Thoughts on Data-Driven Audience Measurement

A Conversation with Scott Portugal of PulsePoint

What are some best practices for the modern digital marketer? Cookie-based data makes knowing your audience easier  than ever. Developing accurate audience profiles, optimizing campaigns based on audience composition, and validating audience reach are all critical components for marketers doing targeted digital campaigns. I recently spoke with Scott Portugal, long time digital media veteran and currently VP of Business Development for PulsePoint, who has been working with PulsePoint’s Aperture audience measurement offering, what marketers should be thinking about when it comes to measurement.

Scott Portugal: First and foremost, marketers must really understand the goals of the campaign. “Branding” vs. “Performance” aren’t goals – they are notional indicators of goals. “Increase brand awareness amongst men passionate about health and fitness by 50%,” is a goal. The more specific, the better. It eliminates the guesswork that agencies have to do around media tactics, and most importantly, specificity in KPIs means everyone knows which data sets to use along the way.  Also, a modern marketer knows that buying digital media isn’t an on/off switch. Once the buy starts, the work starts. Prepare to optimize everything you can – look at performance across targets, media partners, creative (the most important and often least optimized variable), etc.. Good digital marketers are like good scientists: ask plenty of questions, account for all variables, and constantly test to find success.

What new tools are out there to assist in audience measurement, and supplement the standard offerings from Comscore, Nielsen?  

SP: Data is ubiquitous – some might say commoditized. But there are a few platforms out there that are taking novel approaches to audience measurement. Certainly our PulseAudience platform is among that group. We’re able to build audience profiles at the domain level, meaning at a very granular level we can infer the audience composition of a page even without a cookie. Another new player is Korrelate, founded by the guys who ran TACODA. Korrelate is in the business of helping marketers understand how different data sets perform across different platforms – essentially helping a buyer know what data segment to buy when and where. At a broader level, audience measurement platforms are starting to look cross-media, bringing together disparate data sets that show impact of a campaign on ALL digital activity, not just clicks.

What about social data? How are technologies like Facebook and Twitter enabling a more concise view of audiences, and helping marketers validate their choices?  

SP: If you think about Facebook and Twitter NOT as destinations, but as communication tools, then you can start to see where a more holistic audience view can be created. Social media is more than updates – it’s sharing news, communicating about brands, raising hands about interests, and more. Social data, when done right, is true first party data that goes above and beyond standard behavioral data. Marketers can understand not just when a user engages, but how, where, and how valuable that engagement actually was (likes, shares, tweets, etc.). it should validate a marketers choice around creative and placement, but only if the creative and placements actually include social elements. Social data is powerful, but it’s only powerful if it’s part and parcel to other data sets and targeting mechanisms used in conjunction with social media. Nothing happens in a vacuum, and nothing happens ONLY in one channel.

Your company owns Aperture. Can you provide some examples of how progressive media organizations are using audience measurement data? Is it about audience validation? Optimization? Upselling clients?  

SP: It’s about delivering value via insights up and down the funnel. It sounds like ad jargon but it’s what we strive to do with every single engagement. Cookie targeting works, but we believe that there is real value in modeling at other points of content interaction – insights that help guide and inform at all points of the campaign. Our RTB partners can leverage some of this data in real time; our non-programmatic partners work with our data and insights group to go even deeper via custom reporting and deeper dives on how to get consumers to engage. Data availability and normalization—what we do—is what makes the tide rise to lift all boats.

How can (the right) measurement data influence brand advertising? Is this the key to bringing more brand dollars online?

SP: Brands will feel safe moving dollars over from television to digital when they can do two things: ensure the environment is safe and ensure that they are reaching the right audience with minimal waste. Does television have massive amounts of waste in it? Of course – but as an industry we promised the world that we would eliminate much of that problem via targeting and optimization, so we have to lay in the bed we made. So measuring not just reach & frequency, but the impact of that reach & frequency is critical. Did search queries go up relative to their competitors? Did social commentary increase? Are there more tweets about campaigns in other platforms (did you create awareness that increases awareness in other channels as well)? Like I said earlier – understanding the specific goals of that branding campaign, and ensuring that the right creative is matched with the right tactics, will allow for the right measurement data to be used.

What’s next in measurement?  

SP: To me it comes down to cross-platform impact. Devices and screens aren’t truly linked yet, but the audience at the other end of that ad campaign is the same person. They tweet, they promote, they like, they friend, they blog, they comment, they shop….but they do it across multiple screens in the home, the office, and on the street. The best measurement companies are going to be those that can build an impact assessment across ALL platforms and show the points of interconnection. It’s a big task – but the ones who get it right will be the ones working directly with marketers and become embedded into everything they do. The next big push will be to show marketers that social, search, display, video, and mobile are all tactics inside the same strategy…and then show them how each tactic impacts the other.

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.

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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]