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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

The Growing Importance Of Customer Identity Graphs

When we read a good article, we like to promote it. And, with the recent article published by Analytics Center of Excellence entitled ‘The Growing Importance of Customer Identity Graphs’, we are doing just that. It stresses the importance of customer identity and how brands need to be able to create a single, deterministic view of each unique customer across multiple channels and devices. Read on…
It’s crunch time for customer identity. Today, brands are capturing more customer data than ever before across a multitude of channels and touch points. But many companies struggle with using that data to construct useful customer identity profiles – “identity graphs” – that can greatly improve marketing measurement capabilities and accuracy while helping you create better customer experiences.

Recognizing customers across multiple channels and devices while using multiple data points to create a single view of that customer has never been more important or relevant. Traditional customer data sources such as CRM platforms can’t really help much. Their customer identity capabilities are rudimentary. They merely manage customer contact data, not actual identity. That requires triangulating a great deal more data.

Establishing true identity is a complex and nuanced task. But if you resolve and manage identity right, it will allow you to understand a tremendous amount about customers at specific moments in time. Customer identity can be one of the most valuable, unique and proprietary assets that a brand owns.

Accurately establishing customer identity is one of the most challenging issues facing brand marketers today. But it’s a challenge that marketing organizations must meet since customer identity can be one of the most valuable, unique and proprietary assets that a brand owns.

A good starting point is to create a solid framework for resolving cross channel, multi device, online-offline identity.

Identity Graph Basics
An identity graph is a tool that lets you connect and unify data on individual customers gathered from a variety of channels, devices and touch points. The more channels and devices in play, the more customer identity data there’ll be. You can use it to create more personalized messages and fine tune your marketing measurement.

Identity graphs are not cookie-cutter friendly. They come in many shapes and sizes. As you continue adding data to an ID graph it becomes smarter, deeper and more valuable for targeting customers and improving customer experience.

Marketers deploy a variety of methods for matching data to customers, including direct, or “deterministic” where the customer has facilitated the match via cookies, through a credit card purchase, or some other authenticating action. This creates “persistent” identity which is another way of saying with certainty that you know (at a minimum) who a customer is, where they are and what device they are using.

Another, more scalable method uses sophisticated probability algorithms to put the pieces together. Done right, this can also be highly accurate, though not perfect.

Such AI-based approaches can sniff out data and seek identity matches continuously, and provide answers in real time.

Creating a customer identity framework is vital to filling the inevitable gaps in your customer intelligence.

Filling Identity Gaps
Inefficiencies in how many organizations currently approach the task of identifying customers creates data gaps – or sometimes more like data chasms. Such gaps make it hard to scale marketing, provide personalize customer experience and measure results.

To help fill these gaps, successful companies build a framework that links customer identifiers (name, IP address, phone, mobile, email, cookies, device IDs and physical address) while also adding on a unique (persistent) ID that can be used across channels. Companies also need to install privacy safeguards that are verified and compliant with all privacy standards.

If you can’t accurately and consistently identity customers across devices – quickly and at scale – your results will suffer. You’ll be unable to consistently target customers with the right type of content, at the right time, in the right context – and do it across multiple devices.

The amount of time it takes for you to tap into customer identity data, connect the dots and establish identity is critical. If you can’t do it fast – and that basically means in real time – your efforts may go for naught.

Identity Graph Benefits
Here are six reasons that building out an identity graph framework should be a strategic imperative for your marketing organization:

1. Extend Marketing Accuracy and Reach at Scale
A solid identity management framework will allow you to identify customers as they interact with your brand in real time. This greatly increases your ability to understand what otherwise would look like anonymous customers and prospects. With a solid identity management framework in place, you can engage customers with far greater accuracy and increase marketing reach by as much as 50%.

2. Build More Personalized Customer Experiences
Improving customer experiences depends heavily on being able to accurately identify someone and personalize any interaction. Whether your efforts involve current customers, prospects, or both, the experience you provide ultimately determines the quality of your relationship with that individual. In order to optimize results, identity data should be continuously enriched, updated and shared across the entire organization to facilitate greater control and personalization.

3. Improve Measurement Accuracy and ROI
As the number of consumer touch points grows, so does the quantity of data collected, causing analytic teams to struggle at providing an accurate view of marketing ROI. With a well-constructed approach to identity, you can create a single, holistic view that more accurately measures customer interactions across all channels including digital, mobile, website and phone.

