event horizon

Building On-Ramps to Media Futures Markets

It's been a long time since technologists started building electronic systems for trading forward media . To be fair, it has been at least 20 years since the first companies gave it a shot. In fact, we have a little symbolic graveyard in our office to commemorate our fallen comrades. Some pivoted, some got acquired, some went out of business, and some were divisions inside big tech firms that faded into the background. All made valiant efforts. All failed to build a real exchange for forward media. Let's pour one out in their honor.

Fallen Comrads.jpg

Our fallen comrades who had taken on forward media buying and failed, could not effectively prove to both sides that their platforms and technologies drove greater efficiency and led to better deals.  

So how is it that so much media is bought on a guaranteed basis, but none of it actually trades. As far as we know, no one in the market provides a biddable environment for forward media. To have real markets supply must compete with other supply and demand must compete with other demand in the same market. But, competition is not just the application of a single bid to a single inventory opportunities, it also means the application of multiple bids to multiple inventory opportunities simultaneously. Without this, there is no real exchange, without a real exchange, none of these technologies were able to reach critical mass.

We can ask ourselves if there was a theme among all of these heroic attempts. Is there a common reason that we can point to? Is there one thing we can identify that tripped up silicon valley greats like Google, Yahoo, and Microsoft? We think there was. 

When we look at any of these "marketplaces", some of which still exist in one form or another, we see that they all actually provided ecommerce platforms for media, not exchanges. While that was true, most were marketing their products as if they were exchanges. If a searchable list of products that can be purchased, from multiple buyers,  through a website is a marketplace, than why is doing the same thing over the phone with a sales rep not a marketplace? (Because automated sales wasn't sexy or VC fundable)

Media buying is much more like portfolio management than buying electronics from Amazon. Setting aside the fact that none had an environment in which anything can be traded, none provided the types of solutions that enabled demand from other channels to flow into these forward media marketplaces. In real-time impression markets, pricing and analytics tools facilitated the movement of budget to increasingly targetable audiences. This in turn enabled the measurement of greater efficiency from real-time impressions markets than traditional insertion orders.  In short, buyers and sellers could measure how good of a deal they were getting and simply followed the money. And so, many left the on-ramps to their highways unbuilt. It was too hard to get into the markets, so none got traction. 


I Got 99 NP-Hard Marketing Problems...

We have spent a fair amount of time thinking about why some problems faced by data driven marketers are so much harder than others, advertising's P vs. NP problems. We were looking to find a reason why these hard problems have not been solved.

In short, we realized that there are two ways of thinking about solving data problems, the first is by modeling (think algorithms) and the second was a new idea. The new idea is that there are problems in marketing whose solutions are emergent from the data itself. In other words it can be measured but not modeled. Effectively it means that some problems can only be solved by working out every possibility and measuring which work and which don't. It means that the way we design our data models really matters.

Emergent solutions arise when new properties and behaviors "emerge," with no one directing and no one able to foresee the new characteristics from knowledge of the constituents alone.

In chemistry, for example, the taste of saltiness is a property of salt, but that does not mean that it is also a property of sodium and chlorine, the two elements which make up salt. Thus, saltiness is an emergent property of salt.

17th century economist Adam Smith described an "invisible hand", an emergent property of markets, that guides markets to produce just the amount and variety of goods that the public needs. The stock market has its own "invisible hand." The purely self-interested actions of thousands of buyers and sellers result in the purely blind workings of the stock market—the sudden shifts in activity and valuations, the bubbles and crashes—as well as the market's notorious properties of stupendous intricacy and frustrating unpredictability.

There are some problems in media whose solutions will only emerge from data. The key challenge is usually measuring and identifying the source of the issue. Once a problem can be identified and measured, it’s solution emerges from the data. Patterns can be identified and changes can be made.

Solutions for marketing problems such as attribution are data-emergent in nature. As evidence, look to the current models used for attribution. Frankly, the market has just begun measuring attribution and the models in the market kind of suck. We can’t even model how and why people click on ads, just that they did, let alone understanding why they made a purchase.

To do that we need to change the way we understand attribution data. Books are a good analog to attribution. What if we stored the data for a library in such a way that every book's data contained its translation to every language on earth. Fundamentally, War and Peace is the same story in every language, but understanding all translations at the same time will surely yield understanding that we couldn't get from just the English version. This exemplifies how storing the same basic data in different ways unlocks knowledge that was simply inaccessible before. The old way of storing that information made it invisible. Every language has its own unique way of capturing and communicating ideas. If a story can be told in every language, the underlying fidelity of the ideas in the story are far more well defined. The improved definition is the emergent property in this case.

Many of the problems we face in marketing are as difficult as they are to solve because the way we describe the underlying information needs to be improved. We are simply unable to see a solution because we are unable to identify the source of the problem.

The Dangers Of Scaling Today’s Addressable TV Buying Methods

Originally Published on AdExchanger

For more than a decade, some of the biggest names in television have been hard at work on achieving household-level addressability across their footprints.

Leaders in the space, such as Cablevision, Comcast and Dish, have deployed a great deal of hardware and software to ensure the right creative can be delivered to the right household. But what about the way in which the media is transacted?

Many in the television space have girded themselves for the onslaught of technology they fear will undermine their positions the way display publishers believe early open-market RTB eroded their pricing power.

The problem is that even though there is no real-time bidding in addressable television, the forces that weakened publishers’ pricing power in RTB are rearing their heads in addressable television. But it is not the programmatic or real-time technology that hurts a seller’s pricing power – it is the flow of information.

