Creating the Fabled 360 View of the Consumer

ImageDespite years of online targeting, the idea of having a complete, holistic “360 degree view” of the consumer has been somewhat of a unicorn. Today’s new DMP landscape and cross-device identification technologies are starting to come close, but they are missing a key piece of the puzzle: the ability to incorporate key social affinities.

In the nearby chart, you can see that online consumers tell us all about themselves in a number of ways:

Viewing Affinities: Where they go online and what they like to look at provides strong signals of what they are interested in. Nielsen, comScore, Arbitron and others have great viewership/listenership data that is strong on demographics, so we can get a great sense of the type of folks a certain website or show attracts. This is great, but brands still struggle to align demographic qualities perfectly with brand engagement. 34 year old men should like ESPN, but they could easily love Cooking.com more.

Buying Affinities: What about a person’s buying habits? Kantar Retail, OwnerIQ, and Claritas data all tell us in great detail what people shop for and own—but they lack information on why people buy the stuff they do. What gets folks staring at a shelf to “The Moment of Truth” (in P&G parlance) when they decide to make a purchase? The buying data alone cannot tell us.

Conversational Affinity: What about what people talk about online? Radian6 (Salesforce), Crimson Hexagon, and others really dig into social conversations and can provide tons of data that brands can use to get a general sense of sentiment. But this data, alone, lacks the lens of behavior to give it actionable context.

Social Behavioral Affinity: Finally, what about the actions people take in social environments? What if we could measure not just what people “like” or “follow” online, but what they actually do (like post a video, tweet a hashtag, or engage with a fan page)? That data not only covers multiple facets of consumer affinity, but also gives a more holistic view of what the consumer is engaged with.

Adding social affinity data to the mix to understand a consumer can be a powerful way to understand how brands relate to the many things people spend their time with (celebrities, teams, books, websites, musicians, etc.). Aligning this data with viewing, buying, and conversational data gets you as close as possible to that holistic view.

Let’s take an example of actionable social affinity in play. Say Whole Foods is looking for a new celebrity to use in television and online video ads. Conventional practice would be to engage with a research firm who would employ the “Q Score” model to measure which celebrity had the most consumer appeal and recognition. This attitudinal data is derived from surveys, some with large enough sample sizes to offer validity, but it is still “soft data.”

Looking through the lens of social data, you might also measure forward affinity: how many social fans of Whole Foods expressed a Facebook “like” for Beyonce, or followed her account on Twitter? This measurement has some value, but fails at delivering relevance because of the scale effect. In other words, I like Beyonce, so does my wife, and so does my daughter . . . along with many millions of other fans—so many that it’s hard to differentiate them. The more popular something is, the broader appeal and less targetability that attribute has.

So, how do you make social affinity data relevant to get a broader, more holistic, understanding of the consumer?

Obviously, both Q Score and forward affinity can be highly valuable. But when mixing viewing, buying, and listening with real social affinity data, much more becomes possible. The real power of this data comes out when you measure two things against one another. Sree Nagarajan, CEO of Affinity Answers, explained this mutual affinity concept to me recently:

“In order for the engagement to be truly effective, it needs to be measured from both sides (mutual engagement). The parallel is a real-world relationship. It’s not enough for me to like you, but you have to like me for us to have a relationship. Mapped to the brand affinity world, it’s not enough for Whole Foods fans to engage with Beyonce; enough Beyonce fans have to engage with Whole Foods (more than the population average on both sides) to make this relationship truly meaningful and thus actionable. When true engagement is married with such mutual engagement, the result is intelligence that filters out the noise in social networks to surface meaningful relationships.”

As an example, this approach was recently employed by Pepsi to choose Nicki Minaj as their spokesperson over several other well-known celebrities.

What else can social affinity data do?

  • Brands can use social affinity data to decide what content or sponsorships to produce for their users. Looking at their users’ mutual affinity between the brand and music, for example, might suggest which bands to sponsor and blog about.
  • A publisher’s ad sales team can use such data to understand the mutual affinity between itself and different brands. A highly correlated affinity between activated social visitors to GourmetAds’ Facebook page and those who post on Capital One’s Facebook page may suggest a previously unknown sales opportunity. The publisher can now prove that his audience has a positive predisposition towards the brand, which can yield higher conversions in an acquisition campaign.
  • What about media buying? Understanding the social affinity of fans for a television show can produce powerful actionable insights. As an example, understanding that fans of “Teen Wolf” spend more time on Twitter than Facebook will instruct the show’s marketing team to increase tweets—and post more questions that lead to increased retweets and replies. Conversely, an Adult Swim show may have more Facebook commenters, leading the marketer to amplify the effect of existing “likes” by purchasing sponsored posts.
  • Keyword buying is also interesting. Probing the mutual affinities between brands and celebrities, shows, music acts, and more can yield long tail suggested keyword targets for Google, Bing/Yahoo, and Facebook that are less expensive and provide more reach than those that are automatically suggested. As an example, when “Beavis and Butthead” re-launched on MTV, Google suggested keywords for an SEM campaign such as “Mike Judge” (the show’s creator) and “animated show.” Social affinity data suggested that socially activated Beavis fans also loved “Breaking Bad.” Guess what? Nobody else was bidding on that keyword, and that meant more reach, relevance, and results.

