Is Someone Else Using Your Game’s Monetization IDs?

Looking around app-ads.txt files one of the first things I was excited to check was whether DIRECT publisher IDs show up on other unrelated apps. To do this I ignored any apps that shared the same developer contact URL or Developer IDs. This doesn’t catch 100% of the legitimate publishers I checked, as the examples to follow show, but does provide a good rough estimate of which networks seem to have publishers whose IDs are more mixed.

DIRECT traffic should mean that your app controls the publisher ID listed. But sometimes when looking through ads-txt there are publisher IDs that appear many time on unrelated apps. This could mean that an ad buyer could be buying ads from more than just the app they were intending, which means both good and bad quality could be mixed together.

The worst possible use case is a bad actor ad network mixing unrelated publishers together, then asking each of them to use the DIRECT tag. It turns out this is much less obvious than I first assumed, but it does seem like certain networks do have a higher prevalence of mixed IDs. Let’s look at some examples.

Number 1: Quiz Game(s)

One of the first examples I looked at was Blackpink Quiz Game who’s Unity, Google and other publisher IDs were shared across hundreds of other apps like this Family Guy-Quiz. As you can see, the two titles visually look quite similar. Each of these hundreds of apps belonged to different Google Play developers each with their own developer ids.

In addition to being visually similar, they also the same top level domain hosting their developer page url. For example Blackpink: and Family Guy-Quiz:

Each link does host it’s own app-ads.txt file, but they were nearly identical across each app, containing the same DIRECT and RESELLER publisher IDs. Looking at the top level domain we see that QuickApp is an online tool that lets you create quize games from your browser. When you export the APK from the site it would likely all contain the same ad monetization accounts, controlled by QuickApp. QuickApp does later share some of the ad revenue back to the original creator.

To sum up, while that is an interesting business model, it certainly is not the kind of ad fraud I was originally looking for, and for now I’ll leave this as undecided for how to handle it.

Number 2: Crazy Marvin

Next try found another Unity ID that showed across a hundred plus apps. Checking the apps I didn’t immediately see a pattern this time. One a Dragon Ball Z game by Yodo1, a large game publisher, while the others were smaller other games with different styles and developers. Most of the developers did have several games.


I found it really interesting that the oddest of the titles, Pimple Popper, had a GitHub linked with it’s game open sourced with a group of people working on it. So I reached out to Crazy-Marvin the dev of the Pimple Popper game to see if he could lend some insight.

Marvin was very helpful and wrote back immediately to let me know he didn’t know much about ads, but that he had been contacted by Yodo1 and per their request put the relevant ad SDKs and app-ads.txt file on his site.

Again, this seems to be a monetization partner who runs ads on behalf of the client, but without taking over their developer page or showing any other obvious links.

Number 3:, 24400, DIRECT, 24400, DIRECT, 0bfd66d529a55807, 24400, RESELLER, 0bfd66d529a55807

Finally I found one that looks more suspicious than the others. This ID 24400 is located in 500+ separate developer sites meaning 1000s of apps are pointed to this publisher ID as DIRECT traffic. Oddly, it is almost always paired with a RESELLER line with the same ID. RESELLER here would be fine, as it is being resold, but DIRECT seems highly unlikely. Looking through the apps again they all appear to be unrelated.

Compared to the first and second attempt, Rubicon is not a SDK ad network, but a programmatic SSP.

Outcome: Inconclusive, but a picture is starting to form

While there is a lot of different ways we can put together our monetization accounts, there do seem to be a higher prevalence of mixed/shared IDs on the programmatic only traffic such as Rubicon, PubMatic, IndexExchange. Looking again at our plot from the beginning, highlighting the traditional networks that do both buying and selling, we do see that they tend to rank higher than the pure programmatic SSPs.


I think that the results are pretty inconclusive, but do show some directions to move forward. I learned that there are quite a few situations where shared DIRECT publisher IDs is a natural outcome of some company’s revenue setup, but still some situations appear to be incorrectly labeled publisher IDs. The networks that do have these shared publisher IDs seem to be more prominently programmatic SSPs.

Do you have any specific examples you’d like me to check? Feel free to comment or send me your developer URL and I can help look into your IDs. Do you think there is interest in a tool to help check your app-ads.txt for network quality?