So, what is it about open-market RTB information flow that is similar to the information flow in today’s manual methods of buying household addressable television? Something is causing this asymmetry of the information flow that will undermine seller’s pricing power, even without any programmatic markets in place. When I shared this insight with some in the industry, the initial response was disbelief. After more explanation, they came to see the trends.

Sellers Left In The Dark

The root problem of the information flow asymmetry is a lack of shared understanding of what is being bought and sold. In the traditional model of linear TV buying, both the buyers and sellers understand the audience and placement being purchased. Both sides know how Nielsen defines the audience and both sides have a general understanding of what is being bought and sold. Therefore, the buyer and seller agreement represents a real market price.

Today’s addressable TV buyers are very different. The buyer leverages data from Rentrak and other third parties to identify a list of target households, which defines the media buy. This is where the information asymmetry begins. A seller that receives a list of households has no idea why the buyer targeted those households or what drove the value of the transaction.

If sellers have no idea why their products are valuable, how can they possibly determine a fair price? But that is how addressable TV media buys are like open-market RTB in display. The buyer knows everything about the product and what defines the value of the deal and the seller knows nothing.

In RTB markets, the publisher has no idea why any impression cleared at the price it did. A publisher has no access to the data the buyer is using to bid. This information asymmetry created a huge advantage to buyers through the use of open RTB markets. It is not the technology that undermined publishers’ pricing power – the technology just leveraged a specific type of deal model, which most onlookers conflated with the technology itself.

Repeating History?

So what does this mean for today’s sellers of addressable TV media? It means that sellers who relinquish control over defining products and packages will be doomed to repeat the long-feared loss of pricing power experienced by display publishers. For addressable TV markets to flourish, sellers will need to use first- and third-party data to certify packages and make them discoverable.

A buyer unwilling to define the target of their buy with the seller knows full well that the value of what they are buying is much higher than what they want to pay. It is a signal to the seller that buyer is only willing to buy the inventory if the publisher sells it below its fair value.

While this may be beneficial in the short term, transaction models that strongly benefit one side of the transaction become overly complex and fail to scale to meet market needs. This has been proven by the movement of display media liquidity from open market RTB to private marketplaces, header bidding and programmatic direct. Display publishers have sought alternative channels to regain lost control.

Continuing down this path for addressable TV media transactions will result in yet another technology and vendor map that is as confusing and complex as display. That’s something I’m pretty sure none of the TV media buyers and sellers want.

A Lack Of Market Forecasting Is Holding Back TV Media Trading

Originally published on AdExchanger

This year will be a great one for digital media trading. Digital media trading has jumped the shark and landed safely. Transparency will increase, fraud will decrease and market structures will only get better. Moving forward, the real challenge is to bring what we have learned to drive the growth and expansion of media trading into all other media and create unified platforms.

At this point, TV media is purchased and not traded. But that could change with the creation of better market-forecasting tools, which would improve our understanding of media markets, enhance decisioning and enable faster, effective and efficient technology-driven TV media trading. The inability to model and understand media markets is a leading obstacle holding back the birth of TV media trading.

Sellers of TV cannot reliably predict how trading may impact revenue flows, while traditional channels are reliable with forecastable revenue. If trading is not provably more reliable and revenue-accretive, there is no reason to switch from purchasing to trading. When sellers forecast higher yields via trading, there will be incentive to actively trade.

Unfortunately, display media is the petri dish where the earliest experimentation happens. Once technologies are created in display, companies try to cram those technologies into other media with little success. The trading technology developed in display was designed for machine-based trading, not people.

Discoverable Pricing, Secret Strategies

The challenge to developing appropriate TV market-forecasting technology is two-fold. The transaction space is highly fragmented, and the asymmetry between buyers and sellers makes the collection of granular data only possible on the buy side. Current technology makes market pricing and trading strategies inseparable, so everyone in the markets wants all of their data secret. Markets operate best when pricing is discoverable and strategies are secret.

As demand aggregators, agency buyers have a significant advantage. Buyers see upfronts, scatter, cable, satellite and local supply, while the suppliers only see the demand for their own inventory.

Broadcasters and networks have few tools to help them understand how to use packaging and pricing techniques to address viewership and market-demand fluctuations. Sellers can see the scatter budgets coming their way and what their sellout is, so they see both supply and demand, but only for the tiny part of the market that their inventory represents.

There is little that data sellers can use to understand granular demand across the market. Given this environment, people use their anecdotal experience and intuition to make up for the lack of data, which reduces the use of machines to add value.

No Solution In Sight

What is even more disturbing is that while the asymmetry exists, the lack of an organized market generating this historical transaction data means that no one is even working on this problem. Buyer and seller transaction information is so closely guarded that general trends are the best we can hope for.

No existing technology accounts for campaign goals, budget, available inventory, market prices or historical performance metrics. There is also no technology that offers recommendations for an optimal media plan.

On the other side, media companies lack the sales tools that enable what-if scenario modeling to understand how the demand they expect should be most effectively allocated across the media. In reality, media plans and media packages span multiple media and publications. That is what the deal conversations are all about, not a single impression or a line item for a placement. Buyers and sellers need a better way to forecast what those conversations will be like and decide the best course of action.

In Use Now

TV media buyers and sellers need better tools for making decisions. For real-time display, the tools are more straightforward, as people could never pull the trigger fast enough, so machines bear the brunt of the control.

