Big Data · Data Management Platform · Digital Display · DMP · Real Time Bidding (RTB)

Choosing a Data Management Platform

“Big  Data”  is  all  the  rage  right  now,  and  for a good reason. Storing tons and tons of data has gotten very inexpensive, while the accessibility of that data has increased substantially in parallel. For the modern marketer, that means having access to literally dozens of disparate data sources, each of which cranks out large volumes of data every day. Collecting, understanding, and taking action against those data sets is going to make or break companies from now on. Luckily, an almost endless variety of companies have sprung up to assist agencies and advertisers with the challenge. When it comes to the largest volumes of data, however, there are some highly specific attributes you should consider when selecting a data management platform (DMP).

Collection and Storage: Scale, Cost, and Ownership
First of all, before you can do anything with large amounts of data, you need a place to keep it. That  place  is  increasingly  becoming  “the  cloud”  (i.e.,  someone  else’s  servers),  but  it  can  also  be   your own servers. If you think you have a large amount of data now, you will be surprised at how much it will grow. As devices like the iPad proliferate, changing the way we find content, even more data will be generated. Companies that have data solutions with the proven ability to scale at low costs will be best able to extract real value out of this data. Make sure to understand how your DMP scales and what kinds of hardware they use for storage and retrieval.

Speaking of hardware, be on the lookout for companies that formerly sold hardware (servers) getting into the  data  business  so  they  can  sell  you  more  machines.  When  the  data  is  the  “razor,”   the  servers  necessarily  become  the  “blades.”  You  want  a  data  solution  whose  architecture  enables the easy ingestion of large, new data sets, and one that takes advantage of dynamic cloud provisioning to keep ongoing costs low. Not necessarily a hardware partner.

Additionally, your platform should be able to manage extremely high volumes of data quickly, have an architecture that enables other systems to plug in seamlessly, and whose core functionality enables multi-dimensional analysis of the stored data—at a highly granular level. Your data are going to grow exponentially, so the first rule of data management is making sure that, as your data grows, your ability to query them scales as well. Look for a partner that can deliver on those core attributes, and be wary of partners that have expertise in storing limited data sets.
There are a lot of former ad networks out there with a great deal of experience managing common third party data sets from vendors like Nielsen, IXI, and Datalogix. When it comes to basic audience segmentation, there is a need to manage access to those streams. But, if you are planning on capturing and analyzing data that includes CRM and transactional data, social signals, and other large data sets, you should look for a DMP that has experience working with first party data as well as third party datasets.

The concept of ownership is also becoming increasingly important in the world of audience data. While the source of data will continue to be distributed, make sure that whether you choose a hosted or a self-hosted model, your data ultimately belongs to you. This allows you to control the policies around historical storage and enables you to use the data across multiple channels.

Consolidation and Insights: Welcome to the (Second and Third) Party
Third party data (in this context, available audience segments for online targeting and measurement) is the stuff that the famous Kawaja logo vomit map was born from. Look at the map, and you are looking at over 250 companies dedicated to using third party data to define and target audiences. A growing number of platforms help marketers analyze, purchase, and deploy that data for targeting (BlueKai, Exelate, Legolas being great examples). Other networks (Lotame, Collective, Turn) have leveraged their proprietary data along with their clients to offer audience management tools that combine their data and third party data to optimize campaigns. Still others (PulsePoint’s  Aperture  tool  being  a  great  example)  leverage  all  kinds  of  third party data to measure online audiences, so they can be modeled and targeted against.

The key is not having the most third party data, however. Your DMP should be about marrying highly validated first party data, and matching it against third party data for the purposes of identifying, anonymizing, and matching third party users. DMPs must be able to consolidate and create as whole of a view of your audience as possible. Your DMP solution must be able to enrich the audience information using second and third party data. Second party data is the data associated with audience outside your network (for example, an ad viewed on a publisher site or search engine). While you must choose the right set of third party providers that provide the best data set about your audience, your DMP must be able to increase reach by ensuring that you can collect information about as many relevant users as possible and through lookalike modelling.

