"The enemies of advertising are the enemies of freedom.” - David Ogilvy

Exciting news for Yieldbot and lovers of relevance today as we’re announcing a new Series A round of funding led by New Atlantic Ventures (NAV) and RRE Ventures.  Seed Investors kbs+p Ventures, Common Angels and Neu Venture Capital also participated again in this round.

The funny thing about raising money in media technology is that very few VC’s actually understand it and even fewer have vision for where it’s headed. We’re fortunate to bring together a team of investors that live and breathe this stuff and proudly represent New York’s media leadership and Boston’s technology leadership in a way that mirrors Yieldbot’s own corporate footprint.

The funds will be used to continue development and bring to market our Yieldbot or Publishers (YFP) realtime intent-graph™ technology (launched July 2011) and our Yieldbot for Advertisers (YFA) realtime intent marketplace that launched in alpha this month. Together YFP and YFA create a valuable media channel of realtime consumer intent that delivers an order of magnitude more relevant ad matching and performance. 

From day one, two years ago, we wanted to bridge the largest digital inventory source, Web Publishers, with the largest and best digital ad spends, Search advertisers, in a way the brings a more relevant web experience to people. We’ve progressed an extremely long way with a small team and relatively little funding so far. Today we’re putting dry powder in our muskets and continuing to battle. The enemies of freedom are only so because they know not relevance.

 

The first two questions we usually get asked by publishers are:

1) What do you mean by “intent”?

2) How do you capture it?

So I thought it was time to blog in a little more detail about what we do on the publisher side. 

The following is what we include in our Yieldbot for Publishers User Guide.

Yieldbot for Publishers uses the word “intent” quite a bit in our User Interface. Webster’s dictionary describes intent as a “purpose” and a “state of mind with which an act is done.” Behavioral researchers have also said intent is the answer to “why.” Much like the user queries Search Engines use to understand intent before serving a page, Yieldbot extracts words and phrases to represent the visitor intent of every page view served on your site.

Since Yieldbot’s proxies for visit intent are keywords and phrases the next logical question is how we derive them. 

Is Yieldbot a contextual technology? No. Is Yieldbot a semantic technology? No. Does Yieldbot use third-party intender cookies? Absolutely not!

Yieldbot is built on the collection, analytics, mining and organization of massively parallel referrer data and massively serialized session clickstream data. Our technology parses out the keywords from referring URLs – and after a decade of SEO almost every URL is keyword rich - and then diagnoses intent by crunching the data around the three dimensions of every page-view on the site. 1) What page a visitor came from 2) what page a visitor is about to view and 3) what happens when it is viewed. 

Those first two dimensions are great pieces of data but it is coupling them with the third dimension that truly makes Yieldbot special. 

We give our keyword data values derived from on-page visitor actions and provide the data to Publishers as an entirely new set of analytics that allow them to see their audience and pages in a new way – the keyword level. Additionally, our Yieldbot for Advertisers platform (launching this quarter) makes these intent analytics actionable by using these values for realtime ad match decisioning and optimization.

For example: Does the same intent bounce from one page and not another? Does the intent drive two pages deeper? Does the intent change when it hits a certain page or session depth? How does it change? These are things Yieldbot works to understand because if relevance were only about words, contextual and semantic technology would be enough. Words are not enough. Actions always speak louder.

All of this is automated and all of this is all done on a publisher-by-publisher level because each publisher has unique content and a unique audience. The result is what we call an Intent Graph™ for the site with visitor intent segmented across multiple dimensions of data like bounce rate, pages per visit, return visit rate, geo or temporal.

Here’s an example of analytics on two different intent segments from two different publishers:

For every (and we mean every) visitor intent and URL we provide data and analytics on the words we see co-occurring with primary intent as well as the pages that intent is arriving at (and the analytics of what happens once it gets there). We also provide performance data on those words and pages.

Yieldbot’s analytics for intent are predictive. This means that the longer Yieldbot is the site the smarter it becomes - both about the intent definitions and how those definitions will manifest into media consumption. And soon all the predictive analytics for the intent definitions will be updated in realtime. This is important because web sites are dynamic “living” entities - always publishing new content, getting new visitors and receiving traffic from new sources. Not to mention people’s interests and intent are always changing. 

I hope this post has served a good primer on Yieldbot for Publishers and maybe even gotten you interested in seeing it in action on your site. One of the best parts of what we do is seeing people’s faces when they first see the product. If you are a publisher and would like a demonstration please email info <at> yieldbot.com

 

Wired had two amazing pieces on online advertising yesterday and while Felix Salmon’s piece The Future of Online Advertising could be Yieldbot’s manifesto it is the piece Can ‘Serendipity’ Be a Business Model? that deals more directly with our favorite topic, intent.

The piece discusses Jack Dorsey’s views on online advertising and where Twitter is going with it. I had a hard time connecting the dots.

