I was a co-founder and CTO of NetGravity back in 1995, and I spent a lot of years building ad servers. So I can say this from experience: ad servers are hard to build. There’s a lot of expertise that goes into delivering the right ad to the right person where response times are measured in milliseconds and uptimes are measured with five nines. Now it’s been almost four years since I co-founded Yieldex, and I can say another thing from experience: predictive analytic systems are also hard to build. Moving, managing and processing terabytes of data every day to enable accurate forecasting, rapid querying, and sophisticated reporting takes a lot of expertise. And, as it turns out, it’s very different from ad server expertise.

When I ran an ad server team, all we wanted to work on was the server. We delighted in finding ways to make the servers faster, more efficient, more scalable, and more reliable. We focused on selection algorithms, and real-time order updates, handling malformed URLs and all kinds of crazy stuff. Our customers were IT folks and ad traffickers who needed to make sure the ads got delivered. The reporting part was always an afterthought, a necessary evil, something given to the new guy to work on until someone else came along.

Contrast that with the team I have now, where we obsess over integrating multiple data sources, forecasting accurately, and clearly presenting incredibly complex overlapping inventory spaces in ways that mere humans can understand and make decisions about. Reporting is not an afterthought for us, it’s our reason for being. Our success critera are around accuracy, response time, and ease of getting questions answered through ad-hoc queries. Our customers are much more analytical: inventory managers, sales planners, finance people, and anyone else in the organization working to maximize overall ad revenue.

These teams have completely different DNA, so it’s not surprising that a team good at one of them might not be so great at the other. This is why so many publishers are unhappy with the quality of the forecasts they get from their ad server vendor, and one of the reasons so many are signing up with Yieldex. Good predictive analytics are hard to build, and nobody has built the right team, and spent the time and effort to get them right for the digital ad business. Until now.

Okay, I admit it, I stole the headline for this post from an excellent article by Ben Barokas in the AdMeld Partner Forum Executive Outlook booklet. In fact, I’m going to quote his entire paragraph:

For most digital publishers, ‘ad operations’ really means ‘revenue operations’ and that means a lot of data analysis. Data is the key to exceeding the expectations of clients, partners, and shareholders. Data tells ops how much revenue is being generated from specific sales channels, ad agencies, advertisers, site sections, and even users. Data enables them to make smarter decisions, not only on behalf of their own company, but for the benefit of their clients: what to sell, who to sell it to, and under what conditions to maximize value for everyone.

At Yieldex, we couldn’t agree more. Many major publishers use both Yieldex and AdMeld as complementary tools to get the “revenue radar” they need to dramatically increase CPMs and revenue. AdMeld co-sponsored our own 2010 Yield Executive Summit, leading the agency panel. Their 2011 AdMeld Partner Forum was a terrific event, and we appreciated the opportunity to attend.

Any great partnership should be greater than the sum of its parts. Whether a marriage, artistic collaboration, or business partnership, great partners build on each other’s strengths to achieve much more than either could alone.

In that spirit, I’m delighted to announce Andrew Nibley as the new CEO of Yieldex. I’ve spent quite a bit of time with Andy over the last few months, and I feel that our skills complement each other perfectly to take Yieldex to the next level. More importantly, I like working with him, and we have good chemistry, which is an essential ingredient for any great partnership.

Andy has a terrific background in digital media, having been CEO of five different media companies. Most recently he ran WPP’s Marsteller, a digital agency, and among many other successes was a co-founder of Reuters New Media. He also is an excellent leader of people, and has experience managing rapid growth, and many other skills we will take full advantage of as Yieldex drives tremendous growth in 2011.

I look forward to focusing on my strengths in product vision, strategy, and business development. My favorite part of the day is when I get to solve hard (and valuable) customer problems with cutting-edge technology. Yieldex is well positioned to continue to drive dramatic uplift in revenues for our customer base as we broaden our platform, and I look forward to helping drive that.

I am proud of the success Yieldex has enjoyed so far, and humbly grateful to the team that has worked so hard to make it happen. I am excited that Andy and I are partnering together to continue to drive value for our customers and for Yieldex, through 2011 and beyond!

For more info, see our press release and PaidContent article. Thanks to Ryan Graves and his blog 1+1=3 for the idea for this post.

