EConsultancy: Best Practices in Data Managment

A Google Hangout with Eric Picard, CEO of RareCrowds; Chris Scoggins of Datalogix; Andy Monfried, CEO of Lotame; and Chris O’Hara, author of Best Practices in Data Management. Hosted by Stefan Tornquist of EConsultancy.

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The Data-Driven CMO

talkdataMark Zagorski, the CEO of data management platform eXelate has worked with dozens of big marketers to help them put all kinds of data to work, including their own.

“Right now most organizations are dealing with terabytes of data. Over a third [manage] more than 10 terabytes of data and one-fifth will manage half a petabyte of data within three years,” Zagorski tells me. “The key objective for marketers seeking to harness the power of big-data is to make it actionable.”

As a marketer, it is likely that you have access to a great deal of data, and maybe even the kind of big-data we’ve been hearing so much about. CRM data grows every day; point-of-sale data gets easier and less expensive to store; tag-collected data from websites and social sites expands daily; and there is a seemingly infinite amount of third-party data available for purchasing and mixing in with your own.

The modern CMO must find a way to value the data assets she has, learn to listen for the real signals among the noise, and find a way to put that data to use. Mostly, that means understanding customer attributes, what drives them to transact, and how much it costs to get them to do so more frequently.

For Darren Herman, in charge of digital media at forward-looking agency Media Kitchen, data is all about the way it can be leveraged for his clients. “We care less about big-data and more about actionable data. Our clients have tons of first-party data,” Herman tells me, adding that the real challenge is in “uniting the data between silos (usually within client organizations) and making them available and actionable for advertising and marketing decisions. Much of the time, the clients’ data is available through the IT organization, and it’s not quite understood how it will be used for marketing decisions.”

In many ways, data-driven CMOs face two challenges: Firstly, winning the internal battle with the CTO to get access to disparate data sources, and bringing them together in a way that creates the opportunity to glean global insights; and secondly, building the platform that enables them to normalize many discrete data types, query that data quickly, and “activate” that data to produce a sales outcome.

Think of a large, global consumer products organization. A company that sells soap suds around the world may have up to 20 regional operating companies, and as many as 200 separate datacenters throughout the organization. Within all of those data silos are digital stories of marketing success and failure. Imagine if you could duplicate the promotional dynamics that drove a 20 percent increase in Italian diaper sales across the entire global organization, or leverage the learnings that one operating company had when a key discounting scheme failed?

These types of insights can be obtained when the CMO asks the right questions, and when he has data management platforms behind him that can make it possible to get the answers. Being a data-driven marketer isn’t about how much data you can centralize in a single platform. The data may be big, but ultimately the data you store is only as valuable as your ability to extract insights from it — and act upon it.

[This post originally appeared in the CMO Site on 3/20/13]

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.]

Why We’re all Thinking Big Data (Jump Magazine Q+A)

Interview by Heather Taylor

One trend that is dominating conversation across marketing and wider business practices, is big data. How we measure it, how we store it and how we use it to inform the work that we do. We spoke to Chris O’Hara, domain expert on platform technology to find out about big data, why it’s important and how it is changing the marketing world.

Q Why is big data such a big deal and how did it get that way?

A: I think the term “Big Data” is getting thrown around a lot lately. There’s “data” that’s maybe too big for some companies to handle, and then there is truly “BIG DATA,” like you would find in the human genome, or Google search. The simple fact is that data has gotten a lot cheaper to store, and infinitely easier to access.

Big data is a big deal because people are leveraging technology to get insights from data they have never been able to get without spending more than those insights are worth. In short, understanding data makes money for those smart enough to leverage it, whether you are a digital agency, CPG marketer, or hedge fund. As the recent McKinsey report points out, “The volume of data that businesses collect is exploding: in 15 of the US economy’s 17 sectors, for example, companies with upward of 1,000 employees store, on average, more information than the Library of Congress does. New academic research suggests that companies using this kind of “big data” and business analytics to guide their decisions are more productive and have higher returns on equity than competitors that do not.”

Q: Why and how is big data moving us toward a more integrated marketing approach?

