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.


Underneath the Funnel

How Social Data Flips and Extends the Purchase Funnel

The traditional purchase funnel hasn’t changed much since its invention in 1898. Although there are many different versions of it, the basic “AIDA” model (awareness>interest>desire>action) remains the same:


  • Awareness: The traditional digital customer funnel starts at creating product awareness through impression-based display advertising and sponsorships.
  • Interest: The consumer continues down the purchase path when consumers demonstrate intent through behavioral and contextual signals. Those consumers can be targeted using a large variety of pre-packaged 3rd party segments.
  • Desire: Digital marketers capture a user’s desire, when they demonstrate affinity by clicking on an ad or visiting a product’s website. These consumers can be reached digitally through retargeting.
  • Action: Finally, the consumer purchases the product, at which point he “drops out of the funnel.”

Until recently, once the consumer entered the company’s CRM, he was marketed to in a more traditional way, via e-mail, postal mail, and telemarketing. In the case of digital media tactics, the consumer could reasonably be expected to be bombarded with retargeting ads for the remainder of his life (or, until he cleared his cookies), but that was the extent of things. Fast forward a few years, and all of the sudden Salesforce and Oracle are snatching up social media and measurement companies like they were going out of style. As I was writing my recent report on data management, I wondered:

Did they see this?


The perfect storm of advanced, extensible CRM platform technology, near ubiquitous availability and scale of social signals, and ability to activate first party data has extended the purchase funnel. Once the consumer “drops through” the real action starts.

  • Joins: Once in the customer database (CRM), the post-purchase journey starts with a commitment beyond the sale, when a consumer joins an e-mail list or signs up for special offers on the company’s site.
  • Likes: The next step is an expression of social interest, when the consumer agrees to make public his “like” for a company or brand by “friending” a company’s Facebook page, following a company’s Twitter account.
  • Recommends: Beyond the like or follow is true social activation, wherein the consumer actively (not passively) recommends the product or service, through commenting, sharing, or other active social behaviors, thus showing his brand affinity.
  • Sells: The final step is having the consumer sell on your behalf (directly via affiliate programs or, in the softer sense, as a “brand ambassador”).

To navigate the consumer from brand awareness, all the way through to actually selling on behalf of a brand takes an understanding of data and its application to each step in the journey. The most successful companies leveraging this new inverted funnel paradigm are aligning their first party CRM data with social affinity data to get a 360-degree view of their typical consumer—and modeling against that view to produce repeatable marketing outcomes.

What does that mean? It is not enough to understand your brand’s core demographic (e.g., male, aged 25-36, single family home, income >$125,000). That data is important, and you can certainly make somewhat efficient digital media decisions with it. Once that person expresses “desire” by visiting your website, you can certainly retarget him. And, once he finally purchases, you can pretend you “own” him, and deploy the various traditional CRM marketing tactics to create return purchases. All well and good.

The challenge is getting that person to like you back, and mutually engage with your brand. Once he is in your CRM, are you prepared to deliver new content to him via social media channels? Can you find the linkages between him and his internet friends, and get downstream of his activity via social affinity signals? Ultimately, can you create enough incentive, through affiliate programs, social gaming, couponing, or other active programs, to enable him to actually sell on your behalf? That is today’s digital marketing challenge—and it resides inside an integrated social CRM.

That’s why Salesforce bought Radian6 and Buddy Media, and why Oracle bought Vitrue and Involver. It will take some time for these new social data tools to get properly embedded into the traditional CRM, and even longer for marketers to get adept at leveraging them at scale—but we are now living in an inverted funnel world. Be prepared to turn your thinking about digital marketing upside down.

[This post originally appeared in ClickZ on 12/21/12]

What is Data Science?

A Conversation with Ankur Teredesai, Data Scientist, nPario

These days, the term “bid data” is all the rage and over a dozen data management platforms are competing for the right to manage audience segmentation, targeting, analytics, and lookalike modeling for advertisers and publishers. I recently sat down with noted data scientist Ankur Teredesai to help understand data science, and how ad technology companies are using data science principles to help publishers and marketers understand audiences better. Ankur is the head of data science for nPario, a WPP portfolio company focused on data management, and he is also a professor at the University of Washington.

You hear lots of digital advertising people talk about “data science.” Does their perception differ from the broader, academic understanding of what data science is? Is the notion of utilizing data science in digital marketing applications new?