For forward-media buying, in which TV is the 900-pound gorilla, people bear the brunt of control, and technology plays more of a support role. While there are some systems that help manage yield after the deal is made, today, the selection and decisioning of what to offer a potential client and the price at which to offer it is nearly completely manual.

With everything we have accomplished, we still face obstacles that will challenge this momentum moving forward. First and foremost among those is the revolution of market forecasting. Unified platforms require unified – not homogenized – tools and analytics to support omnimedia trading.

While media mix modeling has been around for decades, buyers’ and sellers’ ability to transact an ever-increasing granularity of content and audience programmatically is driving an increased need for a revolution of market-forecasting technology.

Momentum is moving in the right direction to help buyers determine what they can buy, how they can buy it quickly and easily and whether it turned out as expected. The technology for modeling what TV media to buy and sell has not kept up.

Everyone Needs To Understand These Media Trading Models (But Few Do)

Originally published on AdExchanger

I have a confession to make: Before writing this, I couldn’t really say that I, or anyone I know in ad tech, fully understood how different ways to trade media compared with each other.

There are all these different ways that technology brings buyers and sellers together to trade media. This stuff is so confusing and hard to wrap your mind around that I never really felt like I had a solid grip on it.

Buyers and sellers choose the trading models they want to use based on their objectives, technology and access to data. Each media trading model fills a niche in our ecosystem. Understanding the different media buying models is not about picking winners or declaring one better than another. That is up to you, given your business needs.

I set out to map these media trading models so that everyone can pick what works best for them and have a far better understanding of what is going on. To understand the different types of media trading models, I spent a few days talking to folks throughout the industry, which made it clear that it was time to decipher this enigma.

I decided to focus the comparison on three areas of a transaction where key business decisions are made by buyers and sellers. These are the handling of inventory before the trade, managing of the negotiation and how the deal’s final price is determined. The outcome of the effort to understand media trading models resulted in a visual landscape for media trading models that I call a “Media Tradescape.”

I designed it to compare 10 key areas where we can differentiate each of the nine trading models used in display media; I give more detail on each trading model below the tradescape. I’ve also included a breakdown of each trading model with corresponding characteristics as part of my “Table of Trading Models.”

What did I learn from my investigation? There is significant upward price pressure from demand within media trading environments. Since the advent of real-time bidding, an increasing number of media trading models have risen to allow buyers to access inventory at increasingly higher prices. Trading models developed in the last couple of years focused on unlocking trades for more effective and higher-quality inventory, at higher prices. The environment for transacting high-quality and high price-point inventory via technology is very much in flux.

You can download a PDF version here.

Inventory availability: Control by sellers to allow buyers to see forward supply. Some models empower sellers to give buyers a searchable supply, others empower sellers to answer requests for specific supply and, in some, sellers can't expose forward supply at all.

Inventory priority: The ability to guarantee inventory amount and price to a seller. Some models focus on reserved inventory, others on unreserved and some on both.

Inventory allocation: The way in which supply is allocated to demand. Once an impression is received by the publisher's ad server it attempts to allocate the supply to demand, based on the outcome of its yield optimization. If no desirable outcome is found by yield optimization, an outside auction is used to determine the outcome.

Inventory units: The definition of what is supplied and demanded. Units of audience are a mix representing all of the consumers of the supplied media, and “impressions” are units of supply that all share a common set of attributes.

Inventory targeting: The ability to apply granular definitions to the supply using a specific set of agreed-upon attributes. Some trading models enable targeting definitions from the supply side or enable use of definitions from the demand side, while others enable both definitions for filtering.

Pricing model: The units of price used by buyers and sellers. Most trading models are based on CPM pricing, while two support multiple pricing models, including CPM, CPC and CPA.

Seller offers: The ways in which supply is represented in each trading model. Some use hidden floors in an auction to set the lowest price a seller will accept. Others use a set of rules to represent offers to buyers and some use manual processes to set the sell offers used for trading.

Buyer bids: The ways in which demand is represented in each trading model. Trading models can be divided into environments where buyers can bid or buy using a take-it-or-leave-it price. Rules-based bidding allows buyers to set the price at which they are willing to purchase the supply. No-bidding environments provide an ecommercelike experience where buyers can only buy at the offered price.

Negotiation: The ability to consider both the seller’s offer price and buyer’s bids in determining the clearing price. Some models do not account for any negotiation since the price is determined manually by humans before technology is involved. Other trading models support a negotiation and the final price is determined by a technology.

Deal priced by: The layer of technology that determines the clearing price. Regardless of how the negotiation occurred, the final price at which each impression clears is either determined by the publisher's ad server, or that decision is outsourced to a technology outside the publisher's ad server.

This is my interpretation of the landscape of media trading models. I want to have a shared understanding of these trading models so let’s have a conversation. Please add your perspective to our shared understanding of the space. I and many others would be very happy if we could clear this up.

Below is the "Table of Trading Models," which breaks down this information by trading model. You can download a PDF version here.

Four reasons why ‘Infinite’ media is a lie

The idea of infinite digital inventory has been batted around as a concept among colleagues for a long time. Every time I hear this, I get pissed. The reason for this reaction is that I know some folks are turning a blind eye to what is actually happening in advertising markets. There are four ways in which the lie of infinite media is created.

In reality, there is too little real supply.  If real supply and demand are in balance, there is very little room for shenanigans.

