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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.

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, 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 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.

Data, Data Onboarding

Data Management, Data Onboarding Are The Paths To Marketing Success

The amount of data that retailers now generate and have access to is unprecedented. From customer data to product data to competitor data and more, retailers are collecting more information than ever before. In fact, digital data in the universe is doubling every two years, according to Patrick Wolfe, executive director of the University College of London’s Big Data Institute.

However, all of that data is worthless if it isn’t captured and maintained accurately, and then leveraged to make informed business decisions, whether it be for marketing to customers, merchandising your website, managing your inventory, or a host of other things.

Proper Hygiene is the Key to Onboarding Data Accurately
With the abundance of data, many retailers are turning to data onboarding companies to help them manage and improve its intelligence and value. Data onboarding provides the connection between a retailer’s offline CRM list to customers’ online devices such as PCs, laptops, smartphones, smart TVs and tablets. When evaluating data onboarding companies, there are a number of factors retailers need to pay close attention to. Paramount is finding a data onboarder that can standardize and clean your data. This process ensures that the data you’ve collected — likely from disparate sources, such as offline CRM data, online transaction-level data, marketing campaigns, etc. — is accurate and ready to be used.

“Once a CRM list has been correctly onboarded, retailers can accurately target their customers anytime, anywhere, across all devices,” says Paul Chachko, CEO of Throtle Onboarding. “Without precise data onboarding, personalization and optimization are not effectively attainable.”

In addition to clean data, you want to increase its intelligence so that you’re getting a 360-degree view of each customer and prospect, including matching their offline behavioral data with their online behavioral data. For example, a retailer needs to make sure that the customer that opens its email on their smartphone and then visits one of its brick-and-mortar stores before ultimately making a purchase on its website is accurately tracked and identified across every touchpoint, allowing for more relevant communications with that customer in the future. Your data onboarding partner can make this level of tracking possible by assigning a persistent ID to each customer. That ID travels with that customer no matter how, when and where they interact with your brand, so you know at the exact moment they show up across any channel.

A unified view of customer data enables retailers to target individuals with relevant, personalized messaging across multiple channels (e.g., display, email, social media) and devices. This level of understanding allows for personalization that has proven to increase conversion rates. Furthermore, creating segments of like-minded customers within your database — e.g., 20-25 year-old-women who live in New York City that have purchased boots from your brand in the last six months — allows for personalization at a larger scale.

Enhance What You Have
In addition to cleaning, managing and segmenting first-party data, the right data onboarding provider will be able to supplement a retailer’s database with additional intelligence at the individual level. This influx of third-party data can help retailers learn more about existing customers, making personalized communications to them that much easier — and much more successful. Customer retention is a primary challenge for today’s retailers, particularly online, where the competition is simply a click away. The more you know about your existing customers, the better you can market to them. This helps to build long-term relationships with customers, and keeps them returning to your brand to purchase again and again.

Furthermore, a third-party data provider’s vast data warehouse offers a retailer the opportunity to identify like-minded “look-alike” prospects — e.g., more 20-25 year-old-women who live in New York City that have purchased boots in the last six months, but who are not in its CRM database.

The first step for this type of customer modeling is analyzing the data you have to understand who are your best and most active customers. Then by being able to identify prospects that share common characteristics and behaviors with your best customers, you can target these prospects with relevant messaging. Continuously adding new customers to your database is essential to growing your business, as customer churn is inevitable for all retailers.

Data modeling allows retailers to target new look-alike prospects with a higher degree of certainty, thereby increasing the chances for a successful campaign. In addition, marketing spend can be better optimized by targeting consumers with a higher propensity to convert.

Accurate data is the fuel necessary to power personalized marketing campaigns. Without accuracy, personalization isn’t possible. This is critical because consumers have come to expect and in many cases demand that brands know them, and present them with relevant messaging and offers at every step of the purchase journey.

Working with the right data onboarding partner can help retailers meet this consumer demand. From ensuring that your data is accurate, having the ability to track customer behavior across channels via persistent ID numbers, using segmentation capabilities that enable personalized messaging, and identifying look-alike prospects that will optimize marketing spend and grow your brand, the right data onboarding company will become one of your most trusted partners. Being able to effectively manage and use the immense data that retailers have at their fingertips to make informed business decisions is critical to future success. Ensure that your brand is securing its future by partnering with a data onboarder wisely.

Data Onboarding

How Data Onboarding Improves Marketing Efforts

As email, postal and other marketing channels flood consumers with messaging, brands struggle to meet the highly complex expectations of their hyperconnected customers. Today’s consumers demand more personalized, optimized and unique experiences. While the process known as data onboarding isn’t new, it’s still growing in popularity within the world of marketing.

Data onboarding enables marketers to integrate offline customer data with online identities to target customers across multiple channels and devices — e.g., display, mobile, email, social and addressable TV. To remain competitive, marketers need to include data onboarding as part of their marketing strategy and planning process.

Extend Reach and Targeting Capabilities Online

With data onboarding, marketers can take their offline CRM data sets (purchase, loyalty, behaviors, point of sale, etc.) and convert them into a holistic digital audience for online targeting. Once a CRM list has been onboarded, marketers can effectively target a customer anytime, anywhere, across all devices. Without data onboarding, personalization and optimization is not effectively attainable.

