Programmatic advertising is dead. So says its self-proclaimed inventor, Mr . Brian O’Kelley, CEO of App Nexus. The new era is all about the core ad tech buyers — the advertisers and publishers. The winning technologies of the new age will be those that see customers as equals, that teach customers how to use the powerful tools they are buying.
Sprint's prepaid cell phone business, Boost Mobile, announced this week that customers can opt to receive a $5 monthly discount in exchange for seeing ads on their smartphone lockscreens. For Sprint to break even on this scheme, they either need to charge ultra-premium rates or sell a whole lot of impressions.
Sprint makes money on its new offering by turning a consumer's smartphone lock screen into ad inventory. Each time a user unlocks his phone, Sprint has the opportunity to sell an ad impression and generate a small amount of ad revenue. Open your phone often enough, and Sprint can more than make back the $5 discount.
To make this profit math work, Sprint either needs to charge extremely high rates or sell an enormous number of impressions. For people who check their phones only a handful of times per day, Sprint needs to charge upwards of a $50 CPM. Even for people who check their phones 50 times per day, Sprint's breakeven rate is nearly $5 CPM, well above typical display ad rates.
Keep in mind that Sprint doesn't get to keep all of this ad revenue. The offering is powered by a company called Unlockd (unlockd.com), which keeps some of the ad revenue. And of course there are additional ad serving middle men who also take a cut of ad revenue
The chart above makes what seems like a reasonable assumption about the number of times consumers check their phones, but it turns out to be very wrong. According to Unlockd, the average consumer checks his phone 150 times per day, and the average millennial checks his phone 200 times per day. That's a whole lot of ad inventory, and it means we need to look much further to the right on our breakeven chart.
At 150 unlocks per day, Boost can recoup its investment by monetizing inventory at a $1.11 CPM. Those 200-unlocks-per-day millennials require just an $0.83 CPM. Profitability seems well within reach.
Here's the rub. Imagine having to dismiss an ad every time you want to check your mail. Or answer a phone call. Or turn off your Monday morning alarm. Imagine doing this 200 times per day, every day. Is this hassle worth a $5 coupon? Customer reviews for the Boost Dealz app suggest the value exchange doesn't quite work:
Presumably Unlockd and Boost can improve the user experience with time, but in the end, they're left with a business model that requires delivering a whole lot of interruptive ads. Kudos to both companies for testing new ground on ad economics, but this feels like a short lived experiment.
Facebook is choosing to interrupt its user experience in order to build its identity data assets. Whether you love the decision or hate the decision, give Facebook credit for grappling with issues that aren't even on the radar for most companies. Facebook is playing the ad tech game at a different level.
KardBlock, the browser plugin that removes all references to the Kardashians, was met with great fanfare at its May launch. At its core, KardBlock is a customized ad blocker, and it provides a clean illustration of a technique that is rewriting the rules of ad blocking for the world’s biggest media companies.
KardBlock uses a technique called element hiding to produce a Kardashian-free internet experience. The tool simply looks for pieces of web pages that contain Kardashian content and hides this content from the user. As an example, take a look at the following before-and-after screenshots from E! Online:
Poof! No more Kardashians. But even in the screenshot on the right, the image of Kendall is downloaded to the user’s browser. KardBlock doesn’t prevent your browser from downloading Kardashian content, rather it just prevents the content from being presented on screen. E! Online’s website analytics will report two views of the Kendall image — one for each page view. Meanwhile only one of those two pageviews actually produced on-screen content.
It turns out that ad blockers are increasingly behaving like KardBlock, and this has some ugly consequences for both publishers and advertisers. In an attempt to outsmart ad blockers, publishers are adopting native advertising products that are largely indistinguishable from the site’s primary content. While ad blockers typically cannot prevent native ads from downloading, they can employ a KardBlock-like technique to prevent native ads from rendering.
Here’s an example of a typical Facebook ad unit:
Because these ads all load from the same domain as Facebook’s newsfeed content, traditional ad blocking fails. Enter element hiding.
