Author Archives: Eric Picard

Mar 24 2013
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Ad Ecosystem, Ad Serving, Ad Technology, Online Media, Predictions, Publishers

What everyone should know about ad serving

By Eric Picard (Originally published in iMediaConnection.com)

Publisher-side ad servers such as DoubleClick for Publishers, Open AdStream, FreeWheel, and others are the most critical components of the ad industry. They’re responsible ultimately for coordination of all the revenue collected by the publisher, and they do an amazing amount of work.

Many people in the industry — especially on the business side of the industry — look at their ad server as mission critical, sort of in the way they look at the electricity provided by their power utility. Critical — but only in that it delivers ads. To ad operations or salespeople, the ad server is most often associated with how they use the user interface — really the workflow they interact with directly. But this is an oversight on their part.

The way that the ad server operates under the surface is actually something everyone in the industry should understand. Only by understanding some of the details of how these systems function can good business decisions be made.

Ad delivery

Ad servers by nature make use of several real-time systems, the most critical being ad delivery. But ad delivery is not a name that adequately describes what those systems do. An ad delivery system is really a decision engine. It reviews an ad impression in the exact moment that it is created (by a user visiting a page), reviews all the information about that impression, and makes the decision about which ad it should deliver. But the real question is this: How does that decision get made?

An impression could be thought of as a molecule made up of atoms. Each atom is an attribute that describes something about that impression. These atomic attributes can be simple media attributes, such as the page location that the ad is imbedded into, the category of content that the page sits within, or the dimensions of the creative. They can be audience attributes such as demographic information taken from the user’s registration data or a third-party data company. They can be complex audience segments provided by a DMP such as “soccer mom” — which is in itself almost a molecular object made up of the attributes of female, parent, children in sports — and of course various other demographic and psychographic atomic attributes.

When taken all together, these attributes define all the possible interpretations of that impression. The delivery engine now must decide (all within a few milliseconds) how to allocate that impression against available line items. This real-time inventory allocation issue is the most critical moment in the life of an impression. Most people in our industry have no understanding of what happens in that moment, which has led to many uninformed business, partnership, and vendor licensing decisions over the years, especially when it comes to operations, inventory management, and yield.

Real-time inventory allocation decides which line items will be matched against an impression. The way these decisions get made reflects the relative importance placed on them by the engineers who wrote the allocation rules. These, of course, are informed by business people who are responsible for yield and revenue, but the reality is that the tuning of allocation against a specific publisher’s needs is not possible in a large shared system. So the rules get tuned as best they can to match the overarching case that most customers face.

Inventory prediction

Well before the impression is generated and has to be allocated out to the impressions in real-time, inventory was sold in advance based on predictions of how much volume would exist in the future. We call these predicted impressions “avails” (for “available to sell”) in our industry, and they’re essentially the basis for how all guaranteed impressions are sold.

We’ll get back to the real-time allocation in a moment, but first let’s talk a bit about avails. The avails calculation done by another component of the ad server, responsible for inventory prediction, is one of the hardest computer science problems facing the industry today. Predicting how much inventory will exist is hard — and extremely complicated.

Imagine if you will that you’ve been asked to predict a different kind of problem than ad serving — perhaps traffic patterns on a state highway system. As you might imagine, predicting how many cars will be on the entire highway next month is probably not very hard to do with a pretty high degree of accuracy. There’s historical data going back years of time, month by month. So you could take a look at the month of April for the last five years, see if there’s any significant variance, and use a bit of somewhat sophisticated math to determine a confidence interval for how many cars will be on the highway in the month of April 2013.

But imagine that you now wanted to zoom into a specific location — let’s say the Golden Gate Bridge. And you wanted to break that prediction down further, let’s say Wednesday, April 3. And let’s say that we wanted to predict not only how many cars would be on the bridge that day, but how many cars with only one passenger. And further, we wanted to know how many of those cars were red and driven by women. And of those red, female-driven cars, how many of them are convertible sports cars? Between 2 and 3 p.m.

Even if you could get some kind of idea how many matches you’ve had in the past, predicting at this level of granularity is very hard. Never mind that there are many outside factors that could affect this; there are short-term issues that could help get more accurate as you get closer in time to the event such as weather and sporting events, and there are much more unpredictable events such as car accidents, earthquakes, etc.

This is essentially the same kind of prediction problem as the avails prediction problem that we face in the online advertising industry. Each time we layer on one bit of data (some defining attribute) onto our inventory definition, we make it harder and harder to predict with any accuracy how many of those impressions will exist. And because we’ve signed up for a guarantee that this inventory will exist, the engineers creating the algorithms that predict how much inventory will exist need to be very conservative on their estimates.

When an ad campaign is booked by an account manager at the publisher, they “pull avails” based on their read of the RFP and media plan and try to find matching inventory. These avails are then reserved in the system (the system puts a hold on avails that are being sent back to the buyer based for a period of time) until the insertion order (I/O) is signed by the buyer. At this moment, a preliminary allocation of predicted avails (impressions that don’t exist yet) is made by a reservation system, which divvies out the avails among the various I/Os. This is another kind of allocation that the ad server does in advance of the campaign actually running live, and it has as much (or even more) impact as the real-time allocation does on overall yield.

How real-time allocation decisions get made

Once a contract has been signed to guarantee that these impressions will in fact be delivered, it’s up to the delivery engine’s allocation system to decide which of the matching impressions to assign to which line items. The primary criteria used to make this decision is how far behind the matching line items are for successfully delivering against their contract, which we call “starvation” (i.e., is the line item starving to death or is it on track to fulfill its obligated impression volume?).

Because the engineers who wrote the avails prediction algorithms were conservative, the system generally has a lot of wiggle room when it comes to delivering against most line items that are not too complex. That means there are usually more impressions available when the impressions are allocated than were predicted ahead of time. So when all the matching line items are not starving, there are other decision criteria that can be used. The clearest one is yield, (i.e., of the available line items to allocate, which one of those lines will get me the most money for this impression?).

Implications of real-time allocation and inventory prediction

There’s a tendency in our industry to think about ad inventory as if it “exists” ahead of time, but as we’ve just seen, an impression is ephemeral. It exists only for a few milliseconds in the brain of a computer that decides what ad to send to the user’s machine. Generally there are many ways that each impression could be fulfilled, and the systems involved have to make millions or billions of decisions every hour.

We tend to think about inventory in terms of premium and remnant, or through a variety of lenses. But the reality is before the inventory is sold or unsold, premium or remnant, or anything else, it gets run through this initial mechanism. In many cases, inventory that is extremely valuable gets allocated to very low CPM impression opportunities or even to remnant because of factors having little to do with what that impression “is.”

There are many vendors in the space, but let’s chat for a moment about two groups of vendors: supply-side platforms (SSPs) and yield management companies.

Yield management firms focus on providing ways for publishers to increase yield on inventory (get more money from the same impressions), and most have different strategies. The two primary companies folks talk to me about these days are Yieldex and Maxifier. Yieldex focuses on the pre-allocation problem — the avails reservations done by account managers as well as the inventory prediction problem. Yieldex also provides a lot of analytics capabilities and is going to factor significantly in the programmatic premium space as well. Maxifier focuses on the real-time allocation problem and finds matches between avails that drive yield up, and it improves matches on other performance metrics like click-through and conversions, as well as any other KPI the publisher tracks, such as viewability or even engagement. Maxifier does this while ensuring that campaigns deliver, since premium campaigns are paid on delivery but measured in many cases on performance. The company is also going to figure heavily into the programmatic premium space, but in a totally different way than Yieldex. In other words, neither company really competes with each other.