Let me be the first to introduce you to the hot new TLA (three-letter acronym) for 2010: RTS. 2009 brought you RTB done by DSPs. On the other side, we’ve just been introduced to SSPs, so that means they should do – you guessed it – Real-Time Selling!

I am only partly joking here. As I’ve written before, RTB continues to skew the power in favor of the buyers. A healthy ecosystem requires a balance of power between buyers and sellers. To help restore the balance, publishers need a complementary system – an SSP – and part of what an SSP should provide is real-time selling.

So, what is real-time selling? We define it as the ability for sellers to set floor prices in real time to make sure they’re getting full value for their inventory, in the same way buyers can set bids in real time based on available information at the time of the impression request.

One of the major selling points of DSPs is they can incorporate information from many sources – agency and advertiser data, third party providers, and the publishers themselves – and use it to decide how much an impression is worth. Somewhat less obvious is that the worth of the impression is actually just a ceiling on what the buyer is willing to pay, and sophisticated bid management programs often bid much lower in an attempt to get the impressions for the lowest value. This means the publisher may get much less than the buyer would have been willing to pay.

This is why publishers need to use all available information to set their floor price for an impression. Some of the information they might want is unavailable – agency or advertiser information, or certain re-targeting cookies – but any third party and publisher data can be incorporated. Then, sophisticated analysis is required to detect segments of inventory where floors should be raised or lowered – the closer to real-time the better. These algorithms will no doubt continue to be refined over the next several years.

Premium publishers have a much more complex problem: they also sell guaranteed impressions over a period of time. As long as those up-front CPM-based buys were much more valuable than the secondary channel, this was easy to solve: serve the guaranteed ads first, them look at the exchange or network. But as the secondary channel begins valuing some impressions more highly, the publisher needs to manage global yield optimization across multiple channels, and the floor-setting algorithms need to incorporate that data as well. This may seem like it’s years off, but several of our customers have asked us about this capability, and we’ve applied for patents in this area.

Lots of three-letter acronyms have been proposed in the last couple of years, and it’s way too early to tell what the final landscape will look like. That said, forward-thinking publishers who are looking at technology platforms to serve them for the next decade or so should be asking about their plans for Real-Time Selling.

This post also appears at AdExchanger.com in “The Sell-Sider,” a column written by the sell-side of the digital media community.

Google’s release of the DoubleClick Ad Exchange 2.0 has introduced Real-Time Bidding (RTB) to a much wider audience. While they were not the first, they are probably the biggest, and their entry is starting to legitimize RTB as more than just a niche.

Neal Mohan’s introductory blog post emphasizes the three main principles behind the development of their exchange: simplify the system for buying and selling, deliver better performance, and open up the ecosystem. It’s this last point – openness – that I’d like to explore.

Real-time bidding offers some openness for the buyers: they are delivered each impression, with the floor price, URL, and cookie, and have a fixed amount of time to bid. They are then notified if they win and a request is made to deliver the advertisement. What’s surprising is that unlike a standard auction, at eBay for example, if they lose, the potential buyer has no idea what the winning bid was. Google gets to keep all that information.

Even more incredible is the fact the publishers also aren’t told the winning bid amount. They get an aggregate value for their earnings, but can’t see the value of each impression. This is as if you auctioned 10 things on eBay, and at the end, eBay sent you $100, but refused to tell you what item 1 sold for vs item 2 or item 3.

This information asymmetry is largely to the benefit of Google, but also skews to the buyers. Savvy buying systems can tweak bids up and down in real-time to do crude discovery of the “true” value of different kinds of inventory and how it varies over time. Publishers have no such ability to discover their inventory value at an impression level. Worse yet, while buyers can bid different prices for each impression, publishers have no ability to re-set floor values on each impression to push the bids up. Of course, they would need new tools to do this (SSP, anyone?), but it is much harder without data.

One final point on how the system is stacked against the publishers: any buyer can participate in any of the exchanges, and indeed many of them do. But since Google does not give publishers their impression values, it is very hard for publishers to find out if some of their inventory would perform better on a different exchange. And to add insult to injury, Google makes it almost impossible for non-DFP publishers to participate at all.

Publishers should be wary of using any ad exchange until they get real openness, and the tools – like an SSP – they need to ensure the deck isn’t stacked against them.

This post also appears at AdExchanger.com in “The Sell-Sider,” a column written by the sell-side of the digital media community.