A: The largest change is not that data is being used to drive advertising creative and placement; it is that the data is available immediately, and that creates the opportunity for optimization. I think we are still in the early days, though. Most marketers and publishers are content to use off-the shelf 3rd-party segments to define and target audiences, rather than plumbing the (infinitely more valuable) depths of their own, first party datasets. Take large CPG companies who maintain databases all over the world. In one large company, you might have as many as 200 large databases, across dozens of operating companies all over the world. It is likely that those datasets have never been directly connected, and certainly it is highly unlikely that this data has never been stitched together and plumbed for insights.

The data equation in marketing is quite simple: the more an advertiser knows about you, the better you can be targeted. The real question is whether or not the effort and expense of such targeting is worth the incremental yield that targeting produces. As data gets cheaper and the cost of accessing diminishes, it is obvious that data starts to create real value for marketers.

Q: How is the era of big data changing the practice of digital marketing?

A: One of he biggest ways that data can help is in terms of avoiding waste. Before large amounts of data could be processed easily, there was no easy way to find out, as an example, what an advertisers’ unduplicated reach was across channels that include mobile, video, game consoles, and the Web.

The so-called “sciencification of marketing” is real. If you look at Terence Kawaja’s famous logo vomit slide of the digital display advertising landscape, it is clear that it is 100% driven by data. The underlying data is mostly audience-based, but there is also ad performance data, search data, engagement data, longitudinal data, and attitudinal data driving digital marketing these days.

On the direct marketing side, the transition from using mailing address data and surveys to target households to using IXI financial data to target online audience members via a cookie is not so different. Direct marketers can judge performance in real time with conversion data, and now brand marketers can leverage real-time engagement metrics to measure success.

Q: What are some examples of big data in integrated marketing?

A: The applications to use data in marketing are virtually unlimited. We are moving into a world where everything is interconnected, and we are surrounded by devices that transmit and store data constantly. These days, your supermarket partners with a brand to start a campaign on television, and that drives you to their website, to download a mobile coupon code that goes to your phone, and is used at the checkout line. Your purchase data is then stored, churned, and used to inform the next campaign. A better example of a big data approach to marketing (well, integrated digital marketing) is Google. Ingesting your search habits, video preferences, e-mail content, social network, mobile activity, and internet browsing habits takes a lot of expensive data storage, but it seems to be paying off for Google!

Q Which companies are jumping into the big data business and how will this help (and hinder) us?

A:  In the digital marketing space, you are going to see almost every progressive network, exchange, and data provider stake their claim to helping advertisers and publishers leverage their data. Some of them will be bigger than others. When it comes to managing truly huge amounts of audience data, there are very few companies that have managed to do it outside of the “big five” (AOL, Yahoo, Facebook, Google, and Microsoft). If a company truly has big data (petabytes, terabytes, or exabytes) then you need database software that can scale infinitely, and be able to query massive tables of data and return a result quickly. In marketing, that is starting to mean “real time,” which is not only a software challenge, but a hardware and logistical challenge as well. Marketers should look for a DMP that has actual experience working with massive data sets specifically for marketing applications.

Q What implications will digital marketers face with this big data trend?

A: Just because the data is there, doesn’t make it meaningful. These days it is possible to get a near real-time view of your audience at the creative level for digital display campaigns, but how many marketers can take advantage of the overwhelming amount of data that they receive every day beyond enabling a DSP to “auto-optimize a campaign, based on a single metric, such as conversion rate? Marketing insights that are driven by churning huge amounts of data are only as useful as the marketer’s ability to react to execute against them. That is why you are going to see the technology platforms that specialize in advertising execution team up with data platforms to try and get advertisers a true 360-degree view of the consumer that can be acted upon.

Q How can marketers leverage big data without being overwhelmed by it?

A: Try and learn what data is valuable and what is not. Even though I bought a new car 18 months ago, I am still bombarded with Volkswagen ads every time I check my email. Whoever is buying my “auto intender” cookie isn’t really getting their money’s worth, are they? My advice would be to perform a “data appraisal” that focuses on your own first-party data and see what you have. Even if your daily data is measured in gigabytes rather than petabytes, there is always something to leverage.