First Party Data

  • CRM data, such as user registrations
  • Site-site data, including visitor history
  • Self-declared user data (income, interest in a product)

Second Party Data

  • Ad serving data (clicks, views)
  • Social signals from a hosted solution
  • Keyword search data through an analytics platform or campaign

Third Party Data

  • Audience segments acquired through a data provider

For example, if you are selling cars and you discover that your on-site users who register for a test drive are most closely  matched  with  PRIZM’s  “Country  Squires”  audience,  it  is  not  enough  to  buy   that Nielsen segment. A good DMP enables you to create your own lookalike segment by leveraging that insight—and the tons of data you already have. In other words, the right DMP partner can help you leverage third party data to activate your own (first party) data.

Make sure your provider leads with management of first party data, has experience mining both types of data to produce the types of insights you need for your campaigns, and can get that data quickly.  Data  management  platforms  aren’t  just  about  managing  gigantic  spreadsheets.  They  are   about finding out who your customers are, and building an audience DNA that you can replicate.

Making it Work
At the end of the day, it’s  not  just  about  getting  all  kind  of  nifty  insights  from  the  data.  It’s   valuable to know that your visitors that were exposed to search and display ads converted at a 16% higher rate, or that your customers have an average of two females in the household.  But  it’s   making those insights meaningful that really matters.
So, what to look for in a data management platform in terms of actionability? For the large agency or advertiser, the basic functionality has to be creating an audience segment. In other words, when the blend of data in the platform reveals that showing five display ads and two SEM ads to a household with two women in it creates sales, the platform should be able to seamlessly produce that segment and prepare it for ingestion into a DSP or advertising platform. That means having an extensible architecture that enables the platform to integrate easily with other systems.

Moreover, your DSP should enable you to do a wide range of experimentation with your insights. Marketers often wonder what levers they should pull to create specific results (i.e., if I change my display creative, and increase the frequency cap to X for a given audience segment, how much will conversions increase)? Great DMPs can help built those attribution scenarios, and help marketers visualize results. Deploying specific optimizations in a test environment first means less waste, and more performance. Optimizing in the cloud first is going to become the new standard in marketing.

Final Thoughts
There are a lot of great data management companies out there, some better suited than others when it comes to specific needs. If you are in the market for one, and you have a lot of first party data to manage, following these three rules will lead to success:

  • Go beyond third party data by choosing a platform that enables you to develop deep audience profiles that leverage first and third party data insights. With ubiquitous access to third party data, using your proprietary data stream for differentiation is key.
  • Choose a platform  that  makes  acting  on  the  data  easy  and  effective.  “Shiny,  sexy”  reports  are   great, but the right DMP should help you take the beautifully presented insights in your UI, and making them work for you.
  • Make sure your platform has an applications layer. DMPs must not only provide the ability to profile your segments, but also assist you with experimentation and attribution–and provide you with ability to easily perform complicated analyses (Churn, and Closed Loop being two great  examples).  If  your  platform  can’t  make  the  data  dance,  find  another  partner.

Available DMPs, by Type
There are a wide variety of DMPs out there to choose from, depending on your need. Since the space is relatively new, it helps to think about them in terms of their legacy business model:

  • Third Party Data Exchanges / Platforms: Among the most popular DMPs are data aggregators like BlueKai and Exelate, who have made third  party  data  accessible  from  a  single  user  interface.  BlueKai’s  exchange approach enables data buyers  to  bid  for  cookies  (or  “stamps”)  in  a  real-time environment, and offers a wide variety of providers to choose from. Exelate also enables access to multiple third party sources, albeit not in a bidded model. Lotame offers  a  platform  called  “Crowd  Control”  which  was  evolved  from  social   data, but now enables management of a broader range of third party data sets.
  • Legacy Networks: Certain networks with experience in audience segmentation have evolved to provide data management capabilities, including Collective, Audience Science, and Turn. Collective is actively acquiring assets (such as creative optimization provider, Tumri14) to  broaden  its  “technology   stack”  in  order  to  offer  a  complete  digital  media  solution  for  demand  side customers. Turn is, in fact, a fully featured demand-side platform with advanced data management capabilities, albeit lacking  the  backend  chops  to  aggregate  and  handle  “Big  Data”  solutions  (although  that  may   rapidly change, considering their deep engagement with Experian). Audience Science boasts the most advanced native categorical audience segmentation capabilities, having created dozens of specific, readily accessible audience segments, and continues to migrate its core capabilities from media sales to data management.
  • Pure Play DMPs: Demdex (Adobe), Red Aril, Krux, and nPario are all pure-play data management platforms, created from the ground up to ingest, aggregate, and analyze large data sets. Unlike legacy networks, or DMPs that specialize in aggregating third party data sets, these DMPs provide three core offerings: a core platform for storage and retrieval of data; analytics technology for getting insights from the data with a reporting interface; and applications, that enable marketers to take action against that data, such as audience segment creation, or lookalike modeling functionality. Marketers with extremely large sets of structured and unstructured data that go beyond ad serving and audience data (and may include CRM and transactional data, as an example), will want to work with a pure-play DMP