4. Fill Your Customer Intelligence Gaps
The more data sources there are, the greater the chance gaps will appear if those data sources aren’t properly linked. Marketers can solve that by continuously corroborating, verifying, and appending missing information across customer records, to produce a 360-degree customer view.

5. Better Offline-to-Online Matching
It’s important to have a persistent cross channel identifier to unify, personalize, and activate customer engagement. Linking records across touch points while enriching identity with demographic, behavioral, psychographic and geo-location data provides marketers with a single lens of the consumer across screens.

6. Identify Customers in Real Time
The ability to identify a quality lead or an existing customer in real-time is crucial. With an identity framework in place and implemented, your call center, for example, will be able to identify a prospect the moment a call is received, reducing call times and improving customer service. Your digital team will be able to accurately identify an existing loyalist even if that customer hasn’t authenticated.

Original article can be found here.

Identity Graph

Not All Consumer Identity Graphs Are Created Equal

5 Reasons Why Brands Should Invest in a Deterministic Consumer Identity Graph

A consumer identity graph is a vital tool for brands to have in their marketing arsenal. It allows brands to deliver relevant, personalized customer experiences by linking customer data from a multitude of channels, devices, and touchpoints to create unique customer profiles. However, not all consumer identity graphs are equal. Many companies tout the ability for their identity graphs to make distinct deterministic connections to individuals with sizable scale, but are utilizing probabilistic algorithms to make those linkages. Unfortunately for marketers, this methodology leaves room for a great deal of error because probabilistic matching uses anonymized data signals (location, browsing history) and analysis, not 1-to-1 matching for the verification of identity.

Some identity resolution providers also claim to combine these methods of probabilistic and deterministic matching for the best possible reach and engagement, but don’t be fooled. Only deterministic, individual-based consumer identity graphs can accurately unify fragmented customer data to create a holistic view of each individual for better customer engagement, both offline and online.

So, the question remains, if a brand cannot confidently, accurately and consistently identify its customers across multiple devices, channels, and touchpoints, then is the consumer identity graph really doing its job?

Here are the top 5 reasons why brands should invest in a deterministic consumer identity graph.

  1. Marketing Accuracy – Deterministic consumer identity graphs are data-centric. They use a wide array of data points, including email addresses, postal addresses, mobile numbers, device ids, etc., to validate individual identities consistently and in real-time. Since a 1-to-1 direct match is made, brands are ensured the highest level of accuracy for marketing and customer engagement.
  2. Data Hygiene and Identity Management – Consumer identity graphs are complex, ever-changing entities. Unlike probabilistic consumer identity graphs, which use anonymized data points to make inferred connections, providers with deterministic consumer identity graphs carefully implement and maintain in-depth data hygiene processes to guarantee the accuracy of their 1-to-1 matches. Investing in a deterministic consumer identity graph means you are investing in the technology and data management necessary for effective marketing.
  3. Higher Match Rates – Many brands and owners of data are under the impression that there is a trade-off between accuracy and reach, habitually opting for probabilistic (or combination) solutions because they believe they offer the highest match rates and scale for targeting. But that just isn’t the case! Since deterministic consumer identity graphs have access to a constant stream of consumer data sources (email addresses, device ids, cookies, etc.), they can actively validate both offline and online identities, allotting for better offline to online matching with no sacrifice on reach.
  4. Enhanced Customer Engagement – When using probabilistic, household level matching, the ability to enhance customer engagement for consumers with personalized and relevant campaigns is lost. Deterministic consumer identity graphs enable marketers to understand each customer at the individual level, offering the demographic and behavioral knowledge necessary to determine the correct strategy for interacting with each person. This means that Mr. and Mrs. Smith can now be viewed as Jane Smith, 42, who drives a BMW and John Smith, 45, who loves his Ford pick-up truck, and each person can receive customized messaging that will better resonate with them as unique individuals.
  5. Better Attribution and ROI – With deterministic consumer identity graphs, brands can resolve identities and personalize messaging to each unique individual, reducing marketing waste with generalized campaigns or campaigns delivered to the incorrect recipients (I.E. Female running shoe campaign displayed to males). Brands are also able to use the individual-based, deterministic matching to help connect the dots between purchases made and brand engagement, offering better attribution metrics.
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 MediaPost.com

Data Onboarding

Transparency In Data Onboarding

In today’s digital age, information is only a click away. Social media platforms like Facebook and Instagram entice individuals to increasingly feel the need to share everything. Whether it’s political views, or the need to share you’re heading to the gym, people are living their lives completely out in the open.