The new real-time bidding (RTB) exchanges seem to skew the buying power further in favor of buyers. They can see each impression in real-time, data-enhance it with their own data, and then bid on it. The problem is that most publishers don’t know where the pockets of value exist in their remnant inventory, so they can’t intelligently allocate that inventory in a way that makes them the most revenue.

Publishers need 3 things to maximize the value of their inventory on an exchange:

1. Inventory value. Publishers need to get back from their exchanges the value of their inventory, on an impression-by-impression basis. Just getting high-level average CPMs by section or zone isn’t good enough, this masks the high-value impressions that may exist within those sections.
2. Third-party data. Your buyers are using data to understand the value of your inventory, you need to have the data to fight back. You should be setting floors on your inventory based these data segments, so buyers can’t just cherry-pick your inventory at an overall low value, and you can’t do this without the data.
3. An analytic system to maximize yield. You will need a system that can capture an analyze all this data – terabytes of it – and spit out a set of floor prices by inventory segment. In an ideal world, this system operates in real-time, re-setting floors based on the most recent data. I call this real-time selling – the antidote to real-time bidding.

Not all of these items are easily attainable. The first item is not generally available from exchanges, so publishers need to demand it. The second is becoming more available, from vendors such as Audience Science, BlueKai, and eXelate. The third will be provided by Yieldex, among others. Forward thinking publishers, who put this stack together, should be able to dramatically increase revenue from their unsold inventory.

This article was also published on AdExchanger.com.

Cloud computing means lots of different things, and much of it is hype. At Yieldex, we’ve been using cloud computing, specifically Amazon Web Services, as a key part of our infrastructure for the better part of a year, and we thought we’d pass on a few of our lessons learned. As you might expect, the services we use have trade-offs. If your challenge fits within the parameters, cloud computing can be a huge win, but it’s not the answer for everything.

All of these lessons are the result of the hard work of our entire engineering team, most notably Craig and Calvin. These guys are among the best in the world at scaling to solve enormous data and computation problems with a cloud infrastructure. We could not have built this company and these solutions without them.

For a startup, there are a number of compelling reasons to use a cloud infrastructure for virtually every new project. You don’t get locked into a long-term investment in hardware and data centers, it’s easy to experiment, and easy to change your mind and try a different approach. You don’t have to spend precious capital on servers and storage, wait days or weeks for them to arrive, and then spend a day or two setting them up. If your application scales horizontally, then you can scale additional customers, storage, and processing with minimal cost and time delay. All these things are touted by cloud providers, and basically boil down to: focus on your business, not your infrastructure.

Sometimes, however, you do need to focus on the infrastructure. We provide our customers with analytics and optimization based on our unique and proprietary DynamicIQ engine. Our first customer was a decent sized web property, and we were able to complete our DynamicIQ daily processing on several gigabytes of data using just one instance in less than an hour. Our next customer, however, was 10x the size. And the one after that, 10x more – hundreds of gigabytes per day. Fortunately, we had designed our DynamicIQ engine to easily parallelize across multiple instances. We spent some time learning how to start up instances, distribute jobs to them, and shut them back down again, but because we had designed the engine for this eventuality, we were able to use the cloud to cost-effectively scale to even the largest sites on the web.

We also have BusinessIQ, which is basically an application server that provides query processing and a user interface into our analytics. Initially we started with this server in the cloud too, but as we bumped up against other scalability issues, we found that the cloud doesn’t solve every problem. For example, we provide a sophisticated scenario analysis capability. To calculate a “what-if” scenario requires processing a huge amount of data in a very short time. For our larger customers, a single cloud instance did not have enough memory to perform this operation. Trying to stay true to the cloud paradigm, we implemented a distributed cache across multiple instances, but this didn’t work well because of limitations on I/O. We ended up having to go to a hybrid model, where we bought and hosted our own servers with large memory footprints, so we could provide this functionality.

We have been very happy users of the Amazon Web Services cloud, and not just because we won the award. We would not have been able to get our business of the ground with out the cost effective scalability of the Amazon infrastructure. While it’s not for every application, for the right application, it truly changes the game.

We are delighted to announce our Series B financing. The $8.5m round was led by Madrona Venture Group, a really smart group of investors. We met them through the Amazon AWS Start-up Challenge, and as it turns out they had been looking for a company like ours, so they had done a lot of research on the space. We were impressed by their industry knowledge and their experience, and are excited to be working with them.

