Why 2013 will be the Year of Premium Guaranteed

guaranteed_stampFairfax Cone, the founder of Foote, Cone, and Belding once famously remarked that the problem with the agency business was that “the inventory goes down the elevator at night.” He was talking about the people themselves. For digital media agencies, who rely on 23 year-old media planners to work long hours grinding on Excel spreadsheets and managing vendors, that might be a problem.

For all of the hype and investment behind real-time bidding, the fact is that “programmatically bought” media will only account for roughly $2B of the anticipated $15B in digital display spending this year, or a little over 13% depending on who you believe. Even if that number were to double, the lion’s share of digital display still happens the old fashioned way: Publishers hand-sell premium guaranteed inventory to agencies.

Kawaja map companies, founded to apply data and technology to the problem of audience buying, have gotten the most ink, most venture funding, and most share of voice over the past 5 years. The amount of innovation and real technology that has been brought to bear on audience targeting and optimization has been huge, and highly valuable. Today, platforms like The Rubicon Project process over a trillion ad bids and over 100B ad transactions every month. Companies like AppNexus have paid down technology pipes that bring the power of extensible platform technology to large and small digital advertising businesses alike. And inventory? There are over 5 trillion impressions a month ready to be purchased, most of which sit in exchanges powered by just such technology.

All of that bring said, the market continues to put the majority of its money into premium guaranteed. They are, in effect, saying, “I know I can buy the ‘sports lovers’ segment through my DSP, and I will—but what I really want is to reach sports lovers where they love to go: ESPN.com.”

So, while RTB and related ad technologies will grow, they will not grow fast enough to support all of the many companies in the ecosystem that need a slice of 2013’s $2B RTB pie to survive. NextMark founder and CEO, Joseph Pych, whose company focuses on guaranteed reserved software, has been calling this the great “Sutton Pivot,” referring to the famous remark of criminal Willie Sutton , who robbed banks “because that’s where the money is.”

In order to better inderstand why this is happening, I have identified several problems with RTB that are driving companies focused on RTB to need to pivot:

  • There’s a Natural Cap on RTB Growth: I think today’s RTB technology is the best place to buy remnant inventory. As long as there are low-value impressions to buy, and as long as publishers continue to festoon their pages with IAB-standard banners, there will need to be a technology solution to navigate through the sea of available inventory, and apply data (and algorithms) to choose the right combination of inventory and creative to reach defined performance goals. While the impressions may grow, the real cap on RTB growth will be the most important KPI of them all: Share of time spent. Marketers spend money where people spend their time, whether it’s on television, Twitter, radio, or Facebook. When people spend less time on the inventory represented within exchanges, then the growth trend will reverse itself. (Already we are seeing a significant shift in budget allocation from “traditional” exchanges to FBX).
  • The Pool is Still Dirty: It goes without saying, but the biggest problem in terms of RTB growth is brand safety. The type of inventory available in exchanges that sells, on average, for less than a dollar is probably worth just that. When you buy an $850 suit from Joseph A. Bank—and receive two free suits, two shirts, and two ties—you feel good. But it doesn’t take much figuring to understand that you just bought 3 $200 suits, two $75 shirts, and two $50 ties. Can you get $15 CPM premium homepage inventory for $3 CPM? No…and you never will be able to, but that type of inventory is just what the world’s largest marketers want. They would also like URL-level transparency into where their ads appeared, a limit on the number of ads on a page (share of voice), and some assurance that their ads are being seen (viewability).  Inventory will continually grow, but good, premium inventory will grow more slowly.
  • It’s Not about Private Exchanges: Look, there’s nothing wrong with giving certain advertisers a “first look” at your premium inventory if you are a publisher.  Auto sites have been pursuing this concept forever. Big auto sites guarantee Ford, for example, all of the banner inventory associated with searches for Ford-branded vehicles over the course of a year. This ensures the marketer gets to his prospect when deep in the consideration set. Big auto sites may create programmatic functionality around enabling this type of transaction, but private exchange functionality isn’t going to be the savior of RTB, just necessary functionality. Big marketers want control of share of voice, placement, and flexibility in rates and programs that extend beyond the functionality currently available in DSPs. As long as they are spending the money, they will get—and demand—service.