What A Mobile Ad Monopoly Looks Like

Google’s market share in in-app advertising is unmistakably dominant. The data above was scraped from 100k+ Google Play Store & Apple App Store apps (with a slight emphasis on games) who use advertising for monetization. This public data is made possible by adoption of IAB’s app-ads.txt standard which allows buyers and sellers to cross verify their ad buying is coming from legitimate direct sources by checking public txt files.

So, Why is Google So Dominant?

First, let’s confirm that the data is somewhat accurate. This data can be correlated to similar reporting by Braedon, creator of, a repository of public data from app-ads.txt files (and much more). At 92% his percentage for Google lined up perfectly with the data here, despite likely slightly different app populations.

His tweets were also picked up a few weeks ago by AdExchanger, who pointed out that as you look closer at the long tail of small advertisers, the app-ads.txt files often contain only one single line:

Why is Google dominant in mobile? As the operating system for Android, they are the easiest way to integrate an advertising SDK for a small app. This means that the first ads shown, are often through and if and when the app grows, they will only ever add more competing ad networks, likely never removing the original option. Thus Google is the default advertising option on the device, with other ad networks barely getting a second thought.

Is There No Competing?

Let’s zoom in a bit on the data, using only the top 5% of apps ranked by install/review count, and we see a slightly more competitive world. Here Google is still on nearly every app, but the next closest ad networks are on nearly a 1/3, a healthy increase.

This seems to indicate that once an app a big enough monetization team to manage more than one network, they can much more easily expand to other networks so as to increase their eCPMs. Adding the first ad SDK is a pain, and Google’s moat around their advertising castle is that adding and managing the second SDK takes serious commitment and cost.

What else are we missing?

Let’s start with a caveat: this is programmatic traffic, measured only by those who are hosting their app-ads.txt file. Some ad networks are not so transparent.

For example, Apple Search Ads, with it’s growing stranglehold over the iOS advertising ecosystem is entirely absent in app-ads.txt. This is because Apple does not compete at all in the programmatic markets, but instead uses it’s elevated role as the owner of the App Store to manage it’s own ads.

Another missing piece is that the vast majority of app-ads.txt are polluted now by traffic marked as resellers. Resellers are much more competitive market, fighting over the scraps of what is left after the top networks take their pick of traffic. Unfortunately, there are so many resellers, it is quite hard to understand how app-ads.txt helps with verification at all, as I have seen many app-ads.txt files with hundreds of resellers and only 1 direct account, usually just

Closing thoughts

Despite the doom and gloom, the most important thing is that this information is publicly available. Advertising is a difficult to parse business and this helps us to have some transparency on the marketplace we all share.

Can You Use AI to Generate Marketing Creatives?

Disclaimer: All images and ads in this post are for spec commercial examples only. The originator of the
commercial is not affiliated with, connected to, nor sponsored or endorsed
by Clash of Clans (or Starbucks).

TLDR: Fully formed art, but difficult to control details

AI Text to image models have seen explosive growth the past year matched with an equally explosive growth of an assumption that AI generated images will replace graphic design and artist roles. I am not a graphic designer but in my role of developing advertising automation solutions for mobile games, the creative development process is something I’ve worked near for years.

Input: “A steaming white coffee mug of cappucino with the (starbucks logo), photorealistic”

In short the AI image creation goes from nothing to fully formed image with no effort, but specific inputs or requirements increase the difficulty immensely. AI image generation is the perfect tool for creating a never before seen magical world, but if your requirements are more specific than that, it can feel quite difficult to impart your intentions into the chaos of the random image generation.

For example, this generated photo of a Starbucks coffee mug logo is OK, but the mermaid logo’s face is slightly odd. Further steps can get it closer to the original logo, but ultimately it never will be the exact right logo or the exact right color green, because those requirements ultimately comes from the brand creator and cannot be randomly generated by the AI and would instead later need to be added.

The setup and tools: Stable Diffusion

For our imaginary use cases we will use Clash of Clans as our example spec advertisement. Clash of Clans is a popular mobile game with a proliferation of content, so the image databases used to train the models already have a fair amount of tagged image content. Stable Diffusion is built on models based on 5B images, scraped from the internet, and fully searchable on LAION-5B: Searchable Database of 5B Tagged Images. Stable Diffusion starts from a random noise pattern (like static on a TV) then iterates until patterns begin to form that match your text or image input.

