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Data, Identity Graph, Identity Resolution

5 Trends to Inform your 2020 Identity Resolution Strategy

The old adage, uncertainty is the only certainty, rings true in the new decade, but there’s one other thing we can add to this sentiment with absolute assurance. The future of digital marketing will be identity-driven. A consumer’s appetite for highly-personalized brand experiences is motivated by something far more potent than trends in consumer behavior; it’s their basic human need to be understood, and that need isn’t going away any time soon. Individuals sense that they belong when they feel recognized and understood by a group (aka, your brand). Think about the last time you felt misunderstood. In turn, you probably thought differently about whomever it was that pegged you wrong, and that intuition is difficult to change. Alas, consumers don’t plan to make personalization easy on brands this year. Here’s what’s in store for identity resolution in 2020 and beyond.

Trend #1: Data accuracy is about to become more challenging than ever.

A database full of data doesn’t mean a brand is capable of targeting individual consumers across numerous channels. Accuracy is everything. Unfortunately, accuracy continues to be one of the most prominent pain points for most brands, and it’s about to get worse. On average, consumers use 3 to 4 internet-connected devices throughout the buyer’s journey.  By 2021, that number is expected to escalate to 13 connected devices. In other words, the data flood gates are about to open. Without a strong strategy for cleaning, appending, and keeping data up-to-date, this sudden influx will result in considerably more inaccuracies. Even if brands succeed at synchronizing all of this new data, matching the right data with the wrong customer profile instantly makes the data inaccurate. This is why identity resolution is crucial and necessary for brand survival in the new decade.

Trend #2. Connected TVs will become a priority focal point for brands.

The proliferation of streaming services like Netflix, Amazon Prime, Hulu, Disney+, and Apple TV+ coupled with the widespread adoption of internet-connected TVs opens up a robust new data source for marketers. Over the next few years, marketing strategies will leverage connected TVs in a variety of ways, but the most powerful tactic will be using IP tracking to identify the highest quality leads within a household and bidding strategically to ensure messaging is placed in front of them while they are a captive audience. Using cross-device mapping, brands can then measure the success of their carefully-placed messaging by observing a consumer’s online activity after they viewed an ad—for example, visiting the brand’s website on a device connected to the same WiFi network as the TV.  But beware, deciphering between individual household members is no easy task. IP addresses continuously change, requiring advanced algorithms and data science to identify trends and draw accurate conclusions. Those who succeed at leveraging connected TVs will be few and far between, which represents a major competitive advantage.

Trend #3: Data privacy will put consumer expectations at odds with marketing objectives.

Despite predicting that the CCPA will have a lesser impact than the hype suggests, brands must be prepared for how the emergence of privacy protection laws will change the targeting game. Already, New York, North Dakota, Utah, and Washington are working on legislation similar to California’s. Industry goliaths like Google, Facebook, and Apple are actively implementing measures to prevent certain forms of intelligent tracking. Make no mistake, data sources will be affected by these activities, requiring brands to adapt quickly if they want to sustain a frictionless, personalized, omnichannel brand experience. Agility will depend on widening the scope of identifiers integrated across touchpoints and devices, particularly those that deliver behavior, transaction, and contextual information.

Trend #4: The demand for identity resolution services will skyrocket.

According to a recent Forrester report, less than 25% of marketers feel confident about their ability to manage customer IDs with enough depth, accuracy, persistence, and scale. Nevertheless, they also see identity resolution solutions as “a significant or major assist in achieving their objectives.” Rather than focus time, energy, and budgets on in-house ID resolution solutions that present their own subset of challenges, more brands will look to specialized technology providers with the latest innovations and best practices already built into their services. Here’s why:

  1. Identities are moving targets that constantly change. Marketers admit that they lack the technology, skills, and institutional knowledge required to properly manage data as it evolves over time, which jeopardizes data accuracy and value.
  2. Brands are unable to keep up with the rapid release of MarTech innovations, both in terms of cost and onboarding.
  3. Many marketers still rely on a limited scope of identifiers, which will prevent them from adapting quickly to compensate for changes in privacy and data laws.
  4. As the volume of identifiers, devices, and touchpoints continues to increase rapidly, brands that rely on siloed tracking methods and disjointed consumer identities will miss promising new opportunities to deepen their understanding of individual consumers.