I believe that understanding social affinity data is the missing piece of the “360 degree view” puzzle. Adding this powerful data to online viewing, buying, and social listening data can open up new ways to understand consumer behavior. Ultimately, this type of data can be used to generate results (and measure them) in online branding campaigns that have thus far been elusive.

Want a full view of the people who are predisposed to love your brand? Understand what you both mutually care about through social affinities—and measure it.

[This post originally appeared in AdExchanger on 4.14.14]

 

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Social Affinity

Is your media measurement as dated as this 1970s den?

Is your media measurement as dated as this 1970s den?

The New Panel-Based Audience Measurement for Brands

With the prevalence of social data, yesterday’s panel-based measurement for digital campaigns is starting to look like the wood paneling in your grandmother’s den: A bit out of fashion. Marketers have been trained to buy media based on demographics, and it is natural to want your ads to be where you think your customers are. For BMW’s new entry level sedan, that might mean finding the media that males aged 26-34, who are earning 75,000 or more a year, consume. That makes a lot of sense, but it also means that your paid media will always compete alongside ads for your competitors. That is a big win for websites with premium inventory that fits your demographic, because it means scarcity and high prices for marketers.

What if there was another way to measure what audiences are right for brands? And what if that data were based on a panel of a few hundred million people, rather than a few thousand? Well, thanks to Facebook and Twitter, we have just such a web-based panel of consumers, and they are always eager to share their opinions in the forms of “likes,” “follows,” and (more importantly) engagement. Social listening platforms have been able to tell brands what people think about them directionally, and measure how certain marketing efforts move the social needle. Listening is great, but how do you get to hear what to buy?

A company called Colligent has been going beyond listening, by measuring what people actually do on Twitter, Facebook, and other social sites. “Liking” is not enough (when me, my 10-year old daughter, and my mom “like” Lady Gaga, the audience I am a part of gets too broad to target against). What matters is when people express true affinity by sharing videos, tweeting, and commenting. When people who are nuts about a certain celebrity are also nuts about a certain brand—and that relationship over-indexes against normal affinity, you have struck real social gold: Data that can make a difference. Pepsi recently used such data to choose Nicki Minaj as a spokesperson over dozens of other choices.

What about other media? Nielsen defines television, Arbitron measures radio, and MRI defines magazine audiences by demographics. Now, for the first time, marketers can use social data—gathered from panels nearly as large as the buying population—to define audiences by their own brand and category terms. That’s a world in which Pepsi can purchase “Pepsi GRPs” across all media, rather than GRPs in a specific media.

This is the way brands will buy in the future.

[This post originally appeared on 2/21/13 in The CMO Site, a United Business Media publication]

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.

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

Know Your Audience

Using Audience Measurement Data to Optimize Digital Display Campaigns

These days, advertising and data platforms are giving marketers a wealth of information that can be used to validate their strategies, and optimize their digital campaigns for better performance. There is a lot of data to sort through—some more useful than others. Sometimes, good campaign optimization comes down to the basics: Understanding who your audience is, and why they are doing what they are doing.

Let’s look at a real life example of a digital display campaign, run through the digital ad agency of a popular mattress retailer. The agency wanted to test new inventory sources for the campaign by running broadly on general interest sites, evaluating the demography of audiences that showed purchase intent, and optimize over the course of the campaign to maximize impact.

A theory being tested was that older audiences, who report more difficulty sleeping than younger demographic groups, would respond more favorably to the retailer’s online display ads. Campaigns were initially skewed to sites that over-indexed against audience composed of 50 and older.

Figure 1: Age of Ad Viewer, by Impressions.

As Figure 1 shows, a bulk of impressions during the discovery portion of the campaign were delivered to visitors aged 46-65 years of age, which was the desired demographic. After analysis of those who viewed or clicked on a display ad, and then went on to purchase, the audience composition was remarkably different. As shown in Figure 2, the bulk of conversions came from those aged 18-45.

Figure 2: Age of Mattress Purchaser (Conversions).