Better Understand Who Your Customers Are
Data onboarding is breathing new life into the millions of dollars retailers invest in their CRM systems. Most marketers map their offline CRM records against a robust database of demographic, lifestyle and purchase data to gain a 360-degree view of each customer. Armed with this detailed customer information, marketers can separate this data into specific online segments that can then be easily targeted.

Track the Online Customer Journey From Start to Finish
CRM onboarding also paves the way for closed loop marketing. Using unique IDs, retailers can track customer engagement from online campaign interactions through final purchase. Marketers can gain insights about the entire customer journey and then use those insights to create hyperpersonalized and engaging experiences. Customer journey analysis is now considered the most valuable conversion rate optimization measurement method, according to eConsultancy.

With data onboarding, marketers can use the wealth of their CRM data in online advertising to gain greater consumer insight, develop stronger campaigns, and use that knowledge to create personalized experiences that every consumer craves.

Data, Data Onboarding

Drawbridge Probabilistic Cross-Device Technology Patent

Throtle and Drawbridge previously announced their partnership, stating that the Drawbridge Connected Consumer Graph® will help Throtle’s brand and agency clients reach consumers more efficiently across multiple devices. Throtle will onboard and match its clients’ data with the Drawbridge graph in order for brands to extend and enhance their reach within their demand-side platform (DSP) of choice.

Throtle is proud to be a partner with this leading anonymized digital identity company, as they just announced they were awarded Patent No. 9,514,248 from the United States Patent and Trademark Office. This patent validates Drawbridge’s complexity and scale of the innovation and science that the company has developed as the heart of its technology stack.

“As global device adoption and proliferation reaches unprecedented levels, it has become increasingly difficult for brand and enterprises to understand consumers across multiple devices,” said Drawbridge Founder & CEO Kamakshi Sivaramakrishnan. “The Drawbridge Connected Consumer Graph, built on this patented system, offers an independent, accessible, scaled solution for digital identity that can be leveraged to make customer experiences on the internet more personalized – from advertising and content optimization, to product recommendations and fraud detection.”

To read the full article, please click here.

Data, Data Onboarding

Ad Exchanger Article – Mo’ Match Rates Mo’ Problems

Mo’ Match Rates Mo’ Problems As Cross-Device Vendors Aim For Scale – by James Hercher // Monday, October 10th, 2016

Cross-device identity match rates have shot up in recent years, but brands and agencies remain skeptical of the results.

“We were consistently disappointed with cross-device identity matches,” said David Kohl, CEO of the digital media advisory firm Morgan Digital Ventures. “There’s a gap in understanding of what’s possible between vendors and the buy side, [which leads to] frustration (on both sides) over unclear expectations or results.”

Some point to industry consolidation as the driver behind rising match rates. But as many cross-device vendors increasingly deploy probabilistic matching to manufacture scale, aggregated data is added and verifiable identities are sacrificed.

“This approach can present challenges for advertisers who may want to run verification, as there is no reliable way to measure accuracy of the match,” said Keith Johnson, EVP of strategic data solutions at Wunderman.

An agency can use a client’s CRM data or other owned personally identifiable information (PII) to verify identities if a person is served an ad to his or her phone or desktop and then visits the client’s site and logs in, makes a purchase or provides PII in some way. It’s a messy, laborious process.

“Although many clients like the idea that they can run these tests, in reality it very rarely happens today,” Johnson said.

There is no industry norm or consensus on what is a “good” match rate because they deliberately vary by type of campaign, brand industry category or first-party data used.

Say a brand wants its attribution provider to connect people who were exposed to TV and digital ads with retail foot traffic via a location analytics vendor. Stringent attribution requirements would call for a highly deterministic data set and a match rate greater than 90%, said Auren Hoffman, the former CEO of LiveRamp who now helms a startup called SafeGraph.

An automotive marketer more concerned with long-term exposure frequency and greater reach, however, may be happy with a more probabilistic set matching 30% to 40%.

This is why straightforward questions like “What are your match rates?” can be counterproductive.

Marketers are frustrated, but it’s a frustration born of optimism, said Kate Clough, a media-planning VP at MRM//McCann.

“It used to be that (cross-device) plans were breaking down in the execution. Now we see the opportunity to provide those connected experiences, but it can be pretty daunting because buyers and sellers speak very different languages,” she said.

Morgan Digital Ventures and MRM//McCann are pilot partners in an initiative launched recently by the DMA to standardize cross-device RFP templates and establish the basics with a glossary of industry jargon. Terminology can have different meaning for marketers and cross-device vendors, and it leads to confusion over results.

For example, marketers may use the words “accuracy” and “precision” interchangeably, but those are discrete terms in the cross-device space. “Accuracy” is the percentage of correctly identified matches plus correctly identified non-matches – so a campaign with a low match rate could actually have high accuracy. “Precision” is the percentage of all possible matches correctly identified by the vendor.

“Recall” is a media-buying metric to judge an ad’s impact on consumers, but a cross-device vendor considers recall to be the percentage of overall identified matches divided by all known true matches.