This expectation for transparency has extended beyond personal interactions and is now a reality in business. The topic of transparency has never been more important to the business environment.

Why?
The answer is simple: transparency conveys trust.

There’s a misconception out there about transparency. Too often, companies see transparency only as a tool to be used when owning up to a mistake or righting a wrong. This approach is shortsighted and isn’t an effective way to build trust. Clients will be far more forgiving of mistakes if a company has a history of being forthright with all interactions, not just the negative ones.

Transparency implies openness, and accountability. Operating in a transparent manner means operating in such a way that it is easy for others to see what actions are performed.

So, why has this industry accepted the notion of anything less than transparency? Why doesn’t a client know what their data looks like prior to onboarding and post onboarding? Why doesn’t the client understand what kind of data they are providing? And more importantly, why aren’t clients getting insights to their data after onboarding?

As this industry matures and grows, transparent results are a necessity in making proper strategic marketing decisions. Marketers want their customer data onboarded accurately and need to understand the following:

• What data was onboarded in a deterministic way vs. a probabilistic way
• What data is household-based vs. individual-based
• What is the recency and frequency of the results
• How many devices are associated per record

Does it make sense to include a consumer into a segment if you know that the cookie is probabilistic and has been seen once in 90 days? Is that a true targetable customer?

Digital marketing is becoming more and more sophisticated and data is driving decisions and results. The trusted firms connecting consumers to devices need to make sure that all data is of high quality and is accurate. They must also deliver transparent reports to verify the matches. Marketers also need to know the status of the data that they provided (quality, NCOA, duplications, errors).

Ideally, an onboarder should be able to process a client’s data so that it is of the highest quality prior to matching the data to any device: The nature of data is that it is constantly changing, the population changes millions of times daily, for instance:

There were 3.9 million babies born in 1999 – This year they are targetable 18 year olds.
There were 2.7 million deaths in 2015 alone – They need to be removed.
There were 124 million households in 2015 of which only 81 million were families – Which household should be targeted?
The average household size in 2015 was 2.54 – Wouldn’t you want to know more about that household?
The point is, that in order to develop an effective marketing strategy that targets individuals (not households) and produces accurate results, the data being utilized needs to be accurate and be shared openly with the client.

There are many ways to onboard a customer today (offline to online, online to online, and online to offline). Once the data has been processed for onboarding and successfully matched, the client should fully understand exactly what data was matched. It is the transparency of these results that will allow for better planning, strategy, and analysis. For instance, transparent reports should include:

Pre-Onboarding Data Results:
• Address standardization
• Email append rate
• Attribute append rate
• Record and device duplication removal
• Deceased removal
• Unusable email and postal records
• Net usable records

Post-Onboarding Report:
• Individual match rate
• Household match rate
• Probabilistic match rate
• Deterministic match rate
• Cache clearers
• Frequency
• 30-60-90 day recency
• Segments & quantities

Brands and data providers should not settle for a simple match percentage. Instead, they should demand to know the results so they can better market to their existing customers and strategize to attract new ones. No longer will a one line match rate do. TRANSPARENCY should now and always be the new normal in data onboarding.

Data Onboarding

7 Questions Marketers Should Consider When Weighing The Quality Of Their Data

Sets of numbers can be misunderstood if they lack integrity

The media landscape has realigned itself with lightning speed to the power of data. After a century of being limited by “brute force media,” marketers quickly glommed onto the vast potential of digital and addressable audiences.

The data “exhaust” from the digital experience has been a game changer for marketers, powered first by sites, then by ad networks and finally by programmatic. Money shifted from reach-based to “people-based” planning, augmented by powerful new data companies, monetizing the categories and groupings of people brands want to reach in ever finer granular detail.

But somewhere along the way, the proposition fractured. We discovered that data itself is not the key to addressable marketing and better business outcomes—quality data is. And the difference between data and quality data, or data with integrity, is difficult to see. To use a buzzword of the day, the market for data is not transparent. To use another, it’s a swamp: an opaque, poorly understood mess. If you want to be a data-driven marketer, you need to make your way through the morass to interrogate your data. So here are seven questions to keep in mind in assessing data integrity:

Is your data fresh or stale? The average life of a cookie is 30 days. About 55 million people change their phone carrier every year, 60 million physically move, 43 percent of customer records are out of date or invalid, and 60 percent of data is incorrect within two years.