“…all of that following, all of that interest expressed, is intent. It’s a signal that you like certain things,” 

Following a user on Twitter is not any kind of intent other than the intent to get future messages from that account. If it’s a signal that you like certain things it’s a signal akin to the weak behavioral data gleaned from site visitations.

Webster’s dictionary describes intent as a “purpose” and a “state of mind with which an act is done.” Intent is about fulfilling a specific goal. Those goals fall into two classes, recovery and discovery.

Dorsey goes on:

When it (Google AdWords) first launched, Dorsey says, “people were somewhat resistant to having these ads in their search results. But I find, and Google has found, that it makes the search results better.”

At the dawn of AdWords I sat with many searchers studying their behavior on the Search Engine Results Pages. What I and others like Gord Hotchkiss who also studied searcher behavior at the time learned, was people were not as much resistant to Search Ads as they were oblivious to them. People did not know they were ads!

Search ads make the results better because they are pull. Your inputs into the system are what pull the ads. So how does this reconcile with the core of Twitters ad products that are promotions? Promos need scale to be effective. Promos are push. Precisely the opposite of Search where the smallest slices of inventory (exact match) produces the highest prices and best ROI.

Twitter is the greatest discovery engine ever created on the web. But discovery can be and not be serendipitous. Sometimes, as Dorsey alludes to, you discover things you had no idea existed. More often, you discover things after you have intent around what you want to discover. This is an important differentiation for Twitter to consider because it’s a different algorithm. 

Discovery intent is not an algo about “how do we introduce you to something that would otherwise be difficult for you to find, but something that you probably have a deep interest in?” There is no “introduce” and “probably” in the discovery intent algo. Most importantly, there is no “we.” It’s an algo about “how do you discover what you’re interested in.”

Discovering more about what you’re interested in has always been Twitter’s greatest strength. It leverages both user-defined inputs and the rich content streams where context and realtime matching can occur. Just like Search.

If Twitter wants to build a discovery system for advertising it should look like this.

At Yieldbot our focus is on collection, organization and realtime activation of visit intent in publisher content. We do this not as a network but on a publisher-by-publisher basis because of this simple fact; every publisher has a unique audience and unique content. What that means is that even if the keyword is the same across publishers, the intent associated with it varies in each domain. 

The original purpose of this post however was not to point out the flaws of networked based keyword buying vs the performance advantage of Yieldbot’s publisher direct model. Nor was the purpose to show you how much we truly understand publisher side intent at the keyword level and how use that intelligence in an automated way to achieve the highest degrees of relevant matching. 

The original purpose of the post was to meet the request of a few people that had asked me to share some more data visualization of our Intent Streams™ after we originally shared a few on our recent blog post about our data visualization methods.

It occurred to me the other day that the best representative example over the last month was intent around “Steve Jobs” so below we are sharing our 30-day Intent Streams™ from four publishers. 

If you’re new to our streamgraphs the width of the stream is the measure of pageviews of intent associated with the root intent “Steve Jobs.” The other useful data points in these visualizations are the emergence, increases, decreases and elimination of the associated intent over time. As well as how many terms are seen to be associated with the root intent.

Another way we visualize intent data is across a scatter plot. Here you see the performance of the “Steve Jobs tribute” compared to the other intent related to Steve Jobs looking at the number of entrances (aka landings) on the y-axis and the bounce rate of that intent on the x-axis. 

It’s important to note in this scatter plot visualization that the analytics are predictive. We are estimating performance forward over the next 30 days. The four streamgraph visualizations were based entirely on historical data –in their case a 30-day look back as noted on their x-axis.

We hope you find this intent data as interesting as we do.

 

The business of the web is traffic. Always has been. Always will be. That’s why I was a bit surprised with how much play Rishad Tobaccowala’s quote received last week in the WSJ:

"Most people make money pointing to content, not creating, curating or collecting content."

The original lessons of how to make money on the web got lost because at some point everyone with a web site thought they could go on forever just selling impressions. They didn’t foresee two things:

A) The massive amounts of inventory being unleashed on the web. In the last two years alone Google’s index has gone from 15B pages to 45B pages.

B) The explosive rise of the ad exchange model with its third party cookie matching business that gave advertisers the ability to reach a publisher’s audience off the publisher site and on much cheaper inventory.

Fortunately the more things change on the web the more they stay the same – especially the business models.  Traffic arbitrage, the web’s original model, is a more viable model than ever for publishers and likely the only hope to build a sustainable business in the digital age.

The web is literally built around traffic. Here’s what it looks like right now.

From Search to Email to Affiliate the value and monetization of the web occurs in sending and routing these clicks or as Tobaccowala said “pointing out” at a higher return than your cost of acquiring the traffic.