The agency adjusted the ad buy to heavy up on sites that over-indexed for a younger audience, and opted out of buys tailored to the older demographic. As wasted impressions were trimmed down in the overall plan, conversion rates increased dramatically. Testing and validating your instincts with data on an ongoing basis is the key to success in digital display advertising. The mattress retailer, who experienced better sales from older store visitors (offline), found a more responsive younger audience online. Although it seems obvious, having the initial data means being able to smartly allocate marketing capital, and having access to ongoing data means not having to rely on old insights in a changing marketplace.

Another offline theory the mattress retailer sought to validate was the mattress life cycle. After collecting brick and mortar sales data for years, the retailer knew that the average life of a mattress was approximately 7 years, and that the single greatest life event influencing the purchase of a new mattress was moving. Therefore, it made sense to target audiences based on length of residence (>7 years), and target content around buying or renting a new home.

Inventory was bought from a wide range of home-specific and moving sites, and measured using Aperture audience measurement populated with data sets from Experian, IXI financial, V12 demographic, and Nielsen PRIZM data.

 

Figure 3: Length of Residence, by Impressions.

Figure 4: Length of Residence, by Click.


As Figures 3 and 4 amply demonstrate, the mattress retailer was targeting the bulk of impressions towards individuals reporting over seven years residence in a single location, and clicks among that group indexed the highest in aggregate. That data validated the approach of buying into sites with a strong audience of self-reported homeowners. However, a deeper look into audience data revealed a strong distinction between renters and buyers.

Fig 5Comparing Impressions and Conversions by home ownership status.

As noted in Figure 5, although the bulk of impressions in the campaign were served to homeowners, renters were the ones buying the most mattresses. This learning did more than any other data point to drive campaign optimization.

Naturally, the next step in the campaign optimization process was to focus inventory delivery to sites that promised a concentrated audience of home renters. Sites such as ForRent.com, ApartmentGuide.com, and Renters.com were added to the optimization plan.

More insights came as the Aperture data was collected. Despite purporting to have a heavy concentration of renters, two of the more popular sites actually index much higher among homeowners, as shown in Figure 6. It looked as though homeowners that were looking into renting made up the majority audience—a fact that helped the retailer tailor specific messaging to them.

Figure 6: In this example, a media site aimed at renters, over-indexes against current homeowners.

Figure 6: In this example, a media site aimed at renters, over-indexes against current homeowners.

For this particular campaign, the ability for the retailer to validate certain audience assumptions using real demographic data was critical, as well as the ability to leverage the distinction between two types of potential customers: home owners, and renters. Additionally, getting real audience metrics beyond a publisher’s media kit or self-declared audience information enabled the retailer to craft its creative and messaging in a highly specific way that increased conversions.

When it comes to audience validation and campaign optimization, here are three keys:

  • Know Your Data: In today’s technology-driven marketing world, knowing how to leverage the data available to you is critical to both understanding and targeting your audience. Make sure your marketing investment decisions are driven through the analysis and usage of 1st party data, including registration data for demographic modeling; 2nd party data, such as ad server and search data for behavioral modeling; and 3rd party data, such as available audience segments from providers like Nielsen and Datalogix, for audience validation, matching, and lookalike modeling. Data is not just about buying audience segments for targeting; it’s about trying to get a 360-degree view of your ideal customer.
  • Choose the Right DMP: There are DMPs for every marketer, so be careful to choose the right one. Big Data needs call for pure play DMPs that can stitch together highly disparate data sets that include all data types, and make both insights, audience segments, and lookalike modeling available in real-time. Marketers looking to buy from a variety of 3rd party audience segment providers should choose a data marketplace such as Exelate, or be willing to access a more limited number of data sources inside a DSP such as AppNexus.
  • Leverage Audience Measurement: Finally, there is a lot that audience segments can bring to the table in terms of audience insights. Understanding the audience composition of who saw, clicked on, and converted after seeing your campaign gives you the ability to learn about your target customers, their online behaviors, and (most importantly) find more of them. Your DMP should have the ability to marry audience and campaign data to give you a highly granular level view of your best (and worst) performing audience types—down to the creative level.

Learnings from this case study, and other valuable information, can be found in my upcoming “Best Practices in Digital Display Media,” coming in January 2012 from eConsultancy.com.

[This article originally appeared in ClickZ on 1/4/2012]

 

Traffiq integrates Nielsen site audience data (Interview)

Media management software firm Traffiq has partnered with audience measurement company The Nielsen Co. to provide advertisers access to Nielsen’s target-marketing platform @Plan. The integration will go live on Sept. 21 for all roughly 400 registered customers of Traffiq’s display ad-buying platform, said Chris O’Hara, SVP of sales and marketing at Traffiq.

