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Identity Graph, Identity Resolution

MAIDs: Effectively Leveraging Mobile Ad Identifiers

Looking back at the advertising industry, even as little as five years ago, it’s easy to see how quickly the industry has been forced to focus on cross-device. In just two years’ time, the average number of connected devices per person is expected to climb as high as 6.58 (Statista).

In this always connected world, consumer expectation has changed. Deliver seamless ads and content experiences between all of a consumer’s digital formats or don’t even bother. While still a difficult task for advertisers to muster, the industry is finally coming together to coordinate data sets between devices in order to deliver the cohesive experiences consumers desire. Enter mobile advertising and mobile ad identifiers (MAIDs).

So, what are MAIDs?
A MAID is similar to a cookie in that it helps advertisers identify the person behind a device. A MAID serves the same purpose for marketers as a cookie does in a web browser. Using these identifiers that are connected to certain devices, marketers can reach users based on the data sent by their mobile device. Since MAIDs are set by the operating system, data is easily available, allowing for a more complete and deterministic picture of a consumer.

But the benefits don’t end there. MAIDs are bringing major opportunity to marketers that never before existed on digital. Mobile devices are quickly becoming the go-to platform for just about everything, and businesses have to adapt rapidly to ensure that they are responsive to this unwavering trend. Statista has found that mobile traffic globally has a larger share of internet usage than desktop traffic. (Actually, mobile traffic has been ahead since 2015.)

Here is why you should care:

Identifying A Unique Owner
Computers and desktop browsers can be black holes for marketers. Computers often live within multi-person households and can be used by many different people. This leaves assigning a unique identifier to a distinct person all the more difficult. With one unique owner, marketers can collect a diverse pool of data about their users, all the way from demographics to behavioral data, both of which are often difficult to pinpoint when multiple users are frequenting a single device.

The Key To Personalization
The higher the level of personalization, the better the user experience and consumer retention rate. MAIDs represent a more efficient way for customizing ads based on customers’ interests and the biggest differentiator when it comes to serving users with content that is not only relevant to their needs, but also personally tailored to them. Overall, data from MAIDs comes through more rich and precise and allows advertisers to connect the pieces of a disparate and complicated puzzle.

Connecting Data Across Channels
In this day in age, it’s impossible for marketers to rely on IDs from one channel. MAIDs are the missing link. MAIDs can serve as the bases for a single individual’s online identity and through tools like identity graphs* and company CRM data, linking desktop cookies, MAIDs and other forms of data across channels can be more precise. (*When it comes to identity graphs, the only reliable way to get good cross device data at scale is through an individual level identity graph. Click here to learn more about Throtle Identity Graph and our individual based approach.)

Whether you are trying to reach a specific audience within a device or retarget an audience across other similar devices or connecting consumer identities to a larger data pool, MAIDs are the next generation of cookies that will take your marketing and advertising campaigns to the next level. Consumer are expecting more. It’s time to deliver.

Want to learn more about MAIDs and how Throtle can take your marketing and advertising campaigns to the next level? Contact us today.

Identity Graph, Identity Resolution

A Case For Accuracy In A Multi-Device World

Data drives the marketing industry. From a marketer’s perspective, data is time; data is money. Marketing dollars are trustingly thrown at data to build campaign strategies and targeted advertising across all marketing channels.

Despite the time, money and sophisticated technology shifting towards data mining, many marketers still can’t determine proper attribution across channels in a way that accurately measures their brand’s lift and consumer experience. At the root of this is a persistent lack complacency when it comes to properly matching data to an actual consumer in a way that allows advertisers to reach them with relevant and appropriately timed messages.

Unfortunately, for the most part, data targeting has been focused on scale over accuracy, resulting in an upside down model that benefits everyone but the advertiser. The result – the digital data and identity matching industry gets a bad rep by marketers for high cost and low returns. For consumers, the experience is forgettable at best and, at worst, full of skepticism about the source and quality of data fueling targeted ads. The problem is the glut of low fidelity data that has flooded the marketplace over the past decade in both identity resolution across device and data insights. Yet, advertiser priorities have not changed while accuracy is being scrutinized, verified and measured in more ways than ever before.

Today, accuracy in identity resolution and data targeting is a requirement for all marketers. But all this technology has focused so heavily on proxies rather than delivering individual-level targeting, because resolving to the right individual, onboarding data correctly and serving the right consumer relevant ads is not an easy proposition.