Data becomes obsolete quickly, yet many providers continue to use stale data because it provides the illusion of scale. The only data that matters is accurate data. Make sure you understand how data is collected, whether it’s corroborated against authoritative identity standards, and how often obsolete data is purged.

Is your data 3-D or flat? In the world of data, there are six key areas that matter to marketers: demo (age, gender, income); geographic (where they live/roam); attention (what they concentrate on); consumption (what they buy); behavioral (what matters to them); and intentional (what they’re about to do).

Data providers act as if people exist independently in each of these areas, as if any of the above is sufficient to define a person. I’d ask, are you just a demo? Just a measure of attention? Just a signal of intent? No. Real humans are a combination of all of the above. Collectively, consumers are diverse. Individually, they are multifaceted. Flat data (an individual attribute) is just a signal.

How modeled is your data? Here’s a truth: All data is modeled. Here’s another: At some point, models falter. Do you know at which point?

In order to be useful, data needs to have scale. Marketers seek a balance of specificity and reach. It’s important to understand the size of the initial seed audience versus the size of the total audience to develop a degree of confidence in the data you’re using. If it’s significantly modeled, how certain can you be that you’re still reaching your target?

How transparent is the modeling? Do you know your look-alikes? The data market tends to be opaque, and with data, the devil is in the details. If you don’t know how a look-alike audience is formed, you have no idea whether it can be trusted.

For example, most data sets use only digital identifiers and connections. Definitive email-to-cookie linkages generate only a 30 to 50 percent match rate. So the data you’re starting with may be less than half right. Statistical modeling creates hypothetical look-alikes off the total (which is less than half right), exacerbating the issue. If you don’t know the model, you can’t interrogate the veracity of the data set.

Is your data connected? Most data is digital, but I don’t know of a single person who lives life online only. The world is connected—online and offline. Connected data encompasses both. Most data is based on digital attributes only and is neither linked to offline identity nor normalized versus the population. In other words, it captures a small portion of reality. Data needs to be connected to reflect people’s 3-D lives.

Are you targeting individuals or households? Unless you’re targeting age or gender, you’re better off targeting households than individuals. Here’s an example: It’s amazing how many marketers still target women instead of adults, as if only women are shoppers. Today, 40 percent of primary grocery shoppers are men, and the majority of households share grocery shopping chores.

Targeting only individuals misses a big portion of the grocery-shopping population. Worse yet, most purchase data is generated from shopper card data, which exists at the household level. But generalizing the data from individual to households requires a connection to the offline world (see prior question). Does your data capture this?

Finally, how many profiles are there? There are 220 million adults in the United States. If your data provider has 3 billion profiles, it isn’t marketing to people, it’s marketing to data points. The data stream that we rely on as marketers grows exponentially each year. Today, there are more IP addresses for devices than people. New ways of parsing, organizing and leveraging data will be invented that will make the media landscape even more addressable and exciting.

But buyer beware, if you don’t kick the data tires and get a more complete understanding of the modern principles of data integrity, you may just be getting crap.

This article is a repost from AdWeek.com, and written by Julie Fleischer, VP of Product Marketing at Neustar. #bravo

Data Onboarding

Onboarding Is A Data Business

Onboarding companies are multi-faceted and considered a fundamental part of the digital eco-system. An onboarder is the root of where all targeted display ads emanate. Below is a sample of what a traditional data onboarder facilitates:

  • Pixel tags
  • Publisher traffic aggregation
  • Platform integrations
  • Pixel syncs
  • Cross device identification
  • Persistent IDs
  • Digital audience targeting across media, devices, and formats including: display, email, social media, addressable TV, and mobile (leveraging cross-device IDs)
  • Digital audience segmentation
  • Site personalization
  • Customer or audience insight development for market research, modeling, and planning
  • Measurement and attribution for both online and offline applications

These capabilities sound very technical and they certainly are. Yet, as technical as these processes may be, each process an onboarder performs connects back to the core of onboarding – data. If you do not have capabilities to manage, manipulate, analyze, and understand data, you cannot onboard correctly. The technology only enables the data to be utilized, distributed, and analyzed efficiently, but if you do not have an excellent data pedigree, none of the technology will repair poor data pairings.