Google’s recent release of its dynamic allocation features for the ad exchange (sort of the evolution of the Admeld technology) also plays heavily into real-time allocation and yield decisions. Specifically, the company can compare every impression’s yield opportunity between guaranteed (premium) line items and the response from the DoubleClick Exchange (AdX) to determine on a per-impression basis which will pay the publisher more money. This is very close to what Maxifier does, but Maxifier does this across all SSPs and exchanges involved in the process. Publishers I’ve talked to using all of these technologies have gushed to me about the improvements they’ve seen.

SSPs are another animal altogether. While the yield vendors above are focused on increasing the value of premium inventory and/or maximizing yield between premium and exchange inventory (I think of this as pushing information into the ad server to increase value), the SSPs are given remnant inventory to optimize for yield among all the various venues for clearing remnant inventory. By forcing competition among ad networks, exchanges, and other vehicles, they can drive the price up on remnant inventory.

How to apply this article to your business decisions

I’ve had dozens of conversations with publishers about yield, programmatic premium, SSPs, and other vendors. The most important takeaway I can leave you with is that you should think about premium yield optimization as a totally different track than discussions about remnant inventory.

When it comes to remnant inventory, whoever gets the first “look” at the inventory is likely to provide the highest increase in yield. So when testing remnant options, you have to ensure that you’re testing each one exactly the same way — never beneath each other. Most SSPs and exchanges ultimately provide the same exact demand through slightly different lenses. This means that barring some radical technical superiority — which none have shown me to be the case so far — the decision most likely will come down to ease of integration and ultimately customer service.

Jan 25 2013
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Ad Ecosystem, Ad Technology, Online Media, Startups

Can That Startup Stand Alone, Or Is It Just A Feature?

by Eric Picard (Originally published in AdExchanger 1/24/13)

One of the most common conversations I have on a regular basis about startups, especially in the ad technology space is whether a company has legs. Will they be able to grow large and “go it alone,” or are they really just a feature of a bigger system or platform that hasn’t been added yet? This line of thinking is super important for investors to understand, and a key part of the decision-making process for entrepreneurs as they begin building a company.

Why does this matter?

Investors all have their own set of criteria for how they evaluate investments. Angels and Micro-VCs frequently are willing to invest in companies that are built for a relatively short term flip. They ask, “Can the company build a robust set of features that attract a set of customers in the short term, run at a low burn rate (or even quickly become profitable), integrate with one or more existing platforms, and then get bought within one to three years for a decent return?” In this case, building a company that’s a feature is completely viable and acceptable.

This approach is great if the startup can either bootstrap (not take any money) or just pull in a small amount of investment capital and get profitable (or exit) quickly. For angels and micro-VCs, this kind of investment is great because they can get fantastic returns on a short investment horizon, and sometimes it gets them stock in a larger high flying tech company at a great multiple. Sometimes these will be companies at Series B or C rounds that they couldn’t get into, sometimes they’re large publicly traded companies that the investor gets stock in at a significant market discount.

If the startup is going to need significant capital, it must be able to build a large business that has significant revenues and long sustainable growth. It needs to be able to stand alone for three to six years, and during that time build a large company with an opportunity to exit for greater than $100M – and have a credible story for why they could be worth more than $1B and exit via either an IPO or a significant acquisition.

There have been several examples in the past few years of companies that have been funded as if they were a full standalone company that could build massive revenue and exit big. They’ve taken tens of millions of dollars in funding, and need to either IPO or exit for greater than $100M to be seen as a win by their investors. In these cases, the investors couldn’t properly evaluate if the startup was really a feature or a standalone. So it’s important to have a way to evaluate this in order to avoid making those mistakes – both as an investor and an entrepreneur.

Question 1: Will Google or another big company just add this functionality to its existing product?

Many big companies with significant platform investments will constantly extend their product set over time. Big companies (Google, Microsoft, Yahoo, Amazon, AOL) and the big ad-tech specific companies (AppNexus, Rubicon, Pubmatic, MediaMath, etc.) have large engineering teams, and the reality is that it’s most efficient for large engineering teams to work on large, complex and technically prohibitively problems. They tend to add smaller features on a much slower cycle. That doesn’t mean they won’t add them – it just means they have bigger problems to solve that are higher priority – especially around scale, stability, and redundancy. But eventually they’re going to need to add those features, and they’ll do it either through acquisition or committing resources to build the features. At that point, they’ll be doing what’s known as a “build/buy analysis” to determine where to invest the capital to get the product to their customers. The analysis is going to look something like this:

  1. How many man-hours of development will this take to build?
  2. What’s the cost to develop it in-house, both the actual cost of development and the opportunity cost in both time to market, and the other features that the team won’t be able to work on during the development process?
  3. The answer is likely to come out between $6 and $30 million – with a rare outside answer landing around $50 to $80 million, depending on the complexity of the feature.
  4. That means that for most “feature” companies, exits will be in those ranges, with perhaps a 10 to 20% premium applied. The likelihood of a premium relates directly to how much revenue the company brings with it, minus any costs of both operating the company and integration costs.

This means that startups need to think through a bunch of things if they’re building for acquisition. They should be cheap to buy – ideally in the first range we just discussed. They should be easy to integrate. They should have as few redundant systems and features as possible. They should be architected for scale – to handle the kinds of load that one of those big companies will need to transfer onto their systems post acquisition. But they should be cost effective operationally.

One very smart thing “feature” startups should do as early in their life cycle as possible is integrate with as many of those companies as possible. Thus when that build/buy analysis is done by the acquiring company, the startup is already integrated, is top of mind with the acquirer, and already is familiar with how to develop against their APIs. And in many cases the developers within the startup will already be known by at least some of the developers within the larger company.

Another big question that will come up during due diligence by an acquiring company is whether the startup has any redundant systems that will need to be decommissioned or moved into internal data centers. This is more important than many would realize – especially when it comes to datacenter redundancy. Very few startups have the kind of security and cost efficiencies that a Google, Microsoft or Amazon have in house. So if they’ve invested in their own infrastructure, they’re going to need to move this out of their datacenter and into the bigger company’s infrastructure. Datacenter contracts should be crafted to facilitate this – and hardware is probably just a loss. Building in the cloud can solve this in some cases, but in others, might cause problems of their own – e.g. any real time systems built in the cloud are highly unlikely to scale to cover the needs of a big company. Architecting for scale, even if the startup doesn’t scale up right away, is a critical consideration.

Question 2: How do we define a “standalone” business, and what justifies the higher acquisition costs?

First and foremost consideration in this case is revenue. Companies with deep customer relationships and lots of revenue can both run standalone, and/or be acquired for a large amount of money at significant multiples. Think of a few companies that have been acquired on that scale: AdMeld built a real and significant business that had deep customer relationships, real revenue, and was acquired by Google for a significant premium at a high price. Same goes for Invite Media and DoubleClick, both bought by Google; aQuantive, bought by Microsoft; and Right Media, bought by Yahoo.