Here’s How Throtle Does it Differently

Identify Sources and Context
One key to building robust, accurate data is to know where the data comes from, including its timeliness and its context. Keep in mind the demographic and other online/offline behaviors, not just transactional data in a single moment in time. For example, we all shop for gifts, whether or not they are personally relevant to us. A consumer may at one point have shopped for a baby gift for an expectant friend. Five months later, that consumer has ended up on a mailing list for baby formula and incessantly receives free samples in the mail, although the consumer in point is not a mother nor expecting in the near future. You can see where the discrepancy lies and why knowing the source and context makes this data all the more accurate.

A Fully Deterministic Approach
Marketers must have a deterministic understanding and approach to their data to ensure accuracy. This means ensuring that a single consumer is accurately matched, with definitive proof, to an organization’s corresponding first-party data. This approach ensures that the person is a single user and not generalized under a household or group of consumers. Measuring every interaction across devices can be challenging, but attribute something to the right person and you can identify trends and patterns across all marketing channels. Without accuracy and deterministic matching at the individual level, cross-device attribution is impossible to obtain.

The Identity Graph
Creating this deterministic approach is easily accomplished through a consumer identity graph. An identity graph ensures that all first-party and third-party identifiers for a single consumer reside and are resolved in one place. This is imperative for marketers who want to accurately read consumer behavior and interactions across multiple touchpoints. If you look at everything separately, the picture is hazy and the 360 degree view of a consumer’s core identifiers are lost.

The Results of This Complexity

CONSUMERS: They want to see relevant, harmonized experiences across content. There will be those that complain that after browsing online for new living room furniture, they see cross-device ads from multiple couch vendors for the next three weeks. However, in a world where customer experience is the majority of the battle (according to Gartner and 89% of marketers), when a consumer experience is accurate and relevant, a brand elevates itself above the competition.

ADVERTISERS: Highly accurate targeting is required to reach the same consumer on different channels and devices. Targeting, attribution and defendable measurement can’t happen using inaccurate data; campaigns will produce better ROI because a marketer is able to determine which specific activities are producing the best results

Our Commitment
In short, many of the old approaches to collecting accurate data no longer work. Ad and MarTech companies should be held to a higher standard and greater level transparency when it comes to the data that they source and provide.

Throtle is a 2nd generation data onboarding company focused on deterministic matching, identity resolution, and closed loop enablement. Our data centric onboarding approach guarantees the highest level of accuracy, scale, and responsiveness for our clients.

Data, Identity Resolution

Taking Larger Strides Toward Data Transparency – A Call To Action

There has long been a desire for transparency and specific guidelines around data within the advertising industry. Recently the Advertising Research Foundation partnered with the Coalition for Innovative Media Measurement to propose a data labeling initiative. Still in the early stages of development, the proposal is surrounded by a plethora of open questions about how an initiative such as this can come to fruition.

While we fully support the idea of data labeling and affirm that this is a move in the right direction, a need still exists to take this initiative further. Sure, slap a label on something, but how do we know what that label means or represents? What are actual steps that should be taken to rid advertising of skepticism and uncertainty? As a group, we need to move toward confidence and knowledge that data is being sourced and analyzed correctly. Without these specific instructions and action items around a standard operating procedure when it comes to data transparency, we’re still staring at a problem that will only continue to grow with the current data revolution.

Why we need more help:

No one wants to be labeled a ‘bad actor’. Most data providers are quick to tout the accuracy of their data and create noise around why their product is better than the next. Maybe the data is accurate, but there is still a black box around how that data is collected, built and validated. As a result, brands and marketers don’t truly trust the data they receive. Although they continue to use that data, doubt still abounds. Advertiser Perceptions even recently found that 80% of advertisers use audience insights, but that only 33% say they ‘completely trust them’.

In nearly every industry outside of advertising, there is data regulation. There is clear delineation on how data is sourced, kept and secured. Take HIPAA legislations, for example, which put highly strict regulations on what type of medical data can be shared and where personally identifiable information should be withheld. These type of regulations don’t exist for the mass amounts of general, publicly available data that exist. There is no official standard. And this is not acceptable. On top of data labeling, this is the standard that needs to be set and how we all get there together.