Ankur Teredesai (AT): It’s very interesting to see that the digital advertising folks have started deep conversations with data scientists. Data science is a very interesting space these days where data mining and database management technology is now helping variety of disciplines in establishing a scientific approach to decision making. Data science dealing with the problems of finding patterns in large amounts of data is not a new concept for digital marketing. What is new is the advent of technologies that now support finding useful patterns in large variety and velocity of data in addition to volume; thereby advancing the state of the art in marketing analytics.

Do ad technology companies really rely on data science? What does being a data-driven organization really mean?

AT: No ad-tech company can afford to NOT rely on data science in some shape or form. The power of predictive modeling is quickly differentiating the players who are making quick inroads by using the low-hanging fruits of data science for these domains from the ones that are treating data science as a passing buzzword. My advice to all ad-tech companies is to get at least one data scientist in their ranks; even if they don’t like the term data science for some or the other reason. Machine Learning, data mining and big data analytics are all equally acceptable today.

Describe the concept of data modeling for the non-academic user. What kind of models are being built for digital marketing applications?

AT: A variety of problems in digital marketing are being addressed using predictive modeling. Some examples of the work we are doing at nPario are (a) look-alike modeling that helps find targetable audiences to “rightsize” or expand a particular segment, (b) recommend cost-aware segments that are similar to desired audience segments for targeting, (c) provide comparative analytics for exploring the unique properties of a given segment, (d) enabling real-time audience classification to reduce the time to target in an efficient and effective manner.

Is the concept of lookalike modeling legitimate? How does this work? Is LAM a scalable targeting practice?

AT: Given customer behavior data, the lookalike model estimates and exploits the variations and similarities in behavior across various segments. Once the model figures out which similarities or differences are robust enough in the audience to be useful for predicting future behavior, it exploits these attributes in the data to expand a particular segment’s size by including those customers that are similar to the base segment but were not included because they did not meet the segment definition criteria. This allows fairly restrictive criteria to be relaxed using data science methods such as association mining and regression analysis to expand the segment size accurately, confidently, and in a scalable manner.

The entire digital advertising ecosystem is driven by data. What are the most valuable types of data for targeting? How do you see the future for the ecosystem? Will those with the most data win?

AT: This is central to success of the entire digital advertising world and the benefit of end consumers in finding the right and useful advertising. If we have to understand the user and focus on making digital advertising useful without making it adversarial, we have to focus carefully on the types and granularity of the data being collected. At both nPario and the Institute of Technology, University of Washington Tacoma where I hold an academic appointment, we stress the need for developing an ecosystem of data collection, management and mining that is customer centric with highest regards for security through robust multitenancy, cryptography and privacy aware practices.  The entire technology stack at nPario is for example, data agnostic. We decided very early on in the company to not be supplies of data but to be data pool neutral to allow our clients to bring their own first, second and third party data. Our platforms help clients derive value from the variety of big datasets while at the same time ensuring that customer and end-user privacy is preserved and not compromised through our actions in any manner whatsoever. To address your question if those with most data win I would like to quote that : Everybody has some data and some just have lots of data. It is the ones that have the right tools at the right time that will monetize their data the best.

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.

Death of the Digital Media Agency (Redux)

ImageLast year, I wrote that the digital agency was dead. I was mostly talking about how platform technology was going to knock a lot of digital media agencies out of business. In a world where over five trillion banner impressions are available every month, I argued, it was simply too much for humans to navigate through the choices and wring branding effect and performance out of campaigns. Well, digital media agencies are still around—but they continue to lose share to platforms as the amount of programmatically bought media increases. With RTB-based spending estimated to rise at an annualized rate of nearly 60% a year, according to market intelligence firm IDC, we could see as much as $14 billion in spending by 2016, or 27% of total display spending. Looks like the machines are slowly taking over.

Fairfax Cone, the founder of Foote, Cone, and Belding once famously remarked that the problem with the agency business was that “the inventory goes down the elevator at night.” That’s a big problem for an industry that relies on 23 year-old media planners to work long hours grinding on Excel spreadsheets and managing vendors to produce fairly mediocre media plans. Cone was talking about IP—what, exactly is the digital agency’s core intellectual property when the majority of the work seems to be hard labor? Digital creative agencies have no such worry. In this world of ubiquity, where everyone has access to wonderful SaaS-model technology that enables real-time bidding and access to trillions of exchange-based advertising impressions, the one place an agency can make an impact is on the creative side. Agencies that can create the miracle of getting more than 1 in 10,000 people to click on an ad, or watch a :30 pre roll video to completion are considered geniuses. But, what about the media shops? Can they really buy more efficiently than machines? More importantly, can they leverage the right machines to once again own the middle position between the advertiser and his prospect?