“Agencies typically have a clear idea of how they’d like match rates to be calculated, but if there’s any ambiguity in their guidance to the supplier the results can be inflated,” Clough said. “Are you talking about matched devices or unique individuals? In some cases it seemed accidental, but it left people vulnerable to potential manipulation.”

Michael Schoen, VP of marketing services at Neustar, was less charitable about some linkage claims: “As with everything in ad tech, where there’s a financial incentive, there’s fraud.”

It’s now common for a third-party measurement firm like comScore to verify cross-device graph results when they’re applied in advertising, said Yael Avidan, VP of product at the mobile DSP Adelphic.

Even with verification, some marketers still aren’t convinced the results are accurate.

“If I see match results I don’t like – they seem too high maybe or I’m just not confident what I’m seeing is correct – comScore verification isn’t going to change that,” said one major toy brand marketer who said she couldn’t comment publicly due to NDAs with multiple data-matching vendors.

Much of the overall confusion over match rates is due to a lack of vendor transparency, said LiveRamp Chief Product Officer Anneka Gupta.

“There are lots of people out there saying they do deterministic matching, but you have to be careful about what’s being layered into the data,” Gupta said.

Many cross-device vendors and data aggregators regularly pay publishers to help them connect a customer’s data to web traffic or email sign-ups. The inconsistencies plaguing publisher data monetization – bot farms juicing numbers with fake emails or actual people using throwaway addresses on non-billing accounts – can be passed on to device graphs.

To shake buy-side concerns about finding quality data at scale, some firms are “moving toward a shared, authoritative match set,” said Jay Stocki, SVP of data and product at Experian.

Stocki alluded to Experian’s partnership with Neustar. The two longstanding titans in cross-channel identification began selling a shared profile-matching product earlier this year.

Acxiom, another legacy giant, pursued a similar goal when it purchased LiveRamp in 2014. The European telco Telenor and Oracle each bought a cross-device ID firm this year (Tapad and Crosswise, respectively).

Executives from Acxiom/LiveRamp, Oracle and Experian/Neustar each attributed a steep growth in match rates over the past two years to industry consolidation and cooperation.

Clough anticipates more consolidation as the wider ecosystem seeks enough shared scale to mollify marketers and offset the advantages enjoyed by Google, Facebook and Amazon, all of which have their own proprietary cross-device data.

“[Cross-device vendors] are willing to work with a variety of partners,” Clough said, “because that’s actually the only way for them to differentiate.”

Article page here.

Data Onboarding

Data Onboarding 101

Since the popularization of data, messages and priorities have continued to evolve. First, it was all about collecting data. Using past information about individual customers to answer hypotheses and in some instances to predict future needs and purchases. Next, technologies and intelligence (enter Data Scientists) became readily accessible to the benefit of many.

Data service providers and user-friendly platforms would accept your data, process it, and return it to you cleaned and ready for action. However, as shopping and purchase behaviors have become more complex, increased destinations coupled with increased devices and methods of purchases, a new solution is needed. Enter, data onboarding.

Data onboarding or offline matching is the process of transmitting offline data to an online environment where it is made available for marketing needs. Offline data can be matched at the individual level enabling marketers to produce and delivery highly targeted messaging, or at the household level for more general yet relevant messaging.

The ability to combine offline intelligence with online connectivity has unveiled an abundance of opportunities for marketers.

1. You have more to learn: No matter how much you know about your customers, there’s always more to learn. Data onboarding is the fastest and most effective way to gather more information about your customers. In addition to basic information such as gender, date of birth, demographics, and geographics, you can learn more about their personalities, online browsing habits, search habits, and their favorite places to shop both on and offline.

2. Your data is dynamic: CRM databases are only up to date for a brief period of time. Once a customer gets married, moves, or even changes their preference of ice cream brands, what was once current information becomes dated and effectively incorrect. Onboarding takes your offline data online allowing you to access the most up to date information for accurate targeting, profiling, and segmentation every time.

3. Increase your data value: Onboarding your data to where it can routinely undergo hygiene processing, be continuously enriched with key characteristics, and made available for online customer targeting increases its value both online and off. Lists and segments existing within a data management platform are readily available for monetization and lists pulled and extracted are guaranteed to contain the most up to date information available.

4. Expand your reach: Today’s customer is an omni-channel shopper. From tweens to Baby Boomers, consumers are shopping in store, in apps, on phones, and online. A rich customer profile complete with the most up to date information is required to reach your existing and potential customers wherever and however they choose to shop. Onboarding your data ensures that you have the most effective means of targeting your ideal audience.

5. Enhance the customer experience: Onboarding will make searching, segmenting, cleaning, enhancing, and monetizing your data simpler than ever before, but it will also enhance customer experience . Not only are messages more fine-tuned and appropriate to their intended recipients, they are delivered via the most appropriate channel for maximum convenience.

6. Directly attribute success: Successful campaigns make clients happy, but being able to directly attribute success to the use of your data makes clients loyal. Data onboarding provides a closed-loop solution that can track each individual customer through the sales funnel across all channels and devices over time.

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