First, let’s prove media is not infinite. Then, let’s examine why people think it is. Lastly, let’s contrast the idea of infinite inventory with the idea of finite attention. I’ll use the analogy of data transfer. To illustrate, let’s think of inventory as the volume of data and attention as bandwidth. The idea of infinite inventory is like saying you have unlimited data on your internet service. Everyone can effectively download an infinite amount of data, the question is how fast.  If you try to watch 4K video over DSL, you will get 10 seconds of buffering for every 1 second of video. In this same way, the idea of infinite media ruins the attention experience. If a single user has limited attention bandwidth than the sum of all users also has a limited attention bandwidth and therefore media is finite. The essence of media is the content consumed and attention opportunities it creates not the number of ad placements served.

Now, let’s examine the lie of infinite inventory. Let’s start by assuming it is correct, there is infinite inventory. If true, it is important to consider some possible causes.

1.      There are so many users and so many ads served that there is not enough demand to meet the supply. Supply is growing faster than demand making supply effectively economically infinite.

2.      Infinite supply is produced to syphon off known demand: bots and non-viewable inventory.

3.      The accuracy of and transparency of 3rd party audience derived from lookalike models.  Overly broad segment definitions can create the illusion of far more inventory than actually exists.

4.      The way in which the amount of inventory is measured yields a false count.

Now, let’s address these one by one.

In terms of users, yes, the number of users and the amount of time spent online has grown significantly over the last decade. But let’s be real, finding the right audience, in the right environment, at the right time is pretty darn hard, let alone finding an infinite amount of it. Further, supply can be exponentially increase by flooding ads on a page, turning an article into a ten page slide show, and many other tricks that provide for lots more ad inventory crammed into the same attention bandwidth.

In relation to bots and non-viewable inventory, tomes have been written. In reality, this is inventory that provides zero real audience attention. If media really was infinite, than fraud and ad blocking would not be real issues. Blocking ads, in other words removal of supply, is not a problem when supply is infinite. Further, adding supply via bots, should also have no influence on the market as we could simply replace the lost and fraudulent inventory with real inventory. Further, if $7.2 billion of fraud will take place in 2016 as the ANA study finds, it should leave a pretty big ‘mark’ in the marketplace. It is really hard to hide $7.2 billion. If all programmatic spend in 2015 was $14.2 billion, according to Magna Global, there are a lot of folks making a significant portion of their revenue via fraud.  I’m not a tin-foil-hat conspiracy kind of guy, but if some players in the industry are profiting from this fraud there must be many other players who are happy to look the other way. Said otherwise, if you are willing to buy fraudulent traffic, there is an infinite amount.

Overly broad audience segmentation models are also perpetuators of this lie. For example, a couple of hours of research shows that about 6% of US households replace their car every year. Add to that the fact that the buying consideration period is about a month, then only 0.5% of the population is actually in market for a car at any given point in time. Said otherwise, any data provider that tells you that a general interest or news site can sell you auto intenders that represent more than 0.5% of their audience over any period of time, is likely broadening the model to ‘create’ audience inventory. Using lookalikes may yield lots of opportunities, but do you know how many of those auto intender lookalikes are really auto intenders and not just people with similar behavior? What behaviors drive the model? Just because I visited ford.com and gm.com does that mean I’m going to buy a car?

Last is the way in which we measure inventory. Some buyers and sellers have overcome this measurement problem by transacting on share of voice. Using this model, deals are a commitment for a portion of the overall attention instead of the number of impressions. Measurement by impressions is very easy to manipulate, measuring by attention or share of voice is nearly impossible. If you were choosing between two inventory sources at the same price and you knew that one had 20 ads on the page and one had 4, you would choose to run your ad in the media that was less cluttered. Share of voice or share of attention based deals remove a publisher’s incentive to cram ads and guarantee the buyer is buying the attention that they bargained for.

It’s time to open our eyes. It’s time that legitimate advertisers and publishers take control of the market. Because in the end the lie of infinite inventory only hurts the good guys and makes money for the bad guys.

Analogy Makes It Obvious: The RFP Is Bad News

Technological tillers teach us that sticking too much to old perspectives and ideas is a surefire way to fail. We need to stop thinking of the automated RFP as the product itself, but rather a framework allowing buyers and sellers to access media transactions. If the framework can be avoided while simultaneously creating a better experience for media transactions, then that framework should be avoided.

If technological tillers are ignored new products and technologies fail. For those who acknowledge and solve the problem, it generally  results in a revolution and massive success.

I recently watched a great keynote that I thought applies directly to transacting media and specifically media trading. In  short, the main point is that sticking too much to old perspectives and ideas is a surefire way to fail. The term  for this behavior is 'technological tiller.'Scott Jensen, product strategist at Google, gave this great talk on this subject and came up with this great new term, the  'technological tiller.'  Scott defines a technological tillers as happening when we sticking an old design onto a new technology wrongly thinking it will work.  He derived the term from a tool called a boat tiller, which was, for a long time, the main navigation tool known to man. Hence, when the first cars were invented, rather than having steering wheels as a means of navigation, they had boat tillers.

Cars that used boat tillers to steer were horribly hard to control and prone to crash. Cars could only get widely adopted after the steering wheel was invented and added to the design.

With an understanding of technological tillers, the two main types of media buying, real time and forward,  look very different. For everything that was built using the RTB protocol, there was pretty much no old perspective to stick to when building the technologies and processes, so many problems were avoided and there were no tillers to speak of. For forward media trading, this lens of the past has driven most technology to be developed using the old perspectives. RFPs are a technology tiller. The context and the technology have changed dramatically in recent years.