Trend #5: The demand for greater data transparency will drive new standards in ID resolution.

Without a doubt, security breaches and consumer distrust will follow us into the new decade. If marketers are to regain consumer confidence, they need to know that the data they receive is accurate and responsibly sourced. In other words, a mere data match percentage isn’t going to cut it. Moving forward, identity resolution firms will be held accountable for having a transparent data supply chain and stringent criteria for measuring data quality. They will need to implement a rigorous, multi-step data process to ensure that all data is properly cleansed and prepared. Furthermore, brands deserve a transparent, comprehensive report that discloses how the data was collected, built, and validated. Identity resolution provider should showcase the following:

  • The recency and frequency of the data
  • Email and attribute append rates
  • Address standardization
  • Number of devices per record (after removal of record/device duplicates)
  • Net usable records and unusable records
  • Distinction between
    • proprietary data vs. white labeled data
    • individual match rates vs. household match rates
    • probabilistic match rates vs. deterministic match rates

Simply put, there is no competing with a brand that can track a single consumer’s omnichannel journey and customize their buyer experience with relevant, valuable information and offers—period. Responsibly onboarding all the data required to achieve this level of personalization consistently amid changes in data sources, data capturing tactics, privacy regulations, and MarTech will require an identity resolution strategy far more advanced than the solutions of yesteryear.

Data, Identity Graph, Identity Resolution

Why Identity Should Be at The Forefront of Your 2020 Marketing ‘Resolutions’

It is no secret that identity has become a fundamental component of the digital ecosystem. The increasing volume of consumer data and sheer number of devices and channels in-market, coupled with privacy-first policies like GDPR and CCPA, has created increased demand for identity resolution.  Though identity resolution brings much promise, with it also comes many challenges.

As the number of touchpoints grow and become more complex, so will the challenges for marketers as they attempt to collect, analyze and build a unified customer profile. A study on the state of identity resolution strategies in marketing conducted by Forrester Consulting reveals that less than half of marketers are fully capable of identity resolution management.

If marketers plan to be grow and continue their success into the upcoming year, identity resolution needs to be at the forefront of their strategies. Below, we highlight some of the most important benefits of identity resolution for marketers:

Effective Personalization: If you don’t know with confidence who the customer is, you can’t personalize your messages or experiences to them. That, in turn, lowers the quality (and probably the duration) of your relationship with that individual. With trusted identity resolution, marketers can obtain an in-depth understanding of their customers, ensuring that the right message gets delivered to the right customer, on their preferred channel and device.

Better Customer Insights: Identity resolution helps marketers better understand who’s on the other end of a browser, mobile app, CTV, or IP address.  Accurate identity resolution allows companies to create a true single view of their customers that can be consistently communicated and deployed across brands, business units, and product lines. You should be able to continuously enrich, update, and share identity data across your entire organization in order to facilitate greater control and personalization.

Privacy Compliant Identity: Well-executed identity resolution embraces industry best practices and principles that ensure data is harnessed in ways that are ethical, compliant, and privacy safe. In the wake of GDPR, CCPA and others to soon follow, identity resolution ensures secure data transfer and encrypted storage, data processing controls and access restrictions as well as regulatory compliance.

Data and Consumer Accuracy: To properly execute 1:1 marketing, the data and methodology you use to identify customers on a truly individual basis must be accurate. If it isn’t, you’ll risk sending the wrong messages to customers at the wrong time. Identity resolution provides a constant stream of data to dynamically validate customers as they interact in offline and online channels. Accuracy data is vital to the success of personalization efforts.

