The Five (New) Things to Expect from a DMP

The-5-Pillars-of-Health

In early 2012, when data management technology was somewhat nascent, I wrote about “the five things to expect from a DMP.” They were: To unlock the power of one’s first party data; decrease reliance upon third party data; generate unique audience insights; use data to audience power new channels; and create efficiency. A little over three years later, those things still continue to drive interest in DMP technology—and great value for both publishers and marketers.

The “table-stakes” functionality of DMPs—segmentation, lookalike modeling, targeting, and analytics—continue to resonate. Even the least advanced DMPs have those abilities, and this is what people who buy DMP software should expect from any system. Unfortunately, there are now dozens of “platforms” that claim DMP technology. Some are legitimate players, born from the ground up to be “first-party” DMPs. Some have been created as “lightweight” DMPs to collect and distribute cookies for display advertising. And still others are legacy tag management or network platforms that have bolted on DMP functionality as they work towards a fuller “stack” solution that marketers say they want.

Writing this article again, three years later, I would still encourage software buyers to evaluate their DMP choice on the ability of their partner to meet the above-listed criteria. But, there has been so much nuance and development over the last several years. Therefore, additional selection criteria present themselves if one is expected to make a reasonably informed choice in DMP selection going forward.

Here’s what the modern DMP consumer should be looking out for:

  • Lookback: Three years ago I talked about “lookback windows” in the context of giving publishers the ability to attribute future conversion events to ads shown previously on their site. That is still a compelling publisher user case. What “lookback windows” really refer to is whether or not your DMP can capture 100% of the raw, log-level user event data—and store it. This necessitates an open taxonomy (because “you don’t know what you don’t know,”) and also the ability to store tons of data and make it accessible quickly. This is considered to be complete data architecture. Many DMPs operate with a rigid, defined taxonomy and only collect segment IDs—not the underlying data. That’s a problem for businesses that need to move fast and activate new segments opportunistically. Ask how—and for how long—your DMP stores data.
  • Onboarding: Lots of DMPs claim to have the ability to easily ingest CRM and other offline data and match it to cookies, but the truth is everyone depends on a limited set of “onboarding” vendors to provide the matches. That’s fine, but there are some nuances and subtleties involved in the process by which offline data enters the online identity space (hashing). DMPs should enable seamless connection to all three major onboarding providers, the ability to select the methodology by which offline identity is matched to online, and also be able to automatically choose which onboarding partner is right for each identity. Ask how each DMP you evaluate works with each vendor, what kind of match rates you can expect, and how each stores persistent user identity to insure better matches over time.
  • Measurement: Let’s face it, the ability to tweak programmatic audience delivery to online video viewability numbers up a few percentage points is great, but nothing moves the needle like linear television. Marketers spend a ton of money there, and will continue to do so for the foreseeable future—all the while moving incremental percentages of their budget into the digital channels where folks are spending an increasing amount of time. But, they are never really going to go full throttle with digital until they can reconcile reach and frequency across channels—and those channels must include linear! Your DMP should be able to handle overlap reporting, light attribution, and cross-channel media performance—but it should also start making some highly informed guesses about how linear audiences map to digital ones, in order to enable true attribution and media mix models. Ask how your DMP is positioned to tie the linear and digital strings together from a measurement perspective.
  • CDIM: Three years ago, we were still waiting for the “year of mobile” to occur, so “cross device identity management” was still largely pre-funded slideware on some entrepreneur’s computer. Jump to today, and “CDIM” and “CDUI” are at the tip of every ad tech tongue! As more and more people move from device to device—almost none of which support the traditional cookie as an identifier—marketers and publishers desperately need to map devices to people. It’s the only way to deliver the fabled “360 degree view” of the user. Ask your DMP vendor how they are prepared to deliver deterministic matches and, more importantly, how they reconcile identity without seeing a user logging in across devices. Doing great probabilistic matching necessitates not only strong algorithms but, more importantly, scale of users which breeds precision models. What is the size of their “truth set” of user data with which to probabilistically determine user identity? The quality and scale of that data will determine your choice.
  • Data Governance: I think the biggest question to ask a potential DMP vendor is their philosophy on data ownership. For both marketers and publishers, audience data is likely one of their top three assets. Trusting such data to a technology vendor is not something to be considered lightly. How is that data stored? What are the policy controls available to help you share that data with trusted partners? What about privacy and governance? How can my platform help me activate data in different places, where different rules about PII and data collection and storage apply? Knowing the answers to these before you buy can save lots of heartache (and legal fees) later. More importantly, how independent is your data? Is your partner also in the business of selling media or data? That can create some conflicts of interest—especially if your data might be valuable to a competitor. Finally, what if you want your data back? You have the right to get it out quickly, and in a useable format.