Worse still, most of the language of media buying and selling technologies was created in the realm of inventory management. Before media could be bought and sold effectively, the very first pioneers of media technologies created technologies such as cookies and ad servers. This first generation of technologies was not developed to support media buying and selling, it was designed to facilitate an understanding of what, and how much, inventory would be available at some time in the future, to track the activity of users for the media owner to provide a good experience, or other non-transactional reasons.

For media trading, this is a valuable lesson:  context or technology changes most often require a different approach. In our car example, the new technology that added a motor engine to a horse carriage needed the new design of the steering wheel to make the resulting technology, the car, reach its full potential.

For forward media trading to reach its full potential we need something different than the RFP. Linda Boff, chief marketing officer at GE, recently wrote a great article Marketers: It's Time to Say RIP to the Media RFP

"...when I think about the many ring-around-the-rosy conversations that characterize the standard RFP process, I literally want to cry. In a post-RFP world, agencies and publishers don't waste time on proposals that will never go anywhere, and brands don't devote resources to sifting through cookie-cutter submissions. Instead, time and talent can be invested where it matters: in identifying breakthrough experiences that are good for users and drive attention -- the only metric that really matters."

The only thing that Linda does not address in this great article is what the solution is. In other words, if automating the RFP process is a technological tiller, then what is the steering wheel solution for media buying? And that my friends is why media futures technology is so very necessary.

Media Agencies Will Use Exchanges To Find New Clients

In the modern age of media buying, there is no added value in processing a transaction anymore. Technologies have brought those costs down so much that little expertise is required for simple transactional execution. The role of the agency is to get more bang-for-the-buck than the client can do on their own. We recently sat in a board room listening to the lamentations of a management team talk about the complete collapse of margins in media buying, over the last three years. I liken this to the rise of discount brokers on Wall St. In the 1980s, your stock broker would charge you 3%-4% of the transaction to place a trade on your behalf. As we moved into the 90s and beyond, technology providers effectively disintermediated these high broker fees through technology. Sound familiar? In reality, the high fees charged by brokers also came with advice.

The first generation of financial trading technology, effectively unbundled the trading service from the advisory service, and packaged the trading service in an electronic form. Over time, the brokers realized that in order to make up for the lost trading business, their advice business would need to scale as well. The problem with scaling an advisory business is that it very rarely requires technological innovation to drive the scale. More specifically, these types of businesses need to solve a distribution problem: how to differentiate the quality of the service, inform potential clients of the offering, and make it very easy to purchase.

The innovation for achieving this scale was already in the market, it was the fund structure. Instead of investing all your money with a firm to manage it, the firm created little units that an investor could buy. The firm managed all these units together as a single portfolio. In essence, the financial engineering innovation that brought us the fund (the mutual fund to be exact) enabled firms to create units of service that can be bought and sold easily. Furthermore, it meant that the unit (minimum investment with the fund) could be much smaller than the minimum account size required to have all your money managed by the firm. Said otherwise, you are buying a unit that authorizes the fund company to invest it in the way they believe best achieves the fund goals. We think agencies need to learn how to do the same.

The lesson to be learned by media agencies is that the current market transformation will require a new way to distribute their media buying service. Too complex you say? Too individualized? Well, lets count how many different ways asset managers have created to package up specialized investment management? As of 2013, the answer is 75,000. Yes, there are more than 75,000 different types of funds in financial markets today. Each fund represents a specific strategy. Agencies can use this same approach. For example, creating units of a media buy where the strategy is a CPG food product targeted at 18-34.

By creating units of their service, agencies can present these offers at all times and to all market participants, via an exchange. Many more buyers can afford to purchase these services as their minimum transaction sizes are much smaller, and agencies can leverage trading technologies and automation to scale services (media strategy, planning, and buying) using a well understood model proven to enable scale and efficiency for advisory services, related to the investment of capital. The innovation in this case is that the advisory services  focus on buying media instead of stocks or bonds.

We believe that such a change in the market dynamic will be good for the good guys and bad for the bad guys. If you really are good at what you do, it will shine. If you're not, it will be very hard to hide it.

What Is A Fair Price?

A small question with some pretty big answers.  The simplest one is as follows: the amount of currency traded in exchange for a product or service. That is the premise for a market. A more complex definition, and one that I think more people will adhere to is: the amount of currency traded in exchange for a product or service, where both parties have knowledge of the price of past transactions and the price at which others may be willing to do the same deal. This is the premise of an exchange. Which definition would you pick if you were buying and selling in this market?

In some markets, the technology decides the price of the transaction only using buyers' bids to determine price. When I first heard this, i had a little 'Wait... What?!?' moment. That simply did not make sense to me. How come only the buyers determine the price? In most transactions, the seller sets a price and the buyer agrees to that price or offers a counter bid. So, we got to thinking that building an environment where buyers get to set a price and accept bids would probably result in a more fair price. In fact, we would argue that a fair price is more important than an economically efficient price.  An economically efficient price is what the current ad technology stack is trying to do in growing the private marketplace solutions. There is a problem here, open auctions don't let sellers set a price (a floor, yes), private markets let sellers set prices but bid and historical data are not available to the market. Per the above latter definition, fair price includes knowledge of the market and the ability for both sides to set the price.