All of these companies built significant businesses, with large numbers of customers, decent margins, and were not bought for their technology. In each of those cases, the core technology of each company was completely re-architected and redeveloped by the acquirer (in aQuantive’s case, a little less so).

So when starting a company, and when evaluating that company for investment, one must consider how much revenue, and how many customer relationships, the company can effectively build over a three to six year period. If the answer to both questions is “lots,” and a credible case can be made – then consideration for this kind of investment (both in time/effort from the entrepreneur and cash from the investors) is justified.

Other considerations for whether the business will stand the test of time:

How “sticky” will those relationships be? Ad serving is the penultimate example of a sticky ad technology. It’s so complex and cost prohibitive to switch out your ad server – either for publishers or agencies – that it almost never happens. That’s one reason ad servers with lots of traction were bought for such huge premiums, even though there was significant price compression over time. If the technology is sticky because of integrations into either infrastructure or business processes, it’s a good candidate to be a standalone.

Does the company sell a product, service or commodity that has transfer-pricing pressure?

Or, does the company either create the product it sells from scratch or process the product of another company in a way that adds significant value? Ideally in the latter scenario it would do this in a way that is difficult to replicate – for example, by analyzing big data with some unique and differentiated methods in order to add intelligence and value to what it sells.

Transfer pricing pressure is critical to understand: The best current example of transfer pricing pressure is music. Any business that has started in the music space has been doomed to either incredibly poor margins or instability. This is because the music companies can simply increase the costs of the final good being sold (songs or albums) any time they please. As soon as one of the companies selling music digitally begins to get profitable, the music industry can throttle their margins by increasing price. In the advertising space this is similar to a problem early ad networks had – the ad rep firms. Because the ad rep firms didn’t own the inventory they sold, and re-sold the product of the publisher directly with no additional value, they were doomed to low multiple exits and low valuations if they managed to get large enough to IPO.

A recent example of transformation in a category that shows how some of these standalone companies have become more resilient is the data companies. They started out with relatively undifferentiated mechanisms to create targeting segments that led to the creation of ad networks, then were integrated with data marketplaces on the exchanges, and now have transformed themselves into Data Management Platforms. Lotame is a great example of a company that has made this transition – but many others have as well.

By applying this type of analysis to opportunities to create a company or invest in a company, entrepreneurs and investors can make smart decisions about what to build, and what to invest in.

Jan 25 2013
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Ad Ecosystem, Ad Technology, Media Agencies, Online Media, Publishers

What the heck does “programmatic” mean?

By Eric Picard (originally published on iMediaConnection.com 1/10/13)

This article is about programmatic media buying and selling, which I would define as any method of buying or selling media that enables a buyer to complete a media buy and have it go live, all without human intervention from a seller.

Programmatic is a superset of exchange, RTB, auction, and other types of automated media buying and selling that have mainly been proven out for remnant ad inventory clearing mechanisms up until today. So while an auction might or might not be involved in programmatic buying and selling, the roots and infrastructure behind the new programmatic world is based on the same infrastructure that the ad exchanges, DSPs, SSPs, and ad servers have been plumbing and re-plumbing over the last five years.

Let’s talk first about so-called “programmatic premium” inventory, as this is what I’m seeing as the most confusing thing in the market today. Many people still think of programmatic media as remnant inventory sold using real-time bidding. But that’s far from the whole truth today. All display media could (mechanically) be bought and sold programmatically today — whether via RTB or not, whether it’s guaranteed or not, and whether it’s “premium” or not. Eventually all advertising across all media will be bought and sold programmatically. Sometimes it will be bought with a guarantee, sometimes it won’t.

What we’re talking about is how the campaigns get flighted and how ad inventory is allocated against a specific advertiser’s campaign. In premium display advertising, this is done today by humans using tools, mostly on the publisher side of the market. In the programmatic world, all buys — even the up-front buys — will be executed programmatically. So when I say that all ads will be bought and sold programmatically, I mean that literally. If Coke spends $50 million with Disney at an upfront event, that $50 million will still be executed programmatically throughout the life of that buy. The insertion order and RFP process goes away (as we know it) and is replaced by a much more efficient set of processes.

In this new world, sales teams don’t go away. They become more focused on the value that they can add most effectively. That’s in the relationship and evangelism of their properties and the unique content and brand-safe environment that they bring to the table. Sales teams will also engage in broader, more valuable negotiations with buyers — doing more business development and no “order taking.”

In a programmatic world, prices and a whole slew of terms can be negotiated in advance. Essentially what’s happening is that the order-taking process, the RFP, and the inventory avail “look up” that have been intensely manual for the past 20 years are being automated. And APIs between systems have opened up that allow all these various tools to communicate directly and to drive through the existing roadblocks.

Here are five things everyone in our industry should know about programmatic media buying and selling.

It’s inevitable

Programmatic buying and selling is coming, is coming big, and will change the way people buy and sell nearly all media — across all media types — over the next five to 10 years. This will be the case in online display over the next two to three years.

It’s comprehensive

Programmatic is not just RTB, is not just “bidding,” and is not one channel of sales. It’s comprehensive, it’s everything that will be bought and sold, and it’s all forms of media across all sales channels. That’s why I’m hedging by saying five to 10 years, as it will take more than five years to do all these things across all media. But certainly fewer than 10. And a lot is transitioning over the next two years, especially online.

Prices will still vary

In non-programmatic buying and selling (old-fashioned traditional relationship sales), different customers are charged different prices all the time for exactly the same product. That doesn’t go away. Different advertisers get different prices for all sorts of reasons. In the worst case, the buyers might be worse negotiators. But it could be that the advertisers spend more than $1 million monthly with that publisher and therefore get a huge discount on CPM. There are all sorts of reasons that this happens. The same exact thing will happen programmatically. Various advertisers will have hard-coded discounts that are negotiated by humans in advance. Price will drop as thresholds on overall spend are hit. Prestigious brands will get preferences that crappy or unknown brands don’t get. This can all be accommodated right now — this very minute — in almost every major system out there. It’s here. Now.

Complexities will remain

All the various “ad platforms” of the past and the new true ad platforms of today have opened up APIs and can communicate with each other programmatically. This is the way the infrastructure is powering programmatic buying and selling. I can’t stress to all of you how fundamental this change is. It’s not about bidding, auctions, futures, hedges, etc. — although those things will certainly exist and proliferate. It’s about automating the buying and selling process, removing friction from the market, and providing immense increases in value across the board. People talk about how complex the LUMAscape map of ad tech vendors is, but what they miss is that there’s plenty of room for lots of players when they can all easily connect and interact. I do believe we’ll see consolidation — mainly because there’s too little differentiation in the space (lots of copycat companies trying to compete with each other). But I do believe that the ecosystem can afford to be complex.

