“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.
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