I ran all these images through the Web UI provided by AUTOMATIC1111, this UI allows you to run edit and play with Stable Diffusion in your own browser. The images were run on a regular laptop with 4GB GPU memory, so all images were output with the bare minimum settings. Of course, if you have the recommended 12GB GPU memory your outputs should look much better. In general I found that the iterative approach worked best, so was able to break the project into smaller steps which usually fit nicely into my 4GB memory.

AUTOMATIC1111 Web UI with convenient sliders, buttons and toggles for Stable Diffusion inputs

Use Case 1: Resizing an Ad from Landscape to Portrait

A common situation marketers encounter is after a set of creatives are created, at some point in the future they may encounter an advertising platform with different dimension requirements.

To start the following creative is 1140×700 pixels but we need to transform it to 900×1600 pixels. This is quite difficult as you not only need to resize the image, but also fill in the additional empty space. Stretching the image would distort the creative to the point of being unusable, putting in blurry backgrounds is another simple fix, but still an aesthetically lacking option.

First Try: Img2Img

Using Img2Img with a prefilled image will set a tone for the image to be generated. I tried several rounds of pure Img2Img, and while some basic contents were similar (note the characters on left, white light background on right) the content was so wholly different that it invalidated the original use case to use the original art. I want to keep as much of the original art as possible, so this doesn’t cut it.

Step in the right direction: InPaint

Using Inpaint and a custom black and white image, both made in a separate image editor.
Input: “clash of clans mobile game ad rushing towards battle cry in the style clash of clans”

In our next attempt, we use InPaint which has the tool for creating a mask (the white and black part), which select which parts of the image should remain original and which should be generated. In this case, the white and black below correspond to the generated and untouched parts of the image respectively. The remaining image (blue sky / some of the original image and green for grass) are then used for Img2Img inputs.

This gets us much closer to something that looks OK. We have a purple character above and a semi-convincing charge of men below. The line between the images does not blur very well and the their body positions are illogical. The entire scene at the very bottom is the worse as the men closer to monsters than Clash of Clans characters.

Keep the image: Fix the remaining issues

We can try to fix these by further rounds of InPainting, focusing on specific sections. For example, let’s remove the purple sky man in favor of clouds.

Img2Img Inpainting over the giant purple face, creating clouds instead.

Trying to get the faces to be more “on brand”

The two faces in the bottom of the image were still bothering me as they didn’t quite look like the Barbarian character from Clash of Clans. The main issues were the lack/odd eyes and non yellow beards. What initially felt like a quick fix turned into a much longer process of trial and error, generating dozens of attempts.

Inpainting the terrifying faces away

Finally, I tried searching through LAION-5B database, which was used to train the Stable Diffusion model. I found that probably the best input for getting two good Clash of Clan’s barbarian faces was to simplify my input to “clash of clans icon yellow”. In the search on LAION-5B database this returned the highest density of typical Clash barbarian characters.

LAION-5B search for clash of clans

Use Case 1: Final Resized Image

After getting the last two faces’ beards fully yellow, I had arrived at what my dwindling patience told me is the final product. The outcome is something only a parent can be proud of, and while interesting, would not be something that any branded advertisers would be comfortable using as an advertising creative.

As close to final as I could get

The difficult in controlling the coloring and themes leads to character faces are very similar to Clash of Clans branding, but ultimately not on brand.

A higher order issue is that while we did solve our initial problem to resize the original image, we did not do it in a way that is flexible for future changes. The end product is a fully rasterized image, which means that it is even less flexible were it needed to be further resized for another set of dimensions. A typical work file from a creative team would include each section or character in their own folders and layers, allowing for quick modifications later.

A time breakdown of generating this image was as follows:

  • Setup: 0.5hr
  • First images to the clouds: 1hr
  • Generating the two faces: 1.5hr

Overall generating this use case felt like it became a slower and slower process as I tried to exert a high level of control over the image being output.