TV comparisons do not apply

People keep using the television upfronts as the analog to online premium inventory, and the television scatter market as the analog of remnant inventory. That’s not the right metaphor; they’re not equivalent. And even TV will move to programmatic buying and selling in the next decade. But let me lay this old saw to rest once and for all:

  • In television, the up-front is a discount mechanism. Buyers commit to certain spend in order to get a discount. Publishers use the upfront as a hedge in order to mitigate later-term risk by the seller that they will not sell out their inventories.
  • The scatter market is still the equivalent of guaranteed inventory online (although it’s more “reserved” than guaranteed). It’s just sold closer to the date of the inventory going live. I’d argue that with the exception of the random “upfronts” run by some publishers online today, all online premium ad sales is the equivalent of the television scatter market.
  • Remnant is a wholly other thing in television — and isn’t part of the scatter market. TV limits remnant significantly (in healthy economies to about 10 percent of total inventory). We’ve mucked that all up online by selling every impression at any price, which has lowered overall yield and flooded the market with cheap inventory — most of which is worthless.
Nov 30 2012
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Ad Ecosystem, Ad Technology, Media Agencies, Online Media, Publishers, Rich Media

Time for a New Mobile Ad Format

By Eric Picard (Originally published on AdExchanger.com 11/19/12)

I’ve been designing, prototyping and deploying new ad formats in digital advertising for more than 15 years. I started one of the first rich media advertising companies, where we pioneered many of the ad formats that today are standard offerings – starting in late 1997.  And I ran a team at Microsoft that focused on building new ad unit prototypes for emerging media for several years, which created hundreds of prototypes that were shown to dozens of brands and creative agencies for feedback, and we ran many studies.

I tell you all this not to toot my own horn, but to explain that when I suggest that I’m going to propose a new ad format for the mobile industry, I’m not doing so idly.  I’ve been doing this professionally for my entire career, and the problem of putting new ad formats on devices is one that I’ve thought an awful lot about.  In fact, the format I’m going to suggest is very similar to one I proposed for another new device that was an innovative music and media player.  But I’ll hold off on the specifics for a bit.

The biggest problem with mobile advertising today is the ad formats being deployed. The screen is very small, and even a very small ad unit that isn’t well integrated into the screen experience of an app or mobile website causes a dissonance that is simply unacceptable to most users.  And app developers see this clearly – using the presence of ads not really to drive ad revenue, but rather to annoy the crap out of consumers in order to push them to pay for the premium version of an app.  This is a very backwards approach to advertising experience, but one that has been used repeatedly over the course of digital evolution.

Ad experience design is a very tricky problem – because ad experiences need to walk the razor’s edge between grabbing the audience’s attention while not pushing them over into frustration.  For this reason, in many ways, ad experience design is far harder than standard user experience design for applications.  Unfortunately the vast majority of startups and even large companies that deploy apps and mobile sites simply slap tiny mobile banners onto their applications, and wait for the dollars to roll in. That isn’t likely to happen for most companies, because the ad experience is horrible.

So I decided that instead of simply complaining about the ad experience of mobile, I’d propose to the industry through this article a new ad format that I believe will fix the core problems with mobile ads. I hope somebody picks it up and runs with it, because I firmly believe (after many years of thinking about these problems) that it’s the right solution for mobile. And I believe the same principles behind the format I’m suggesting would work well for tablets.

Let me start by saying something perhaps a little controversial – it’s impossible for companies to differentiate from each other by using proprietary ad formats.  Note that all rich media companies offer essentially the same ad formats.  And this is one reason I’m not bullish on the current so-called “native ad format” movement.  Unless you’ve got the reach of Google, Twitter or Facebook, rolling your own ad format is stupid.  Advertising’s core principle is about reach – advertisers try to reach as broad an audience as possible that matches their target customer persona. Creative teams can’t cost effectively create unique ad formats for every publisher. This means that once a proprietary format starts getting scale, other companies copy that format and they begin to perpetuate. But we’ve only seen this happen with “native” formats when the company that created the format had large scale.  So all paid search ads pivot off Google’s format, because the same creative needs to be uniform across search engines. And when formats that were pioneered by one rich media company got traction, every other rich media company adopted those formats too.  Ad formats don’t work as product differentiators.

So what will work?

First:  The format must be integrated into the design of the application (app or web).

App and M-Web developers must build their user experience *around* the ad vehicle. So the ad vehicle can’t be crappy – it needs to fit neatly into a standard App experience. It needs to provide utility to the advertiser (enable attention to be captured, and ideally to drive activity) and it needs to nestle carefully into the utility of the application it sits in.

Second: The screen is small, so the ad needs to use the whole screen.

Interstitial advertising has been around since the early days of the web – Unicast promoted the format broadly with its Superstitial format, which then was copied by all the other companies.  But most of us who use mobile apps will probably agree that a straightforward interstitial experience is incredibly disruptive and annoying.  So putting up a full screen ad that a user has to stare at before accessing their content is simply unacceptable. What’s the answer?

Way back in 2006 I wrote an article that discussed the (then) current trend toward trying to drop short 5 second videos at the end of pods of content on television to combat fast forwarding on DVRs.  We did some research back at the time at Microsoft that showed that consumers got annoyed with advertising content about 5 seconds into the roll of a video.  We surmised at the time that for non-video content, a few seconds of static “sponsorship text” would be a good way to introduce pre-roll videos – that placing a short sponsorship message in front of the ads would soften the transition – especially if it was limited to a couple of seconds.  We’ve seen this deployed to great effect at Hulu.

I believe the answer is simple: Create a multi-part ad format that has different stages and experiences.

First it’s a full screen ad unit with basic sponsorship text: This app is proudly sponsored by “insert advertiser name here.”  Maybe the company logo can go on this screen as well. At the bottom of the full screen sponsorship, in small text, a message states: To see more, swipe the ad.

Then the full screen unit should shrink down to a “leave-behind” banner that needs to be persistent, needs to be small, and needs to be “swipeable”.  The leave-behind needs to take up the entire bottom of the screen – from left-to-right side.  The creative content in this banner may not take up the entire screen width, but can be centered in the ad unit ‘space’ and ideally the background color of the ad should be matched by the background on the two edges of the ad unit (this is technically easy to do in apps) such that it doesn’t ‘hover’ in the middle of the screen. It’s also important that the same is done with the first and second interstitials – they should cover the whole screen, and be centered in the screen – not locked “off center” to the upper left-hand corner.

The banner should have no more than three to five message transitions (animation points) that can tell an enticing message to the user to facilitate them swiping the ad.  That should be followed by a short “swipe here” animation that is instructional for users to see that they need to swipe the ad to open a bigger ad experience.

Upon swiping the ad unit, a full-screen ad should expand out of the banner unit that is fully interactive and immersive. This can be a “mini-game” experience, a video, an interactive unit that enables commerce or opting into something (a Facebook Like, tweeting a message to your friends, etc).  It should not open a mobile web browsing experience that bounces the user out of the App experience, because once a user is trained that doing anything in the ad is going to bounce them away from their game, they’ll never interact with another ad.

For Apps that have natural transition points (e.g. Moving from level-to-level in a game, or similar), the ad unit can expand out for no more than 5 seconds, and if the user chooses to interact with it – can stay up until the user closes it.

The ad transitions are extremely important to get right.  The initial interstitial unit should smoothly slide down off the screen leaving the banner unit there. The expansion of the banner to take over the whole screen also should be extremely smooth and feel “well crafted” to the audience. Also the initial interstitial should only be shown once per session to avoid annoying the audience. This won’t preclude multiple ads or advertisers per session – but will create scarcity and value to the session sponsor.