Fair prices result from sellers having control over supply and the price at which that supply is represented in the market and buyers having control over demand and the price at which that demand is represented in the market. Currently, publishers lack the technology to understand how to optimize the availability of supply to create bid tension in the market (this is not yield optimization), as such there is very little control of the flow of supply in the markets. Moreover, since the exchanges determine transaction prices using only buyer's bids, there is very little control over the price at which that supply is represented in the market. So, is it any surprise that display advertising prices have been in a complete free-fall for over a decade? When one side of the transaction has far more control on their side of the fence, the economic consequences are well understood.

Our most frequently heard detraction is that there is no common language for describing media; the attributes that drive value and determine the price of a transaction. The focus is always on standardization. The hole in this logic is that it uses standardization as a proxy for reduction in complexity. In fact, standardization can be done via nomenclature instead of categorical classification. What that means is that buyers and sellers have a uniform way to describe the transaction in terms of price and media. Now that everybody is speaking in the same language, we can create larger conversations around trades. More conversations, more market forces, fairer prices.

In short the easiest way to determine a fair price is to understand how much other buyers like me are paying, the changes in the market since those other transactions, and a positive or negative impact on that previous price driven by my needs and the changes in market conditions.

The Importance Of The Trader

At the cutting edge of today's technology driven advertising industry reside a group of professionals with an interesting job title: Programmatic Trader. As good as that sounds, given the current realities of media buying, that title is a bit of an anachronism. That said, that job title might not be that out of place given its brethren anachronisms of "exchanges" and "trading desks." By most definitions, a trader buys and sells for the purpose of making a profit. Generally traders are buying and selling on their own account (using their own money, not the client's.) In financial services contexts, those who buy and sell on behalf of others are referred to as brokers - "One that acts as an agent for others, as in negotiating contracts, purchases, or sales in return for a fee or commission." When we think about what programmatic traders do, it is more along the lines of a broker than a trader. With that said, I would argue that will soon change. Over the coming years, facilities and technologies will quickly enable programmatic traders to act more and more like traders and less like brokers.

An interesting parallel could be seen in financial services when the Glass-Steagall Act was repealed. (I could go through a whole prosaic and fact-filled blah blah about the history of the act, but I'll save you the headache.) In short, banks used to use their clients money, deposits on account, to trade and keep the profit for the bank. When the FDIC was created,  our regulators and politicians thought it would be a bad idea for the government to insure deposits while the banks take it to the 'casino'. In 1999 the Gramm–Leach–Bliley Act pretty much repealed that. And so, proprietary trading was eventually resurrected at all the major banks - the banks using their own funds to trade for profit. Why is this parallel to advertising? Well, as soon as agency buyers can actually buy and sell advertising inventory to profit from its price movements they all will.

To us it seems obvious. Media buying practices at the major agency holding companies 'see' more supply and demand than any other market participant, they have the best overall view of the performance of that inventory, and they have the best understanding (and data) in the market to value the transactions. As in all markets, those with the most information, exploit their information asymmetry for a profit. That is not a bad thing!

Helping the market figure out the best price for a transaction is a critical economic mechanism used by markets to efficiently allocate capital. Like most commodity markets, few end consumers (publishers or advertisers) see enough supply or demand to actually understand what is happening in the markets - that is why we hire brokers (agencies) to help us make the trade.  So what is it about the trader that creates value?

In a price discovered market, the trader buys from a seller selling at a price that is too low, sells to a buyer at the right price, and provides other forms of 'insurance' for price fluctuations. Alternatively, traders may buy at a price that is too high and set the correct price through a loss. In a price discovered environment, this activity is a service benefiting the entire market. Why? In price discovered markets,  transaction prices and pricing data is available to all market participants. So, if you bought from me at a price that is too low and re-sold at a higher price, I will know. If I know, I will not make that mistake again! In fact, traders help us all understand what things are worth.

It is very important to note here that the main point was not transparent markets it was price discovered markets There is a big difference. In transparent markets, everyone knows the buyer's identity, the seller's identity, and the price. In a price discovered market the price is known, but buyers  and sellers can flex the level of transparency they afford the market.

The problem we now face with the market structure in RTB and private markets is the lack of price discovery. In such environments, traders can be rent-seekers -  by arbitraging against their clients. Not all do,  but some do so openly.

The fact that markets work better when better market structures underpin them is key to MASS Exchange.

Markets vs. Exchanges - Setting The Ad Tech Record Straight

It's time to set the record straight. It's time to pause, listen, and contemplate a very basic question: how is a market different from an exchange? In many industry conversations, I find myself running headlong in to a common misconception that conflates exchanges and markets. Let me begin by asking you to think of your definition for both of those and ask you to temporarily forget them. So, let's level-set, let's define...