As I write in my recent report, looking at the history of display advertising, the future doesn’t favor the agency. In the beginning, agencies’ favored relationships with publishers made them a great way to buy media. Publishers aligned their content with the audiences that advertisers wanted (ESPN for sports enthusiasts), and largely controlled their inventory and audience data. Soon enough, the Network Era told hold, and smart companies like Tacoda started segmenting audiences based on context and behavior. By using technology to understand audiences better than the publishers themselves, they put yet another layer of IP between agencies and audiences. Then the DSP Era started, which further decoupled audiences from media. Agencies scrambled to create new vendor relationships with the MediaMaths of the world—but grew nervous that they would be disintermediated, and formed their own trading desks. This era is now evolving in the DMP Era.

After all of promises of easy audience targeting and automation, advertisers are looking at the same disturbingly low click-through rates, near impossibility of true attribution measurement, and spending waste—and determining that their own data is more valuable than most data that they can buy. Their desire to activate their “first party” data has given rise to the “DMP era.” Andy Monfried, who has brought his company Lotame through this transition, sees it this way, “Agencies are attempting to become technology providers for their clients, and from our perception, clients are hesitant to adopt. The larger agency holding companies have made an attempt at understanding first-party data but have come to be just a solution for clients to leverage third-party data. This is due to the lack of agency technology and lack of trust that clients put in agencies accessing their first-party data in a raw state.”

So, what happens now? Are advertisers simply going to license DMP technology, and build small practice groups for audience segmentation, targeting, and analytics? Or, are agencies going to adopt and learn how to become the centralization point for evaluating and helping clients implement new advertising technology? Media Kitchen digital head Darren Herman thinks the way through the trees is through education: “We are super bullish about teaching our strategists to learn the skills of data scientists. While the average media strategists will probably not have the skills of a robust data scientist with a PhD, from Stanford, an entire organization that learns to embrace data and make it useful will be more powerful than a few data scientists sprinkled [through] many. Knowledge of how to action data must both come from the top down and bottom up and be embraced by all. Building a culture that does this is hard as many people resist, but retooling and finding people who want this type of career is what we’re doing.”

Is your agency ready to hire a data scientist? Looks like the days of agencies hiring armies of English majors is over, and the next MIT recruiting session you see may have a few agency folks in attendance. Are digital media agencies dead? The data says not yet.

This post originally appeared on the EConsultancy blog on 12/13/12.

The Role of the Agency in Data Management

A Conversation with David Spitz of WPP Digital 

When it comes to the role of the agency in data-driven digital media, few holding companies have put their money where their mouth is to the extent that WPP Digital has. After setting the tone with a bold acquisition of 24/7 Real Media, the holding company has gone on to place strategic bets on a variety of sectors within the Kawaja map. The question for marketers is whether or not they should be relying upon their agencies when it comes to technology and data. Many argue that the agency model cannot support the type of deep domain expertise needed for the complicated integrations, data science, and modeling that has become an everyday issue in modern marketing. So, should data management be the sole province of the Adobes and IBMs of the world, or is there room for agencies to play? I recently reached out to EVP of Strategy and Corporate Development David Spitz to ask about how he sees agencies working with large brands to define their data strategy.

WPP is working with some of the world’s largest brands. I suspect that many have siloed pockets of valuable data across their enterprises. What are the data challenges and, more importantly, opportunities for global brands?

David Spitz (DS): You are right; there are many data challenges across large enterprises. They range from organizational issues (what group or department should even be running these programs?), to legal and commercial issues (do we have the right to the data we want to use?), to skill set gaps, to challenges posed by legacy technologies, to lack of data standards across channels, brands, regions, or even campaigns. In my experience, though, one thing is clear: it is rarely lack of data that is the problem.

The most common single question we hear from the world’s largest brands is “Where do I start?” It helps to have a clear understanding of the opportunities and choose one or two to focus on to build confidence and momentum while keeping in mind what could come next. “Think big, start small” is one of my favorite phrases when it comes to data programs. In terms of what those opportunities are, it really boils down to what I’ll call the 4 R’s – Reach, Relevance, Resonance and ROI.