This post is an excerpt of Best Practices in Digital Display Advertising: How to make a complex ecosystem work efficiently for your organization All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without prior permission in writing from the publisher.

Copyright © Econsultancy.com Ltd 2012

Advertising Agencies · AppNexus · Big Data · Big Media · Data Management Platform · Demand Side Platform (DSP) · Digital Display · Digital Media Ecosystem · DMP · Marketing · Media Buying · Media Planning · Online Media · Platforms · Real Time Bidding (RTB) · Remnant Monetization · Uncategorized

Best Practices in Digital Display Media (Interview)

Digital display is remarkably complex. Standard campaigns can involve multiple vendors of different technologies and types of media.

Today, eConsultancy launches Best Practices in Digital Display Advertising, a comprehensive look at how to efficiently manage online advertising. We asked the author, Chris O’Hara, about the report and work that went into it.

Why did you write Best Practices in Digital Display Media?

In my last job, a good part of my assignment was traveling around the country visiting with about 500 regional advertising agencies and marketers, large and small, over three years. I was selling ad technology. Most advertisers seemed extremely engaged and interested to find out about new tools and technology that could help them bring efficiency to their business and, more importantly, results to their clients. The problem was that they didn’t have time to evaluate the 250+ vendors in the space, and certainly didn’t have the resources (financial or time) to really evaluate their options and get a sense of what’s working and what isn’t.

First and foremost, I wanted the report to be a good, comprehensive primer to what’s out there for digital marketers including digital ad agencies. That way, someone looking at engaging with data vendors, say, could get an idea of whether they needed one big relationship (with an aggregator), no data relationships, or needed very specific deals with key data providers. The guide can help set the basis for those evaluations. Marketers have been basically forced to license their own “technology stack” to be proficient at buying banner ads. I hope the Guide will be a map through that process.

What was the methodology you used to put it together?

I essentially looked at the digital display ecosystem through the lens of a marketer trying to take a campaign from initial concept through to billing, and making sure I covered the keys parts of the workflow chain. What technologies do you employ to find the right media, to buy it, and ultimately to measure it? Are all of these technologies leading to the promised land of efficiency and performance? Will they eventually? I used those questions as the basis of my approach, and leveraged the many vendor relationships and available data to try and answer some of those questions.

What’s the biggest thing to take away from the report?

I think the one thing that really runs through the entire report is the importance of data. I think the World Economic Forum originally said the “data is the new oil” [actually, the earliest citation we can find is from Michael Palmer in 2006, quoting Clive Humby] and many others have since parroted that sentiment. If you think about the 250-odd technology companies that populate the “ecosystem,” most are part of the trend towards audience buying, which is another way of saying “data-driven marketing.” Data runs through everything the digital marketer does, from research through to performance reporting and attribution. In a sense, the Guide is about the various technologies and methodologies for getting a grip on marketing data—and leveraging it to maximum effect.

There’s an explosion of three letter acronyms these days (DSP, DMP, SSP, AMP, etc) that marketers are still trying to sort out. Do we need all of them? Is there another one around the corner?

I am not really sure what the next big acronym will be, but you can be certain there will be several more categories to come, as technology changes (along with many updates to Guides such as these). That being said, I think the meta-trends you will see involve a certain “compression” by both ends of the spectrum, where the demand side and supply side players look to build more of their own data-driven capabilities. Publishers obviously want to use more of their own data to layer targeting on top of site traffic and get incremental CPM lift on every marketable impression. By the same token, advertisers are finding the costs of storing data remarkably cheap, and want to leverage that data for targeting, so they are building their own capabilities to do that. That means the whole space thrives on disintermediation. Whereas before, the tech companies were able to eat away at the margins, you will see the real players in the space build, license, or buy technology that puts them back in the driver’s seat. TheBest Practices in Digital Display Advertising Guide is kind of the “program” for this interesting game.