Facebook loads this particular ad unit in the following HTML element:
<div class=”ego_column pagelet _y92 _5qrt _1snm”></div>
EasyList, the most commonly used set of ad blocking filters, contains the following rule, which hides this specific Facebook HTML element:
facebook.com##div[class=”ego_column pagelet _y92 _5qrt _1snm”]
When you visit Facebook with an ad blocker, all of the content inside this HTML element will be hidden from the user:
Poof! No more ads. But just like KardBlock, ad content is downloaded and then hidden, and that creates a real advertising mess.
Traditional ad blocking is bad, but element hiding is worse. In our example, Facebook will record the impression, but J Crew’s reporting system will not, leading to volume disputes between the companies. Without a record of the impression, J Crew’s performance measurement systems will also be unable to record any advertising success metrics like viewability, ad engagement, and post-impression sales. Facebook’s performance will appear weak. And depending on contractual terms between the two companies, J Crew may even be responsible for paying Facebook for this hidden impression. Through no fault of either company, their business relationship may be damaged.
So what are publishers and advertisers to do? The good news is that there are techniques for both publishers and advertisers to stay one step ahead of element hiding. The bad news is that the ad blocking playbook largely contains short term cat-and-mouse tactics, not long term strategic solutions. Brace yourself for some hand to hand combat between the ad economy and the ad blocking community.
For more ad tech thinking, visit the Jounce Media Blog
Facebook Instant Articles is back in the news this week. After a bit of a sleepy start, Facebook is expanding the list of participating publishers and increasing the number of users who have access to Instant Articles content. Lots of people are going to start consuming content directly in the Facebook app rather than through embedded webpages. And that has some big implications for ad economics.
As a point of reference, consider three consecutive ads delivered to me in the Facebook app:
These ads paint a picture of who I am — a small business owner working in the tech space. Targeted advertising at work.
Next, I tapped on a Business Insider article that was posted in my Newsfeed. This loaded an embedded webpage within Facebook’s app. Refreshing this same page a few times, I saw the following ads:
A Spanish language car promotion, a cooking ingredient ad, and an offer for a trip to the Catskills? Who do these advertisers think I am? The answer is that they have no idea who I am because Safari (the embedded browser in all iOS applications) rejects third party cookies. Ads served in Facebook’s native app can be targeted based on my device ID, but ads served in Safari don’t support any user targeting. Inside Facebook Newsfeed, advertisers know who I am and can employ highly targeted campaign tactics. But within embedded webpages, audience targeting goes out the window.
The result of this targeting disparity is that advertisers are willing to pay much higher rates for in-app inventory than mobile web inventory. Facebook knows this and so do publishers. Facebook Instant Articles brings in-app economics to mobile content consumption, unlocking audience targeting capabilities for advertisers and boosting yield for publishers. Sure, faster article load times, lower bounce rates, and greater sharing activity are nice. But the real value of Facebook Instant Articles is ad economics.
Facebook isn’t the only platform looking to provide in-app hosted content and monetization tools to publishers. Snapchat, Twitter, and even Google are rumored to be building content distribution platforms to help publishers migrate away from the mobile web. Apple’s plan to starve the web of cookies just might be working.
Identity is the new battleground in digital marketing, and deterministic data is the gold standard. Companies like Facebook and Google have rich records of user logins that enable cross-screen ad targeting and measurement, and the prevailing wisdom is that these deterministic data sets paint a grim picture for probabilistic identity players. But deterministic data has significant blind spots, and savvy marketers are embracing a hybrid approach to identity management.
Google’s Contributor product is an interesting experiment in how much I value my own attention. For $10 per month, I’m zapping a good portion of the banner ads that used to disrupt my daily web browsing. The implementation is dead simple, streamlines my online experience, and improves publisher economics. But it forces me to consider how much I really value an ad-free internet. More specifically, do advertisers value my attention more than I do?