A market - A medium that allows buyers and sellers of a specific good or service to interact in order to facilitate a transaction. - investopedia

"[counter-parties] convey their bid and ask quotes and negotiate execution prices over such venues as the telephone, mass e-mail messages, and, increasingly, instant messaging. The process is often enhanced through the use of electronic bulletin boards where [counter-parties] post their quotes. The process of negotiating by phone or electronic message, is known as bilateral trading because only the two market participants directly observe the quotes or execution. Others in the market are not privy to the trade, although some brokered markets post execution prices and the size of the trade after the fact. But not everyone has access and not everyone in the market can trade at that price. Although the bilateral negotiation process is sometimes automated, the trading arrangement is not considered an exchange because it is not open to all participants equally." - The International Monetary Fund

An exchange - A marketplace in which securities, commodities, derivatives and other financial instruments are traded. The core function of an exchange is to ensure fair and orderly trading, as well as efficient dissemination of price information for any securities trading on that exchange. - investopedia

"An exchange centralizes the communication of bid and offer prices to all direct market participants, who can respond by selling or buying at one of the quotes or by replying with a different quote. When two parties reach agreement, the price at which the transaction is executed is communicated throughout the market. The result is a level playing field that allows any market participant to buy as low or sell as high as anyone else as long as the trader follows exchange rules." - The International Monetary Fund

For advertising, key factors such as brand protection and the protection of pricing strategies for both buyers and sellers need to be accommodated. Not every buyer wants to do business with every seller and vice versa. So, for a true exchange to work for advertising, significant additional innovations are needed above and beyond what fin tech offers, to protect inventory from commoditization. In other words, enabling buyers and sellers to understand what things are worth without necessarily divulging who the buyer or seller was to the broader market. We call that a price discovered market with counterparty transparency and transactional opacity.

An exchange is a marketplace with information that is relatively standardized and is much more readily available to all market participants. An exchange is better in establishing a 'clean and well lit place' for buyers and sellers to come together. The easier is is for buyers and sellers to make deals and understand what they are buying and selling, the greater the benefit to the aggregate economy. Dark and opaque markets only benefit a small number of participants, usually at the expense of the buyers and/or sellers using those markets.

There is actually a way to measure this, its called 'Kaldor–Hicks efficiency'. Under Kaldor–Hicks efficiency, an outcome is considered more efficient if it can be reached by arranging sufficient compensation from those that are made better off to those that are made worse off so that all would end up no worse off than before, a win-win. That is the economic objective of the exchange: make sure there is sufficient benefit to both sides so that both sides prefer transacting in an exchange over a market. In advertising, that means advertisers may pay higher CPMs, but the efficiency created would mean that actual eCPMs are lower. In other words, pay a higher price, buy less, but get more value; a better deal for both sides.

The Innovation Wasteland of Ad Tech

Being a New York based advertising technology company means that the annual Ad Tech conference is a pilgrimage not to be missed. This year, ad tech was a wasteland of ad networks, hosting providers, and a whole bunch of white label beauty and vitamin supplement providers. In fact, one of the Jonas brothers was the most hyped keynote speaker. Seriously? We walked around the floor confused and dazed. Not a single company that matters (except Microsoft promoting their Surface device) was there. Worse still, there were no startups doing anything in the advertising transaction space. Put simply, there were no new ideas. How is this possible? How can it be that there are no new ideas worth spreading in ad tech?

This is bad. This is bad for all of us. If we do not innovate, if we as an industry can't figure out new and better ways to do things, our industry will be commoditized. Let's put this in perspective - if you want to see the most commoditized technology environment globally, look at fin tech. NASDAQ OMX  and NYSE Euronext, two of the most dominant financial exchanges in the US and across Europe, together generated less than $6.5 billion in annual revenue in the last four quarters (12/13). To put that in perspective, that is a month and a half of Google revenue! Think about how much trading happens in the exchanges in dollar terms... Today, these exchanges transact in excess of $500 billion a day! That is more dollars per day than impressions across all of ad tech. Again, $6.5 billion in annual revenue for $500 billion of trades a day. That's a clear and present danger of commoditization that should be obvious to the ad tech industry.

Today, a trade of 100 shares on a US exchange, regardless of transaction value, generates about $0.02 of revenue to the exchange. To be clear, buying or selling 100 shares of Google, a $100,000 transaction, generates $0.02 of revenue. For my ad tech brethren who charge 5%,10%, or 15%, consider this a warning. Fin tech has not figured out new ways to trade stocks in over 30 years, since the start of electronic trading. While there has been lots of innovation driving faster and more efficient trading, the exchanges have not been able to innovate in ways that create more value. Don't get me wrong, efficiency is absolutely important, but finding new ways to create value is how industries expand their share of the economy.

So, to my ad tech colleagues, I implore you, unshackle old ways of thinking, take risks, break rules, and most importantly, build new and groundbreaking technology!

Demand Optimization: A New Ad Tech Function

As a buyer of upfront media, understanding the optimal way to spend your money is probably your number one priority. Is there a better way to spend the money to buy upfront media, so that the outcomes are greater than those from the last time I purchased? To answer that question, a buyer needs to understand two things: the value of the media and the price of the media in the market. What's the difference? Value, is a way to understand how much separate items are worth to you as compared to each other, while the market price indicates how much the last buyer and seller agreed that a single item was worth, between the two of them. Markets are created because different buyers and different sellers each have a belief of what their value is, and they would like to find each other and transact. So, markets are created when there is a landscape of bids to buy and offers to sell that can be meaningful to both the supply side and the demand side. This way, buyers can find sellers and sellers can find buyers more easily. That is precisely what our two-sided market model for upfront media does. (explained in an older post)

An illustrative example: you are standing on the floor of the imaginary New York Advertising Futures Exchange, you want to buy a piece of inventory at $10 CPM for February flighting. You shout out "I have a buy order for Males, 18-25, in an automotive context at $10 CPM, 10 million impressions, for a February flight." It just so happens that there are a bunch of publishers, sellers, on the floor of the exchange. When you shout out your order, they can hear it. If they like your price, the publisher can offer what they have that matched the specification of what you ordered. Alternatively, none of the publishers may like your offer, so you hear a bit of silence and then a shout back " 7 million impressions, males, 18-25, in an automotive context, $10.80 CPM, for a February flight." Now there is a market! Other publishers may step up and offer at a lower price, or another buyer may find the offer priced correctly and take that inventory off the market. What that means for buyers is that they can buy the inventory they find valuable, at the offering price, or they can choose to put in bids on inventory to tell the seller that they want that inventory, just not at the price at which it is being offered. This means that supply is competing for demand, not just demand competing for supply.