Use Case 2: Themes

Sometimes for holidays you may want to modify existing creatives to add a holiday theme. This is usually a pretty simple process for creative teams, but for smaller companies there might not be enough time or manpower to dedicate for creating a steady stream of themed content. Let’s see how well we can take our original creative and add a Halloween theme.
Let’s start with some fall colors and see what we get.

And now let’s add some pumpkins!

Losing the plot, trying to get more of the Halloween theme

Trying to take the Halloween theme further it is really easy to quickly get off track. The image below is just an example, but most images quickly become 100% unrelated to the original image. Again this underscores the true power of Stable Diffusion: that it can so quickly generate incredibly unique and interesting images, but attempting to exert precision over them is difficult.

Use Case 2: Final Product

Trying to prevent Stable Diffusion from generating it’s own images is a bit tiring, so let’s just add more pumpkins and call this done. The final product got a little bit of a fall vibe to it, but let’s be honest, not much. I didn’t document it, but I went on a long tangent attempting to turn the background sky dark / night time, but without any success. I found it difficult to truly give this a good fall motif without losing the original art. Again, this was 2+ hours of experimentation for what in the end felt a bit like bad clip-art. This use case didn’t feel anywhere near as good as the first one, though it did seem promising at first.

Case Study 3: Learn from our mistakes, try again

If Stable Diffusion is so good at generating completely new images, perhaps we shouldn’t try to start with a base creative, however let’s see what we can generate completely new? Let’s retry the two original case studies but from that angle.

Case Study 1: Input “clash of clans advertisement crowd of charging guys running towards a clash of clans giant” total time ~10 minutes, minimal tweaking settings

What a huge change, allowing the Text to Image to manage itself allows for some very unique and interesting scenes, and allows me to do a bit more “gambling” simply allowing the random generation of images to do the heavy lifting to find a good image.

Getting back to our themes, lets make a few themed images. Here’s two groups of four images for both Halloween and Christmas. It was so quick, these took a few minutes each and I didn’t even play with settings to fix anything up on them.

In total I spent mere minutes crafting the above sets of images, a fraction of the time as others, and arguably better looking scenes came out of it. To me, it felt like the less precise control I tried to exert, the better the results.

Final Thoughts & What’s Next for Marketers

Image generation needs more modular components for artists and marketers alike to manipulate. For example, outputting backgrounds, characters and objects as separate images and then composing them. This would allow flexibility later when working with the image. The landscape of image generation has been changing quickly and more and more the tools built around Stable Diffusion. I foresee further chaining together multiple AI algorithms and editing tools to enable more diverse use cases. I think what may come out of these are new specialized roles for artists who can create images from these tool sets.

Canva now has built in Stable Diffusion, though the results are much too constrained for this use case.

Marketers meanwhile will find new uses for newly created images that are not quite on brand. Think rapid concept art prototyping and much wider ranges of A/B testing. Think social media marketers able to respond to simple messages with witty replies that contain unique images tailored to that day’s news or memes.

I see Stable Diffusion as a boon, the clip-art of our generation bringing art closer to everyone and the tools for marketers to create better experience ads.

If you have any questions, feel free to contact me or reach out .

The Future of VR: Open Frontier or Walled Gardens

There is a growing discrepancy between the vision and reality of VR. For decades, science fiction and technology visionaries foresaw a future where consumers were able to freely traverse virtual reality like surfing the web. Virtual reality was seen as an open platform where millions of people could share, consume and explore content. Instead of this vision, we are building walled gardens without connectivity between different content. To open VR to its true potential requires innovation around linking content and open platforms. VR is inherently social, but first we must leap the walls tech oligarchs are constructing across the new virtual reality landscape.

Virtual Reality: The Vision

“The sky above the port was the color of television, tuned to a dead channel.”

Thus began 1984’s science fiction classic Neuromancer, which popularized the concept of cyberspace. Gibson’s virtual reality is notable because it was conceptualized before the web existed. This was explored in depth in early cyberpunk works such as TronNueromancerGhost In the Shell, and Snow Crash which shared a future vision of the internet where three-dimensional metaspaces were linked and could be traveled at will. More recently this idea has resurfaced in the the popular, Ready Player One, which also contains visions of three dimensional metaspaces, though they technically exist within one game. Each of these works imagines society taking advantage of a networked cyberspace and the benefits of three dimensional spatial semantics.