The bad news is that choosing a DMP isn’t any easier than it was three years ago. It’s a lot more complex, and you really need to dig in deeply to understand the very small nuances between platforms that appear, on the surface, to be very much the same. The good news is that there is a great deal of selection available, and some very high quality vendors to choose from. Take your time, put your vendors through a very rigorous process that includes asking the questions outlined above, and choose wisely!

[This post originally appeared in the EConsultancy blog on 5.11.15]

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CDIM is Table Stakes in the Data Management Wars

IdentityCrisisA recent analyst report made an astute observation that all marketers should consider: It’s not about “digital marketing” anymore – it’s about marketing in a digital world. The nuance there is subtle, but the underlying truth is huge. The world has changed for marketers, and it’s more complicated than ever.

Most consumers spend more time on web-connected devices than television, creating a fragmented media landscape where attention is divided by multiple devices and thousands of addressable media outlets. For marketers, the old “AIDA” (attention, interest, desire and action) funnel persists, but fails in the face of the connected consumer.

When television, print and radio dominated, moving a consumer from product awareness to purchase had a fairly straightforward playbook. Today’s always-on, connected consumer is on a “customer journey,” interacting with a social media, review sites, pricing guides, blogs and chatting with friends to decide everything from small supermarket purchases to big investments like a new house or car.

Marketers want to be in the stream of the connected consumer and at key touch points on the customer journey. But, in order to understand the journey and be part of it, they must be able to map people across their devices. This is starting to be known as cross-device identity management (CDIM), and it is at the core of data-driven marketing.

In short, identity lies at the heart of successful people data activation.

Until very recently, managing online identity was largely about matching a customer’s online cookie with other cookies and CRM data, in order to ensure the desktop computer user was aligned with her digital footprint. Today, the identity landscape is highly varied, necessitating matching ID signals from several different browsers, device IDs from mobile phones and tablets, IDs from streaming devices and video game consoles and mobile app SDKs.

Matching a single user across their various connected devices is a challenge. Matching millions of users across multiple millions of devices is both a big data and data science challenge.

Real one-to-one marketing is only possible when the second party – the customer – is properly identified. This can be done using deterministic data, or information people volunteer about themselves, in a probabilistic manner, where the marketer guesses who the person is based on certain behavioral patterns and signals. Most digital marketing companies that offer identity management solutions take what data they have and use a proprietary algorithm to try and map device signals to users.

The effectiveness of device identity algorithms depends on two factors: the quality of the underlying deterministic data – the “truth set” – and its scale.

Data Quality Matters

There is data, and then there is data. The old software axiom of “garbage in, garbage out” certainly applies to cross-device user identity. Truly valuable deterministic data include things like age, gender and income data. In order to get such data, web publishers must offer their visitors a great deal of value and be trusted to hold such information securely. Therefore, large, trusted publishers – often with subscription paywalls – are able to collect highly valuable first-party user data.

Part of the quality equation also relates to the data’s ability to unlock cross-device signals. Does the site have users that are logged in across desktop, mobile phone and tablet? If so, those signals can be aggregated to determine that Sally Smith is the same person using several different devices. Publishers like The Wall Street Journal and The New York Times meet these criteria.

Scale Is Critical

In order to drive the best probabilistic user matches, algorithms need huge sets of data to learn from. In large data sets, even small statistical variances can yield surprising insights when tested repeatedly. The larger the set of deterministic data –the “truth” of identity – the better the machine is able to establish probability. A platform seeing several million unique users and their behavioral and technographic signatures may find similarities, but seeing billions of users will yield the minuscule differences that unlock the identity puzzle. Scale breeds precision, and precision counts when it comes to user identity.

As digital lives evolve beyond a few devices into more connected “things,” having a connected view of an individual is a top priority for marketers that want to enable the one-to-one relationship with consumers. Reliably mapping identity across devices opens up several possibilities.

Global Frequency Management: Marketers that leverage multiple execution platforms, including search, email, display, video and mobile, have the ability to limit frequency in each platform. That same user, however, looks like five different people without centralized identity management.

Many marketers don’t understand what ideal message frequency looks like at the start of a campaign, and most are serving ads far above the optimal effective frequency, resulting in large scale waste. Data management platforms can control segment membership across many different execution platforms and effectively cap user views at a “global” level, ensuring the user isn’t over-served in one channel and underserved in another.

Sequential Messaging: Another benefit of cross-device identity is that a user can be targeted with different ads based on where they are in the consumer journey. Knowing where a consumer is in an established conversion path or funnel is a critical part of creative decisioning. Optimizing the delivery of cross-channel messages at scale is what separates tactical digital marketers and enterprise-class digital companies that put people data at the heart of everything they do.

Customer Journey Modeling: Without connecting user identity in a centralized platform, understanding how disparate channels drive purchase intent is impossible. Today’s models bear the legacy of desktop performance metrics, such as last click, or have been engineered to favor display tactics, including first view. The true view of performance must involve all addressable channels, and even consider linear media investment that lacks deterministic data. This is challenging but all but impossible without cross-device identity management in place.