What does all of this mean? RTB-enables ad technology is not going away, but some of the companies that require real time bidding to grow at breakneck speed to survive are going to pivot towards the money, developing technologies that enable more efficient buying of premium guaranteed inventory—where the other 85% of media budgets happen.  I predict that 2013 will be the year of “programmatic guaranteed,” which will be the label that people apply to any technology that enables agencies and marketers to access reserved inventory more efficiently. If we can apply some of the amazing technology we have built to making buying (and selling) great inventory easier, more efficient, and better performing, it will be an amazing year.

[This post originally appeared in ClickZ on 1/22/12]
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Managing Data in [real] Real-Time

A Conversation with Srini Srinivasan, Founder and VP Operations of Aerospike

Even today, the notion that a consumer can go to a website, be identified, trigger a live auction involving as many as a dozen or more advertisers, and be served an ad in real-time, seems like a marvel of technology. It takes a tremendous amount of hardware and, even more than ever, a tremendous amount of lightning-fast software to accomplish. What has been driving the trend towards ever faster computing within ad technology are new no-SQL database technologies, specifically designed to read and write data in millisecond frameworks. We talked with one of the creators of this evolving type of database software, who has been quietly powering companies including BlueKai, AppNexus, and [x+1], and got his perspective on data science, what “real time” really means, and “the cloud.”

Data is growing exponentially, and becoming easier and cheaper to store and access. Does more data always equal more results for marketers?

Srini Srinivasan: Big Data is data that cannot be managed by traditional relational databases because it is unstructured or semi-structured and the most important big data is hot data, data you can act on it in real-time. It’s not so much the size of the data but rather the rate at which data is changing. It is about the ability to adapt applications to react to the fast changes in large amounts of data that are happening constantly on the Web.

Let’s consider a consumer who is visiting a Web page, or buying something online, or viewing an ad. The data associated with each of these interactions is small. However, when these interactions are multiplied by the millions of people online at any moment, they generate a huge amount of data. AppNexus, which uses our Aerospike NoSQL database to power its real-time bidding platform, handles more than 30 billion transactions per day.

The other aspect is that real-time online consumer data has a very short half life. It is extremely valuable the moment it arrives, but as the consumer continues to move around the Web it quickly loses relevance. In short, if you can’t act on it in real-time, it’s not that useful. That is why our customers demand a database that handles reads and writes in milliseconds with sub-millisecond latency.

Let me give you a couple examples. [x+1] uses our database to analyze thousands of attributes and return a response within 4 milliseconds. LiveRail uses our database to reliably handle 200,000 transactions per second (TPS) while making data accessible within 5 milliseconds at least 99% of the time.

This leads into the last dimension, which is predictable high performance. Because so much of consumer-driven big data loses value almost immediately, downtime is not an option. Moreover, a 5-millisecond response has to be consistent, whether a marketing platform is processing 50,000 TPS or 300,000 TPS.

What are some of the meta-trends you see that is making data management easier (standardization around a platform such as Hadoop? The emergence of No-SQL systems? The accessibility of cloud-hosting?

SS: Today, with consumers engaged more with Web applications, social media sites like Facebook, and mobile devices, marketers need to do a tremendous amount of analysis against data to make sure that they are drawing the right conclusions. They need data management platforms that can absorb terabytes of data—structured and unstructured—while enabling more flexible queries on flexible schema.

In my opinion, classical data systems have completely failed to meet these needs over the last 10 years. That is why we are seeing an explosion of new products, so called NoSQL databases that work on individual use cases. Going forward, I think we’ll see a consolidation as databases and other data management platforms extend their capabilities to handle multiple use cases. There will still be batch analysis platforms like Hadoop, real-time transactional systems, and some databases like Aerospike that combine the two. Additionally, there will be a role for a few special-purpose platforms, just like in the old days we had OLTP, OLAP and special purpose platforms like IBM IMS. However, you won’t see 10 different types of systems trying to solve different pieces of the puzzle.