In late 2014, Google quietly released a product called Contributor. People who sign up for Contributor pay Google a few dollars per month, and in exchange, Google removes some ads from the websites that they visit. Each time Google removes an ad, it delivers a subtle gray image in its place and pays the publisher for the unutilized ad space. The product is still in closed beta, but you can request an invite here. Publishers can also request to be included in the program by sending an email to contributor@google.com.
Based on the lack of a press release (or really any coverage at all from Google), Contributor appeared to be a fringe experiment in processing micropayments, but the fact that it’s still humming after 9 months suggests it may be gaining traction with consumers and publishers. I’ve now spent 2 weeks taking Google Contributor for a test drive, and I’m starting to understand the value. The change is noticeable, and the stats back it up. Google has blocked over 200 ads, and it is paying publishers an incredible $21 CPM for the privilege.
So how is the little bit of marketing magic happening? Google hasn’t disclosed much about the way Contributor works, but based on my experience with the product, here’s what I think is happening behind the scenes:
Google creates a Contributor ad campaign that is targeted to just one user — me. When Google’s ad exchange conducts an auction for my ad space, the Contributor campaign bids aggressively to win the impression. Other bidders continue to participate in the auction, but the Contributor campaign’s high price often wins, and when it does, it serves a subtle ad that thanks me for being a contributor. Google pays the publisher for the ad spot just like it would for any other campaign.
This same experience happens across multiple websites and even follows me across multiple devices (as long as I’m logged into my Google account). But it doesn’t happen everywhere. In particular, it seems that only websites powered by Google’s ad exchange get the Contributor treatment. To be clear, Google’s buy-side products allow advertisers to deliver ads across the entire web, including websites whose inventory is powered by a non-Google ad exchange. But Google prevents its Contributor product from accessing this non-Google ad space. It’s an odd choice that arbitrarily restricts Contributor’s ability to create a truly ad-free consumer experience.
Renewal time is fast approaching, and I need to decide whether I’m ready to pony up another $10. Even assuming Contributor had full access to all websites, I’m not sure I value my attention enough to justify the cost. I’m competing with advertisers to buy my ad space, and I’m just not willing to pay all that much. I’d rather keep the $10 and have an ad-supported internet, and I suspect most people would agree. Advertisers seem to value my attention more than I do, and that’s fine by me. Advertisers, you can have my attention. I’ll take the free content.
Today, I’m excited to release the Little Black Book of Ads, Jounce Media’s guide to the fundamentals of programmatic advertising. The Little Black Book outlines 15 must-know concepts that underpin the ad tech ecosystem. Master these 15 concepts, and you’ll be armed to tackle any programmatic advertising problem.
I remember joining Turn in 2010 and trying to wrap my head around real time bidding. Each time I thought I had mastered a concept, I learned about a new exception to the rule. Turn is full of some of the best minds in ad tech, and we still struggled to make sense of the space. Without an authoritative guide to the ecosystem, we were forced to cobble together an understanding of our industry through a slow process of trial and error. A programmatic education required years of hands-on-keyboard experience, and very few people could credibly claim to be programmatic experts.
Five years later, learning the programmatic space is more daunting than ever. Each Lumascape drawing is more dense than the last. Struggling for differentiation, ad tech vendors inject new terminology into the market to trumpet new twists on old capabilities. Industry publications amplify the confusion, acting as megaphones for the ad tech flavor of the month.
We’re overdue to make sense of the programmatic space.
A programmatic education shouldn’t take years. At Jounce Media, we think we can supercharge your path up the ad tech learning curve, and we think the Little Black Book is the key.
In one marathon day, we’ll take you and your team through each of the 15 fundamental ad tech concepts. We’ll give each member of your team a copy of the Little Black Book, and we’ll discuss each concept’s application to your marketing activities. At the end of the day, you’ll walk away with a much deeper understanding of programmatic marketing, and you’ll also walk away with own Little Black Book — your quick reference for all things programmatic.