The other critical issue here is transition timing.  The initial ad experience needs to be no more than 3 seconds.  The animation frames of the leave-behind banner need to last no more than 7 to 10 seconds. Any automated expansion of the ad unit should leave the expanded page up for no more than 5 seconds.

I believe that if this ad unit were deployed uniformly across apps and mobile web experiences, the industry would see CPMs increase significantly, and the mobile advertising space could enjoy an interesting renaissance. I’m sure there are other answers to this problem – other formats for instance – that would work equally well, or even better.  But if the industry doesn’t lock to a standard format quickly – I fear that the space will continue to languish and won’t see the growth it deserves.

As I said above, ad formats don’t work as product differentiators.  But the largest players do have the ability to use their reach as a driver of format adoption – which is good for the industry. Apple, Google and Microsoft should work together here to drive adoption of a great uniform ad unit that can work across mobile devices.

Follow Eric Picard (@ericpicard) on Twitter.

Nov 08 2012
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Ad Ecosystem, Ad Serving, Ad Technology, Economics, Media Agencies, Online Media, Publishers

How ad platforms work (and why you should care)

(By Eric Picard, Originally Published in iMediaConnection, 11/8/12)

Ad platforms are now open, meaning that startups and other technology companies can plug into them and take advantage of their feature sets. The ad technology space is now API driven, just like the rest of the web technology space. The significance of this change hasn’t hit a lot of people yet, but it will. The way this change will affect almost all the companies in ad technology will have an impact on everything: buying, selling, optimization, analytics, and investing.

Companies in our space used to have to build out the entire ad technology “stack” in order to build a business. That meant ad delivery (what most people think of as “ad serving”), event counting (impressions, clicks, conversions, and rich media actions), business intelligence, reporting, analytics, billing, etc. After building out all those capabilities, in a way that can scale significantly, each company would build its “differentiator” features. Many companies in the ad technology space have been created based on certain features of an ad platform. But because the ad platforms in our space were “closed,” each company had to build its own ad platform every time. This wasted a lot of time and money and — unbeknownst to investors — created a huge amount of risk.

Almost every startup in the ad platform space has existed at the whim of Google — specifically because of DoubleClick, the most ubiquitous ad platform in the market. When Google acquired DoubleClick, its platform was mostly closed (didn’t have extensive open APIs), and its engineering team subsequently went through a long cycle of re-architecture that essentially halted new feature development for several years. The market demanded new features — such as ad verification, brand safety, viewable impressions, real-time bidding, real-time selling, and others — that didn’t exist in DoubleClick’s platform or any others with traction in the space.

This led to the creation of many new companies in each space where new features were demanded. In some cases, Google bought leaders in those spaces. In others, Google has now started to roll out features that replicate the entirety of some companies’ product offerings. The Google stack is powerful and broad, and the many companies that have built point solutions based on specific features that were once lacking in Google’s platform suddenly are finding themselves competing with a giant who has a very advanced next-generation platform underlying it. Google has either completed or is in the process of integrating all of its acquisitions on top of this platform, and it has done a great job of opening up APIs that allow other companies to plug into the Google stack.

I’ve repeatedly said over the years that at the end of the natural process this industry is going through, we’ll end up with two to three major platforms (possibly four) driving the entire ecosystem, with a healthy ecosystem of other companies sitting on top of them. Right now, our ecosystem isn’t quite healthy — it’s complex and has vast numbers of redundancies. Many of those companies aren’t doing great and are likely to consolidate into the platform ecosystem in the next few years.

So how does the “stack” of the ad platform function? Which companies are likely to exist standalone on top of the stack? Which will get consumed by the stack? And which companies are going to find themselves in trouble?

Let’s take a look.

How ad platforms work (and why should you care)

Pretty much every system is going to have a stack that contains buckets of services and modules that contain something like what you see above. In an ideal platform, each individual service should be available to the external partner and should be consumable by itself. The idea here is that the platform should be decomposable such that the third party can use the whole stack or just the pieces it needs.

Whether we’re discussing the ubiquitous Google stack or those of competitors like AppNexus, the fact that these platforms are open means that, instead of building a replica of a stack like the one above, an ad-tech startup can now just build a new box that isn’t covered by the stack (or stacks) that it plugs into. Thus, those companies can significantly differentiate.

This does beg the question of whether a company can carve out a new business that won’t just be added as a feature set by the core ad platform (instantly creating a large well-funded competitor). To understand this, entrepreneurs and investors should review the offering carefully: How hard would it be to build the features in question? Is the question of growing the business one of technical invention requiring patents and significant intellectual property, or is it one of sales and marketing? Is the offering really a standalone business, or is it just a feature of an ad platform that one would expect to be there? And finally, will the core platforms be the acquirer of this startup or can a real differentiated business be created?

The next few years will be interesting. You can expect these two movements to occur simultaneously: Existing companies will consolidate into the platforms, and new companies will be created that take advantage of the new world — but in ways that require less capital and can fully focus on differentiation and the creation of real businesses of significance.

Oct 25 2012
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Ad Ecosystem, Ad Technology, Economics, Media Agencies, Online Media, Predictions, Publishers, Startups

How Do Companies Make Any Money in Digital?

(By Eric Picard, Originally Published in AdExchanger 10/25/12)

In 2007 I wrote a paper that analyzed the lack of investment from 2001 to 2006 in the basic infrastructure of ad technology.  The dot-com bubble burst had a chilling effect on investment in the ad tech space, and as an industry we focused for about six years on short term gains and short term arbitrage opportunities.

This period saw the rise of ad networks and was all about extracting any value possible out of existing infrastructure, systems, and inventory.  So all the “remnant” inventory in the space, the stuff the publisher’s in-house sales force couldn’t sell, got liquidated at cheap prices.  And those companies with the willingness to take risk and the smarts to invest in technology to increase the value of remnant got off the ground and succeeded in higher efficiency buying and selling, and lived off the margins they created.

But we lost an entire cycle of innovation that could have driven publisher revenue higher on premium inventory – which is required for digital to matter for media companies. There’s been lots of discussion about the drop from dollars to dimes (and more recently to pennies) for traditional media publishers. And while the Wall Street Journal and New York Times might be able to keep a pay-wall intact for digital subscriptions, very few other publications have managed it.

In 2006 the ad tech ecosystem needed a massive influx of investment in order for digital to flourish from a publisher perspective.  These were my observations and predictions at the time:

  • Fragmentation was driving power from the seller to the buyer. Like so:
  • A lack of automation meant cost of sales for publishers, and cost of media buying management for agencies, were vastly higher in digital (greater than 10x what those things cost for traditional on both the buy and sell side).
  • Prices were stagnated in the digital space because of an over-focus on direct response advertisers, and the inability of the space to attract offline brand dollars.
  • Market inefficiency had created a huge arbitrage opportunity for third parties to scrape away a large percentage of revenue from publishers. Where there is revenue, investment will follow.
  • There was a need for targeting and optimization that existing players were not investing in, because the infrastructure that would empower it to take off didn’t exist yet.
  • Significant investment would soon come from venture capital sources that would kick start new innovation in the space, starting with infrastructure and moving to optimization and data, to drive brand dollars online.