The visionaries of science fiction foresaw virtual reality like the web in that content from these worlds is linkable through addresses. Thus anyone with a virtual reality device may visit any address and experience its content, as well as click on links to other virtual reality spaces. For example, a quiet cyberspace cafe, might contain links to coffee bean information, virtual chatrooms, ecommerce stores, bulletin boards or news content. Some of this might be hosted by the cafe, some might be links to other virtual reality spaces or web content.

In 2016 the VR platforms are controlled by tech oligarchs. Oculus Rift, HTC Vive, PlaystationVR, GearVR and even Google Cardboard all focus on building walled gardens. Current VR requires you to purchase devices to experience the content created for that specific VR device, similar to XBox and Playstation consoles. An extreme example is that Samsung GearVR doesn’t have native YouTube support as they see this as competition to their own content. These VR companies are backed by billionaires and technology giants, why would they mess this up?


Explanation via Analogy, 1994: The Information Superhighway

“This emphasis on profit has some in congress worried that the coming information superhighway may be dominated by a handful of big boys.”

Before the Internet was the popular form of the web we use today, there were competing models for how to network devices to deliver content. One famous example is the Information Superhighway which would connect ecommerce, videos, business, music to users through their living room televisions. Again, this idea was slightly before the web, and at the time was a competing model for what the Internet could be.





Brian McCullen, @brianmcc, said in his Internet History Podcast, that Bill Gates “along with his buddies in cable and telecom” thought they could bring the Information Superhighway to users “in a prepackaged and pre-designed way.” Yet, Microsoft and cable networks’ walled gardens “didn’t anticipate that everything the Information Superhighway hoped to be, would bubble up from somewhere else.” The world wide web exploded onto the scene with the first major web browser, Netscape, which opened up the idea of the web and an Internet controlled by no one.


The idea of cable networks controlling the access to the networks through television sets crumbled with Netscape’s free browser that could be downloaded onto any computer, for free, and then access any content across a growing web of mostly free content. Netscape also pioneered technology that made websites more accessible, safer and visually appealing. Their contributions to the web were open source and free for websites and competitors alike to adopt. As Netscape jumpstarted the dotcom boom that would define the 1990s, the concept of the walled gardens of the information superhighway was mostly forgotten.


2016: Bring Down the Walls


1. Linking

The idea of a shared virtual spaces has already been present in many popular games. Second Life, 2003, allowed users to create worlds and experience them together on the internet. Minecraft, 2010 alpha, user’s created and shared worlds was also the core mechanic. Still, these games are walled gardens. A user in Minecraft use a link to take him into a world in Second Life or anywhere else.

Links will give the ability to link any object in a metaspace to another metaspace or web page. Hyperlinks are the simple idea behind the web that makes it open and free. Anyone can link to any other page, and it is expected to be able to quickly load on your browser. iPhones and Androids were around for years before DeepLinking was created, this time we need to begin utilizing linking in VR content right away. Linking will force other content to be open and platform agnostic.

2. VR & the Cloud

This is an area that is ripe for innovations. High intensity graphic processing will need to be done in the cloud and streamed to VR browsers. Cloud Gaming has in development and deployment across a variety of platforms, yet very little of this has been open sourced. Google and Apple have also both taken steps to run apps on their servers and stream the content to interested users before they download the app.

3. Open VR Protocols

This part is moving forward, though slowly. It is an area that needs innovations and companies to lead the way forward. Currently we have browser giant Mozilla is leading the way with MozVR and open WebVR protocols with competitors like startup JanusVR creating it’s own JanusVR Markup Language. In March 2016 Googlesimultaneously released open source Web iframes for VR as well as Android app support for VR View.


We are in the early days of VR. If we want it to exhibit the open culture of the web, we need to begin building that openness now. That way when VR does become a useful tool for society, it will benefit the largest number of people rather than the current of VR’s walled gardens.

If you’re interested in the art and cover art please check out Kilian Eng.

To Love & Hate in the World of Advertising

“I hate ads, who likes to see ads?”

Recently my mother came to visit me in Taipei. On the drive from the airport we talked about my startup, Bubbleye, and how we predict a user’s interests in mobile ads. Her response: “I hate ads, who wants to see ads?”