The fact is we are beginning to see the creation of a whole new market to address the question, “How do you produce insights and do so at scale?”

One of the biggest challenges for marketers has been that useful data is often in silos and not shared. What are some of the new techniques and technologies making data collection and integration easier and more accessible for today’s marketer?

SS: Many of our customers are in the ad-tech space, which is generally at the front-end of technology trends adopted by the broader marketing sector. We are just beginning to see a new trend among some of these customers, who are using Aerospike as a streaming database. They are eliminating the ETL (extract, transformation, load) process. By removing the multi-stage processing pipeline, these companies are making big data usable, faster than ever.

The ability to achieve real-time speed at Web-scale, is making it possible to rethink how companies approach processing their data. Traditional relational databases haven’t provided this speed at scale. However, new technology developments in clustering and SSD optimization are enabling much greater amounts of data to be stored in a cluster—and for that data to be processed in milliseconds.

This is just one new way that real-time is changing how marketers capitalize on their big data. I think we’ll continue to see other innovative new approaches that we wouldn’t have imagined just a couple years ago.

Storing lots of data and making it accessible quickly requires lots of expensive hardware and database software. The trend has been rapidly shifting from legacy models (hosted Oracle or Neteeza solutions) to cloud-based hosting through Rackspace or Amazon, among others. Open source database software solutions such as Hadoop are also shifting the paradigm. Where does this end up? What are the advantages of cloud vs. hosted solutions? How should companies be thinking about storing their marketing-specific data for the next 5-10 years?

SS: A couple years ago nearly everyone was looking at the cloud. While some applications are well suited for the cloud, those built around real-time responses require bare metal performance. Fundamentally it depends on the SLA of the applications. If you need response times in the milliseconds, you can’t afford the cloud’s lack of predictable performance. The demand for efficient scalability is also driving more people back from the cloud. We’re even seeing this with implementations of Hadoop, which is used for batch processing. If a company can run a 100-server cluster locally versus having to depend on a 1,000-server cluster in the cloud, the local 100-server option will win out because efficiency and predictability matter in performance.

What are top companies doing right now to leverage disparate data sets? Are the hardware and software technology available today adequate to build global, integrated marketing “stacks?”

SS: Many of the companies we work with today have two, four, sometimes more data centers in order to get as close to their customers as possible. Ad-tech companies in particular tell us they have about 100 milliseconds—just one-tenth of a second—to receive data, analyze it, and deliver a response. Shortening the physical distance to the customer helps to minimize the time that information travels the network.

Many of these firms take advantage of cross data center replication to include partial or full copies of their data at each location. This gives marketers more information on which to make decisions. It also addresses the demand for their systems to deliver 100% uptime. Our live link approach to replication makes it possible to copy data from one data center to another with no impact on performance and ensures high availability.

Over the last year, we’ve have had customers experience a power failure at one data center due to severe weather, but with one or more data centers available to immediately pick up the workload, they were able to continue business as usual. It comes back to the earlier discussion. Data has the highest value when marketers can act on it in real-time, 100% of the time.

This interview, among many others, appears in EConsultancy’s recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger.

This post also appeared on the iMediaConnection Blog 1/11/12.

Matching Offline Data for Online Targeting

A Conversation with Live Ramp’s CEO Auren Hoffman

When all marketers have universal access to an entire world of third party online segmentation data, advertisers are increasingly turning offline for an edge. Leveraging established and deep CRM data, marketers are matching their customer databases to online cookies for targeting and retargeting, and going beyond basic demographic data by bringing multiple data sets into the digital marketing mix. I recently interviewed Live Ramp’s Auren Hoffman to learn more about how traditional databases are getting matched to online cookies, and made available for targeting.

Offline data versus online data. You hear first-party data talked about like it’s the gold standard. Just how much more valuable is a company’s first party data?

Auren Hoffman (AH): First, some clarification: Offline does not equal first-party data; nor is online equivalent to third party data.

The gold standard is not first-party data. It’s the rich knowledge (and capacity for segmentation) that lies in a company’s CRM database, typically tied to a name/address or an email address (including purchase history, direct mail, email campaigns, and loyalty). That knowledge, which is largely (but not exclusively) first-party data, exists almost exclusively offline.