We want the Little Black Book to bring clarity to programmatic marketing and become your essential guide to ad tech. We want the Little Black Book to earn a spot on the top of your desk. We want you to carry it with you to meetings. When you go on a business trip, we want the Little Black Book to go with you.
Ready to take your programmatic game to the next level? Let’s get started.
With the massive scale of open RTB markets comes significant brand safety risk for advertisers. During peak periods, advertisers must evaluate over 1 million programmatic ad buying opportunities every second. This firehose of bid requests contains an extremely diverse pool of inventory — everything from YouTube to YouPorn. Successful buying in the RTB market requires real-time decision making to identify which impressions are worthwhile investments and which could lead to a PR mess.
Each bid request received by a DSP contains basic information about the available ad impression. One piece of information included in every bid request is the site’s URL. Companies like Integral Ad Science and MOAT specialize in rating each URL against brand safety standards, and bidding systems can leverage these URL ratings to ensure advertisers only purchase ad impressions on brand-safe inventory.
To test the URL-based approach to brand safety, I triggered a few thousand bid requests from an ad exchange called Index (formerly Casale), rotating the declared URL between drudgereport.com and youporn.com. The Drudge Report bid requests captured lots of demand — 85% of auctions yielded a bid of $2.00 CPM or more. But bid requests from youporn.com fell on deaf ears. No advertiser ever bid for these impressions.
The URL-based approach to brand safety evaluation works well for most sites, but it fails in a very big way for sites that have highly personalized content. Is Twitter inventory a safe place to advertise? The answer depends on your Twitter feed. Follow a few NSFW accounts, and your Twitter feed quickly becomes a risky place to advertise.
As content personalization proliferates across both social media and traditional publications, advertisers are left with three options for managing brand safety on their programmatic campaigns:
The web is getting personal, and the brand safety game is changing. Advertisers who quickly adapt will benefit from preferred access to consumers, and those who sit on the sidelines will find fewer and fewer advertising outlets.
To trigger your own Index bid request, just load this URL.
More than $15 billion of ad spend will be transacted through programmatic channels this year, and that budget will flow through a famously fragmented set of technology systems. But what specifically are the steps behind a programmatic transaction? How does a single ad move across the Lumascape? In this post, we will break down the 5 key steps of every programmatic impression.
In a recent AdExchanger article, Hagai Schechter delivered an ugly dose of reality to advertisers:
A campaign with a small but significant quantity of fake traffic would outperform on paper a campaign comprised entirely of real traffic.
How could this be, and what’s an advertiser to do?
Imagine that an advertiser works with an ad network that delivers above-average clickthrough rates of 0.5%. This network also appears to have strong inventory quality controls. Ad viewability is an impressive 73%, and only 13% of ads are delivered to non-human bot traffic.
The campaign appears to be a home run, but the advertiser is likely to find that it achieves superficial success metrics without delivering any real business value. Over time, the advertiser will uncover that clicks don’t translate to on-site engagement. Bounce rates will be high, time on site will be low, and very few leads will materialize. What went wrong?
It turns out the advertiser bought a scrambled eggs campaign. The campaign is a mixture of two pools of inventory:
There are some impressions that deliver clicks, and other impressions that deliver viewability, but almost no impressions that deliver both. The challenge for marketers is that these two inventory pools are very hard to separate. There aren’t two tactics called “Fraud” and “Human.” The inventory is comingled within a single campaign, and the advertiser can only measure the campaign’s blended performance. The eggs are scrambled.
Rather than being fooled by the campaign’s average performance metrics, the advertiser needs to understand the characteristics of the ads that are clicked, and this required unscrambling the eggs.
The classic approach to combating ad fraud is to identify fraud-resistant metrics. Clicks are notoriously prone to fraud — bots are good at clicking on ads. Measuring a campaign’s success on post-click activities like time on site or lead form submissions can significantly improve a campaign’s resilience against ad fraud. Unfortunately for advertisers, fraudsters are good at what they do, and bots are quickly becoming more adept at exhibiting human-like behavior. Fraud becomes a game of escalations, and advertisers must constantly adjust success metrics to stay one step ahead of the fraud machine.