To learn more about the Best Practices in Digital Display Advertising Guidedownload the report here.

Big Data · Data Management Platform · Digital Display · DMP · Sales

The Five Things to Expect in a DMP

Getting back control over their inventory is giving publishers a lot to think about.

“We want to make sure that we’re controlling what happens with data . . . we want to make sure we control pricing. Control’s a very important message. We don’t want there to be a cottage industry built on our backs” – Nick Johnson, SVP, NBC Universal

What do publishers really want? It’s simple, really: Power and control. In order to survive the ad technology era, publishers need the power to monetize their audiences without relying on third parties, and complete control over how they sell their inventory. In this era of “Big Data,” there is a fire hose stream of tremendously valuable information for publishers to take advantage of, such as keyword-based search data, attitudinal survey data, customer declared data from forms, page-level semantic data, and all the 3rd party audience data you can shake a stick at.

All of this data (cheap to produce, and ever-cheaper to store) has given rise to companies who can help publishers bring that data together, make sense of it, and use it to their advantage. Currently, ad technology companies have been using the era of data to their advantage, utilizing it to create vertical ad networks, ad exchanges, data exchanges, DSPs, and a variety of other smart-sounding acronyms that ultimately purport to help publishers monetize their audiences, but end up monetizing themselves.

Rather than power the ad tech ecosystem, what if data could actually help publishers take back their audiences? If “data is the new gold” as the pundits are saying, then smart publishers should mine it to increase margins, and take control of their audiences back from networks and exchanges. Here are the five things a good data management platform should enable them to do:

  • Unlock the Value of 1st Party Data: Publishers collect a ton of great data, but a lot of them (and a LOT of big publishers) don’t leverage it like they should. Consider this recent stat: according to a recent MediaPost article, news sites only use in-site audience targeting on 47% of their impressions, as opposed to almost 70% for Yahoo News.  By leveraging site-side behavioral data, combined with CRM data and other sources, it is possible to layer targeting on almost every impression a publisher has. Why serve a “blind” run-of-site (ROS) ad, when you can charge a premium CPM for audience-targeted inventory?
  • Decrease Reliance on 3rd Parties: The real reason to leverage a DMP is to get your organization off the 3rd party crack pipe. Yes, the networks and SSPs are a great “plug and play” solution (and can help monetize some “undiscoverable” impressions), but why are publishers selling raw inventory at $0.35 and letting the people with the data resell those impressions for $3.50? It’s time to turn away those monthly checks, and start writing some to data management companies that can help you layer your own data on top of your impressions, and charge (and keep) the $3.50 yourself. Today’s solutions don’t have to rely on pre-packaged 3rd party segments to work, either, meaning you can really reduce your data costs. With the right data infrastructure, and today’s smart algorithm-derived models, a small amount of seed data can be utilized to create discrete, marketable audience segments that publishers can own, rather than license.
  • Generate Unique Audience Insights: Every publisher reports on clicks and impressions, but what advertisers are hungry for (especially brand advertisers) are audience details. What segments are most likely to engage with certain ad content? Which segments convert after seeing the least amount of impressions? More importantly, how do people feel about an ad campaign, and who are they exactly? Data management technology is able to meld audience and campaign performance data to provide unique insights in near real-time, without having to write complicated database queries and wait long times for results. Additionally, with the cost of storing data getting lower all the time, “lookback windows” are increasing, enabling publishers to give credit for conversion path activity going back several months. Before publishers embraced data management, all the insights were in the hands of the agency, who leveraged the data to their own advantage. Now, publishers can start to leverage truly powerful data points to create differentiated insights for clients directly, and provide consultative services with them, or offer them as a value-added benefit.
  • Create New Sales Channels: Before publisher-side data management, when a publisher ran out of the Travel section impressions, he had to turn away the next airline or hotel advertiser, or offer them cheap ROS inventory. Now, data management technology can enable sales and ops personnel to mine their audience in real time and find “travel intenders” across their property—and extend that type of audience through lookalike modeling, ensuring additional audience reach. By enabling publishers to build custom audience segments for marketers on the fly, a DMP solution ensures that no RFP will go unanswered, and ROS inventory gets monetized at premium prices. 
  • Create Efficiency: How many account managers does it take to generate your weekly ad activity reports? How much highly paid account management time are publishers burning by manually putting together performance reports? Why not provide an application that advertisers can log into, set report parameters, and export reports into a friendly format? Or, better yet, a system that pre-populates frequent reports into a user interface, and pushes them out to clients via an e-mail link? You would think this technology was ubiquitous today, but you would be wrong. Ninety-nine percent of publishers still do this the hard (expensive) way, and they don’t have to anymore.