Most companies that label themselves as “DMPs” are focused on Reach (e.g., targeting) or sometimes ROI (e.g., campaign evaluation, attribution), and mostly only in a digital sense. That might be a good place to start. However, I have also seen relevance (personalization) and resonance (social amplification) as the jumping off point for some brands. Either way, because these tools exist and can be deployed at relatively low cost, it is often best to start with digital-only applications before expanding the data program into multichannel territory.

Whether you are thinking digital or not, these four areas–Reach, Relevance, Resonance and ROI–probably represent 80% of the data opportunity for big brands, and between them you can usually identify at least one solid quick win.

When it comes to marketing, are these brands looking to their agencies for answers, or are they looking to the IBMs of the world? It seems like the agency’s ability to make an impact ends with the marketing team. Can you extend the agency’s value through to IT teams, and get everyone working together?

DS: When it comes to marketing, brands are absolutely looking to their agencies for answers. It is one thing to come up with an “enterprise architecture” and quite another to have it implemented. In many marketing functions, agencies are on the front lines of where the dollars get expressed, customer engagement happens, and [you are able to] understand what it takes to get data into a place where value can be realized.

Still, do agencies need to do a better job of partnering with CIO’s? Without a doubt. Various WPP companies have in place major partnerships with IBM, Adobe and Infosys to do just that, and at WPP Digital we recently invested in a company called Fabric and acquired a company called Acceleration, both of which specialize in marketing technology systems and, essentially, gap bridging between the CIO and the CMO.

You are working on putting many of WPP’s global data resources together (the “Data Alliance”). Tell us about the project. Is this a global data exchange? Are there unique types of data within the Alliance that are unavailable elsewhere?

DS: Data is at the heart of a lot of what WPP does. You have to realize, WPP is not only the world’s largest communications services group, but if you looked at some of its operating companies as standalone you’d find inside WPP the world’s largest media buying company (GroupM), the second largest market research company (Kantar), and, with $4.7b in revenues coming from digital, including the likes of 24/7 Media, OgilvyOne, Wunderman, AKQA, VML, and Possible, WPP is the seventh largest digital company in the world – behind Google and Apple, but ahead of Facebook right now. So you can imagine, WPP as a whole is dealing with a lot of data.

What we are trying to do with The Data Alliance is analogous to the airline industry, where independently operated carriers have come together to create these inter-company frequent flyer programs (as in the Star Alliance) and coordinated route maps. The whole idea is to provide a more seamless customer experience while at the same time providing efficiencies for the member organizations. Without going into too many details, The Data Alliance is focused on three things: Creating greater interoperability across its members’ platforms and data sets, streamlining how we as a group engage with third-party partners (to make it easier on an Acxiom or Exelate, for example, to work with us broadly), and creating a more seamless experience for clients who are working with us more than one discipline (e.g., media, market research, CRM, and digital).

How we do this will involve many different tactics over time, for example, pooling of certain technology development efforts and greater standardization around certain things like policies, data structures, commercial terms, and API’s. You can speculate about some of the new products and business models that would result out of a program like this, but right now the primary focus is simply on creating the best solutions we possibly can for the top 30 clients who are our “frequent fliers” if you will.

Unlike some other holding companies, WPP has taken an active role in investing in, and acquiring, digital media technology. The “stack” that you are assembling at 24/7 Media, and some of the social media technology investments you have made suggest a commitment to being more than just a typical agency. The Data Alliance initiative is also instructive. Tell us what you look for in differentiated technologies.

DS: WPP comes at it very much from a client-side perspective and has partnered with technologies like Omniture and Buddy Media that share that view. In the cases of those two businesses in particular, both of which WPP invested in, there was beyond the obvious criteria also a strong cultural fit with the management team and a good amount of overlap between WPP’s customer base and theirs, so it just made a lot of sense.

In the case of 24/7, while they were known as a publisher-side technology before WPP acquired them in 2007, the intent was always to leverage their audience reach and technical know-how to build what people would now call a DMP/DSP – the tools that now power Xaxis. There were not any established players doing this at the time, so the 24/7 acquisition enabled GroupM to build these capabilities much faster than they could have otherwise. The acquisition of iBehavior, which operates a DMP of a different sort (mostly offline transactions), is also consistent with this strategy and is similar in that it’s accelerating Wunderman’s route to market with several new initiatives.