  1. Providing clear and accurate reports – Inaccurate reporting often stems from identifying sample size and consumers at the individual level versus the household level. In these cases, the baseline of the data collection is left unknown as the data suppliers create false match rates simply using an address and last name, thus making sweeping assumptions about a household as a whole. Everyone in that household is essentially ‘matched’, but without transparency on the report, simple qualifiers at an individual level — age, ethnicity, occupation, gender — can be extremely wrong and suddenly all the information is invalid for the consumer of that information. To avoid this, data providers should be required to disclose the level at which their data is being represented and whether or not that sample is consistent with other information in the database or identity graph.
  2. Describing how the data is built – Data can be collected in any number of manners, so as data providers, we should be sharing the methods we use with clients. This boils down to whether or not data is collected in an appropriate manner, that it is recent and updated regularly, what the point of collection is (online surveys, transactions, web scraping, via phone, with consumer notice that we are collecting data). Again, was this data collected at the household (inferred) or individual level?
  3. Clarifying proprietary vs. white labeled data – Let’s look at a scenario. Company A may want to license data from company X and Y, but company A doesn’t realize that company X is white labeling from company Y and that the data is the same. In this scenario, without full transparency, company A is potentially left buying the same data twice and wasting time and money. If you’re buying data, you’d like to know that you are dealing with the originator of the data or at the least, where that data’s origins are. Companies can sell data, but we often see white labeled data presented as proprietary, presenting a substantial need for more transparency around where data is truly coming from.
  4. Creating a standard sample set — When data providers supply samples they often try to put their best foot forward. It’s easy to mask what they are doing poorly if they are allowed to drive what that sample is and only supply the best data upfront. For this reason, there should be a standard sample set that people can adhere to. If there were standards around what the samples should be, there would be no way to hide this and everyone would get a clear, accurate sample of data to test before committing fully.
  5. Developing data accuracy compliance — It’s difficult to imagine a world where companies fully disclose the names of their sources or are truly 100% transparent. Yet if every other industry has data security and privacy standards, why shouldn’t the advertising industry? In addition to labeling data, we need to develop a compliance checklist that ranks companies in terms of their level of transparency. This would give brands and advertisers the option to be selective about the firms they choose to work with based on whatever standards they deem fit.

This may require giving up a few sources or opening up previously closed doors, but if the whole industry was held accountable to this standard, it would be common practice. If you feel strongly about your data you will be willing to follow these standards and stand together as we take more steps toward full transparency.

Identity Graph, Identity Resolution

Identity Graph: The DNA For Individual Identity Resolution

Identity has become a fundamental component of the digital ecosystem. The more brands understand about their customers and what channels and devices they use, the more effectively they can connect with them.

Everyday, individuals switch between multiple screens (smartphones, laptops, tablets). Trying to understand the individual behind the ever-changing screen through multiple data points is critical for marketing success and the reason why creating a single view of that individual has never been more important. It’s time to look at the consumer holistically from every angle, rather than in silos.

What is an identity graph?

An identity graph allows brands to connect and unify all the known identifiers that correlate with an individual customer.

Each customer has multiple personal identifiers — email addresses, postal addresses, mobile numbers, mobile ad identifiers (MAIDs), logins, usernames, and cookies from web browsing. The identity graph collects these personal identifiers and connects them to an individual profile, which ‘lives’ in the identity graph, along with other types of data, such as demographic, behavioral, lifestyle and purchase data.

What is the Throtle Identity Graph and why do I need it?
The Throtle Identity Graph utilizes a deterministic data matching methodology to absorb cross-channel and cross-device customer information (such as anonymized log-in data or hashed email addresses) to pinpoint an individual identity. Throtle uses this data to locate individuals on whatever device they may be using.

Throtle’s Identity Graph then creates the opportunity for marketers to produce relevant messaging for customers as they switch between various devices. For example, an individual interested in booking a vacation might browse a hotel’s website using their laptop and then again on their mobile device. The hotel’s marketing team can use the data from Throtle’s Identity Graph and onboarding process to ensure a personalized and targeted ad appears as that customer switches devices. This results in a higher conversion rate and more optimized budgets.

Identity Graph, Identity Resolution

Managing Consumer Identity In A Digital World: Ensuring Accuracy Over Everything

When we look at how the advertising industry has changed over the last five years, it’s astounding to see how digital snuck up and rewrote the rules of engagement. Instead of making assumptions, digital forced advertisers to have a more accurate and deterministic understanding of a consumer’s identity to ensure true personalization.

In her annual Internet Trends Report, Mary Meeker painted an eye opening picture of what advertisers are dealing with when it comes to digital. Meeker noted that the amount of time adults spent with digital media in 2016 grew significantly over the past year, with average time increasing to 5.6 hours a day.

As the number of devices and time spent on digital platforms grows, the advertising industry is recognizing that the identity behind those devices is more important than ever. Knowing specific details about the physical being that sits behind something as abstract as pixel or cookie is essential to targeted, meaningful and genuine delivery of ads. Now, everyone is running as fast as they can to get to the point of understanding who those people are before grouping them into segments, showing them advertisements that aren’t relevant and sending consumers running for the adblock button.