This appears in the current edition of eConsultancy’s Jump Magazine, which you can download here.

The Five Things to Expect in a DMP

Getting back control over their inventory is giving publishers a lot to think about.

“We want to make sure that we’re controlling what happens with data . . . we want to make sure we control pricing. Control’s a very important message. We don’t want there to be a cottage industry built on our backs” – Nick Johnson, SVP, NBC Universal

What do publishers really want? It’s simple, really: Power and control. In order to survive the ad technology era, publishers need the power to monetize their audiences without relying on third parties, and complete control over how they sell their inventory. In this era of “Big Data,” there is a fire hose stream of tremendously valuable information for publishers to take advantage of, such as keyword-based search data, attitudinal survey data, customer declared data from forms, page-level semantic data, and all the 3rd party audience data you can shake a stick at.

All of this data (cheap to produce, and ever-cheaper to store) has given rise to companies who can help publishers bring that data together, make sense of it, and use it to their advantage. Currently, ad technology companies have been using the era of data to their advantage, utilizing it to create vertical ad networks, ad exchanges, data exchanges, DSPs, and a variety of other smart-sounding acronyms that ultimately purport to help publishers monetize their audiences, but end up monetizing themselves.

Rather than power the ad tech ecosystem, what if data could actually help publishers take back their audiences? If “data is the new gold” as the pundits are saying, then smart publishers should mine it to increase margins, and take control of their audiences back from networks and exchanges. Here are the five things a good data management platform should enable them to do:

  • Unlock the Value of 1st Party Data: Publishers collect a ton of great data, but a lot of them (and a LOT of big publishers) don’t leverage it like they should. Consider this recent stat: according to a recent MediaPost article, news sites only use in-site audience targeting on 47% of their impressions, as opposed to almost 70% for Yahoo News.  By leveraging site-side behavioral data, combined with CRM data and other sources, it is possible to layer targeting on almost every impression a publisher has. Why serve a “blind” run-of-site (ROS) ad, when you can charge a premium CPM for audience-targeted inventory?
  • Decrease Reliance on 3rd Parties: The real reason to leverage a DMP is to get your organization off the 3rd party crack pipe. Yes, the networks and SSPs are a great “plug and play” solution (and can help monetize some “undiscoverable” impressions), but why are publishers selling raw inventory at $0.35 and letting the people with the data resell those impressions for $3.50? It’s time to turn away those monthly checks, and start writing some to data management companies that can help you layer your own data on top of your impressions, and charge (and keep) the $3.50 yourself. Today’s solutions don’t have to rely on pre-packaged 3rd party segments to work, either, meaning you can really reduce your data costs. With the right data infrastructure, and today’s smart algorithm-derived models, a small amount of seed data can be utilized to create discrete, marketable audience segments that publishers can own, rather than license.
  • Generate Unique Audience Insights: Every publisher reports on clicks and impressions, but what advertisers are hungry for (especially brand advertisers) are audience details. What segments are most likely to engage with certain ad content? Which segments convert after seeing the least amount of impressions? More importantly, how do people feel about an ad campaign, and who are they exactly? Data management technology is able to meld audience and campaign performance data to provide unique insights in near real-time, without having to write complicated database queries and wait long times for results. Additionally, with the cost of storing data getting lower all the time, “lookback windows” are increasing, enabling publishers to give credit for conversion path activity going back several months. Before publishers embraced data management, all the insights were in the hands of the agency, who leveraged the data to their own advantage. Now, publishers can start to leverage truly powerful data points to create differentiated insights for clients directly, and provide consultative services with them, or offer them as a value-added benefit.
  • Create New Sales Channels: Before publisher-side data management, when a publisher ran out of the Travel section impressions, he had to turn away the next airline or hotel advertiser, or offer them cheap ROS inventory. Now, data management technology can enable sales and ops personnel to mine their audience in real time and find “travel intenders” across their property—and extend that type of audience through lookalike modeling, ensuring additional audience reach. By enabling publishers to build custom audience segments for marketers on the fly, a DMP solution ensures that no RFP will go unanswered, and ROS inventory gets monetized at premium prices. 
  • Create Efficiency: How many account managers does it take to generate your weekly ad activity reports? How much highly paid account management time are publishers burning by manually putting together performance reports? Why not provide an application that advertisers can log into, set report parameters, and export reports into a friendly format? Or, better yet, a system that pre-populates frequent reports into a user interface, and pushes them out to clients via an e-mail link? You would think this technology was ubiquitous today, but you would be wrong. Ninety-nine percent of publishers still do this the hard (expensive) way, and they don’t have to anymore.