To your broader point about being not just a typical agency, I don’t believe agencies need to control all of the underlying technologies, but I do think that the techniques involved in connecting and analyzing diverse data streams – and doing so in a scalable, efficient and privacy-safe way – are too important a skill set for a company like WPP to outsource entirely. When digital is the direction most marketing channels are headed, and the ability to measure everything and act on data is a large part of what makes digital so exciting, not having a data integration and data sciences function (granted, it may be called something else) inside an agency holding company in ten years will seem as unusual as not having a media group would today.

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.

This post also appeared on the iMediaConnection blog on 12/18/12.

What I Learned Writing Best Practices in Data Management

Today data is like water: free-flowing, highly available, and pervasive. As the cost of storing and collecting data decreases, more of it becomes available to marketers looking to optimize the way they acquire new customers and activate existing ones. In the right hands, data can be the key to understanding audiences, developing the right marketing messages, optimizing campaigns, and creating long-term customers. In the wrong hands, data can contribute to distraction, poor decision-making, and customer alienation. Over the past several weeks, I asked over thirty of the world’s leading digital data practitioners what marketers should be thinking about when it comes to developing a data management strategy. The result was the newly available Best Practices in Data Management report. A few big themes emerged from my research, which I thought I would share:

Welcome to the First Party

Digital marketing evolves quickly but, for those of us working as digital marketers or publishers for the past 10 years, we have seen distinct waves of transformation impact the way we use data for audience targeting. Early on, audience data was owned by publishers, who leveraged that data to control pricing for premium audiences. The Network Era quickly supplanted this paradigm by leveraging tag data to understand publishers’ audiences better than the sites themselves. Buying targeted remnant inventory at scale created new efficiencies and easy paychecks for publishers, who found themselves completely disintermediated. The DSP Era (which we are still in) continued that trend, by completely separating audiences from media, and giving even more control to the demand side. Today, the “DMP Era” promises a new world where publishers and advertisers can activate their first party data, and use it for remarketing, lookalike modeling, and analytics.

The ubiquity of third party data (available to all, and often applied to the same exact inventory) makes activating first party data more valuable than ever. Doing so effectively means regaining a level of control over audience targeting for publishers, and being able to leverage CRM data for retargeting and lookalike modeling for the demand side, as well as a deeper level of analytics for both sides. If there has been one huge takeaway from my conversations with all of the stakeholders in the data-driven marketing game, it is that getting control and flexibility around the use of your own first-party data is the key to success. As a marketer, if you are buying more segments than you are creating, you are losing.

The New Computing Paradigm

In order to successfully activate all of the data your company can leverage for success takes a lot of work, and a lot of advanced technology. Whether you are a publisher trying to score audiences in milliseconds in order to increase advertising yield, or an advertiser attempting to deliver a customized banner ad to a prospect in real-time, you need to store an incredible amount of data and (more importantly) be able to access it at blazing speeds. In the past, having that capability meant building your own enormous technology “stack” and maintaining it.  Today, the availability of cloud-based computing and distributed computing solutions like Hadoop has created a brand new paradigm or what former Microsoft executive and current RareCrowds CEO Eric Picard likes to call the “4th Wave.”

“Being a Wave 4 company implicitly means that you are able to leverage the existing sunk cost of these companies’ investment,” says Picard. That means building apps on top of AppNexus’ extensible platform, leveraging Hadoop to process 10 billion daily transactions without owning a server (as Bizo does), or simply hosting portions of your data in Amazon’s cloud to gain speed and efficiency. As digital marketing becomes more data intensive, knowing how to leverage existing systems to get to scale will become a necessity. If you are not taking advantage of this new technology paradigm, it means you are using resources for IT rather than IP. These days, winning means applying your intellectual property to available technology—not who has the biggest internal stack.

Social Data is Ascendant

One of the most interesting aspects of data management is how it is impacting traditional notions of CRM. In the past, digital marketing seemed to end below the funnel. Once the customer was driven through the marketing funnel and purchased, she went into the CRM database, to be targeted later by more traditional marketing channels (e-mail, direct mail). Now, the emergence of data-rich social platforms had actually created a dynamic in which the funnel continues.