Identifying Devices Over People
Amidst all the scrambling, most advertisers are still using probabilistic methods to determine who they think that customer is behind the screen. To do so, they start by looking at, from a device perspective — where that device is resting over time, where that device going, what traffic is coming from that device, what apps are being used, etc. Herein lies the problem.

However, more often than not, those device attributes don’t have a valid email associated with that particular assumed individual. There could be an email tied to that device, but who is validating it?

Artificial Identity Management
In an attempt to validate, digital advertisers have also turned to artificial intelligence. According to Juniper Research, “machine-learning algorithms used to enable more efficient ad bids over real-time bidding networks will generate $42 billion in annual ad spend by 2021, up from an estimated $3.5 billion in 2016.”

While AI has its benefits — it has certainly improved accuracy in analyzing consumer behavior and enabling real-time campaign optimization — the true extent of AI’s capabilities is identifying and learning about an individual on a persistent basis. When analyzing data, outside of other response information obtained, it’s hard to determine the true identity of the person associated with that response. AI can show that an ID is aligned with a particular device and activities on that device over a given period of time, but AI alone can’t determine the WHO. Yes, the data says there is a person there, but who are they really? And with so much data from so many different devices flying around, how are brands supposed to take that data and use it efficiently to gain a deterministic understanding of their customers?

Personalization Lies In Accuracy
We already know that when US internet users receive personally relevant content from brands, it increases their purchase intent. But there must first be an association between various touch points on an individual on a deterministic basis to help advertisers create the level of personalization that consumers so deeply desire. Because in the end, it all comes down to creating meaningful connections with customers by meeting the needs and wants of each unique individual.

Since this level of personalization all circles back to the accuracy of a consumer’s identity, it’s imperative to start by connecting the dots between siloed data points and understanding who that person is. Furthermore, advertisers should look beyond the device level. This is where most advertisers end up getting stuck. Without drilling down past the device or the pixel, advertisers are missing core demographics, psychographics and lifestyle attributes that give us deep insights into these individuals — that personalization is completely lost.

So how can brands fix this? Simple — by bringing into a consumer ID graph authenticated email addresses that are associated down to an individual and matching them to existing data to create a connecting point. This same sequence can then be applied to mobile numbers, IP addresses, social handles, physical addresses — wherever a connector can be found. Now an advertiser has a single individual at the center of each record that is tied to multiple connectors or types of attributes, which provides a more robust picture of who that person truly is. Once you know all of this, you can associate it with a persistent ID at the individual and household level. This allows advertisers to track consumer behavior across any website or location when an ad is presented to that customer. Talk about accuracy and personalization! A win-win for both the advertiser and the consumer.

In the end, we believe that all advertisers should be digging down to the core level of understanding deeply who a person is and then CONFIRMING through multiple instances that that person does indeed belong to a specific record. There aren’t many, if any, that are using a truth set to do that. But we’re here to help initiate that change in the industry and reinstate accuracy and personalization in the world that digital ever so slyly created.

Identity Resolution

Linking Data To Identities: Deterministic And Probabilistic Explained

Cross-device tracking is the latest Holy Grail of digital advertising. At stake is the marketer’s ability to recognize the same consumer when he or she interacts with a brand, whether that interaction happens on a laptop, tablet, smartphone or connected TV.

Until recently, marketers had no choice but to view these engagements in silos — essentially treating one consumer as three or four distinct people, one for each device they used. What’s the impact of that? Plenty. From the brand’s perspective, basic tasks like managing frequency, attributing and allocating conversion credit and sequencing messages all suffer.

But new and more accurate tools and techniques that link our various digital aliases have come to market recently. And those methodologies for linking these digital identities fall into two buckets: deterministic and probabilistic. Right now, the industry is debating whether probabilistic or deterministic is the best approach to cross-device measurement and targeting of digital advertising.

Those in the deterministic camp believe that definitive proof of a consumer’s identity is the only way to go. Such solutions rely on known facts about people, typically revealed when they log in to sites such as Facebook or Twitter. Once a consumer logs into the same site from her desktop, mobile phone and tablet, it’s a fairly straightforward process to link all of those devices together to a particular alias.

So why isn’t the whole world embracing deterministic matching? While highly accurate, it struggles to scale. No one — not even behemoths like Google and Facebook — can build deterministic device graphs for every consumer. And that means marketers can’t truly achieve their campaign goals using just deterministic data.