We also are excited to have Amazon participate. They are the clear leader in their space, and their vision has proven out time and time again. We certainly hope they are right about this investment, too! Their AWS platform is what enables us to scale so cost-effectively.

Finally, a big thank-you to our Series A investors Sequel Venture Partners and First Round Capital, for their advice, counsel, introductions, and yes, their support in the Series B. We could not have build this complex and innovative technology without their support.

This is just the beginning for us. We are now well positioned to succeed, and our success is largely within our own control – just the way we like it. Great athletes always want the ball when the game is tight – we now have the ball, and we will win this game.

This is an exciting week for us at Yieldex, we are finally launching our first product, BusinessIQ! We’ve been working hard for quite a while on this, so it’s very liberating to be able to tell the world about it. Read our press release, or see the MediaPost article, for the details.

Having built enterprise software before, we know how important it is to have real customers banging on the product to make it solid. We are delighted to have Martha Stewart Living Omnimedia as our debut customer, and look forward to announcing more in due course.

MSLO has been a great beta partner for us, give us tons of constructive feedback and being patient through our inevitable growing pains. We have been able to iterate the product very rapidly to address their needs, and we are continuing to improve by leaps and bounds. We are excited by the value we are providing to them; we love seeing our hard work start to bear fruit.

Congrats to the team for this milestone – let’s enjoy it! Okay, that’s enough, get back to work.

The doom-and-gloom set have been getting a lot of press lately, and the conventional wisdom seems to be that display advertising will die off in favor of performance-based marketing. But don’t write the obituary yet. There’s an interesting new study from MarketingSherpa and ad:tech that surveys 1200 marketers and concludes that most actually plan to increase display ad spending in 2009. From the report summary:

The greatest shift in budgets is for behaviorally targeted ads. About one out of five marketers (21%) are cutting their budget while more than half (52%) are investing more money. Slightly more than 30% of the respondents said that behaviorally targeted ads were providing a great ROI, as noted in the first chart.

Surprisingly, 46% of marketers reported that they are increasing spending on rich media ads, despite the fact that more marketers reported that they deliver a poor ROI (27%) than a great ROI (23%), as noted in the second chart.

Traditional online ads will get more spending from 29% of marketers. That tops the 24% who said they’re cutting budgets in this area. This is surprising as well since about 1 out of 3 respondents (34%) said banner ads deliver a poor ROI and only 13% said they were great.

Perhaps the branding effect of the ads, while not directly attributable to revenue, is seen as vital. Almost half of marketers (47%) are holding steady in this category.

Surprisingly, this seems to suggest that marketers are not exactly running away from traditional online ads, but instead could actually be increasing their spend, particularly for rich media. And behaviorally targeted ads are typically display ads too, just targeted at audiences instead of content. So while performance-based advertising is very important to every marketer’s mix, let’s not lose sight of the fact display advertising, in various forms, is critical too.

I was recently asked what advice I would give to aspiring company founders. While there are many mistakes I’ve made that I would try to warn others about, the best advice I can give is to work with great people.

Great people are easy to communicate with, and will give (and take) honest feedback. Great people make it enjoyable to come to work, and help turn a job into a passion. Great people argue passionately, then come to agreement, then work closely together to get the job done. Great people can be trusted.

Startups are hard places to work. The hours can be long, and the disagreements are often heated. Everybody has their ups and downs, nobody is perfect. There are big wins and crushing losses. But great people with great relationships get through these patches much more smoothly.

At Yieldex, I’m privileged to work with great people. Every member of the team works well with the others, and we can feel the momentum building. The result is we are executing very efficiently, and getting more done in less time than any other team I’ve worked with. Best of all, I love the feeling that I like everyone I work with, that I would be happy hanging out with them and their families. At our company holiday dinner, I was delighted that everyone, including the spouses, seemed to have a very enjoyable time. These are the times we will look back on years from now and remember fondly.

I used to row crew competitively in high school and college. Crew is a sport that epitomizes team “flow”. A boat full of great individual contributors will get beaten by a boat that is rowing smoothly together every time. We are rowing smoothly together here at Yieldex. One of my biggest jobs is to not screw it up as we grow.

Life is too short to work with jerks. Work with great people.