Customers “are able to come into Traffiq, throw on a campaign and get @Plan data appended by Nielsen [which is] really great demographic information and do decisioning on whether they should advertise based on that information,” O’Hara said.

Nielsen’s @Plan platform will display websites’ number of monthly unique visitors as well as site visitors’ demographic information, such as gender, age, education level, household income, ethnicity and marital status.

Previously, Traffiq customers only had access to publishers’ self-reported data, which was “not that accurate,” said O’Hara. In addition to supplementing site audience data for the 3,000 publishers available on Traffiq’s platform, the partnership adds data for 7,000 publishers collected by Nielsen, he said.

O’Hara said the partnership marks the first time Nielsen has made the @Plan platform available to non-@Plan customers.

[This post appeared in Direct Marketing News on 9/21/11]

TRAFFIQ Talks Private Marketplaces and Other Platform Enhancements

ADOTAS – Demand-side digital media management platform TRAFFIQ expands its offerings so much that it’s hard to keep up. Fortunately, we were able to hit Senior Vice President of Sales and Marketing  (and regular Adotas contributor) Chris O’Hara with questions regarding the platform’s latest upgrades (including customized and private publisher portfolios and enhanced financial management tools) as well as the many partnerships the company has formed since the beginning of the year.

ADOTAS: Terence Kawaja’s infamous display ecosystem landscape places TRAFFIQ in “media management systems” with companies like Centro — closer to the supply side than DSPs. Do you think this is a fair placement and why?

 

O’HARA: I don’t think we should put too much emphasis on placement in the landscape chart. Many companies belong in one or more buckets—and some of the logos should appear much larger than others, based on overall impact within the landscape itself. TRAFFIQ, for example, could appear in many of the categories (DSP and Ad Serving being two of them), but I believe there is a revenue threshold to be met before LUMA will place you in multiple buckets.

That being said, I think TRAFFIQ is in the right category. Eventually, the notion is that TRAFFIQ would appear as an overlay to multiple sections of the map, providing dashboard level access to an advertiser’s entire vendor toolset.

How does a media management system differ from a DSP? Confused agency people want to know.

Mostly, it’s nomenclature. I think the term “demand-side platform” is a great term for a technology tool that helps advertisers manage their media. The reality is that now “DSP” means “technology tool for real time managing exchange buying.” Agencies have every right to be confused, as companies within the landscape are changing from network to “platform” and from data provider to “DMP.”

The difference is simply that a “management system” should provide tools that cover inventory discovery, vendor negotiation, offer management, contracts, ad serving, analytics, and billing; DSPs handle a sliver of the overall media buy. For example, TRAFFIQ customers will be able to manage several DSPs within our platform at once.

It seems like the new Private Marketplaces tool allows advertisers to customize publisher and exchange lists — fair assessment, or is there more, so much more?

Right now, TRAFFIQ private marketplaces enables advertisers to buy outside of our curated list of 3,000 guaranteed inventory sources, which is especially important in terms of giving agencies the control they need over media. Publishers increasingly want the convenience and efficiency of exchange buying…without exposing their quality inventory to the world.

Demand side customers like the reach and price efficiency they can achieve with exchange-buying—but still struggle with brand safety and transparency. Our next-generation system will offer both sides a lot more control over who they work with, and that is sorely needed in our business right now.

Can this tool also offer hookups into the increasingly popular private exchanges, such as The Weather Channel’s Category 5 and Quadrant One?

Yes, as long as the demand-side partner has a business relationship in place with the inventory supplier, TRAFFIQ will be able to enable the connection.

Why are agencies going gaga over your new finance management tools?

If agency CFOs could actually go “gaga,” they may be doing so over our new tool for the simple reason that most digital platforms don’t take the vagaries of agency pricing into account. At TRAFFIQ, we have to manage several different pricing scenarios at once.

What is the agency’s margin, and how do they want that margin reflected in the pricing (baked into the media cost, or shown transparently)? How about data and technology fees? Those can be added to the gross media cost, or shown separately as well. Also, handling net and gross costs with publishers has always been challenging.

Smart systems should recognize these fundamental business needs, and expose the correct pricing to everyone within the system, eliminating confusion and duplicative work.

Can you explain how the multiple user permissions work? Why is this important for your agency clients and how can they best be deployed?

For the demand side, multiple user permissions means giving access to a subset of clients for an individual account team. On the supply side, it means having the ability to put the right publisher rep with the right demand side customer.

