Ad network scale is important for a couple of reasons. First, an advertiser or agency only wants to deal with a small number of ad media suppliers (networks and publishers). So lots of little ad networks are going to fall off the bottom of the agency/advertiser's list. If you work for an advertiser or agency, I'm sure you really enjoy the dozens of calls you get every week from network sales reps. At the moment, there's a niche to be exploited in offering particular inventory or verticals, which can justify (for an agency or advertiser) the hassle of dealing with a small, specialist player. But ironically, the more such players who enter the market, the more of pain it is for the media buyer. And once the bigger networks start to offer these services, they'll pick up a big chunk of business through sheer inertia.

The second reason scale is important is related to the third and fourth points above: scale enables optimization of ad delivery and the ability to enrich inventory with targeting data. The reasons are simple: firstly, if you're looking for the best place to serve an ad, the more places you have to choose from, the better your chances of getting the best price for that ad. And secondly, if you're using cross-network behavior of users to add value to inventory (for example, noting that people who visit baby sites also visit pizza delivery sites), the more sites in the network, the better chance you have of spotting these correlations and using them to add value to the inventory (i.e. selling pizza delivery companies ads on baby sites).

All of which means that many of the newer/smaller companies I saw at ad:tech last week will likely fail to achieve the critical mass needed to make an ad network business model work. This may be ok for them, since I'm sure every single one is hoping to build up a bit of momentum and then sell up to a bigger network, but there will certainly be losers as the game of musical chairs comes to an end - you don't want to leave selling up too late, or your competitors will simply be able to steal your customers rather than paying you for them.

So you can expect to see continual consolidation in this industry, and a few large-scale players emerge, offering a range of inventory types and verticals, and focusing their offerings on adding value in delivery optimization and behavioral targeting. But who might these players be? This post is long enough already, so tune in for another installment where I'll give my (not so) considered opinion on how the market'll shake out.

The mysteries of measuring marketing response, part 3: Reconciling post-click behavior

Apologies for the rather slower pace of posts of late, but life has been a little busy here, what with moving house in Seattle, taking a trip to London to attend E-metrics, and succumbing to a nasty cold this week. Hopefully this post, the latest in my 'mini-series' on online marketing measurement techniques, will make up for things.

In my first and second posts on this topic, I discussed the techniques that ad servers (and other kinds of marketing delivery systems) and web analytics apps use to decide which marketing has delivered a click to an advertiser's website. If you know what marketing has delivered a specific visit, you can associate any 'conversion behavior' (e.g. the customer buying something) with that marketing, and use this information to generate ROI information about that marketing. Here's a simple example:

• Keyword "bananas" delivers 1,000 visits to www.bananas-r-us.com
• CPC (cost per click) for this keyword is $1; so campaign costs $1,000 to run
• Of 1,000 visits, 30 include a purchase
• Average purchase value is $50 (that's a lot of bananas)
• Total revenue generated - 30 x 50 = $1,500; so ROAS (return on ad spend) is 50% ((1,500 - 1,000)/1,000)

So far, so much egg-sucking tutorial. The wrinkle is that not all visitors who click through the ad will go on to buy something there and then (that is, within the visit that started with the ad click). A proportion will visit the site, undertake some serious banana research, and then go home in the evening to consult with their spouses about whether their family really needs a 60lb bargain bucket of over-ripe bananas., and then place the order. In the industry, this is known as a deferred conversion.

In certain retail sectors, such as technology, travel, and insurance, deferred conversions are the norm. So you might be spending lots of money on search engine marketing, but your web analytics tool is telling you that no one is buying anything on the back of your keywords, whilst what is in fact happening is people are coming back later of their own accord and buying stuff.

Individual marketing delivery systems (like Google Adwords) solve this problem by giving the user a cookie when they first click through an ad, and then tying subsequent conversions (typically within 30 days) back to this click if that same user (identified by the cookie they still have) comes back and buys something. However, as I mentioned in my first post on this topic, this doesn't work well when you are using multiple marketing channels (e.g. search + e-mail), as they will 'compete' to claim credit for the same conversions, and over-report the ROI of the marketing you're doing.