It’s time for publishers to dig into their data, and start mining it like the valuable commodity it is. Data used to be the handcuffs which kept publishers chained to the ad technology ecosystem, where they grew and hosted a cottage industry of ad tech remoras. The future that is being written now is one of publishers’ leveraging ad technologies to take back control, so they can understand and manage their own data and have the freedom to sell their inventory for what it is truly worth.

That’s a future worth fighting for.

[This post originally appeared in ClickZ on 2/29/12]

Big Data · Data Management Platform · Digital Display · DMP · Media Planning · Uncategorized

Signal to Noise

What Data Should Inform Media Investment Decisions?

The other day, I was updating my Spotify app on my Android device. When it finally loaded, I was asked to log in again. I immediately loaded up a new playlist that I had been building—a real deep dive into the 1980s hardcore music I loved back in my early youth. I’m not sure if you are familiar with the type of music that was happening on New York City’s lower east side between 1977 and 1986, but it was some pretty raw stuff…bands like the Beastie Boys (before they went rap), False Prophets, the Dead Boys, Minor Threat, the Bad Brains, etc. They had some very aggressive songs, with the lyrics and titles to match.

Well, I put my headphones in, and started walking from my office on 6th Avenue and 36th street across to Penn Station to catch the 6:30 train home to Long Island…all the while broadcasting every single song I was listening to on Facebook. Among the least offensive tunes that showed up within my Facebook stream was a Dead Kennedys song with the F-word featured prominently in the song title.  A classic, to be sure, but probably not something all of my wife’s friends wanted to know about.

As you can imagine, my wife (online at the time), was frantically e-mailing me, trying to tell me to stop the offensive social media madness that was seemingly putting a lie to my carefully cultivated, clean, preppy, suburban image.

So why, as a digital marketer, would you care about my Spotify Facebook horror story?

Because my listening habits (and everything else you and I do online, for that matter) are considered invaluable social data “signals” that you are mining to discover my demographic profile, buying habits, shoe size, and (ultimately) what banner ad to serve me in real time. The only problem is that, although I love hardcore music, it doesn’t really define who I am, what I buy, or anything else about me. It is just a sliver of time, captured digitally, sitting alongside billions of pieces of atomic level data, captured somewhere in a massive columnar database.

Here are some other examples of data that are commonly available to marketers, and why they may not offer the insights we think they might:

— Zip Code: Generally, zip codes are considered a decent proxy for income, especially in areas like Alpine, New Jersey, which is small and exclusive. But how about Huntington, Long Island, with an average home value of $516,000? That zip code contains the village of Lloyd Harbor (average home value of $1,300,000) and waterside areas in Huntington Bay like Wincoma, where people with lots of disposable income live).

— Income: In the same vein, income is certainly important and there are a variety of reliable sources that can get close to a consumer’s income profile, but isn’t disposable income a better metric? If you earn $250,000 per year, and your expenses are $260,000, then you are not exactly Nordstrom’s choicest customer. In fact, you are what we call “broke.” Maybe that was okay back in the good old days of government-style deficit spending but, these days, luxury marketers need a sharper scalpel to separate the truly wealthy from the paper tigers.

— Self-Declared Data: We all like to put a lot of emphasis on the answers real consumers give us on surveys, but who hasn’t told a little fib from time to time? If I am “considering a new car” is my price range “$19,000 – $25,500” or “35,000 – $50,000?” This type of social desirability bias is so common that reaearchers have sought other ways of inferring income and purchase behavior. When people lie about themselves, to themselves (in private, no less)  you must take a good deal of self-declared data with a hearty grain of salt.