She’s right, no one wants to see intrusive popup ads. People hate advertising, especially digital. It is seen as both annoyingly intrusive and ethically unacceptable in its casual invasion of privacy. Yet despite all this advertisements are human inventions, labors of love and ingenuity.

“Doublethink means the power of holding two contradictory beliefs in one’s mind simultaneously, and accepting both of them.”  
George Orwell, 1984

For those of us working in the advertising we both despise advertising, yet enjoy our jobs. As creators of the ad world this doublethink is our reality. We love ads, we hate ads. I love my job, yet I also encourage my friends to use ad blockers to improve their browsing experience. In Orwell’s 1984, doublethink is a propaganda tool used to edit understanding of truth, yet I see it also as a sophisticated way of understanding the world. To accept two opposing views is to have both knowledge and faith in the forces of the industry to naturally stabilize between the two. Similar to faith that supply and demand’s equilibrium is a place we want to be, I trust that the ad industry will maximize both ad supported free content and enjoyable browsing experiences.

So, lets break down these two sides of love and hate for the ad industry and see what we think the future might hold.

Did you know that ‘apology’ used to mean not an admission of guilt but a defense of one’s position? Plato’s Apology is a retelling of Socrates’s self defense at his trial. As a way of tying together the love and hate of the ad world, I would like to deliver both the modern apology and older definition of an apology for advertising.


I Hate Ads: An Apology for Advertising


The picture below is a screen cap of the highly personalized invasive advertising of the future as portrayed in 2002’s Minority Report.

Ads which cause unsolicited intrusions into your life which produce negative experiences is the cardinal sin of the advertising industry. Louis D. Lo Praeste recently articulated this “ethical conundrum” of the ad world’s ability to inject advertising into every opportunity in the digital experience but only with the hope of a positive user experience and the reality of mostly negative experiences.

The second great sin of advertising is violation of privacy. Societies are still deciding where it is an isn’t appropriate for advertising to collect information, but this effort to understand big data behind advertising is obfuscated by the unseen world of data collection. In addition, it is difficult to decide where to draw the line about individual pieces of nearly meaningless data, which once collected and analyzed can show potentially sensitive information about an individual. Society still has difficulty fathoming what it means that corporations can know my name, friends, locations, habits, and interests.


I Love Ads: An Apology of Advertising

“The best minds of my generation are thinking about how to make people click ads. That sucks.”
–Jeff Hammerbacher, 2011

Hammerbacher’s infamous quote is where I would like to start my apology of advertising. That advertising generates wealth for small businesses and content creators is something that is well understood and currently being discussed across many digital mediums. Less popular are the ideas that advertising both spreads information and breeds innovation.

Seeing an ad spreads information. Given that information is of high value and true, this is an extremely positive thing. The information is being spread in a non natural way, funded by money. Marketing campaigns can help the smallest and biggest players accelerate their natural word of mouth growth. Thus more efficient advertisements are more efficiently accelerating economic growth. For example, Tesla’s ability to hype its products is not only accelerating their growth, but accelerating the mainstream acceptance of electric vehicles around the world. This of course can have runaway effects and has caused governments to regulate commercials targeted to children, or possibly addictive activities. Advertising’s power to spread of information is overall a positive effect as it is moderately regulated.

Advertising breeds innovation because that is where the money is ready to be spent. While that may seem materialistic, some ad product concepts are expensive and difficult to develop. Capital support is necessary for research and development, which will later spread to other sectors. For example, big data research has been at the forefront of adtech companies. These innovations benefit machine learning and data mining in other industries which are not directly related to advertising yet benefit from this boon of advances.

 Conclusion: Accepting Evolution

 The evolution of the advertising industry is not lockstep with the society’s understanding of the advertising industry. Advertising has evolved so fast that society is currently still grappling with the wider implications of these changes. It is important for both insiders and outsiders to understand the forces moving advertising, and weigh the benefits and costs. This evolution of the advertising industry can be influenced by those outside of it, as it is currently by the uptake in ad blocking technology. This is a positive force that should be accepted. At the same time ad industry insiders are evolving the industry to show less but more targeted advertising. Balanced together these forces push advertising, innovation and society to better places.