According to Winterberry Group, consumer data onboarding refers to the process of linking offline data with online attributes. More specifically, it is the matching of two audience data sets: a first-party CRM data set belonging to a marketer and a digital data set belonging to a data provider. The match process uses a common identifier or match key to link the records. The match—with a certain degree of accuracy—provides the privacy-compliant identity resolution necessary to activate a distinct and expanding set of data-driven marketing use cases.

How many times did you see the word data in the description above and how many times did you see the word technology? [Data won 6-0] To onboard accurately and properly assist marketers in delivering relevant targeted ads to consumers, data must be the focal point.

It was not long ago, at the start of this component of the tech stack, that a connection made anonymously between an offline email and an online hashed email was considered acceptable for targeting. Or that any one associated in that email address’s household was also sufficient for targeting and helped to provide “scale”. But things are different now. Today, we can and do understand individuals online by utilizing data to deterministically match a CRM file to a known hashed email address. This process enables the identification and connection to each individual customer. (Device Ids can also be used to create connections).

But what about the universal concern of anonymity and PII? Creating associations and deterministically matching only helps group individuals into more targeted and accurate segments. All individuals are still anonymized for targeting and no PII is ever utilized within platforms – that is the job of the onboarder.

At the end of the day, onboarding must be data-centric. Marketers and brands demand individual and deterministic matching. The only way to accomplish this is by doing a lot of data work up front. First, an onboarder needs to work on the data being provided. This includes data hygiene, deduplication, NCOA (National change of address) procedures, and reviewing for data gaps within emails, postal addresses, or intelligence. Ensuring the brand’s data is standardized and able to be accurately matched is the most crucial first step of the onboarding process.

The second step is ensuring that the brand’s data has the greatest chance of being found online. This takes more data work to append as many email addresses, device IDs, mobile numbers etc. as possible to each individual record. Third, that data must then be matched against an onboarder’s identity graph to conclude what the individual deterministic match rate is. Again, a pure data function.

The building of an accurate identity graph is also pure data work. An identity graph is not a licensed consumer file, it is a living breathing entity that needs to be cared for and cultivated in real time. Each day, people experience transitions in their lives. Whether that be a birth, death, new email, new phone, new TV, new home, new car, or college etc., these individual transitions need to be captured, validated, and updated consistently. A true identity graph can achieve this and is therefore considered the foundation for all accurate matching, targeting, performance, ROI, attribution, analytics, programmatic selection and most of all results.

If you are serious about onboarding, stop worrying about scale for scale’s sake and devices by the ton. Instead, focus on making sure your data is being matched accurately on an individual deterministic basis. You will then not only see an increase in the performance of your campaigns, but an increase in your customer’s sense of appreciation and value.

Data

Six Questions Marketers Need To Ask About Data Quality

Data-driven advertising requires good data. But lots of bad data and questionable data practices can harm a marketing campaign.

Marketers need to know when to use their own data, and when to rely on partners. They need to understand the trade-offs between cost, accuracy and scale. They need to know where their data came from, and how to test it cheaply. And they need to know how to evaluate multiple data sources.

Question One: How Is The Segment Created?
Finding out how segments are created is arguably the most important question of the bunch. When a marketer is targeting “auto intenders” or “beauty buyers” or “people who visit coffee shops,” they need to know how that segment is built and whether it was created using their own data or that of a third party.

“Third-party data can be very valuable when it’s segmented very carefully,” said Ana Milicevic, principal and co-founder at Sparrow Advisers, a boutique data-focused consultancy.

“If someone is targeting ‘auto intenders,’ they may not think about what it signifies,” she said. “Is it someone buying a car this weekend? Or someone interested in cars in general? If you don’t have this defined, it’s very easy to lump together widely defined segments.”

Data providers can use different methods to come up with segments. Some data can be “totally probabilistic, based on assumptions you never asked about,” warned Oleg Korenfeld, Mediavest Spark’s ad tech/platform EVP.

“On the other side,” he said, “you can know the list of the email addresses where they were created and matched against a database, like a supermarket loyalty card. That’s as deterministic as it gets, without any cookies involved.”

Other segments are created using modeling, which can improve scale, but reduce quality.

“We want to know now exactly what percentage of a segment is modeled versus seed data,” said Jonny Silberman, director of digital strategy and innovation at Anheuser-Busch InBev, at LiveRamp’s RampUp conference in San Francisco Tuesday.