We won! Out of nearly 1000 startups who applied, we won!

This is a great validation of our fantastic technical team. We have been chosen as one of the most innovative users of Amazon’s cloud computing technology. We could not have done this without your hard work. Thank you!

This was a great experience for us. The Amazon team was very professional throughout, the event was well-managed, and they even made a cool video featuring our development team. The press release went out tonight, and there was even a blog post that beat mine.

Here’s the blow-by-blow, for those who want all the details:

We pitched the panel of judges, all senior execs of Amazon, at 1pm. They had 50min presentations from each of the seven finalists, and had been going since 7am. We did our standard pitch, and did a great job talking about how important AWS is to us. They seemed to appreciate the presentation, but were somewhat poker faced, so while we felt we did a good job, it was hard to tell their reaction.

Later in the afternoon, we had a “VC speed-dating” event, where we had 10 minutes with each of 5 VCs. The firms were all first-rate (BlueRun, Hummer Winblad, Madrona, Greylock, and CMEA). Our product is pretty complex, so it’s hard to get across in 10 minutes, but we did our best, and each of the VCs seemed to get it quickly enough. All were interested in following up, but again, hard to tell how we ranked.

Then there was a reception while the judges and the VCs deliberated. They had invited 200 other startup people to come hear how the seven finalists were using AWS. My guess is closer to a hundred people were in the room, and we had to do another 10 minute presentation on AWS, with slides, to this group. We managed to do it in only 5-6 minutes and get our message across. Finally, around 9pm, it was time to announce the winner. We were jubilant when they picked us – I let out a shout of joy and a fist pump, to the delight of the audience.

Andy Jassy, the SVP of AWS said some nice words and gave us the traditional golden hammer. We were then invited to take a whack at an old rackmount server they had, to symbolize the destruction of our own servers. John and I both hammered it pretty hard, but we barely dented it – those steel frames are tough.

Then everyone came up to congratulate us, and we shook hands with big grins on our faces. We took a couple of pictures, approved the quote in the press release, and talked with the Amazon folks some more. All great stuff.

Finally, we headed out for a celebratory dinner. Once again, thanks to everyone in the company for your hard work – we did the talking, but we could not have done this without all of you.

Hooray!

We were delighted to learn yesterday that Yieldex was selected as a finalist in the Amazon AWS Startup Challenge. Seven companies were selected out of probably 100’s that applied (apparently 900 applied last year, don’t know how many applied this year). We even got a TechCrunch mention – can’t wait to see what that does to our web site traffic. For the record, our web site is intentionally vague – we didn’t want to really launch the company until we had a few customers up and running – and we’re getting close.

We view this as a great validation of our sophisticated technology for solving the complex and difficult problem of maximizing the value of premium inventory for large web sites. By using cutting-edge software that takes advantage of the unique capabilities of the cloud, coupled with our patented engine, we are able to tackle these challenges that have resisted commercial solutions for a decade or more. Congratulations to our development team – they deserve this!

Ah, the first customer. No other single event is as important and nerve-wracking to an enterprise software company as getting your first customer up and running. Fail, and the ensuing reputation damage could kill your business. Succeed, and the reference will set you up for a much easier sales effort to the next one. Not to mention the effect on employee morale, investors both current and potential, and (heaven forbid) your revenue line.

You know there are a few things not quite done about your product yet, but you think most of it is there, and you can manage around the rest. You know that you’ll learn some things from how the customer will use it, but exactly what is still unclear. You think you know how to deploy it, but some of the integration challenges are still opaque. As somebody said, you’re 90% of the way there, and the last 10% takes the other 90% of the time.

Choosing the right first customer is essential. Someone with a champion willing to work with you through the inevitable obstacles that will crop up. Someone with a problem hard enough that your solution delivers significant value, but easy enough that your alpha-quality solution doesn’t choke. Someone name-brand enough to provide a strong reference.

In my experience, setting expectations is the hardest part. You don’t actually have any idea how long it’s going to take to deploy, but the customer wants an estimate. You don’t know exactly what the main value propositions are, because you don’t know exactly how they’ll use it, but you had to say something to sell it to them. And you want to leave yourself enough wiggle room that when you finally do deliver something of value, you can declare success and say that was your plan all along. This is a balance beam that’s hard to walk.