Google of course is the biggest player in the arb business. 75% of the intent Google harvests costs them nothing. They’ve been able to leverage all the intent generation created in other channels like TV that shifts to Google for free. Just take a look at how much TV drives Search. But Google also pays for traffic. Last year they spent over $7.3 Billion or 25% of revenue on what they call TAC (traffic acquisition cost) to get intent. Of course you need this kind of blended model to be successful as Google is with arb.

With the growing (and free) traffic generating intent to publishers it is time they got in this game. Organic Search continues to drive higher percentages of traffic to top publishers (and the Panda update has pushed that even higher). YouTube, Facebook and Twitter are also sending more traffic all the time at no cost to Pubs. The seeds of a huge arb model continue to be sown.

Vivek Shah CEO of Ziff Davis speaking to Ad Exchanger about ‘What Solutions are Still Needed For Today’s Premium, Digital Publisher’ put it this way:

“…you need to invest in technology that can sort through terabytes of data to find true insights into a person’s intent… not just surface “behaviors.”

This speaks directly to the arb model and how it becomes a true revenue engine for publishers. Once you understand the intent that is present on your site you can then quantify its value. Once you quantify the value you can figure what intent you need to get more of, seed that intent with new content and how figure out how much you can spend to drive traffic to it. This is exactly the model we’ve been working with publishers - using Yieldbot to qualify and quantify the intent on their sites.

So the future of the web looks a lot like the past. There will be marketing levers and technologies that optimize what traffic you drive in and there will be marketing technologies that optimizes where and at what value that same traffic goes out. Everything else you will do in your business will support those value creation events. Yieldbot just wanted to “point out” that for publishers.

 

There are few main theses that I’ve spent my 14 year career online successfully operating under. The most successful one has always been to leverage the fact that the web is the only user controlled medium. The more ability you give for visitor events to define run-time or dynamic rules the better your ability to deliver relevance. Put another way, there is no better segmentation than self-segmentation.

In marketing terms this means pull instead of push. The best representative example of this is, of course, Search. A visitor action provides an input (query) and everything else processes based off that rule*. Relevance is delivered (and the most successful advertising technology is born) by pulling content to data input rules. Dynamic landing page optimization works the same way.

So why does this matter to publishers whose business is one of “pushing” content? It matters because of one incredible fact about Search that seems to get lost on Publishers. Neither the intent that precipitates the query or the content used to deliver relevance to it belongs to Search. People bring their intent to Search and Search sends it to content created by digital publishers.

Search maybe the meat in the intent sandwich but you can’t have a sandwich without bread. Bread is the media generating intent and receiving intent. Publishers are the bread in the intent sandwich and bread is what publishers have been leaving on the table.

Two years ago I wrote about the opportunity to build an intent harvesting platform for publishers and Chris Dixon followed up with a piece Why Content Sites are Getting Ripped Off. In the ensuing time our team went ahead and built that platform. Because of the complex level of intelligence and scale needed it has taken until now to bring Yieldbot to market. In fact we spent a full-year in invite only beta doing nothing but learning. Now that we’ve released Yieldbot we’re finding out even more amazing things about intent on the Publisher side and the opportunities are obvious and bode very, very well for the future of ad supported publishers.

The most important thing may be that publisher inventory for realtime intent dwarfs Search. As David Koretz figured out a couple years ago the top 200 pubs generate 2000% more pageviews than Google. We see that dichotomy everyday in our data. The amount of inventory using first party (better) data also dwarfs third-party data as Chris O’Hara at Traffiq recently pointed out.

"You have an entire ecosystem built around audience targeting using 3rd party data. The problem? The companies with better and deeper first-party data have a lot more audience"

We also see that most of the advertisers who are buying this intent in Search are not buying from the Pubs that have the exact same intent on their site. Even better the pubs intent is more down-funnel, has greater context, is less competitive and has the influential power of being in a branded domain and can leverage creative in ways search cannot. Publisher side intent should be more valuable to advertisers than Search with its SERP landscape of crowded text link ads and arcane rules.

As we work with pubs to understand this information arbitrage opportunity it’s clear that intent matched with timing and context can improve visit monetization by an order of magnitude. And why shouldn’t it? Yieldbot structures the data from the page that is clicked on and the subsequent pages that are clicked to. This scenario happens millions of times a day on large sites and these (click) streams of data provide rich realtime intent data that fuel our intent classifications and matching rules.

The bottom line is publishers are in the unique position to both classify the intent on their sites and use pull rules once that intent is recognized to deliver the highest levels of relevance in realtime - just like Search. Even better, they never lose ownership of their data and can monetize it directly with advertisers - even using their own ad server. This is game changing. This is the bread in the Intent sandwich. This is Yieldbot.

The Yieldbot team will be writing a lot more about our data and technology right here on our blog. We hope you join the conversation about the power shift to publishers and their data in the ad ecosystem.

* there are additional rules that are used with the query as well such as geo, temporal, query number, however the query itself is the primary rule.