The ubiquity of personal technology has transformed today’s consumers into “digital natives” who seamlessly switch between devices, controlling the way they transmit and receive information. Marketers and publishers alike must adapt to a new reality that puts them in control of how editorial and advertising content is accessed. Delivering the right consumer experience is the new battleground for CMOs. Unlocking identity is the first step in winning the war.

[This post originally appeared in AdExchanger on 3.16.15]

How Can Advertisers Bypass The Industry’s Walled Gardens?

own-walled-gardenIn this increasingly cross-device world, marketers have been steadily losing the ability to connect with consumers in meaningful ways. Being a marketer has gone from three-martini lunches where you commit to a year’s worth of advertising in November to a constant hunt for new and existing customers along a multifaceted “customer journey” where the message is no longer controlled.

Consumers’ attention migrates from device to device, where they spread their limited attention among multiple applications. It’s become a technology game to try and track them down, and starting to become a big data game to serve them the “right message, at the right place, at the right time.”

Modern ad tech is supposed to be the marketer’s savior, helping him sort out how to migrate budgets from traditional media, such as TV, radio and print, to the addressable channels where people now spend all of their time. Marketers and their agencies need a technology “stack,” but they end up with a hot mess of different solutions, including various DSPs for multiple channels, content marketing software and ad servers.

Operating and managing all of them is possible, but laborious and difficult to do right. Worse still, these systems are nearly impossible to connect. Am I targeting the same consumer over and over through various channels? How to manage messaging, frequency and sequencing of ads?

Since all of these systems purport to connect marketers to customers on the audience level, the coin of the realm is data. It’s not just “audience data” but actual data on the individuals the marketer wants to target.

Marketing is now a people game.

Yet, in the cross-channel, evolving world of addressable media, connecting people to their various devices is difficult. You need to see a lot of user data, and you have to not only collect web-based event data, but also mobile data where cookies don’t exist. Deterministic data, such as a website’s registration data, can lay the foundation for identity. When blended with probabilistic data and modeled from user behavior and other signals, it becomes possible to find an individual.

Right now, the overlords of the people marketing game are platforms like Google, where people are happy to stay logged in to their email application on desktop, mobile and tablet, or Facebook, which knows everything because we are nice enough to tell them. Regular publishers may be lucky enough to have subscription users that log in to desktop and mobile devices, but most publishers don’t collect such data. Their ability to deliver true one-to-one marketing to their advertisers is limited to their ability to identify users.

This dynamic rapidly makes the big “walled gardens” of the Internet the only place big marketers can go to unlock the customer journey. That might work for Google and Facebook shareholders and employees, but it’s not good for anyone else. In our increasingly data-dependent world, not all marketers are comfortable borrowing the keys to user identity from platforms that sell their customers advertising. Soon, everyone will have to either pay a stiff toll to access such user data, or come up with innovation that enables a different way to unlock people-centric marketing.

What is needed is an independent “truth set” that advertisers can leverage to match their anonymous traffic with rich customer profiles, so they can actually start to unlock the coveted “360-degree view of the user.” Not only does a large truth set of users create better match rates with first-party data to improve targeting, but it also holds the key to making things like lookalike modeling and algorithmic optimization work. Put simply, the more data the machine has to work with, the more patterns it finds and the better it learns. In the case of user identity, the probabilistic models most DMPs deploy today are very similar. Their individual effectiveness depends on the underlying data they can leverage to do their jobs.

In the new cross-device reality: If you can’t leverage a huge data set to target users, it’s time to take your toys and go home. Little Johnny doesn’t use his desktop anymore.

Think about the three principle assets most companies have: their brand, their intellectual property and products and their customer data. Why should a company make a third of their internal value dependent upon a third party, whether or not they pledge “no evil?” Those that offer a “triple play” of mobile, cable television and phone services are also part of the few companies that can match a user across various devices. The problem? They all sell, or facilitate the sale of, lots of advertising. Marketers are not sure they want to depend on them for unlocking the puzzle of user identity.

Some of the greatest providers of audience data are independent publishers who, banded together, can create great scale and assemble a truth set as great as Facebook and Google. Maybe it’s time to create a data alliance that breaks the existing paradigm. The “give to get” proposition would be simple: Publishers contribute anonymized audience identity data to a central platform and get access to identity services as a participant. This syndicate could enable the deployment of a universal ID that helps marketers match consumers to their devices and create an alternative to the large walled gardens.

The real truth is that, without banding together, even great premium publishers will have a hard time unlocking the enigma of cross-device identity for marketers. Why not build a garden with your neighbors, rather than play in somebody else’s?

[This post was originally published in AdExchanger on 12.11.14]