Final piece of advice: listen to your customer. Don’t just listen politely, but really dig in and listen hard. Spend a lot of time with them, both in the office and out. Learn why they think you can help them, and understand their own motivations. Watch them in their daily work, both with your product and without, to understand what they do and how they see the world. These things will help you work through the challenges, deliver a product that they love, and give you the foundation of a successful business.

Analytics have long been an important part of content management, with companies like Omniture and Quantcast building nice businesses helping media sites understand how people are finding and using their sites. In contrast, the analytics focused on the revenue side of the business, particularly the display advertising, are surprisingly primitive.

One big reason for this is how much data is thrown away. Take DoubleClick DART for Publishers (DFP), for example. If you are a DFP user, you have access to some very basic information, like delivery count for each ad yesterday. But if you want to know which zones of your site a run-of-site ad was delivered on, you’re out of luck, they don’t keep that around. If you want that kind of data, you need to turn on “data transfer” and analyze the logs yourself, which despite their protestations of “it’s your data”, is an extra cost service (more on that below). And here’s a little-known fact about DoubleClick’s data transfer: it still doesn’t give you all the data. The DFP ad server strips out any publisher-specific tag attributes that aren’t used to target an ad. So, if you have your site divided into categories, with a key-value in the tag specifying the category, even with the detailed logs you still have no idea what categories a run-of-site ad was run on. They do have a workaround: duplicate your entire tag into a new parameter “u=”, with a new set of delimiters, and then you can re-parse it on the other side. In summary: re-tag your entire site, to get back “your data” that you are paying them to give you. The other ad servers are not much better.

If you ask your ad serving provider for the raw logs, they will claim that this is a lot of data, so they need to charge you for storage, bandwidth, etc. Let’s take a look at cost for a minute. If you are a pretty large site, you might generate 10GB of logs a day. For a reasonable comp, let’s look at what it would cost to use Amazon S3 for this service. 10GB of data transfer each day is $1/day to get it in, and $1.70/day to get it back out. To store it for 30 days would be $1.50. So, about $150/mo to move and keep 30 days of data, at the top end, with a healthy margin for Amazon. Remember that you pay extra to Amazon for the flexibility of scaling up and down easily, a dedicated hosting center would almost certainly be cheaper. So, if your ad serving provider is charging you much more than that (one DoubleClick customer I know was quoted $3500/mo for about 100MB of data per day, they negotiated it down but are still paying way too much), you should push back. Memo to ad server companies: storage is a lot cheaper than it was 10 years ago.

Ad sales and operations groups are starting to realize that they need detailed analytics to do their jobs. The first step of that is good underlying data, and getting that is way too hard. This needs to change.

We are riding the typical start-up roller coaster. Some days are absolutely great, others not so much. Here’s a good example.

I’m in a contract negotiation process with a major media company. Awesome – they want to use our product! And they’re willing to pay for it! Wow, okay, let’s do it. I send them a proposed contract. They get their lawyers involved. They decide they want to use their contract template instead. Hmm – then why did I spend the money to have my lawyer do the first draft? Their template is totally one-sided, but we work with it, mark it up and send it back. They send it back again. We finally put together a conference call with their business folks, their lawyer, my lawyer, and me. The call takes a while, but goes well: we hammer out all the substantive issues, and get close to signature.

Here comes the kicker. The next day, their lawyer goes on paternity leave. Hey, I know how that is, some things you can’t plan. Bummer is, the new lawyer they put on it looks at the contract, says “no, no, no” and shoves in a dozen more major redlines, completely changing the deal. Argh! Another round of back and forth markups, another major conference call, a late-night conference with my lawyer to get it turned around, and finally we’re done again. It cost an extra week and a few more thousand dollars, but we got it done. Now, the real work begins.

In a previous post I made reference to the lack of technology available to publishers to increase the value of their premium inventory, which in many cases makes up 80+% of their total revenue, even though it is often a much smaller percentage of their total impressions.  One of the main ways to make it more valuable to brand advertisers is to give them a more targeted audience, and charge higher CPMs because of the increased efficiency to the advertiser.  Many publishers are reluctant to do this, however, for two reasons: either the don’t have the data, or their afraid that targeting will fragment their inventory.  They know that without good inventory tools, inventory fragementation could result in lower overall revenue.