Ultimately, identity resolution is a must have, not a nice-to-have for 2020 and beyond.  To remain competitive in the age of the empowered, connected consumer, marketers must be able to connect customer data into a cohesive whole. Those who leverage identity resolution will be able to provide a seamless customer experience that is consistent and personalized across channels, creating increased engagement and relevant interactions that improve loyalty, thus ensuring long-term success.


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Data, Identity Graph, Identity Resolution

Identity Resolution and Why It Matters: Q&A with Throtle CEO, Paul Chachko

Understanding who and where your customers are across all screens and channels is no easy task. Identity resolution creates a valuable asset for brands. However, the explosion of devices and touch points, combined with siloed or inaccessible data, has left most brands with only a fragmented view of their customers.

We sat down with Paul Chachko, founder and CEO of Throtle, to get his take on the importance of identity resolution and the advice he has for brands.


How do you define identity resolution?
At Throtle, we define identity resolution as the continuous mapping of disparate consumer data accurately associated with a single individual.

As the industry is starting to realize, an accurate identity resolution strategy is critical to the success of every marketing campaign executed today. It not only fuels customer experiences, but it allows brands the ability to create and build richer customer profiles overall. With the ability to view the entire picture surrounding one individual customer, their buying behaviors, demographic and geographic data, a brand can create more tailored marketing messages and campaigns that drive loyalty, engagement, and success.

In your words explain why brands need identity resolution?
Today, consumers own an estimated 3-4 different devices, and use these devices in different ways to research, browse, view and purchase products.

Without identity resolution, brands can’t associate and understand a customer from one device to another leaving them with a very fragmented view of the consumer. Identity resolution is the process by which a firm collects all of these various identifiers across all devices and touchpoints and unifies them into one holistic view of the individual behind them and makes them available for targeting.

As an identity resolution provider, how do you keep up with the industry and the ever-changing consumer?
Throtle continues to heavily invest in not only our underlying data technology, but also in sourcing the most accurate and rich data currently available in market. We are constantly looking to expand our network of like-minded data and media partners that are leaders in their spaces and are very tech forward. Accuracy is one of the most crucial data elements of identity resolution, and unfortunately, we see this lacking most in current data practices. If you don’t have a constant stream of integrated multi-sourced customer data such as email addresses, MAIDs, cookies, etc., then you will fail to have accurate identifiers to validate individual customers on a deterministic basis.

For brands looking at identity resolution providers, what advice do you have for them?
First and foremost, ask if their identity graph is deterministic or probabilistic. Deterministic matching is the gold standard. It is important to understand that what some companies call “deterministic” matching, really isn’t. Some think that if they find a single 1:1 match between two data points that it’s a confirmed deterministic pairing. Others, including Throtle, hold themselves to a much higher standard of quality and accuracy that requires multi-source corroboration of all data before declaring that a match is deterministic.

I would also suggest having identity providers explain the logic behind their match rate. A match rate depends heavily on a steady flow of multi-source data that is accurate and validated. At Throtle, we view the match rate as our ability to match to a unique individual rather than unique cookies or device IDs. If we match to John Doe and we see he has 3 devices, we count him as (1) unique match because all 3 devices belong to one unique individual. Other providers count each device or cookie match as a unique match regardless if they belong to the same (1) unique individual. As such, match rates are often inflated and do not accurately depict the match rate against unique individuals in a client’s file. We always advise to ask for apples-to-apples data from all partners you are evaluating so that you can be sure that match rate definitions and calculations are consistent across partners.

Finally, the best identity resolution vendors deploy a variety of “data hygiene” steps such as email validation, de-duplication, National Change of Address (NCOA), etc. to ensure a customer’s data is at the highest level of fidelity PRIOR to match to allow for the highest possible match rate. Lastly, we always recommended a data match test before any contracts are signed. It’s important that both sides understand the depth and wealth of the data and how it might or might not perform prior to entering into a partnership.