For example, an individual agency account team may buy from Fred at ESPN for one client, and Joe at ESPN for another. It is also necessary for agencies to be able to manage which of their end-clients gets to view certain reports. Multiple user permissions adds the layer of flexibility that enables TRAFFIQ users to expose the right data to the right set of customers.

What kind of agencies are you working with these days and what kind do you hope to add to your client base? Are you working with brands directly as well?

For the past several years, our focus has been getting total product adoption from the small to mid-sized agency market. Some are the types of shops that have a thriving traditional media practice, but not necessarily the right tools to tackle digital media. Still others are strong in digital, but are struggling with multiple tools, and having a hard time putting all of the pieces together efficiently.

We partnered with some of the great agency groups like TAAN, Magnet Global, AMIN and Worldwide Partners to reach these shops, and have been quite successful. We have also done some work with the holding companies, but mostly on a campaign-by-campaign basis, rather than getting the large shops to adopt our solution fully.

The product features we are working on now will actually enable big agencies to adopt TRAFFIQ by enabling API connections to their existing systems (ad serving, billing, etc). You can’t walk into an agency and ask them to drop all of their vendor relationships at once… You have to be able to work seamlessly with what they have.

What sets apart your attribution services from your media management peers as well as other attribution providers? What kind of extra insight do you provide?

Right now, a lot of our customers are working with our embedded Aperture audience measurement reports. Unlike other platforms, we make it fairly easy to take those demographic campaign  learnings and take action against them. So, it’s not just click- or view-based data; it’s using third-party data to understand who is seeing your campaign, clicking on it, and ultimately converting against it.

We are the only platform that can help marketers react to that data through guaranteed buying—and RTB. In the near future, we will be able to show how our efforts in initial media budget allocation and optimization are driving performance. We also see a great opportunity to get some key attribution metrics out of search and display, once out customers are doing both types of media in the platform at scale.

How does TRAFFIQ integrate first-party and third-party data into audience buying efforts?

Right now we have over 15 data segmentation partners. Some of them work directly with our Trading Desk (we apply those segments to exchange buys), and some of our partners provide both targeting and media execution. We see our role as a platform as provisioning our advertising clients with the right best-of-breed partners, no matter what the targeting need.

That means Proximic and Peer39 for semantic; AlmondNet (now Datonic) for search keyword retargeting; Media6Degrees and 33Across for social targeting; Nielsen, Lotame and eXelate for demo targeting, etc. We also have the ability to match any first-party data with available audience within our real-time bidding system, and find that audience as well.

Do you foresee more mobile partnerships in TRAFFIQ’s future or is Phulant your one and only?

TRAFFIQ is an open platform, and that means we must be willing to integrate partners based on our clients’ needs. We see Phluant as a key TRAFFIQ partner for mobile ad serving, and have plans to work closely with them to define and grow our mobile capabilities. We want to see more standardization around mobile workflow, and that means making it easier for marketers to allocate budgets across different media types (social, search, mobile, video, and display) in one system.

Phluant has developed amazing technology to help marketers take rich media for display  and bring it to mobile devices. That’s a great starting point… and something that can be leveraged across multiple mobile inventory vendors.

Regarding your partnership with Bizo, what kind of opportunities lie in the realm of targeted B2B display?

Bizo is doing an amazing job of bringing the power of B2B to display advertising. Until recently, B2B marketers stayed away from display advertising (or struggled to get online reach with smaller, niche business publishers). Now, they can take the success that they are used to having with targeted direct mail in B2B, and apply that in real time display.

We believe that there are some real opportunities to make both B2B and local display digital advertising more manageable, scalable, and accountable.

Besides its “interesting” name, what about Oggifinogi (recently acquired by Collective Media) attracted TRAFFIQ to make it your video and rich media network partner?

Our customers use Pointroll, Mediamind, Spongecell, and all kinds of third-party rich media vendors, but we needed a reliable “go-to” partner that could help our registered demand-side client base tackle rich media and video more easily. We saw that “Oggi” had a strong commitment to both technology and customer service, and we felt that we could work with their team well. I think Collective media validated what a great partner choice we made there!

TRAFFIQ appears to have spread itself out pretty well across digital marketing channels, so what area is next on the agenda? Social?

The first big channel we are going to tackle after display is search. In a few months, TRAFFIQ will feature bid management tools for search engine marketing right in the platform—along with access to the Facebook self-service ad inventory. This means that, for the first time, guaranteed display, real-time display, search, and social can be managed within the same “media management system.”

It’s going to be exciting, but the real challenge will be making it seamless for marketers—and getting some great insights out of all the data that such an integrated platform will produce. That’s what we’ll be working on over the next several months.

[This interview appeared on 7/2711 in Adotas]