Question Two: Is The Data Worth The Cost?
If males are half the population, but it costs three times as much to target them, is buying a gender-based segment even worth it? Sometimes.

Spending money on data to serve the right creative can be worth it, especially for brand marketers. “If you are bombarding them with messages they don’t want, because it’s cheaper to do it, you are going to annoy them and they will shut down ads in general,” said Accenture’s Matt Gay, senior manager of the media and entertainment practice.

But for performance marketers, spending on data only makes sense if it improves outcomes.

“You can have the most accurate, amazing data in the world. But if it’s 15 times more expensive than anything else, maybe it’s not worth the squeeze,” said Mindshare Chief Data Officer Rolf Olsen.

Performance-based marketers have the luxury of testing to see if expensive data still drives stronger results. “I look at cost and quality in concert with each other,” said Shutterstock CMO Jeff Weiser, who comes from an analytics background. “If there is going to be a higher cost to acquire better data, it’s got to be justified by a higher ROI.”

Mediavest Spark looks at data’s ability to drive efficiencies. Since media is “the most expensive things marketers pay for,” using data to buy less media can drive results, Korenfeld said.

“The formula is how many fewer impressions did you buy in order to justify the KPI goals,” he said. “Did you buy 10% less media? If you paid the same amount overall, then the data is wasteful.”

Question Three: What’s the Trade-Off Between Scale and Accuracy?
Bad data sometimes proliferates because crappy data can drive results for an advertiser.

A small, high-quality data segment may work for an email marketing campaign, but is way too small for a media campaign. So data providers futz with data to add more scale, juicing results. Brands need to be aware of lookalike modeling or any other tactics used to gain scale.

“There is always a balance between pure reach and ability to target that makes conversations around data quality difficult to have,” Milicevic said. “If you create a stringent segment of people like women in their 30s who bought a magazine in the past 14 days in these four ZIPs, you realize that’s 30 people. It’s a valuable segment, but doesn’t have reach or scale.”

Buyers reflexively want the most accurate segment, Korenfeld said, “But you lose scale that way.”

Transparency is the best remedy. Going back to question one, if marketers know how the segment is created, they can determine the trade-off between accuracy and scale that make sense for their brand.

Question Four: Can I Test This Segment Without Buying Media?
Traditionally, advertisers test data by buying media against a segment. But media is expensive.

“While we have healthy budgets, we can only test out a few segments every year,” said Anheuser-Busch InBev’s Silberman. “What makes sense for us is brand health or offline sales lift, and that means we need to do long and expensive studies for our campaigns.”

If marketers don’t want to spend money to test a segment, they can try to validate the data against another data segment they have in their DMP or CRM and see if there are any head-scratching results. (Unfortunately, this method doesn’t work as well for a CPG like Anheuser-Busch, which doesn’t sell directly to customers.)

“You don’t need to start with in-market testing,” Shutterstock CMO Jeff Weiser said, who has an analytics background. “You can append to the CRM database, and check the match rate. To the extent that it can be matched, does it have correlations to the rest of the database?”

A lack of correlations indicates bad data, Weiser said. Another red flag would be correlations that don’t make sense, like an outside data set that suggests a marketer has a wealthy customer base when internal data suggests the opposite.

Data that doesn’t make sense can be chucked before going through the expense of testing it with media.

Question Five: How Often Is The Data Refreshed?
Some data – like demographic information or interests – doesn’t change much over time and marketers can use it without worrying it will decay. But other types of data decay quickly. “You are going to want to update SKU-level or transaction-level data more frequently than a lifestyle category,” Weiser said.

Take someone on the cusp of a big purchase, like a car. A consumer enters that phase in a matter of weeks or months, so predictive models of “car intenders” refreshed every year won’t drive results. Pun intended.

“Particularly in the world of behavioral segments, there can be three months out of a three- or four-year cycle where your signals are clear,” Mindshare’s Olsen said.

Brands can run into problems when activating their slow-moving CRM data in a media environment.

“Lots of brand marketing was built with an annual plan or a quarterly plan,” noted Howard Bass, partner and global media and entertainment advisory leader at EY. “Brands need to move to a more near-real-time data exchange. In the digital media ecosystem you’ve got to rethink the rhythms.”