— Automobile Ownership: Want to know how much dough a person has? Don’t bother looking at his home or zip code. Look at his car. A person who has $1,800 a month to burn on a Land Rover is probably the same person liable to blow $120 on mail order steaks, or book that Easter condo at Steamboat. Auto ownership, among other things, is a great proxy for disposable income.

It would be overly didactic to rehearse all of the possible iterations of false data signals that are being used by marketers right now to make real-time bidding decisions in digital media. There are literally thousands—and social “listening” is starting to make traditional segmentation errors look tame. Take a recent Wall Street Journal article that reported that the three most widely socially-touted television shows fared worse than those than shows which received far less social media attention.

Sorry, but maybe that hot social “meme” you are trying to connect with just isn’t that valuable as a “signal.” We all hear the fire truck going by on 7th Avenue. The problem is that the only people who turn to look at it are the tourists. So what is the savvy marketer to do?

Remember that all data signals are just that: Signals. Small pieces of a very complicated data puzzle that you must weave together to create a profile. Unless you are leveraging reliable first-party data, second-party data, and third party data, and stitching that data together, you cannot get a true view of the consumer.

In my next column, we’ll look at how stitching together disparate data sources can reveal new, more reliable, “signals” of consumer interest and intent.

[This article was originally published in ClickZ on 12/2/2011]

Big Data · Data Management Platform · DMP · Real Time Bidding (RTB)

When Big Data Doesn’t Provide Big Insights

The right DMP solution can be golden for finding audiences.

What big marketers should look for in a next generation data management platform

“Big Data” is all the rage right now, and for a good reason. The other day, I was switching computers, and wanted to move about five gigabytes of photos and videos unto my new laptop, and my largest thumb drive was a measly 1 gig. I ended up getting an 8GB thumb drive for about $8 at the K-Mart in Penn Station. Think about how cheap that is. That’s less than half a cent per song, if you consider the typical 8GB MP3 device can hold about 2,000 high-quality recordings. Two terabyte drives are selling for about $130 from Western Digital. I don’t know about you, but I am not at the point where I need 2TB of data storage, and I hope I never get there. The point is that storing tons and tons of data has gotten very inexpensive, while the accessibility of that data has increased substantially in parallel.

For the modern marketer, that means having access to literally dozens of disparate data sources, each of which cranks out large volumes of data every day. Collecting, understanding, and taking action against those data sets is going to make or break companies from now on. Luckily, an almost endless variety of companies have sprung up to assist agencies and advertisers with the challenge. When it comes to the largest volumes of data, however, there are some highly specific attributes you should consider when selecting a data management platform (DMP).

Collection and Storage: It’s all About Scale, Cost, and Ownership

First of all, before you can do anything with large amounts of data, you need a place to keep it. That place is increasingly becoming “the cloud” (i.e., someone else’s servers), but it can also be your own servers. If you think you have a large of data now, you will be surprised at how much it will grow. As devices like the iPad proliferate, changing the way we find content, even more data will be generated. Companies that have data solutions with the proven ability to scale at low costs will be best able to extract real value out of this data. Make sure to understand how your your DMP scales and what kinds of hardware they use for storage and retrieval.

Speaking of hardware, be on the lookout for companies that formerly sold hardware (servers) getting into the data business so they can sell you more machines. When the data is the “razor,” the servers necessarily become the “blades.” You want a data solution whose architecture enables the easy ingestion of large, new data sets, and one that takes advantage of dynamic cloud provisioning to keep ongoing costs low. Not necessarily a hardware partner.

Additionally, your platform should be able to manage extremely high volumes of data quickly, have an architecture that enables other systems to plug in seamlessly, and whose core functionality enables multi-dimensional analysis of the stored data—at a highly granular level. Your data are going to grow exponentially, so the first rule of data management is making sure that, as your data grows, your ability to query them scales as well. Look for a partner that can deliver on those core attributes, and be wary of partners that have expertise in storing limited data sets. There are a lot of former ad networks out there with a great deal of experience managing common 3rd party data sets from vendors like Nielsen, IXI, and Datalogix. When it comes to basic audience segmentation, there is a need to manage access to those streams. But, if you are planning on capturing and analyzing data that includes CRM and transactional data, social signals, and other large data sets, you should look for a DMP that has experience working with 1st party data as well as 3rd party datasets.