Once in the customer database (CRM), the post-purchase journey starts with a commitment beyond the sale, when a consumer joins an e-mail list, “friends” a company’s page, follows a company’s Twitter account, or signs up for special offers on the company’s site. The next step is an expression of social interest, when the consumer agrees to make public his like for a company or brand by “friending” a company’s page, following a company’s Twitter account. Beyond the “like” is true social activation, wherein the consumer actively (not passively) recommends the product or service, through commenting, sharing, or other active social behaviors. The final step is having the consumer sell on your behalf (directly via affiliate programs or, in the softer sense, as a “brand ambassador”).  This dynamic is why Salesforce has acquired Radian6 and Buddy Media.

For digital marketers, going beyond the funnel and activating consumers through social platforms means understanding their stated preferences, affinities, and that of their social graph. Most companies already do this with existing platforms. They real key is tying this data back into your other data inputs to create a 360 degree user view. That’s where data science and management platforms come in. If you are not ingesting rich social data and using it to continually segment, target, expand, and understand your customers, you are behind the curve.

[This post originally appeared on the EConsultancy blog. Get the paper here.]

Choosing a Data Management Platform

A Conversation with Bridget Bidlack

Today, data is like water: Free-flowing, highly available, and pervasive. As the cost of storing and collecting data decreases, more of it becomes available to marketers looking to optimize the way they acquire new customers and activate existing ones. In the right hands, data can be the key to understanding audiences, developing the right marketing messages, optimizing campaigns, and creating long-term customers. In the wrong hands, data can contribute to distraction, poor decision-making, and customer alienation. In order to combat that problem, there are now over a dozen data management platforms (DMPs) configured to help marketers and publishers leverage their first party data, and take advantage of the growing universe of 3rd party data. I recently sat down with a DMP veteran, Bridget Bidlack, to ask how one should approach choosing a platform.

To the unpracticed eye, it seems like many DMPs do exactly the same things. What are some of the subtleties and differences between the major platforms?

Bridget Bidlack (BB): It’s true that, to someone unfamiliar with the technology, the differences may seem subtle, but that’s often the case no matter what you are discussing. I recently came across a catalog that featured a violin bow for $22,000. To me they all look alike but to a virtuoso the right bow can make all the difference in the world.

That’s the way it is for marketers and the technology they rely on every day. DMPs are very different in the capabilities they provide; the approach and level of integration they are capable of; their ability to adapt to future media channels and market demands; how well they can scale in terms of the amount of data they can ingest, manage and store; and their ability to deliver actionable analytics regardless of the audience touch point.

Smart marketers who evaluate their needs and assess the full range of solutions to find the one best able to suit their needs will benefit today and in the future.

Many DMPs sprang forth from a network background. Is there an advantage to having a heritage in the online media business? Is it better to leverage a “pure play” DMP that has been built from the ground up?

BB: It’s really important to bear in mind the differences between a DMP designed exclusively for display media and an enterprise DMP designed for the needs major brands that require multi touchpoints.

Too often people behave as though display advertising is the be-all and end-all of marketing, and that’s probably true inside an agency. But enterprise marketers have a much broader palette of customer and prospect touchpoints they need to manage. That’s where a purpose-built enterprise DMP really shows its value. So, what are the differences between a display-focused DMP and an enterprise DMP?

  • First, an enterprise DMP ingests and normalizes data from a wide variety of sources
  • Second, is to automate the way data is organized and segmented
  • Third, is to be configurable enough to use an organization’s unique approach to audience identification and data match key models
  • Fourth, is to make the enterprise’s unique data actionable across ALL touch points in real time
  • Fifth, is to deliver consistent messages and enforce offer eligibility across all channels – not just display,  but important customer channels such as email, click to chat and SMS for example.

You have worked with some of the world’s largest and most aggressive marketers to help them leverage their data. What were some of the challenges you encountered at the enterprise level that surprised you?

BB: This probably doesn’t come as a surprise, but in large organizations it is sometimes difficult for individual departments to put the greater good of the overall organization ahead of their own goals. Typically this is because of the way individual departments are measured. It’s important to understand the needs of all departments and how an enterprise DMP can help meet those needs. The costs and benefits of DMPs are enterprise-wide and their benefits should be evaluated that way.

Some organizations have created systems that provide DMP-like capabilities. In these situations, a company can weigh the total cost of ownership and benefits of building out the full DMP functionality versus working with an available enterprise DMP. There are a number of factors to consider: speed to market, ROI, domain expertise and consumer privacy, to name a few.

