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

Rise of the Machines

Where do People Fit into a World that Promises Endless Media Automation?

Ever since man tied a rope to an ox, there has been a relentless drive to automate work processes. Like primitive farming, digital media buying is a thankless, low-value task where results (and profits) do not often match the effort involved. Many companies are seeking to alleviate much of the process-heavy, detail-oriented tasks involved in finding, placing, serving, optimizing, tracking, and (most importantly) billing digital media campaigns with various degrees of success.

Let’s take the bleeding edge world of real-time audience buying. Trading desk managers are often working in multiple environments, on multiple screens. On a typical day, he may be logging into his AppNexus account, bidding on AdBrite for inventory, bidding for BlueKai stamps in that UI, looking for segmentation data in AdAdvisor, buying guaranteed audience on Legolas, trafficking ads in Atlas, and probably looking at some deep analytics data as well. If he is smart, he is probably managing that through a master platform, where he can look at performance of guaranteed display and even other media types. How efficient does that sound?

To me, it sounds like six logins too many. Putting aside the obvious fact that an abundance of technology doesn’t lead to efficiency (how’s “multitasking” working out for your 12 year old, by the way?), I wonder we aren’t asking too much of digital as a whole. How many ads have you clicked on lately? If the answer is zero, then you are in a large club. Broken down to its most basic level, we are working in a business that believes a 0.1% “success” rate is reason to celebrate. But the “click is a dead metric” some say. Really? Isn’t the whole point of a banner ad to drive someone to your website? When did that change?

All of this is simply to illustrate the larger point that the display advertising industry, for all of its supposed efficiencies, is really still in its very nascent stages. Navigating the commoditized world of banner advertising is still very much a human task, and the many machines we have created to wrestle the immense Internet into delivering an advertiser the perfect user are still primitive. For a short while longer, digital media is still the game of the agency media buyer…but not for long.

Let’s look at the areas in which smart media people add value to digital campaigns: site discovery, pricing, analytics and optimization, and billing.

Site Discovery

In the past, half the battle was knowing where to go. Which travel sites sold the most airline tickets? Which sites indexed most highly against men of a certain age, looking for their next automobile? What publisher did you call to get to IT professionals who made purchasing decisions on corporate laptops? Agencies had (and still have) plenty of institutional knowledge to help their clients partner with the right media to reach audiences efficiently and—even with the abundance of measurement tools out there—a lot of human guidance was needed. Now, given the ability to purchase that audience exactly using widely available data segments, the trick is simply knowing where to log in. I just found the latter IT professional segment in Bizo in less than 2 minutes. So the question becomes: how are you leveraging data and placement to achieve the desired result, and how efficiently are you doing it?

Pricing

It used to be that the big agencies could gain a huge pricing advantage through buying media in bulk. Holding company shops leveraged their power and muscled down publisher rate card by (sometimes) 80% or more with promised volume commitments, leaving smaller media agencies behind. Then, a funny thing happened: ad exchanges. All of the sudden, nearly all of the inventory in the world was available, and ready to be had in a second-price auction environment. Now, any Tom , Dick, and Harry with a network relationship could access relatively high quality impressions at prices that were guaranteed never to be too high (in a second-price auction, the winning bid is placed at the second highest price, meaning runaway “ceiling” bids are collapsed). Whoops. With their pricing advantage eliminated, large agencies did the next best thing: eliminated the middleman by building their own exchanges, which we have been calling “DSPs.” So, you don’t need human intervention to ensure pricing advantages.

Analytics and Optimization

What about figuring out what all the data means? After all, spreadsheets don’t optimize media campaigns. Don’t you need really smart, analytical media people to draw down click- and view-based data, sift through conversion metrics, and build attribution models? Maybe not. Not only are incredible algorithms taking that data and using machine learning to automatically optimize against clicks or conversions—but programmatic buying is slowly coming to all digital media as well.  In the future, smart technology will enable planners to create dynamic media mixes that span guaranteed and real-time, and apply pricing across multiple methodologies (CPM, CPC, CPA). Much of that work is being done manually right now, but not for long.

Billing

Sadly, much of the digital media business comes down to billing at the end of the day. Media companies struggle tremendously with reconciling numbers across multiple systems, and agency ad servers don’t seem to speak the same language as publisher ones. The bulk of a media company’s time seems to be spend just trying to get paid, and an incredible amount of good salary gets burnt in the details of reconciliation and reporting. This is slowly changing, but the advent of good API development is starting to make the machines talk to each other more clearly. The platforms that can “plug in” ad serving and data APIs most easily have a lot to gain, and the industry as a whole will benefit from interoperability.

So, are people doomed in digital media? Not at all. There are going to be a lot less digital media buyers and planners needed—but what agencies are really going to need are smart media people. Right now, you need 4 people to manage 10 machines. In the near future, you will need 1 smart person to manage 1 platform—and the other three people can focus on something else. Maybe like talking to their clients.

[This article originally appeared in ClickZ on 4/14/11]