Six years later, this is where we are. I did predict pretty successfully what would happen, but what I didn’t predict was how long it would take – nor that the last item having to do with brand dollars would require six  years. This is mainly because I expected that new technology companies would step up to bat across the entirety of what I was describing.  Given that the most upside is on brand dollars, I expected entrepreneurs and investors to focus efforts there.  But that hasn’t been the case.

So what’s the most important thing that has happened in the last six years?

The entire infrastructure of the ad industry has been re-architected, and redeployed.  The critical change is that the infrastructure is now open across the entire “stack” of technologies, and pretty much every major platform is open and extensible. This means that new companies can innovate on specific problems without having to build out their own copy of the stack.  They can build the pieces they care about, the pieces that add specific value and utility for specific purposes – e.g. New Monetization Models for Publishers and Brand Advertisers, New Ad Formats, New Ad Inventory Types, New Impression Standards, New Innovation across Mobile, Video and Social, and so on.

So who will make money in this space, how will they make it, and how much will they make?

I’ve spent a huge portion of my career analyzing the flow of dollars through the ecosystem. Recently I updated an older slide that shows (it’s fairly complex) how dollars verses impressions flow.

The important thing to take away from this slide is that inventory owners are where the dollars pool, whether the inventory owner is a publisher or an inventory aggregator of some kind.  Agencies have traditionally been a pass-through for revenue, pulling off anywhere from 2 to 12% on the media side (the trend has been lower, not higher), and on average 8 to 10% on the creative side depending on scale of the project.  Media agencies are not missing the point here, and have begun to experiment with media aggregation models, which is really what the trading desks are – an adaptation of the ad network model to the new technology stack and from a media agency point of view.

The piece of this conversation that’s relevant to ad tech companies is that so far in the history of this industry, ad technology companies don’t take a large percentage of spend.  In traditional media, the grand-daddy is Donovan Data Systems (now part of Media Ocean), and historically they have taken less than 1% of media spend for offline media systems. In the online space, we’ve seen a greater percentage of spend accrue to ad tech – ad serving systems for instance take anywhere from 2 to 5% of media spend.

So how do ad tech companies make money today and going forward? It’s a key question for pure transactional systems or other pure technology like ad servers, yield management systems, analytics companies, billing platforms, workflow systems, targeting systems, data management platforms, content distribution networks, and content management systems.

There’s only so much money that publishers and advertisers will allow to be skimmed off by companies supplying technology to the ecosystem. In traditional media, publishers have kept their vendors weak – driving them way down in price and percentage of spend they can pull off. This is clearly the case in the television space, where ad management systems are a tiny fraction of spend – much less than 1%.

In the online space, this has been less the case, where a technology vendor can drive significantly more value than in the offline space. But still it’s unlikely that any more than 10% of total media spend will be accepted by the marketplace, for pure technology licensing.

This means that for pure-play ad tech companies with a straightforward technology license model – whether it’s a fixed fee, volume-based pricing, or a  percentage of spend – the only way to get big is to touch a large piece of the overall spend. That means scaled business models that reach a large percentage of ad impressions.  It also means that ultimately there will only be a few winners in the space.

But that’s not bad news. It’s just reality.  And it’s not the only game in town. Many technology companies have both a pure-technology model, and some kind of marketplace model where they participate in the ecosystem as an inventory owner. And it’s here that lots of revenue can be brought into a technology company’s wheelhouse. But its important to be very clear about the difference between top-line media spend verses ‘real’ revenue. Most hybrid companies – think Google for AdSense, or other ad networks – report media spend for their marketplaces as revenue, rather than the revenue they keep. This is an acceptable accounting practice, but isn’t a very good way to value or understand the value of the companies in question. So ‘real revenue’ is always the important number for investors to keep in mind when evaluating companies in this space.

Many ad technology companies will unlock unique value that they will be the first to understand. These technology companies can capitalize on this knowledge by hybridizing into an inventory owner role as well as pure technology – and these are the companies that will break loose bigger opportunities. Google is a great example of a company that runs across the ecosystem – as are Yahoo, Microsoft and AOL.  But some of the next generation companies also play these hybrid roles, and the newest generation will create even greater opportunities.

Oct 11 2012
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Ad Ecosystem, Publishers, Rich Media

Why publishers’ ad experiences need to be more flexible

(By Eric Picard, Originally Published in iMediaConnection.com October 11, 2012)

In 2004, I was recruited to Microsoft, where among other things I was put in charge of coming up with a new plan for the overall advertising experience for MSN and, soon after, Windows Live. I spent about eight months digging into the advertising experience as it then existed and tried to rationalize how advertising should work on a major site like MSN and across a variety of user experiences.

In an early meeting with a group of folks from the sales team, Gayle Troberman made a fateful suggestion: “You really need some kind of framework for assessing what kind of ad fits in what kind of experience.” This was a key suggestion because it forced me to assemble a cross-disciplinary team and create a shared language that drove numerous long-term decisions.

The first-order considerations were driven by “user modality,” which is defined as the behavior and related mindset that a user is engaged in during specific activities. We needed to determine which advertising experiences were acceptable in each type of modality that existed across the myriad experiences on our properties. By carefully considering modality, we were able to create a set of guidelines for what advertising should be enabled in each type of environment.

To illustrate the point, let me give a few key examples of what we put together:

  • Users who are reading email are open to advertising experiences that are relevant and non-invasive, but that are not explicitly targeted to that user based on the content of the mail — which just is creepy.
  • Users who are writing email are not open to advertising experiences.
  • Users who have sent email are open to a broader ad experience with a larger format ad.
  • Users who are reviewing a home page or section front are open to a large format ad.
  • Users who are reading an article are open to non-invasive ads that can be large format as long as they don’t encroach on the reading experience.

The guidelines I created with that team quickly became the overall framework used by Microsoft to drive advertising experiences across all content experiences across MSN, Windows Live, and even in a variety of emerging media experiences. My “day job” at the time was managing product planning for emerging media, which at that time included video, over-the-top television, mobile, video games, software applications, and new device formats (e-readers, tablets and other device prototypes, Zune, etc.).

Some key principles that I came up with include the following:

  • Ensure that ad clutter is kept to a minimum. It’s better to have one very large-format ad on a page than five small-format ads.
  • Ensure that ads have enough white space around them.
  • Give the user the ability to give feedback about ads (both positive and negative) — such as rating ads.
  • Be transparent about behavioral targeting of ads, including how an ad was targeted to them and what profile information we stored about users. Enable users to correct and enhance their targeting profiles. (This was the most controversial of my recommendations and was discussed at length.)
  • Enable every ad unit to become “rich media enabled” with specific templatized enhancements, such as a store locator, a pop-up video unit, RFI, and others.

Like many efforts I’ve been engaged in over the years, this one met with a mixture of success and failure. It took almost five years before we enacted most of the privacy and targeting features I recommended. And none of the rich media templates ever saw the real world. But the user modality guidelines were a huge hit — maybe in a sense these were too successful. Sometimes the creation of a set of clearly defined “rules” empowers folks who are embedded more deeply in an organization to say “no” to next efforts very quickly. This is often the case with any standards effort, whether at the industry level or within a specific organization.

I experienced this one day when I was trying to roll out a new set of ad formats for software applications. I sat down with the product manager in charge of the effort, and when I started walking him through the prototypes, he quickly stopped me with a clear set of concerns: “Uhm… look — these ad formats clearly don’t fit the ad experience framework we use here. So I’m just going to have to say ‘no.'”