It’s time for publishers to dig into their data, and start mining it like the valuable commodity it is. Data used to be the handcuffs which kept publishers chained to the ad technology ecosystem, where they grew and hosted a cottage industry of ad tech remoras. The future that is being written now is one of publishers’ leveraging ad technologies to take back control, so they can understand and manage their own data and have the freedom to sell their inventory for what it is truly worth.

That’s a future worth fighting for.

[This post originally appeared in ClickZ on 2/29/12]

When Big Data Doesn’t Provide Big Insights

The right DMP solution can be golden for finding audiences.

What big marketers should look for in a next generation data management platform

“Big Data” is all the rage right now, and for a good reason. The other day, I was switching computers, and wanted to move about five gigabytes of photos and videos unto my new laptop, and my largest thumb drive was a measly 1 gig. I ended up getting an 8GB thumb drive for about $8 at the K-Mart in Penn Station. Think about how cheap that is. That’s less than half a cent per song, if you consider the typical 8GB MP3 device can hold about 2,000 high-quality recordings. Two terabyte drives are selling for about $130 from Western Digital. I don’t know about you, but I am not at the point where I need 2TB of data storage, and I hope I never get there. The point is that storing tons and tons of data has gotten very inexpensive, while the accessibility of that data has increased substantially in parallel.

For the modern marketer, that means having access to literally dozens of disparate data sources, each of which cranks out large volumes of data every day. Collecting, understanding, and taking action against those data sets is going to make or break companies from now on. Luckily, an almost endless variety of companies have sprung up to assist agencies and advertisers with the challenge. When it comes to the largest volumes of data, however, there are some highly specific attributes you should consider when selecting a data management platform (DMP).

Collection and Storage: It’s all About Scale, Cost, and Ownership

First of all, before you can do anything with large amounts of data, you need a place to keep it. That place is increasingly becoming “the cloud” (i.e., someone else’s servers), but it can also be your own servers. If you think you have a large of data now, you will be surprised at how much it will grow. As devices like the iPad proliferate, changing the way we find content, even more data will be generated. Companies that have data solutions with the proven ability to scale at low costs will be best able to extract real value out of this data. Make sure to understand how your your DMP scales and what kinds of hardware they use for storage and retrieval.

Speaking of hardware, be on the lookout for companies that formerly sold hardware (servers) getting into the data business so they can sell you more machines. When the data is the “razor,” the servers necessarily become the “blades.” You want a data solution whose architecture enables the easy ingestion of large, new data sets, and one that takes advantage of dynamic cloud provisioning to keep ongoing costs low. Not necessarily a hardware partner.

Additionally, your platform should be able to manage extremely high volumes of data quickly, have an architecture that enables other systems to plug in seamlessly, and whose core functionality enables multi-dimensional analysis of the stored data—at a highly granular level. Your data are going to grow exponentially, so the first rule of data management is making sure that, as your data grows, your ability to query them scales as well. Look for a partner that can deliver on those core attributes, and be wary of partners that have expertise in storing limited data sets. There are a lot of former ad networks out there with a great deal of experience managing common 3rd party data sets from vendors like Nielsen, IXI, and Datalogix. When it comes to basic audience segmentation, there is a need to manage access to those streams. But, if you are planning on capturing and analyzing data that includes CRM and transactional data, social signals, and other large data sets, you should look for a DMP that has experience working with 1st party data as well as 3rd party datasets.