Why I started up away from home, and you should too

Growing up in the North Bay I was always aware of the Silicon Valley tech scene.  Yet I was more interested in experiencing the world away from home so I traveled to Africa, went to college in LA and for my career I went to Asia. After working for Elex Tech & Cheetah Mobile, I started at my first startup in Beijing, and later transitioned to my current company in Taipei. While working at startups in Asia I have noticed the curiosity in people’s faces when I tell them I am from the Bay Area. They curiously wonder: if you are from the Bay Area, why start a startup in Asia?

“The best entrepreneurs know this: every great business is built around a secret that’s hidden from the outside.” – Peter Thiel

The Outsider Advantage

Positioning yourself and your company in a unique location, different from your comfort zone, will empower you to learn more. Being an outsider allows you to see bigger pictures, understand more connections and see problems that need to be solved.

Different Markets

Different regional markets represent opportunities for you to work with clients who are willing to try new things, and try them at scale. For example, my current company, Bubbleye, is taking of great advantages to work with ad networks in Asia who need new targeting solutions. Sharing your technology with foreign markets might be difficult, but it can also be very rewarding.


Living and working in foreign countries is difficult, but the upside is that through these difficulties you can form relationships that will last a lifetime. Living and working abroad will give you the courage to help others, even when it doesn’t benefit you, but will help the community.

So, my advice if you’re willing, is to pack your bags and head somewhere new. It will create new business opportunities, generate new knowledge for you and build lifelong relationships. So go for it! You can always go home later, but you can’t always take the chances that you have now.

The Future of Ad Networks? Where they might turn for market position.

The decline of ad networks is a chance for innovation as they transition to new market positions which will transform the existing advertising ecosystem. Ad networks are businesses that deal directly with their clients in both advertising and ad publishing. Ad networks directly control the ad inventory and as well as sell the ad inventory to their advertising clients. The weakening of ad networks has been slow and yet the ad networks who evolve too late will die. Ad networks have several options for the evolution of their market position: SSPs, DSPs, DMPs, Agencies and even ad exchanges. Here is a brief explanation of these possibilities.

Sell/Supply Side Platform (SSP) – Supply Side Platforms are the broker for ad publishers to sell their inventory for bidding on ad exchanges. Ad networks with strong publisher bases can grow their relationships with publishers by adding value to their publishers’ inventory and becoming SSPs.

Demand Side Platform (DSP) –Demand Side Platforms facilitate the buying of ads from ad exchanges. Ad networks with strong advertiser platforms have already begun working with other DSPs and may begin to build their own bidder and transition to an independent DSP. This option is a logical transition for ad networks, but they will face dangerously high competition in this space. Existing relationships with advertisers and knowledge of their needs may allow them to corner this market.

Ad Agency – Ad agencies work on behalf of clients to purchase the correct audiences for ads. Their clients are often multinational brands who are interested in procuring specific types of audiences. Ad networks who have strong advertising experience, but have focused less on their technology may see opportunities in beginning to positioning themselves as an ad agency. This would be a radical change from their existing technology, but would represent a positive opportunity for working with the huge branded content ad market.

Data Management Platform (DMP) – Ad networks who have the most data will find themselves the best equipped to move into any of the above areas, but some may opt selling this data to third parties. This is an easy route but may not have long term stability as their core source of market data continues to erode.

Ad Exchange- Ad exchanges are the market place where ads are bought and sold in times measured in milliseconds. Transitioning from an ad network to an ad exchange (similar terminology but vastly different meanings) is the most ambitious option for an ad network. Cornering this market allows for all other products of the original ad network preference. This would be the most difficult transition and is most likely only possible for the largest existing ad networks.

The Innovators Some small ad networks may look at these options and see nothing but costly competition and low profit margins. Instead they will look to create the next opportunities in the advertising world. Their innovations may be incredibly disruptive as they seek to create a market position by changing the ecosystem itself. I look forward most to seeing the ways in which the markets will evolve and the role that innovative ad networks and other small businesses will play.

In conclusion, the continued pressure on ad networks will cause pivots, innovations and failures. This is exciting opportunity will see new technologies coming to the fore. The continuing dominance of mobile, the use of big data and predictive AI to target ads will be the platforms and tools that fuel these massive changes through 2016.