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It’s All About The Data

Gone are the days when brands and marketers were satisfied with investing thousands of dollars on inflated, household level cookie inventory without the ability to target individuals within a data segment. Today’s marketers are extremely savvy and demand that their customer data is matched both individually and deterministically, so they can execute people-based marketing campaigns.

Just like the direct marketing business decades ago and the email business in the early 2000s, digital data advertising is being transformed, replacing probabilistic matching methodologies with deterministic matching. Deterministic matching uses customer data, such as hashed email addresses or log-in data to recognize individuals on various devices with 100% accuracy, whereas probabilistic matching uses IP addresses, browser type data, device type data and anonymous data signals to estimate connections. Although probabilistic matching can produce greater reach, brands have recognized the importance of individual targeting and the value of understanding each customer’s journey for better engagement, analysis, and ROI. Yet for most marketers this capability still seems too good to be true, with many still questioning how this process can be achieved in a non-PII way and still be reliable.

All of the data aggregated by data onboarders, device ID providers, and publisher networks must be collected and stored in a way that allows for individual connections to be made in a deterministic fashion. How are these connections made? Through the use of an accurate identity graph.

An identity graph is an offline consumer database that contains all the known identifiers associated with individual customers. This can include email addresses, postal addresses, mobile numbers, device IDs and IP addresses along with demographic, geographic and lifestyle information on each consumer. Using an accurate identity graph, data onboarders can effectively increase the match criteria for each individual by linking all cookies, device IDs and customer data.

Once the data is connected through the identity graph, a persistent ID is applied, which enables an onboarder or device ID provider to match all data and devices back to an individual. The final addition of the persistent ID is what transforms the identity graph into a consumer ID graph.

At scale, a consistent consumer ID graph empowers marketers with valuable customer insights across all devices. Without it, brands would be unable to accurately segment, target, or monitor attribution both online and in-store.

It’s for these reasons that a consumer ID graph is considered the current ‘holy grail’ of marketing. The providers of consumer ID graphs understand the power of individual level targeting and are investing significant resources to help brands tie online and offline data sets across multiple devices into a single view.

At the end of the day, whether you’re a marketer or a data onboarding provider, everyone will agree that identity and accuracy is the new name of the game, not probabilistic cookies by the pound. The digital marketing industry and the capabilities offered by data onboarding providers are maturing at lightning speeds. In order to enhance customer engagement and optimize marketing performance, brands need to demand individual matching, cross-device alignment, consistent IDs and the ability to do this at scale. With the use of an accurate identity graph and consumer ID graph, marketers will see exceptional and measurable results, without it, well… keep trying.

It’s all about the data.

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.


Extend Your CRM Audience

We sat down with Throtle CEO Paul Chachko to ask him a few questions about the Throtle Extend solution they offer as part of the onboarding process.

What is Throtle Extend?
Throtle Extend is a unique data matching extension process that adds missing online identifiers to an individual’s profile, allowing brands to increase their online reach.

What does Throtle Extend do for a brand?
Consumers regularly switch between devices and email addresses when shopping online, making it more difficult for brands to connect with the right individuals using a single email address or device. With Throtle Extend, brands can append Throtle’s known customer IDs to their pool of anonymous IDs and detect previously unknown customers. Accurately identifying these customers is the key to improving customer experiences and campaign results.

What type of data is matched?
Throtle Extend uses a variety of match criteria including email [hashed 3 ways], device ID, mobile number, and IP address.

How does the Throtle Extend process differentiate from other onboarding processes?
Throtle maintains an exclusive and proprietary database of consumer match keys. Throtle Extend combines the quality of its consumer match keys with the accuracy of its individual and deterministic match pool, to create a process that no other onboarder has at scale.

What is the average match rate increase that you can expect from Throtle Extend?
The average increase varies by brand and is determinant on the accuracy, cleanliness, and age of the CRM file being used. Once Throtle completes the pre-onboarding data processing, the brand usually sees a 10-15% lift in its individual match rate.


Global Advertising Revenue Will Hit $590 Billion In 2017

According to its annual Global Advertising Trends report, research entity IHS Markit, saw big brand budgets and quadrennial events such as the Olympics, European Football Championship and US Presidential Election drive 2016’s global advertising revenue growth to $532 billion, up 7.1 percent.

Strong growth in private consumption also buoyed advertising revenue as brands tried to take advantage of heightened consumer spending. Advertising revenue accounted for 0.69 percent of global GDP in 2016, up from 0.66 percent in 2015, the report said.

Top ten ad markets include US, China, Japan, UK, Germany, Brazil, France, Canada, Australia and India.

Despite the continued growth of Facebook, Google and Snapchat, the TV market remained the largest advertising category, lifted by quadrennial events like the Olympics, the Euro Cup and the US elections. In some countries though, including the UK, online already accounts for almost 50% of total advertising revenue and HIS Markit forecasts that in the next 5 years, TV will be overtaken globally by online.

As for 2017, IHS Markit expects an 11.1% increase in advertising revenue to $590 billion. The strongest growth is expected to come from the Middle East and Africa, followed by APAC, where India and Indonesia are seen to top the list in terms of growth.

Original article posted by Media Post.

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.

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