Do you see a threat with the upcoming California Consumer Privacy Act (CCPA) going into effect in January and identity resolution?
I don’t see a threat at all. In fact, I see this as an opportunity that continually validates identity resolution providers like Throtle, and the steps that we take in championing accurate, privacy compliant, transparent data. CCPA will showcase the ability for a provider to deliver transparency and ensure CCPA privacy compliance. This includes the ability to apply a high level of data protection and security in relation to personal data that our clients and third parties entrust to us.

Data, Privacy

California Releases Draft Rules for CCPA

California’s Department of Justice has developed and released a draft of implementing regulations for the state’s upcoming data privacy law. The rules clarify how the state will enforce the California Consumer Privacy Act (CCPA) and explain what businesses have to do to ensure they are following the law.

The draft implementing regulations for CCPA groups the actions businesses have to take around five key components: how to notify consumers about what data is being collected; how to handle the consumer requests for information; how to verify the identity of consumers making the requests; how to handle requests for information for children younger than 16 years old; and what needs to be done to avoid discriminating against consumers who don’t want their data or sold. The comment period for the draft rules end Dec. 6.

Privacy is an “inalienable right” in California, and CCPA will reset “the power dynamic between [consumers] and businesses,” California Attorney General Xavier Becerra said at a press conference announcing the draft implementation. The CCPA “allows you to pull the curtain back and see what information companies have collected about you, so that if you want, you could have that data deleted.”

The implementation rules lay out the things businesses have to think about as CCPA becomes law. “We want businesses to understand consumers have rights,” Becerra said. “Everyone has an obligation to know their rights and responsibilities under CCPA.”

The CCPA isn’t just for businesses that collect data online. A business that “substantially interacts with consumers offline” also has to notify the consumer about the data being collected and provide an offline opt-out mechanism.

The new law also requires businesses to be “transparent” about the data’s value, so that “consumers know how their information is valuable to the business,” Becerra said. Towards that end, businesses have to clarify the “service difference” a business may offer in exchange of personal information, so that the consumer can make an informed decision.

California may be the first state to have such a far-reaching data privacy law, but it isn’t alone. However, most local laws have focused on one or two aspects of consumer privacy, such as opt-outs and collection. The breadth of California’s law means that companies have to make changes all across the data lifecycle. “We may be the first, but we won’t be the last.” Becerra said.

The law goes into effect on Jan. 1, but the rules implementing and enforcing the law won’t go into effect until July 1, said Stacey Schesser, California’s supervising deputy attorney general.

Data, Privacy

Device IDs: Driving the future of digital advertising

Mention a “cookie” and most people expect a chocolate chip treat to appear. However, when talking about digital marketing, cookies are references text files on a browser that associate bits of data to a specific user.

Cookies are created when a user visits a website to keep track of their movements within the site, helping the user remember their login, preferences, and other information. Many online retailers use cookies to keep track of the items in a user’s shopping cart as they explore the site. Without cookies, your shopping cart would reset to zero every time you clicked a new link on the site. That would make it impossible to buy anything online.

Types of Cookies
The two most common types of cookies are first-party and third-party. Both types of cookies contain browser information and can perform the same functions. However, the real difference between the types of cookies has to do with how they are created and used.

  • First-party: These cookies are originated from the primary domain visited by the user, used to personalize that users experience on that primary domain.
  • Third-party: These cookies don’t originate from the primary domain visited by the user. The most common use of third-party cookies is to track users who click on advertisements and associate them with the referring domain.

Cookies have become the most common method of identifying website users and allowing for a personalized browsing experience on a desktop (we discuss mobile below). However, with growing awareness of privacy issues, the introduction of laws like the General Data Protection Regulation (GDPR), CCPA, Apple’s ITP, Firefox’s ETP and now Chrome’s ITP, some are saying the end of cookies is near.

More on Third-Party Cookies
For the MarTech industry, ad targeting is a huge deal. Third-party cookies are used to gather the information on user behavior such as websites visited, time spend, clicks, location, and more. This information creates a unique profile of the user to show them only relevant and personalized ads.