There are a number of ways to collect and target on better data: behavioral targeting, user registration, and data exchanges are just a few examples. And, there are a number of companies like Tacoda or NextAction that can help provide data that advertisers will pay more for.

The second reason above, fragmentation, hasn’t gotten as much attention.  Brand advertisers want a targeted audience with broad reach, so publishers end up building broad products that take inventory from many different parts of their site.  While it’s clear that selling Moms for $20 CPM is better than selling my Entertainment section (which overlaps by 50%) for $15, if I’m also stealing inventory from my $25 CPM Travel section, that’s not a good thing.  This kind of data can be highly complex, and quickly outstrips an ad operations team’s spreadsheet skills.  The industry is just beginning to deliver the tools that publishers need to really take advantage of better data to generate more revenue, and Yieldex is at the forefront.

There have been a flurry of great blog posts lately about how broken brand advertising is online, mostly because we are trying to measure it the wrong way. My own contribution to this effort notes that click through rates are the “hammer” of online advertising, and when all you have is a hammer, well, you know the rest.

Dave Morgan has a recent one about vertical publishers, where he talks about brand advertisers:

Major brand advertisers are starting to take online very, very seriously; just look at the announcement earlier this week that General Motors plans to shift 50% of its ad spend online within three years. Just look at all of the pioneering online ad work done recently by big offline brands like Coca-Cola, Pepsi, Snapple and P&G. These brands are now making online a big part of their overall marketing programs, and they are not just focused on direct-marketing objectives. No, while performance objectives will always be a big part of the online ad ecosystem, more and more marketers are looking to online ads to help drive consumer perception objectives. Yes, they are using online to drive brand-oriented objectives like awareness, favorability and purchase intent.

He then says “the portals and major online ad platforms are probably not going to be the ones selling it [ad space]“, but that vertical networks will fill the void.

John Battelle wrote on a similar topic, pointing out how technology is winning over talent in the brand wars – and this is not a good thing. He asks:

Do we sell inventory to the highest bidder via algorithms, automated processes, and platforms? Or do partner with marketers and creators of media to build brands – both media brands, and consumer marketing brands?

He then goes on to assert that the obsession with chasing Google has led the likes of Microsoft, Yahoo, and AOL to ignore their strength in brand building.

Cory Treffiletti wrote a good one, too, asking when we are going to stop using clicks as a primary measure? He has a good description of the difference between direct response and brand advertising:

Fundamentally all advertising exists to do one thing: increase sales and/or market share. If the goal is to generate an immediate response, meaning the acquisition of information or a customer in a single session generated via a click, then we can consider that to be a direct response effort. If the goal is to drive an increase in sales, market share or customers over a longer period of time (basically anything beyond that initial single session) we should likely consider that to be a branding campaign, or what is most typically now referred to as a brand response effort.

He then goes on to detail why click-through rates are not necessarily a good proxy for brand campaign success.

Finally, Eric Picard has an excellent interview with Jeff Einstein, where Jeff says:

We’ve been obsessed with our own ability to measure performance (regardless of the metric) since day one online. Our obsession with efficiency and scale all but eliminates the quality of the message from the consideration set, largely because quality is much more difficult to measure and formulize. We can tinker all we want with metrics and formats, but as long as we remain fixated on efficiency and scale as the keys to the kingdom, performance will continue to decline.

I couldn’t agree more. Reminds me of trying to use standardized testing to measure everything kids are supposed to learn in elementary school. Sure, it’s okay for basic math and reading, but our kids aren’t learning how to learn, because we only “teach to the test”. Brand advertising (and teaching) still requires a generous helping of creativity and talent.

UPDATE: Another great blog post by David Koretz asks if increasing CTR can actually hurt your brand:

If an advertiser’s goal is truly branding, then driving clicks by annoying the user is a horrible approach. Not only will it detract from the user experience, but it will also damage the very brand it was intending to build.

Well worth reading.

Brand advertising on the web is suffering from a major problem: too much measurability. To be more specific, too much measurability of the wrong things.

Click-through rates were the first measure of online ad effectiveness, and seemed like the holy grail of ad measurement. Finally, advertisers could figure out which half of their ad dollars were wasted. I think the first well-known demonstration of the shortcomings of CTR as a measurement was the MCI “Shop Naked” ad back in 1996. The ad had tremendous click-through, but nobody actually bought anything.