For inventory from a specific publisher, the exchange manages those conversations so that an order shouted out "I have a buy order for Males, 18-25, in an automotive context at $10 CPM, 10 million impressions, for a February flight, from Yahoo!" will only be heard by Yahoo!

So, what is demand optimization? In short, it is a way for buyers to control a much larger set of conversations with sellers, some public and some private. The more information you have for decision making, the more nuance can be had in the conversation with the seller. MASS Exchange collects the bids from buyers, the offers from sellers, classifies them, stores them, applies market participant rule about who wants to do business with whom, and then makes them searchable. If one of the offers and one of the bids match, the exchange executes that trade for upfront media. This might seem like what other ad exchanges do, which is true. The difference is that MASS Exchange does it for orders for future inventory, not impressions that have already arrived at the publishers page. Hence, an advertising futures exchange.

Demand optimization is drastically different from what today's DSPs do. In short, demand optimization is all about figuring out all the upfront inventory that one could buy to meet the campaign objectives, comparing  those inventory options based on price and previous performance, and empowering media buyers to buy the best performing inventory at the best price. It's all about taking the media buyer's decision making powers to the next level. It's not machines replacing people, it's people making better decisions powered by machine driven data and analytics. Simply put, it is a tool to help organize a massive parallel conversation happening between thousands of and buyers and thousands of sellers. Demand optimization allows buyers to virtually survey the landscape of all inventory available in the market place, identify if it meets their specification, understand the clearing price, and select the best inventory to meet their needs across hundreds or even thousands of sellers, with millions of possible inventory options.

Supply Optimization: A New Ad Tech Function

In our quest to improve and empower buyers and sellers of media , we have spent a lot of time thinking about what actually happens when buyers and sellers come together. Specifically, what happens between buyers and sellers before the ad is served. We quickly realized that ad tech can be broken down into three teams: those who work to understand what has happened after the ad was served, those who work to understand what you should do when the ad is served, and those who work to understand what will happen in the future. We focus our work on understanding what buyers and sellers are telling each other they want to happen in the future. When a buyer and seller share the same desire for opposite sides of a transaction, we bring them together. So, there are three areas on the timeline: the past, the event horizon, and the future. What is the event horizon you ask? (a term borrowed from physics) "The point of no return" - the moment at which an advertising insertion decision needs to be made. In digital, it is the moment at which an ad call is made from the web server. In print, it is the moment at which files go to press. In television, it is the moment at which the broadcast file is finalized. How supply and demand are understood is different in each area on the timeline. From an analytic standpoint, what all that means is that the "math of the past" is different from the "math at the event horizon," which are both different from the "math of the future."

The event horizon is the place on the timeline where we yield optimize; the place where insertion orders meet real-time bids. Yield optimization is a critical function, but how do publishers choose the ideal combination of insertion orders that will maximize yield opportunities? How do publishers know which deals to say "yes" to and which to turn away? The answer is to supply optimize the future. Finding the best way to sell for maximum revenue, not necessarily the highest price. In the past, this was simply impossible. The reason yield optimization at the event horizon is possible is because there are multiple options to choose from when making that final sale decision; multiple insertion orders and RTB bids competing for the same impression.

When a sales team has to deal with sales for future delivery, all they know is what they have been told by the buyers they communicated with. Further, understanding the relationship between multiple buy orders and how they overlap or do not overlap on the supply landscape, in terms of revenue impact, is simply not done. Yes, there are lots of inventory forecasting tools, but that is not the same thing. To optimize supply, the sales force needs to have the tools to see the entire landscape of demand, identify the data points meaningful to the decision at hand, and understand the implications of those decisions.

So, when dealing with inventory for future delivery, sales teams need multiple options to choose from when making that final sale decision. Because MASS Exchange is for advertising futures, programmatic forwards, we can, and did, build just those tools. In the next phase of the growth of our platform. We are building supply optimization tools to answer those questions above. In reality this is not a one-sided function, if supply can more easily find demand, than the opposite is also true: demand can more easily find supply.  Its not about empowering one side or the other, it is about the belief that efficiently finding someone that wants to buy what you are selling or sell what you are buying will drive the best possible outcomes for both sides. At MASS Exchange, we win only when both sides win.

In short, supply optimization occurs when sellers can efficiently map and identify demand from which the can choose to initiate a transaction. This optimization is an outgrowth of our market model: continuous two-sided (RTB exchanges are generally one-sided second price - for those among you that want something a bit wonkier, here's a handy reference Auctions and bidding: A guide for computer scientists) In other words, when buyers express their orders in a market that supports resting orders, those orders can be mathematically optimized to provide a seller the ideal combination of insertion orders that should generate the most revenue. For buyers, the increased efficiency of matching supply and demand enable publishers to sell targeted placements, on an audience attribute and ad placement attribute basis, with significantly smaller transaction minimums, expanding the efficiency of audience targeting at scale beyond the event horizon (RTB) and into the future.

In our next blog post, we will show buyers the other side of this "coin" - demand optimization.