The concept of ownership is also becoming increasingly important in the world of audience data. While the source of data will continue to be distributed, make sure that whether you choose a hosted or a self-hosted model, your data ultimately belongs to you. This allows you to control the policies around historical storage and enables you to use the data across multiple channels.

Consolidation and Insights: Welcome to the (Second) Party

Third party data (in this context, available audience segments for online targeting and measurement) is the stuff that the famous Kawaja logo vomit map was born from. Look at the map, and you are looking at over 250 companies dedicated to using 3rd party data to define and target audiences. A growing number of platforms help marketers analyze, purchase, and deploy that data for targeting (BlueKai, eXelate, Legolas being great examples). Other networks (Lotame, Collective, Turn) have leveraged their proprietary data along with their clients to offer audience management tools that combine their data and 3rd party data to optimize campaigns. Still others (PulsePoint’s Aperture tool being a great example) leverage all kinds of 3rd party data to measure online audiences, so they can be modeled and targeted against.

The key is not having the most 3rd party data, however. Your DMP should be about marrying highly validated 1st party data, and matching it against 3rd party data for the purposes of identifying, anonymizing, and matching third party users. DMPs must be able to consolidate and create as whole of a view of your audience as possible. Your DMP solution must be able to enrich the audience information using second and third party data. Second party data is the data associated with audience outside your network (for example, an ad viewed on a publisher site or search engine). While you must choose the right set of 3rd party providers that provide the best data set about your audience, your DMP must be able to increase reach by ensuring that you can collect information about as many relevant users as possible and through lookalike modeling.

For example, if I am selling cars and I find out that my on-site users who register for a test drive are most closely matched with PRIZM’s “Country Squires” segment,  it is not enough to buy the Nielsen segment. A good DMP enables you to create your own lookalike segment by leveraging that insight—and the tons of data you already have. In other words, the right DMP partner can help you leverage 3rd party data to activate your own (1st party) data.

Make sure your provider leads with management of 1st party data, has experience mining both types of data to produce the types of insights you need for your campaigns, and can get that data quickly. Data management platforms aren’t just about managing gigantic spreadsheets. They are about finding out who your customers are, and building an audience DNA that you can replicate.

Making it Work         

At the end of the day, it’s not just about getting all kind of nifty insights from the data. I mean, it’s big to know that your visitors that were exposed to search and display ads converted at a 16% higher rate, or that your customers have an average of two females in the household. It’s making those insights meaningful.

So, what to look for in a data management platform in terms of actionability? For the large agency or advertiser, the basic functionality has to be creating an audience segment. In other words, when the blend of data in the platform reveals that showing 5 display ads and two SEM ads to a household with 2 women in it creates sales, the platform should be able to seamlessly produce that segment and prepare it for ingestion into a DSP or advertising platform. That means a having an extensible architecture that enables the platform to integrate easily with other systems. Moreover, your DMP should enable you to do a wide range of experimentation with your insights. Marketers often wonder what levers they should pull to create specific results (i.e., if I change my display creative, and increase the frequency cap to X for a given audience segment, how much will conversions increase)? Great DMPs can help built those attribution scenarios, and help marketers visualize results. Deploying specific optimizations in a test environment first means less waste, and more performance. Optimizing in the cloud first is going to become the new standard in marketing.

Final Thoughts

There are a lot of great data management companies out there, some better suited than others when it comes to specific needs. If you are in the market for one, and you have a lot of first party data to manage, following these three rules will lead to success:

  • Go beyond 3rd party data by choosing a platform that enables you to develop deep audience profiles that leverage first and third party data insights. With ubiquitous access to 3rd party data, using your proprietary data stream for differentiation is key.
  • Choose a platform that makes acting on the data easy and effective. “Shiny, sexy” reports are great, but the right DMP should help you take the beautifully presented insights in your UI, and making them work for you.
  • Make sure your platform has an applications layer. DMPs must not only provide the ability to profile your segments, but also assist you with experimentation and attribution–and provide you with ability to easily perform complicated analyses (Churn, and Closed Loop being two great examples). If your platform can’t make the data dance, find another partner.

[This post was originally published in ClickZ on 11/9/11]