Oftentimes, this specific customer knowledge – first-party data belonging to a brand or business – is augmented by complementary third-party data (for example, zip code-based psychographic typing). Also added into the mix is certain online data (largely transactional, where the customer is known) that has been taken offline (into the CRM database).

This deep customer knowledge has – before now – really only been usable offline (to manage direct marketing, for example). Customer segmentation derived from CRM data is commonly used to target certain audiences with specific messages. That same knowledge has not been – could not be – used to achieve better targeting online through display advertising… until recently.

Companies such as LiveRamp take the knowledge about individual customers from offline CRM databases to form useful and rich customer segmentation that can be “onboarded” – taken online and used for highly-focused display advertising, in a safe and privacy centric way. For example, catalog recipients (from a CRM-driven direct marketing campaign) whom it is known both purchase online and focus on a particular product line in their purchases can be transformed into an online audience with a very focused marketing message. This is what LiveRamp does: translate rich offline data (first- or third-party, or both) into anonymized online segments that can be used to create highly targeted and therefore more effective display advertising. LiveRamp is the only company focused solely on providing data onboarding that can be used to achieve “CRM Retargeting” (using CRM data to enable highly-targeted display advertising).

It should be emphasized that onboarded data is anonymized – that is, unlike CRM data which is frequently used in its individualized form (specific customers tied to an email or postal), onboarded data is aggregated based on customer segments (e.g. a possible segment could be customers who have not purchased from the brand in more than six months) who receive a specific message (e.g. special incentive to return to the brand). So the customer’s privacy is protected, while the customer is still able to receive an offer or message likely to be of specific appeal. With CRM retargeting, brands can target last year’s shoppers with relevant ads about the upcoming holiday season to remind them about your brand’s offer, regardless of if, or when they’ve been to your site.

What kind of offline data should marketers consider bringing online? What offline data do you consider to be the most valuable in terms of audience targeting?

AH: Marketers should consider any data that allows them to create more targeted – and therefore more valuable – segmentation for use in online display advertising; which will vary depending on a brand’s business and messaging strategy. The most valuable such data is that which, when linked with focused messaging, is most likely to achieve resonance with the audience segment. Onboarded data, as noted above, is anonymized; consequently the objective is not to track down and message individual consumers (which would be intrusive), but rather to develop creative messaging to groups of (anonymized) customers (e.g. lapsed customers, or those with particular product or service requirements – for instance, customers with car leases about to expire might well be interested in incentives for a new lease).

Though the most valuable data is likely to be based on transactional history or product/service preferences, it is by no means limited to this. The most valuable data is that determined by the brand to create segmentation – and the accompanying messaging – needed to elicit a positive customer response and in turn ROI.

How should marketers manage their data? Now that data is so cheap to collect, transfer, load, and store the tendency is to make almost every piece of data available for analysis. Where should marketers draw the line? What about recency? Does the cost of keeping certain datasets (transaction events, for example) recent outweigh their potential value?

AH: We’re agnostic on this. (That is, we’re not in the business of managing the data, just bridging the offline/online divide with onboarding expertise.) Each marketer must judge for him or herself the value of data in relation to its potential use for targeted segmentation.
How does it work? Please describe, in layman’s terms where possible, the various methodologies for matching offline data with an online consumer. (cookie matching, key value pair match, etc)

  • A brand (or a brand’s agency) provides LiveRamp with an encoded CRM safely through our secure upload portal.
  • LiveRamp matches your offline data keyed off an email address to an anonymous online audience via cookies with extensive coverage and high accuracy.
  • LiveRamp places the online audience on a brand’s existing DSP or DMP (or we can suggest one of our partner platform’s) & the display campaign runs as normal with a larger, more valuable, and more targeted audience.
  • Your customers see a relevant and timely message from your brand
  • LiveRamp does not buy or sell data. We do not collect any data from a site, our cookies do not contain PII, and we do not pass any site audience information to any third party.

This interview, among many others, appears in EConsultancy’s recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger.

This post also appeared on the iMediaConnection blog on 1/3/12.