A more forward-thinking approach to managing fraud is to stop measuring campaign averages and start tracking impression-level performance. By recording a log of each campaign impression, a sophisticated data management platform allows advertisers to investigate just the ads that are clicked. While a campaign’s aggregate performance metrics might look strong, a de-averaged view of click performance might look something like this:
Regardless of the sophistication of the bot traffic in faking human-like post-click activity, a de-averaged view of campaign performance will make it immediately obvious to the advertiser that it has bought a scrambled eggs campaign.
Fraud is likely to be a reality of the advertising ecosystem for the next several years, and it is a total waste of a brand’s ad dollars and creative energy. The goal for advertisers is to manage against fraud with the least possible effort, leaving maximum resources to develop campaign messages and test new media channels. By measuring de-averaged campaign performance, advertisers can quickly identify fraud at the source and avoid the whack-a-mole paranoia of escalating fraud sophistication.
Transaction syncing is a simple way for advertisers to exchange custom data with ad tech vendors. Because the mechanics of transaction syncing are flexible, marketers can unlock lots of use cases to solve their brand’s specific marketing problems.
A standard pixel provides only very basic information to an advertiser. Here’s an example of a simple pixel that a retargeting company called Chango places on the Fresh Direct homepage:
https://www.chango.com/c/1428883653753/
When I visit Fresh Direct’s website, this pixel loads, and Chango records the timestamp of my visit. All Chango knows is that I’ve visited freshdirect.com, nothing else.
But when I make a purchase on Fresh Direct, Chango does something much more interesting. On the checkout page, I see a graphic that confirms my order, including an order number I can use in case I ever need to call customer support. I also see the following Chango pixel:
This pixel is a lot more interesting, mostly because of the snippet highlighted in yellow, which sends my order ID to Chango. This “transaction sync” gives Fresh Direct and Chango a common identifier, unlocking information flow between the two companies. Fresh Direct can now send Chango information about my order — the items in my cart, the price of each, and even any issues fulfilling my order. This information flow enables much more precise advertising.
Like most ad tech building blocks, transaction syncing has applications for both targeting and attribution.
One of the most important choices an advertiser can make is what message to present to a customer. Transaction syncing unlocks smarter creative selection.
FreshDirect’s standard ad creative is a message designed to attract new customers with a 15% discount on their first order. With the knowledge that I’m already a customer, Chango can make an informed decision to deliver a retention message instead of the standard acquisition creative. A second and more refined decision is which specific retention message will resonate best, and this is where transaction syncing can be valuable.
Did I buy baby food in my last order? Produce? Pantry items? This can inform a personalized creative choice, driving up response rates and improving campaign effectiveness.
Transaction syncing also unlocks more precise media attribution. By sharing information about the contents of my order, Fresh Direct can refine its advertising techniques in order to reach consumers with specific buying patterns. At the simplest level, Fresh Direct can assign greater value to campaign tactics that drive high value orders. But Fresh Direct can also go deeper — designing and optimizing campaign tactics that drive specific types of purchases. Need to steer a campaign toward consumers who buy high margin private label products? Transaction syncing can help.
The trick (and the fun) with transaction syncing is that the applications are unique to each brand. Brands in the retail, insurance, and mobile phone spaces have very different marketing needs, but they can all benefit from clever uses of transaction syncing. A few examples:
The use cases are just beginning to emerge. It’s a fun time to be a digital marketer.
At last week’s F8 conference, LiveRail trumpeted the benefits of its unified ad auction, but DSPs will carry a heavy economic burden
Traditional publisher ad servers monetize inventory through a waterfall. Direct sold campaigns get first access to an impression. If a direct sold campaign does not fill the impression, the publisher’s ad server then gives access to a remnant demand source like an ad network or an RTB bidder. This process continues until the ad is monetized. The key characteristic of a waterfall is that demand is checked sequentially — priority level 3 is only notified of the ad’s availability after priority level 2 passes on the ad.