This spurred innovation in measuring conversions. For direct marketers, measuring conversions really is the holy grail, becuase you can immediately understand if your ad is generating more margin than it is costing. The problem is, both of these metrics, CTR and conversion rate, are direct reponse metrics.

Brand advertisers typically have very different campaign objectives. Does Coca-Cola expect you to click through and buy a Diet Coke? Does P&G expect you to click through and buy some Tide? Of course not, but these campaigns are being force-fit into having direct-reponse objectives, because that’s what we can measure immediately on the web.

Campaign objectives for brand advertisers typically include affecting a customer’s attitude and propensity to buy, their liklihood to recommend to a friend, and simple brand awareness. CTRs and conversion rates are not good proxies for these objectives. Measuring the effectiveness of campaigns on the internet probably requires the same old-fashioned techniques as measuring the effectiveness of TV or radio ads: panels, questionnaires, and close analysis of the effect on local sales.

I say “probably” because there are always new measurements on the internet, and some may turn out to be good proxies for brand objectives. Look at some of the innovations that are coming along now: brand engagement metrics with widgets, for example, or some of the video ads that VideoEgg and others are touting. However, those have yet to really prove their effectiveness relative to the real campaign objectives (witness the number of games where the players can’t name the brand that sponsored them). And, they don’t typically have the reach that brand advertisers need to make meaningful buys.

What is particularly effective for brand advertisers on the web is their ability to target. While Oprah may be a pretty good proxy for Moms on TV, on the web it’s possible for Minute Maid to target exclusively Moms. And instead of limiting their reach to content that is aimed at Moms, they can use behavioral targeting and other techniques to find Moms wherever they are on a network of sites. This can make brand buys on the internet much more efficient, assuming they can measure them appropriately.

Another effective brand advertising technique that only the internet really offers is frequency capping. Most publishers and ad networks can offer frequency caps that allow a brand advertiser’s message to reach their audience an optimal number of times, without burning them out. We all have had the experience of seeing the same TV ad so many times you actually change the channel – with the internet, that doesn’t have to happen.

Brand advertisers need to start to embrace online advertising for what it can offer, targeting and frequency capping, and stop trying to use direct response metrics to measure their campaign effectiveness. When all you have is a hammer, everything starts to look like a nail. Click-through rates are the hammer of online advertising, and brand advertisers are getting nailed by them.

I’ve been talking to a lot of publishers recently, and I’ve gotten some very consistent feedback: a relatively small percentage of their impressions often make up a disproportionate percentage of their revenue. This is not news, it’s a classic 80/20 rule (although the numbers may differ – some are 90/10!). And every publisher knows what inventory I’m talking about: it’s perennialliy sold out, and the sales guys are all fighting over who gets to sell it next month. Here’s a slide illustrating what I’m talking about:

Now this is oversimplified, but typically this “premium” or even “super-premium” inventory is sold to brand advertisers. Deals can be time-based sponsorships (typically with impression guarantees) or guaranteed-impression buys at relatively high CPMs. The remaining inventory is generally either sold on a CPA/CPC basis, at much lower eCPMs, or sent to a remnant network or ad exchange for a low CPM or revenue share.

What is interesting about this from an industry level is the amount of time and energy the industry has spent on optimizing the value of the 80% of the impressions that are not premium, and by comparison how little progress has been made on increasing the value of the inventory that makes up the majority of the revenue. Performance-based ad networks have sprung up everywhere, and ad serving providers like DoubleClick have products that are focused on increasing click-through rates, and therefore revenues, from performance-based ads. But these only help turn your $0.25 CPMs into $0.50 CPMs, they don’t do anything for your $10 CPM premium inventory.

The reason is not hard to guess: performance ads, because they are directly measurable, are much easier to optimize. Lots of people have had roughly the same idea: if I crunch enough data, I can more accurately predict who is likely to click on which ads, and therefore I can get more revenue out of less inventory.

Brand advertising is much less amenable to optimization in this same way, because click-through rates aren’t typically a good proxy for brand campaign objectives (look for a future post on this topic). To get more out of your premium inventory, you have to either make it more valuable to the advertisers, by adding more data and targeting more tightly, or you need to utilize it better, so you waste less through fragmentation and inaccurate allocation. Yieldex is focused on providing tools that help publishers maximize the value of their premium inventory, which is the 20% of the inventory that results in 80% of the revenue.