Of course, once he learned that I had written those guidelines, the conversation was reopened. But this is an important lesson. Core principles always need to be flexible enough to allow testing the edges and borders of experiences. Once a new content experience is rolled out, an ad experience needs to be tried out with it. Sometimes that new experience doesn’t fit in the guidelines you’ve created.

Oct 05 2012
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Ad Ecosystem, Ad Technology, Economics, Media Agencies, Online Media, Predictions, Publishers, Startups

5 Important Memes for the Digital Advertising Industry

Oct 05 2012
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Ad Ecosystem, Ad Serving, Ad Technology, Economics, Online Media, Predictions, Startups

Entering the Fourth Wave of Ad Technology

By Eric Picard (Originally published on AdExchanger.com, 9/18/2012)

Ad tech is a fascinating and constantly evolving space.  We’ve seen several ‘waves’ of evolution in ad tech over the years, and I believe we’re just about to enter another.  The cycles of investment and innovation are clearly linked, and we can trace this all back to the late 90’s when the first companies entered the advertising technology space.

Wave 1

The early days were about the basics – we needed ways to function as a scalable industry, ways to reach users more effectively, systems to sell ads at scale, systems to buy ads at scale, analytics systems, targeting systems, and rich media advertising technology.

There was lots of investment and hard work in building out these 1.0 version systems in the space. Then the dot-com bubble imploded in 2001, and a lot of companies went out of business.  Investment in the core infrastructure ground to a halt for years. The price of inventory dropped so far and so fast that it took several years before investment in infrastructure could be justified.

We saw this wave last from 1996 through 2001 or 2002 – and during that dot-com meltdown, we saw massive consolidation of companies who were all competing for a piece of a pie that dramatically shrank.  But this consolidation was inevitable, since venture firms generally invest on a five to ten year cycle of return – meaning that they want companies to exit within an ideally 8 year window (or less).

Wave 2

The second wave was really about two things: Paid Search and what I think of as the “rise of the ad networks.”  Paid search is a phenomenon most of us understand pretty well, but the ad network phase of the market – really from 2001 to 2007 was really about arbitrage and remnant ad monetization.  Someone realized that since we had electronic access to all this inventory, we could create a ‘waterfall’ of inventory from the primary sales source to secondary sources, and eventually a ‘daisy-chain’ of sources that created massive new problems of its own.  But the genie was out of the bottle, and this massive supply of inventory that isn’t sold in any other industry was loosed.

It’s actually a little sad to me, because as an industry we flooded the market with super cheap remnant inventory that has caused many problems. But that massive over-supply of inventory did allow the third wave of ad tech innovation to get catalyzed.

Wave 3

Most people believe that the third wave was around ad exchanges, real-time buying and selling, data companies, and what I like to call programmatic buying and selling systems. But those were really just side effects. The third wave was really about building out the next generation infrastructure of advertising. Platforms like AppNexus and Right Media are not just exchanges; they’re fundamentally the next generation infrastructure for the industry.  Even the legacy infrastructure of the space got dramatic architectural overhauls in this period – culminated by the most critical system in our space (DoubleClick for Publishers) getting a massive Google-sponsored overhaul that among other thing opened up the system via extensive APIs so that other technology companies could plug in.

Across the board, this new infrastructure has allowed the myriad ad tech companies to have something to plug into.  This current world of data and real-time transactions is now pretty mature, and it’s extending across media types.  Significant financial investments have been made in the third wave – and most of the money spent in the space has been used to duplicate functionality – rather than innovate significantly on top of what’s been built.  Some call these “Me too” investments in companies that are following earlier startups and refining the model recursively.  That makes a lot of sense, because generally it’s the first group of companies and the third group of companies in a ‘wave’ that get traction. But it leads to a lot of redundancy in the market that is bound to be corrected.

This wave lasted from about 2005 to 2011, when new investments began to be centered on the precepts that happened in Wave 3 – which really was a transition toward ad exchanges (then RTB) and big data.

That’s the same pattern we’ve seen over and over, so I’m confident of where the industry stands today and that we’re starting to enter a new phase. This third major ad tech wave was faster than the first, but a lot of that’s because the pace of technology adoption has sped up significantly with web services and APIs becoming a standard way of operating.

Wave 4

This new wave of innovation we’re entering is really about taking advantage of the changes that have now propagated across the industry. For the first time you can build an ad tech company without having to create every component in the ‘stack’ yourself. Startups can make use of all the other systems out there, access them via APIs, truly execute in the cloud, and build a real company without massive  infrastructure costs.  That’s an amazing thing to participate in, and it wasn’t feasible even 3 years ago.

So we’ll continue to see more of what’s happened in the third wave – with infrastructure investments for those companies that got traction, but that’s really just a continuation of those third wave tech investments, which go through a defined lifecycle of seed, early, then growth stage investments.  Increasingly we’ll see new tech companies sit across the now established wave 3 infrastructure and really take advantage of it.

Another part of what happened in Wave 3 was beyond infrastructure – it involved the scaled investment in big data.  There have been massive investments in big data, which will continue as those investments move into the growth phase. But what’s then needed is companies that focus on what to do with all that data – how to leverage the results that the data miners have exposed.

Wave 4 will really change the economics of advertising significantly – it won’t just be about increasing yield on remnant from $0.20 to $0.50. We’ll see new ad formats that take advantage of multi-modal use (cross device, cross scenario, dynamic creatives that inject richer experiences as well as information), and we’ll see new definitions of ad inventory, including new ad products, packages and bundles.

So I see the next five years as a period where a new herd of ad tech companies will step in and supercharge the space. All this infrastructure investment has been necessary, because the original ad tech platforms were built the wrong way to take advantage of modern programming methodologies.  Now with modern platforms available pretty ubiquitously, we can start focusing on how to change the economics by taking advantage of that investment.

I also think we’re going to see massive consolidation of the third wave companies. Most of the redundancies in the market will be cleaned up.  Today we have many competitors fighting over pieces of the space that can’t support the number of companies in competition – and this is clearly obvious to anyone studying the Lumascape charts.

Unfortunately some of the earlier players who now have customer traction are finding that their technology investments are functionally flawed – they were too early and built out architectures that don’t take advantage of the newer ways of developing software. So we’ll see some of these early players with revenue acquiring smaller newer players to take advantage of their newer more efficient and effective architectures.

Companies doing due diligence on acquisitions need to be really aware of this – that buying the leader in a specific space that’s been around since 2008 may mean that to really grow that business they’ll need to buy a smaller competitor too – and transition customers to the newer platform.

For the investment community it’s also very important to understand that while Wave 3 companies that survive the oncoming consolidation will be very big companies with very high revenues, it is by nature that these infrastructure heavy investments will have lower margins and high volume (low) pricing to hit their high revenues. They still will operate on technology/software revenue margins – over 80% gross margins are the standard that tech companies run after. But the Wave 3 companies have seen their gross revenue numbers be a bit lower than we’d like as an industry.  This is because they are the equivalent of (very technically complex) plumbing for the industry.  There are plenty of places where they invest in intelligence, but the vast majority of their costs and their value deal with massive scale that they can handle, while being open to all the players in the ecosystem to plug in and add value.