By contrast, a unified ad auction seeks demand from all potential buyers simultaneously. Every demand source participates in every ad impression, and the publisher’s ad server makes a yield-optimizing choice of which demand source should be awarded the impression.
Unified auctions drive better monetization than waterfalls because they consider the full range of potential demand sources, potentially awarding an impression to a high priced RTB bidder over a more modestly priced direct sold campaign. Publishers benefit from improved yield (including some very non-obvious optimizations), and advertisers benefit from improved inventory access.
The unified auction feels like a win-win, but DSPs bear an unexpected burden that could meaningfully damage their economics. The basic financial math of a DSP business looks something like this:
A typical DSP charges advertisers a percentage of the media dollars that flow through its platform. An advertiser that spends $10M per year might pay its DSP $1M. At the most basic level, DSPs make more top line revenue by growing advertiser budgets.
DSP expenses, however, have nothing to do with advertiser budgets. After covering overhead like people and facilities, DSP costs scale with the number of bids the platform must process. The core technology of a DSP becomes more expensive to operate as the volume of incoming bid requests grows. Every lost auction is pure cost to a DSP, driving down its profitability.
The trouble with unified auctions is that they drive up bid requests without bringing more revenue into the system. The value proposition of a unified auction hinges on increased bid density — more demand sources participate in every impression. But the byproduct of increased bid density is decreased win rates. Only one demand source can win an impression, so as more bidders participate, more of them lose. A spike in lost auctions is an economic crisis for a DSP.
DSPs are already struggling to prove their economic model in the face of commoditized technology and market saturation. Facebook’s LiveRail is not the first supply side platform to offer a unified auction to publishers, but it is the first that could capture a meaningful inventory footprint. If Facebook is successful in driving market adoption of unified auctions, pure play DSPs may have an even tougher road to profitability.
In a recent post, I highlighted LiveRamp’s role as a hub in the highly fragmented ad tech ecosystem. Specifically, LiveRamp operates an ID syncing network that enables ad tech providers to exchange data. In this post, I’ll outline how this ID syncing network functions and where you can see LiveRamp execute syncs on the web.
LiveRamp operates an ID syncing network that maintains a centralized mapping of cookie IDs and device IDs for each real-world consumer. Each ad tech company that participates in LiveRamp’s ID syncing network shares its user IDs with LiveRamp. With a complete mapping of IDs for each consumer, LiveRamp can facilitate data transfers between any two network participants.
To illustrate, here is the process that allows LiveRamp to collect my user ID from Adometry:
When you load http://log.dmtry.com/liveramp in your browser, you’ll be redirected to a LiveRamp URL that contains your Adometry user ID.
More generally, every participant in the LiveRamp network creates a URL that LiveRamp can call to request a user ID. This URL is configured to redirect to a LiveRamp URL that takes the following form:
Partner ID is populated by a code that is unique to each ad tech company, and User ID is populated by the code that the ad tech company assigns to your browser.
Click/tap any of the logos below to retrieve your user ID:
Behind the scenes of your daily browsing of the web, LiveRamp will periodically load these URLs, maintaining a constantly updated map of your user IDs.
Want to see the whole process in the wild? Head over to Banana Republic’s website and log in. Banana Republic appears to have an agreement in place with LiveRamp, allowing LiveRamp to use Banana’s login page to perform an ID sync with multiple network partners. All Banana Republic has to do is load the following URL:
That URL triggers a process that selectively performs ID syncs with partners in LiveRamp’s network. Install a debugging tool like Ghostery or Firebug, and you’ll be able to see all the ID syncs that are performed in the background. Depending on the freshness of your LiveRamp profile, you might see just a few syncs execute, or you might see dozens. LiveRamp has lots of similar partnerships with other sites, enabling it to constantly perform ID syncs and maintain a fresh mapping of every consumer’s IDs.