Large organizations have many disparate data sets that are used in many different ways. Sometimes, just getting a list of all the different data sources and attributes is a challenge. Often, there isn’t a shared taxonomy that can be used across departments. Data management and permissions can also become an issue as different departments might have rights and permissions to different data sets that others do not. All of this points to the challenge of finding a unique ID to link all of an organization’s data for a given customer together in a way that makes it accessible and actionable where and when it is needed.

How big is the market for DMPs? How many companies actually have the data challenges that warrant leveraging a “big data” platform for marketing?

BB: The market is growing so fast that this is a difficult question to answer. Any marketer would love to have one platform to reach their customers across any current or future channel. Some marketers might claim they’re comfortable limiting their reach to channel-specific audiences available through specific ad networks or email providers, but that’s rare. Sophisticated marketers want to use the full force of tools, technology and insights at their disposal. They want to use their own data along with third-party data, they want to take into account interactions on their website, as well as those taking place on other marketing channels to inform every message put in front of a consumer. To do otherwise seems like marketing with one hand tied behind your back. Who would choose that?

What are some of the considerations to bear in mind? The number of disparate data systems they are working with, the number of touch points they use to reach their consumers, how frequently the data they depend on is updated, how quickly they need access to the data and the sheer amount of data that they have on their customers. They also need to ask themselves whether their goals can be met with internal systems or by using multiple point products. In most cases it will be more efficient, economical and effective to work with a complete platform able to meet all their needs.

Let’s pretend all current DMPs have exactly the same attributes right now. What should I look for on a DMPs product roadmap to tell me they are going to offer the next great differentiator? Is it Hadoop compatibility? Fast query speeds, based on different storage abilities?

BB: If I were in the market for a DMP and all things were equal, the items I’d like to see in a roadmap would be:

  • A robust and constantly expanding set of self-service tools to allow end users to manage and use their data independently and in a scalable way
  • Continued investment in analytics and modeling to allow customers to understand data in the ways that will make it work best for them. There should also be a balance of pre-defined reports that provide deep insights out of the box, as well as the ability for users to customize them to meet their own specific needs
  • The ability to adapt to emerging market trends and new technologies
  • Attribution modeling that provides the ability to implement custom approaches into the media planning, buying and decisioning processes

Integration seems to be the name of the game. How important are existing server-to-server integrations? Are DMPs becoming truly “plug and play” as they plug into more and more various technologies?

BB: Having open web service APIs is important for any DMP that claims to provide ‘plug and play’ capabilities. This approach makes it fast and flexible and easy to integrate with new partners, channels and data sources. Without this type of framework, integration can become a nightmare of custom code, delays and missed opportunities.

What about data? Does the company with the most data win? Should I select a DMP based on the ability not just to manage first party data, but for their ability to link my data to the wider universe?

BB: The idea that more data equals better performance is much too simplistic. When it comes to data, the things that matter are how it is filtered, analyzed and put to work to inform decisions. Quantity isn’t the key at all; it’s all about having the right data and being able to act on it to reach customers and prospects at the right time through the right channels.

The ability to centralize, normalize and make data actionable through any touch point needs to be at the core of any enterprise DMP. The DMP should also close the loop by ingesting campaign data from all channels and vendors, as well as offline activities like in-store sales and call center interaction. The data can be surfaced in a way that is meaningful to the marketer. This means marketers need the ability to define custom attribution models to reflect their unique sales funnels. Based on this information, marketers can measure ROI and inform future strategies.

Data is key but it has to be available, accurate and actionable for it to have the kind of impact that marketers demand.

Will be still be talking about “DMPs” in 2 years, or is there another acronym coming along that marketers need to be aware of?

BB: In the future, marketers will continue to invest in learning about and tapping into the latest channels, networks and screens through which consumers are living their increasingly digital lives. Whenever new channels, networks and screens emerge, there will be an evolution and expansion of the data available to marketers. This means that the systems and technologies for ingesting, testing and validating data will continue to be valued – probably even more than they are today.

Smart marketers increasingly understand the importance of being customer-centric and this implies the need to be data-centric. Knowing this they will continue to invest in data management technologies. They will also bring these capabilities in-house as they have in the past with their core CRM and operational data. Even as the hardware and software running their data management platform migrates to the cloud, it will still be viewed as an “owned” solution. This means that the technology companies that marketers partner with to develop and execute their marketing campaigns will need to continue to invest in becoming data savvy and fluent with the tools and systems in the marketplace.

 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.

This post also appeared in the iMediaConnection blog on 12/11/12.