Question Six: Where Did This Data Come From, And What Has It Been Combined With?
“No piece of data has a virgin birth,” Shutterstock’s Weiser said. It’s captured, then extracted, transferred and loaded into a database, queried with SQL and transformed in Excel. At each of the steps, “there is a little bit of telephone that can happen to data elements.”

For instance, matching data to cookies or device IDs can degrade data quality.

“You might combine a bunch of data points, but the match rates are so low you end up with a data set that’s not valuable,” Mediavest Spark’s Korenfeld noted.

Conversely, having a data set that plays well with other data sets improves quality.

“We talk about how well a data set integrates with other data sets,” Mindshare’s Olsen said. “If you have to merge three to four data sets to get a clean read on the viewability rate or ad fraud, there is a significant level of complexity in the integration of that data set.”

Call it “the unsexy part of analytics,” as Accenture’s Gay does, but data organization, matching and cleansing impacts results.

And every marketer should ask how data has been combined when bringing in new data or analyzing existing data. “If you don’t understand how the data is built from the ground up, it can lead to very misleading conclusions,” Gay added.

Bringing The Answers To All The Questions Together
Using data in media today requires discipline around quality, but also an acceptance that sometimes still things will get messy. “We are in the early innings,” Gay said.

As digital matures, data quality will likely improve but retain certain flaws.

“The digital land is geometrically more complicated [than TV], because you can get much more granular with data,” Gay said. “We will never get to perfect. It’s going to be an evolution with degrees and shades of gray.”

Article courtesy of Ad Exchanger.

Data Onboarding

What Is Individual-Based Marketing And Why Do Brands Need It?

Odds are you’ve probably heard about “people-based marketing,” the marketing technology phenomenon that, over the past few years, has helped brands activate their offline customer data online. Yet, despite the success brands have achieved using people-based marketing, it still leaves room for error. Individual-based marketing is different. It focuses on connecting brands with each individual customer, not people in a household, so that campaigns can be truly relevant.

Individual-based marketing is 100% deterministic, not probabilistic. Whereas people-based marketing can utilize a combination of deterministic matching and probabilistic algorithms to onboard customers for brands, individual-based marketing only uses 1 to 1 matching. This means that brands can confidently and accurately display personalized online advertisements to their customers at the individual level across multiple devices. Working with an individual-based data onboarding company, brands can connect their offline CRM (first-party) data with online identities to precisely target the right person, on the right device, at the right time.

Still not convinced? Here are the top 5 reasons why brands should incorporate individual-based marketing into their strategy for 2017:

1. Effective Use Of CRM Data – Individual-based marketing is fueled by first-party data, one of the most powerful yet underutilized tools in a brand’s marketing arsenal. Since the data is information aggregated from a brand’s direct interaction with its customers, it provides the greatest amount of insight and knowledge necessary to help make effective marketing decisions. Sadly, many brands misjudge the value of their first party data, often settling for probabilistic, household level, people-based marketing approaches.

2. Optimized Targeting – Consumers now more than ever demand more customized online shopping experiences. Individual-based marketing provides the ability to go beyond basic demographic breakouts (gender, income, age etc.) so that brands can take the guesswork out of content personalization and are not limited by generic marketing strategies. The last thing you want to do as a brand is unknowingly deter your audience because of a broad scale marketing campaign that lacks relevance.

3. Reduction Of Marketing Waste – With individual-based marketing, you can pinpoint the individuals most likely to purchase certain products or services and avoid the rest. This leads to higher conversion rates and improved ROI. The alternative? Continue playing display ad roulette with your marketing spend and hope your boss doesn’t mind.

4. Understand Each Customer’s Journey – Using customer insights (cross-channel/device habits) and closed-loop analysis (purchase behaviors) from individual-based marketing campaigns, brands can gain a true understanding of their customers and create products/services tailored to fit their needs.

5. People-Based Cookies Are No Longer King – Brands that continue to solely rely on the technology of people-based cookie targeting will be plagued with a list of issues. These issues can include poor personalization, lower ROI, inconsistent performance within mobile browsers, and inability to keep up with the progression of technology.

Individual-based marketing is revolutionizing the way that brands connect with their individual customers. It’s proven ability to connect marketers with real individuals has allowed for optimized targeting, the reduction of marketing waste, and a true understanding of each customer’s journey. Without it, brands will struggle to resonate with their customers and effectively reach their marketing goals.

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