Acxiom recently spent a hefty $310M to buy LiveRamp, a company best known for its offline-to-online “data onboarding” service. But the real value of LiveRamp is its ubiquitous ID syncing network. LiveRamp has positioned itself as the switchboard of digital advertising — the router of marketing data across a hyper-fragmented ad tech ecosystem.
Back in 2010, Facebook got a pretty serious slap on the wrist after inadvertently sharing personally identifiable information with third party data companies. In particular, a company called RapLeaf found a way to scrape referring URLs to match a cookie ID to a specific Facebook profile. Using this match, RapLeaf could target ads based on consumers’ Facebook profiles. Marketers loved it. Privacy advocates did not.
On the heels of that snafu, RapLeaf launched a secondary brand, LiveRamp, which would eventually become the face of the company. The LiveRamp service matched marketers’ offline CRM files with online cookie pools, enabling brands like Ford to deliver ads to drivers whose F150 leases were about to end. To make this work, LiveRamp built data transfer relationships with every major marketing platform — focusing initially on publishers and DSPs, and eventually expanding to a growing list of players in the data management, content marketing, and attribution spaces.
LiveRamp’s bet that data onboarding would be a valuable business proved to be correct, but the far bigger source of value would turn out to be a byproduct of the onboarding service — an ID syncing network that positions LiveRamp at the center of the highly fragmented ad tech ecosystem.
For both technical reasons (every ad tech company operates on a different cookiespace) and privacy reasons (anonymous IDs should be rotated every few months), any real world person will have dozens, sometimes hundreds, of different online identifiers. Yahoo might call me user 1234, and Google might call me user 6789. Knowing that Yahoo’s user 1234 and Google’s user 6789 are the same real world person unlocks lots of interesting marketing use cases. With this match, marketers can:
In the context of a hyper-fragmented ecosystem, marketers often work with many different ad tech partners, and ID syncing enables these partners to operate as an integrated system.
Here’s where things get a little gnarly. Imagine that a brand works with 20 different ad tech companies. In order to create seamless operations, each company must know 19 corresponding IDs, and they must establish these 19 matches for every real world consumer.
By way of example, here is a URL that BlueKai can call to request TubeMogul’s user ID:
http://rtd.tubemogul.com/upi/pid/w8wqx7f2?redir=http%3A%2F%2Ftags.bluekai.com%2Fsite%2F4413%3Fid%3D%24%7BUSER_ID%7D
When you load that link in your browser, you’ll be redirected to a URL that looks something like this:
http://tags.bluekai.com/site/4413?id=6092720069689058834
This URL notifies BlueKai that TubeMogul identifies me as user 6092720069689058834. With the first half of the sync in place, TubeMogul now needs to make a call back to BlueKai to request my BlueKai user ID. Similar two-way syncs need to happen for every pair of ad tech partners. Now do that for hundreds of millions of consumers. And keep the matches updated as each partner rotates its IDs. It’s a big job.
An alternative approach is to designate a single company to be the hub of all ID syncs. The hub can collect IDs from each participating ad tech partner and then form mutual ID syncs as needed. Think of this as a match maker who knows the full universe of eligible singles and can then introduce couples. LiveRamp has established itself as this match maker, and its ID syncing process looks something like this:
In hindsight, it’s clear that this match maker role is a coveted position in the ad tech ecosystem — a position that would eventually warrant an 11x revenue multiple. But it wasn’t so obvious when RapLeaf pressed the reset button in 2011 and launched LiveRamp. The bet to pursue a data onboarding service has proven to be a smart move. It enabled RapLeaf to move past its consumer privacy issues and build a new reputation as a connector of the online and offline marketing worlds. It unlocked new revenue streams, allowing LiveRamp to tap into CRM budgets, scale its business, and largely self-fund its growth. But through the process, LiveRamp also backed into becoming the de facto hub of ID syncing, the glue that holds together the ad tech ecosystem. This is the real value of LiveRamp, and it’s the reason Axciom made such a big bet on the company.