The concept of ownership is also becoming increasingly important in the world of audience data. While the source of data will continue to be distributed, make sure that whether you choose a hosted or a self-hosted model, your data ultimately belongs to you. This allows you to control the policies around historical storage and enables you to use the data across multiple channels.

Consolidation and Insights: Welcome to the (Second) Party

Third party data (in this context, available audience segments for online targeting and measurement) is the stuff that the famous Kawaja logo vomit map was born from. Look at the map, and you are looking at over 250 companies dedicated to using 3rd party data to define and target audiences. A growing number of platforms help marketers analyze, purchase, and deploy that data for targeting (BlueKai, eXelate, Legolas being great examples). Other networks (Lotame, Collective, Turn) have leveraged their proprietary data along with their clients to offer audience management tools that combine their data and 3rd party data to optimize campaigns. Still others (PulsePoint’s Aperture tool being a great example) leverage all kinds of 3rd party data to measure online audiences, so they can be modeled and targeted against.

The key is not having the most 3rd party data, however. Your DMP should be about marrying highly validated 1st party data, and matching it against 3rd party data for the purposes of identifying, anonymizing, and matching third party users. DMPs must be able to consolidate and create as whole of a view of your audience as possible. Your DMP solution must be able to enrich the audience information using second and third party data. Second party data is the data associated with audience outside your network (for example, an ad viewed on a publisher site or search engine). While you must choose the right set of 3rd party providers that provide the best data set about your audience, your DMP must be able to increase reach by ensuring that you can collect information about as many relevant users as possible and through lookalike modeling.

For example, if I am selling cars and I find out that my on-site users who register for a test drive are most closely matched with PRIZM’s “Country Squires” segment,  it is not enough to buy the Nielsen segment. A good DMP enables you to create your own lookalike segment by leveraging that insight—and the tons of data you already have. In other words, the right DMP partner can help you leverage 3rd party data to activate your own (1st party) data.

Make sure your provider leads with management of 1st party data, has experience mining both types of data to produce the types of insights you need for your campaigns, and can get that data quickly. Data management platforms aren’t just about managing gigantic spreadsheets. They are about finding out who your customers are, and building an audience DNA that you can replicate.

Making it Work         

At the end of the day, it’s not just about getting all kind of nifty insights from the data. I mean, it’s big to know that your visitors that were exposed to search and display ads converted at a 16% higher rate, or that your customers have an average of two females in the household. It’s making those insights meaningful.

So, what to look for in a data management platform in terms of actionability? For the large agency or advertiser, the basic functionality has to be creating an audience segment. In other words, when the blend of data in the platform reveals that showing 5 display ads and two SEM ads to a household with 2 women in it creates sales, the platform should be able to seamlessly produce that segment and prepare it for ingestion into a DSP or advertising platform. That means a having an extensible architecture that enables the platform to integrate easily with other systems. Moreover, your DMP should enable you to do a wide range of experimentation with your insights. Marketers often wonder what levers they should pull to create specific results (i.e., if I change my display creative, and increase the frequency cap to X for a given audience segment, how much will conversions increase)? Great DMPs can help built those attribution scenarios, and help marketers visualize results. Deploying specific optimizations in a test environment first means less waste, and more performance. Optimizing in the cloud first is going to become the new standard in marketing.

Final Thoughts

There are a lot of great data management companies out there, some better suited than others when it comes to specific needs. If you are in the market for one, and you have a lot of first party data to manage, following these three rules will lead to success:

  • Go beyond 3rd party data by choosing a platform that enables you to develop deep audience profiles that leverage first and third party data insights. With ubiquitous access to 3rd party data, using your proprietary data stream for differentiation is key.
  • Choose a platform that makes acting on the data easy and effective. “Shiny, sexy” reports are great, but the right DMP should help you take the beautifully presented insights in your UI, and making them work for you.
  • Make sure your platform has an applications layer. DMPs must not only provide the ability to profile your segments, but also assist you with experimentation and attribution–and provide you with ability to easily perform complicated analyses (Churn, and Closed Loop being two great examples). If your platform can’t make the data dance, find another partner.

[This post was originally published in ClickZ on 11/9/11]