Being a Wave 4 company implicitly means that you are able to leverage the existing sunk cost of these companies’ investment.  Thomas Friedman talks about this in “The World is Flat” – one of his core concepts is that every time an industry has seen (what he called) over-investment in enabling infrastructure, a massive economic benefit followed that had broad repercussions.  He cites the example of railroad investment that enabled cheap travel and shipping that led to a massive explosion of growth in the United States.  He cites the investment in data infrastructure globally that led to outsourcing of services to India and other third world countries on a massive scale.  And frequently those leveraging the sunk cost of these infrastructure plays make much more money from their own investments than those who created the opportunity.

So what should investors be watching for as we enter this fourth wave of ad tech innovation?

  1. Companies that are built on light cloud-based architectures that can easily and quickly plug into many other systems, and that don’t need to invest in large infrastructure to grow
  2. Companies that take advantage of the significant investments in big data, but in ways that synthesize or add value to the big data analysis with their own algorithms and optimizations
  3. Companies that can focus the majority of technical resources on innovative and disruptive new technologies – especially those that either synthesize data, optimize the actions of other systems, or fundamentally change the way that money is made in the advertising ecosystem
  4. Companies that are able to achieve scale quickly because they can leverage the existing large open architectures of other systems from Wave 3, but that are fundamentally doing something different than the Wave 3 companies
  5. Companies that take advantage of multiple ecosystems or marketplaces (effectively) are both risky but will have extremely high rewards when they take off

This is an exciting time to be in this space – and I predict that we’ll see significant growth in revenue and capabilities as Wave 4 gets off the ground that vastly eclipse what we’ve seen in any of the other waves.

Aug 22 2012
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Ad Ecosystem, Economics, Online Media, Predictions, Publishers

Why Media Companies Are Being Eaten By Tech Companies

By Eric Picard (Originally published on AdExchanger.com, August 20, 2012)

My friend and colleague Todd Herman (LinkedIn) once wrote a strategy paper about video content when we worked together at Microsoft. Called “Don’t be food,” it was a brilliant paper that laid out a strategy for effectively competing in a world where content is distributed everywhere by anyone.  I love the concept of “Don’t be food.”  It applies to so many existing business models, but clearly where Todd initiated it – Media – it applies incredibly well.

The media business is being forceably evolved through massive disruptions in content distribution. In the past, control over distribution was the primary driver of the media model. Printed material, radio and television content required a complex distribution model. Printing presses and distribution are expensive. Radio and television spectrum is limited, and cable infrastructure is expensive. Most media theory and practices have been deeply influenced by these long term distribution issues, to the point that the media industry is quite rigid in its thinking and cannot easily move forward.

One of my favorite business case studies is the Railroads.  Railroad companies missed massive opportunities as new technologies such as the automobile and airplanes began to be adopted. They saw themselves as being in the “railroad business,” and not the “transportation business.”  Because of this they lost significant opportunities and very few of the powerhouse companies from the rail era continue to exist.

In media, new technologies have been massively disrupting the space for more than a decade. And there is an ongoing debate about technology companies stepping in and disrupting the media companies. Google is a prominent example, and its recent acquisition of Frommer’s is yet another case where it has eaten a content company and continued to expand from pure technology into media.  But Google isn’t moving into media based on the existing rules that the media companies play by – it is approaching media through the lens of technology.

But this issue doesn’t only pertain to the oft-vilified Google: Amazon continues to disrupt the book industry by changing the distribution model through the use of technology, and is clearly gunning for magazine, radio and video content as well.  Microsoft is changing the engagement model and subsequently the distribution of content to the living room via its ever-expanding Xbox footprint, and is broadly expanding toward media with Windows 8, its new Surface tablet devices and smartphones – again using technology.  Apple has turned distribution models on their ears by creating a curated walled garden of myriad distribution vehicles (apps on devices), but charges a toll to the distributors – again using technology to disrupt the media space.  Facebook, Twitter and social media are now beginning to disrupt discovery and distribution in their own ways – barely understood, but again based on technology.

Existing media models are functionally broken – and will continue to be disrupted.  Distribution is always a key facet of the overall media landscape, and will continue to be.  But as distribution channels fragment, and become more open, the role that distribution plays will radically change. Distribution is no longer the key to media – it is inherently important to the overall model of media – but it isn’t the key.

Technology is the key to the future of media. Technology can and has profoundly changed the way content is distributed, and will continue to do so. The future of media is wrapped up in technology, and this is an indicator of why technology companies are eating media companies’ lunches, if not actually consuming them in their entirety.

Media companies don’t understand technology because they are not run by technologists. And there is a vast gulf between the executive leadership of media companies and the needs to understand technology. Every media company should be running significant education efforts to pass along the concepts needed to compete in the technology space, but I’m not convinced even that would be enough to fix the problems they face.

At Microsoft I once had an executive explain to me why most of the executives running businesses at the company were from a software background.  He said something along the lines of, “A super smart engineer who can wrap his or her head around platforms and technology issues can probably learn business concepts and issues faster than a super smart business person can learn technology.”  And he was right – it’s that simple.

Business schools should have requirements today for anyone graduating with an undergraduate or graduate degree to learn how to write software, and most importantly to develop a modern understanding of platforms. These platform models are the future of distribution, and are barely understood even among many technologists. The modern platform models used broadly on the Internet and to create software on devices that drive content distribution are relatively new, and are frequently not understood by people with technical backgrounds who haven’t spent time working with them.

Bad business decisions continue to be made by media companies because of the significant lack of technology leadership in both executive and middle management. As technology evolved, the model for many years was that business people figured out “Why and What” to build and “Where” to distribute it, and engineers figured out “How and When” something could be delivered.  Great technology companies break down the walls between Why, What, How, When and Where. Engineers have just as much say in all of those things as the business people. Great technology companies don’t treat engineers and technologists like “back room nerds.”  They recognize that engineering brilliance can be applied to the business problems facing them, and that technology innovation will drive their businesses to disrupt themselves toward future success.

Media companies must evolve away from their historical strengths based on distribution control, and must embrace technology as a key principal.  And they need great engineers to do so. The problem is, great engineers won’t work for mediocre engineers. Great engineers won’t take bad direction from people they don’t respect, especially business people. And many media companies have treated their existing engineering organizations as an extension of traditional IT models. The groups that are responsible for the corporate network, intranet, conference room systems, email servers and laptop support do an important job. But it’s vastly different from building software and inventing new technologies.  Media companies often have not understood this.

For a traditional media company to compete effectively with Google, Amazon, Apple, Microsoft, Facebook, and the thousands of hot startups now competing with them, they must build world-class engineering organizations. This isn’t a light fuzzy requirement, it’s a core fundamental of their ability to survive for the next century.  These companies must evolve forward.  They must find ways to empower internal disruption.

Media companies must build startup organizations within their own internal structures that are isolated from the existing IT leadership and given bold broad empowered charters with the leeway to disrupt other teams’ businesses.  They must build a new technology driven culture within these large media companies that is separate from the existing groups, and then embrace those internal startups as the future of the company.  This isn’t easy.  It’s nearly impossible.  And this very likely will not work the first time it’s tried. But if media companies don’t commit to this kind of change, they are going to be eaten.

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