The lifespan of third-party cookies has been threatened for a while. In 2017, Apple first released ITP aka Intelligent Tracking Prevention for the Safari browser. With ITP 1.0, Safari wanted to prevent third-party cookies from tracking users across different sites. And now, they have ITP 2.2 coming soon to strengthen that protection against user tracking.

Why You Should Care
Chrome is following in Apple’s footsteps and released a new set of controls that allow users to see all of the cookies currently stored by the browser and give them the option of blocking any trackers they don’t like. With Google Chrome accounting for nearly 70 percent of the global desktop internet browser market share, the MarTech industry is getting nervous.

The cookie also poses obstacles in the mobile space — if they even work at all. There is a new term called ‘the unreachables’, the mobile-only users, who don’t really engage on desktop computers and don’t interact with traditional media. No cookies for them.

Goodbye Cookie, Hello Device ID
The cookie isn’t dead yet, but as protection against user tracking, browser privacy and the growth of mobile users continues to grow, device IDs will be redefining the role and the usefulness of cookies.

Cookies don’t deliver the holistic view that device IDs do. Device IDs provide better and more reliable data. They present a clear view of a user based on deterministic data across longer, if not indefinite, stretches of time. Cookies only track a single session and the average “lifespan” of the cookie is no more than three weeks, creating discrepancies when measuring long term user journeys.

Device IDs are a more efficient way of targeting and reaching customers via connected devices more accurately. There is a science of how to properly collect and curate accurate device IDs to an individual and if done right, ad spend will be utilized wisely and profitably, if it’s done wrong, spending and ROI will be disastrous.

Device IDs will drive this future; cookies will not.

CCPA, Data, Privacy

How is CCPA Different from GDPR?

The California Consumer Privacy Act has been coined California’s GDPR, referring to the comprehensive data protection law that took effect in May 2018 in Europe, just one month before the CCPA was passed. The CCPA, which is set to take effect January 2020, creates new rights for Californians and other obligations for businesses handling their information. The CCPA is said to be a model of the GDPR, however, there are some clear differences between each legislation.

Both the CCPA and the GDPR give individuals certain rights to how their personal information is collected and used, but there are several important contrasts to be aware of. Because California has a much larger economy than the UK, the implications of penalties may be even more severe than that of the GDPR. Even though the CCPA does not go into effect until 2020, we are already seeing it influence federal legislation.

Understand the similarities and differences between the GDPR and CCPA.

CCPA GDPR
Who It Protects
‘Consumers’ who are California residents ‘Data Subjects’ in the European Union
Personal Information
Defined as any information that ‘identifies, relates to, describes, is capable of being associated with, or could reasonability be linked directly or indirectly, with a particular consumer or household.” This includes not only identifies like name or address, but extends to browsing history, behavioral data and more. Defined as any information relating to an identified or identifiable natural person, directly or indirectly. This usually mean data like address, license plate numbers, SSN, blood type, bank account information, and more.
Rights Granted
Grants consumers five rights:

1. The right to disclosure

2. The right to deletion

3. The right to access

4. The right to opt-out

5. The right to non-discrimination

Grants data subjects eight rights:

1 . The right to be informed

2. The right to access

3. The Right to rectification

4. The right to erasure

5. The right to restrict processing

6. The right to data portability

7. The right to object

8. Rights in relation to automated individual decision making, including profiling

Right to Deletion
CCPA right to deletion applies to data collected from and about the consumer GDPR right to deletion applies to all data collected about the consumer
Who Must Comply
“California businesses” of substantial size (with regards to revenue or number of consumers affected) that collect consumer personal data Any “data controllers” (who determine the purpose and means of processing the data) and “data processors” (who process this data for the controller) that holds personal data of EU citizens.
Basis for Consent
Allows sites to collect and sell your data if you sign up or make an online purchase and only offers consumers the right to opt-out. Requires consumers to opt-in to data collection by instructing sites to get consent before collecting data.
Time allowed to respond to a request
Responsible parties have 30 days to respond to a request Responsible parties have 40 days to respond to a request
Financial Penalties
Organizations in breach can be fines up to $2,500 per violation for negligent violations and up to $7,500 per violation for intentional violations. Organizations in breach can be fined up to 4% of annual global turnover or EUR 20 million.