Probabilistic device-linking approaches use data analysis to associate multiple devices to a specific consumer or household. Let’s say a marketer serves an ad to a desktop on a certain WiFi residential address. Later, the marketer sees a mobile device using that same Wi-Fi connection. It’s probable — but not certain — that the device is part of that household. As you can see, this approach delivers more scale, but with less assurance that the linkages are accurate.

So which approach is better, deterministic or probabilistic? The smarter way to answer this is to ask yourself: What do you want to accomplish with your campaign, and which data actually gives you the positive marketing returns you seek?

For instance, if you’re selling family vacations or big-ticket items the whole family weighs in on, reaching a household probably makes sense for that type of campaign. But that won’t be the case for all sectors and campaigns. It all goes back to what you want to achieve with your campaign.

And of course, the other important consideration in cross-device targeting is consumer privacy. There is a wide range of risk for each major form of deterministic and probabilistic methodology.

Some identifiers, such as cookies, typically have set expiration dates and consumers have enough familiarity with them that they can control them if they so choose. Device IDs, which are used to reach users in mobile apps, don’t have set expiration dates, but consumers can easily opt out of tracking. Both are common and known to consumers, which means device tracking based on cookies and device IDs won’t feel as invasive to them.

Other identifiers — such as household IDs and carrier IDs — are still emerging, and consumers have low visibility into their use, or how to opt out. Consequently, household and carrier IDs pose more risk.

There are other identifiers used by consumer-tracking vendors that marketers should seriously give pause to before using, such as fingerprinting and zombie cookies. Fingerprinting uses persistent identifiers and gathers new data via methods that are both invisible to the consumer and collected without consent. Zombie cookies are expired cookies that have been brought back to life and are often in direct violation of a consumer’s choice to opt out. Both data types involve a considerable amount of risk of angering consumers.

How do you avoid risk by ensuring you don’t use these two identifiers in your campaigns? You need to ask your vendors explicitly how they compile their consumer data sets — and get it in writing.

Most brand marketers aren’t interested in getting into the weeds of cross-device tracking, and leave it to their vendors to recommend an approach. If we lived in a world where we had the perfect ID that might make sense, but we don’t. Between the embarrassing headlines about wireless companies and zombie cookies, increasing consumer awareness of privacy issues, and even the possibility of government regulation in the form of a Consumer Privacy Bill of Rights, every marketer needs a basic understanding of data, its uses and misuses, as well as consumer attitudes and rights. Ignorance can land you on the front page in a bad way, and go a long way in alienating the customers you worked so hard to win.

By investing some time to expand your understanding by asking specific, tough questions of your vendors, you’ll more likely attain the transparency and flexibility you need to use the right data at the right time for the right campaign.

This article was originally published on

Identity Resolution

‘Transparency’ Voted ‘Marketing Word Of The Year’

“Transparency” was voted the “word of the year” for 2016 by members of the Association of National Advertisers.

The finding, which is the third consecutive year the ANA has polled its members to name a single, defining word representing the industry zeitgeist for a year, follows “content marketing” in 2015 and “programmatic” in 2014.

The selection of “transparency” should not be a surprise, given the amount of action and discussion surrounding the subject in 2016, including the ANA’s release of a landmark transparency study conducted by K2 Intelligence, as well as its own set of recommendations and some pushback by its agency counterpart, the American Association of Advertising Agencies.

Transparency has also been a central topic during numerous other industry developments, panel discussions and publications, including how it relates to everything from advertising and media-buying fraud to the Presidential election.

The ANA surveyed its members the week of Nov. 28th and received 267 responses. It said “transparency” got the most votes, but did not disclose how many.

It said the other top choices for “Marketing Word of the Year” included: “customer experience,” “content marketing,” “influencer” and “programmatic.”

The ANA provided the following verbatim, but anonymous, comments from respondents who selected the word “transparency.”

  • “[Transparency] is the single most important issue in marketing and has the greatest potential benefit in terms of improving marketing ROI.”
  • “Transparency, or lack of transparency, defines all media agency relationships and provides a new perspective to consider these relationships.”
  • “The K2 findings have served as a milestone encouraging change in how clients and agencies partner on media deals.”
  • “Fraud and lack of transparency are killing the digital ecosystem for advertisers.”
  • “Because of the important K2 report and the light it shed on the broken agency/client model, coming to a common ground that works for marketers is crucial to the future of this relationship.”
  • “Transparency affects everything we communicate in marketing, from our product formulations and labels to how we communicate in all channels to our internal culture.”
  • “Consumers want to do business with brands they can trust. That goes to the heart of transparency.”
  • “Trust between agencies and clients has never seemed worse, especially in the world of programmatic and data.”

This article was originally posted by MediaPost.

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