Under the influence

The only way round this is to get your web analytics system (which will not double-count conversions, because each conversion only occurs once in a web analytics system's database) to make some intelligent decisions about what marketing actually drove (or at least 'influenced') the conversion. Consider the following example sequence of visits to a site from the same user (who, for the sake of this example, we can assume has a persistent cookie for the duration of the set of visits):

 

Date	Marketing Source	Purchase value
April 1 2007	Paid Search	$0
April 10 2007	E-mail	$0
April 13 2007	none [direct]	$1,000

In this scenario, how do you decide what (if any) marketing contributed to the $1,000 purchase? There are a number of methods you could use:

1. 'In visit' allocation
This method allocates the conversion to the visit that contained it, and no other. This method would allocate the $1,000 to a "no marketing" or "direct URL" bucket; i.e. the paid search and e-mail campaigns would get no credit.

2. Last marketing source
Here, the last marketing that drove this user to the website gets exclusive credit for the conversion. So in this example, the e-mail campaign would be credited with the $1,000. Paid search gets nothing, despite the fact that it may have been this marketing that alerted the user to the site in the first place.

3. First marketing source
The first marketing that drove the user to the site gets the credit - in this example, the paid search campaign gets the $1,000 in its ROAS calculations. E-mail gets nothing, despite the fact that it drove the customer's most recent interaction with the site. Another problem with this approach is that when users churn their cookies, their "true" first marketing source is lost, and a new (semi-random) one is allocated based upon the first marketing they respond to since cookie-churning.

4. Simple shared allocation
All historical marketing gets a share of the credit for the conversion. So paid search gets $500 and e-mail gets $500. This is probably closer to the truth (both had some hand in creating this eventual conversion), but is a pretty crude model, since different kinds of marketing have radically different 'engagement profiles' associated with them. For example you could argue that paid search clicks have a higher engagement profile than banner ad clicks, since when someone clicks a paid search ad they've already entered a relevant search term, so are clearly in the market to some extent.

5. Age-based shared allocation
Another over-simplification in the last method is that the age of the click (i.e. how long ago it happened) is not taken into account. The e-mail click happened only 3 days ago, whereas the paid search click was 13 days ago. Taking this into account, you could allocate, say, $250 of the $1,000 to paid search, and $750 to e-mail. The maths for doing this systematically are non-trivial - you have to model in influence 'curves' that tail off to zero at some point (say, 30 days back) and then allocate the conversion value on a pro-rata basis based upon each click's position along the curve.

6. Age and channel-based shared allocation
This method combines the idea of age-based and marketing channel-based allocation together to create what is probably the truest (but certainly the most complex) picture of conversion influence. In this method you create influence curves for each marketing channel that you're using, which reflect the different rate at which the 'influence' of each channel wanes over time (the influence of an ad might wane very quickly, for example, whilst an e-mail's influence might linger longer).

For each historical clicks that was driven by marketing, you then locate the position on the influence curve for that kind of marketing and read off the number; and you then add up the values from each curve and pro-rata allocate the conversion value on this basis.

In our example, you might find that the paid search curve yields a value of 4 at the "-13 days" position, whilst the e-mail curve yields a value of 6 at the "-3 days" position, reflecting the fact (or, more accurately, someone's opinion) that paid search click influence lasts longer than e-mail click influence. Allocating the $1,000 on this basis would mean that paid search gets $400, whilst e-mail gets $600.

Confused?

You may be asking, "but where do these influence curves come from?" The answer is, your head. Or mine. or the head of (the head of) your marketing agency. But they don't exist at the moment, and, with all the other uncertainties and lack of standards in the online marketing world, I can't see a commonly agreed-upon set of marketing channel influence curves coming out any time soon.

The tricksy thing about this field is that the more you look at the way marketing is allocated to conversions at the moment, the more you realize how broken and simplistic those methods are. I've never met a client or agency who's implemented anything like nos. 5 or 6 above, though I have seen no. 4 used.

But in a world where everyone increasingly understands that you need to use multiple touch points to reach consumers, intelligently allocating conversions to multi-channel marketing efforts will become increasingly important, even to a little guy running some paid search campaigns with a bit of e-mail thrown in. So if you can come up with some plausible influence curves, turn them into a fancily-named methodology and set yourself up as a consultant, and you can make a bunch of money.