 

While in many ways the GDPR and the CCPA align, there are notable differences between the two regulations. The GDPR’s definitions are often broader, while the CCPA has taken a more specific approach to its scope. That does not mean however that companies that are GDPR compliant don’t need to worry about the CCPA.

 

Don’t expect this to be the last privacy act, either — there are many more on the horizon. Companies should be prepared to meet more stringent data privacy regulations that focus on data discovery, security, and classification. Stay tuned…

CCPA, Data, Privacy

The California Consumer Privacy Act: CCPA 101

Just when you settled into a post GDPR routine, there is a new consumer privacy law looming. The California Consumer Privacy Act of 2018, also known as CCPA, goes into effect on January 1, 2020, and will have implications for marketing to consumers.

In a nutshell, CCPA will empower people to know the types of personal information businesses collect about them, and give them the right not to agree to the sale of their personal data to other parties. More specifically, CCPA introduces the following:

  • Right to know all data collected by a business on you
  • Right to say NO to the sale of your information
  • Right to DELETE your data
  • Right to be informed of what categories of data will be collected about you prior to its collection, and to be informed of any changes to this collection.
  • Mandated opt-in before sale of children’s information (under the age of 16)
  • Right to know the categories of third parties with whom your data is shared
  • Right to know the categories of sources of information from whom your data was acquired
  • Right to know the business or commercial purpose of collecting your information
  • Enforcement by the Attorney General of the State of California
  • Private right of action when companies breach your data

What Businesses Will Be Affected by the CCPA?
While the CCPA could be influential in shaping additional consumer data regulations, for now the law’s scope is limited to mid-to large-sized businesses that do business in California. Companies are subject to the terms of the CCPA when they meet one of the following conditions:

  • Annual revenue exceeds $25 million
  • Company receives data from at least 50,000 people, households, or devices every year
  • Company earns at least 50 percent of its annual revenue from selling personal data

Are There Any Penalties?
Currently, penalties in the law can include up to $7,500 per incident. Meaning that a data breach involving 10,000 customers could end up costing a business as much as $75 million.

When Does the CCPA Go into Effect?
Technically, the CCPA went into effect when it was signed into law on June 28, 2018. However, the requirements will go into effect on January 1, 2020. That said, January 1 is not the end of the line. The California Attorney General has until July 2, 2020 to publish regulations. (Legislation is what the legislative body passes. Regulations are the standards for enforcing the law.) Also, the Attorney General cannot bring legal action against violators of the CCPA until either July 1, 2020 or six months after the final regulations are published, whichever comes first. More to come…

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.

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

Outlook Data 2017 Snapshot Evolving Role Audience Insight

Data is playing an increasingly critical role across a vast range of advertising and marketing applications. Marketers, media buyers, publishers, and digital advertising technology executives said that a renewed focus on measurement and attribution will be the centerpiece of their efforts in 2017 —a shift from 2016, when “cross-device audience recognition” took the lead position. This second annual benchmarking report explores how digital marketing and media practitioners are using audience data, and how they intend to evolve their data-centric practices in the year ahead.


Research Highlights include:
Over the coming year, users said they expect “cross-channel measurement and attribution” will take center stage in the organizations (57.1%)
“Difficulty in proving ROI of our data-driven programs” (45%) and “lack of internal experience (at the functional/operational level)” (45%) were cited as the two biggest obstacles impeding panelists’ efforts to leverage audience data in 2017
More than two-thirds of respondents (67%) increased their spending on data and related services from 2015 to 2016—and even more (71%) anticipate bigger budgets in 2017

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