Using BDEX to Navigate Digital Transformation on

Using BDEX to Navigate Digital Transformation

Whether your organization’s MarTech integration efforts are going smoothly or have created a Frankenstein stack, BDEX has the third-party data solution it needs.

Global CMOs are getting stretched thin these days. On top of their traditional mandates of lead generation and brand building, they are being asked to find and implement technologies needed to extract marketing intelligence from the growing torrent of data their organizations produce each day.

The complexity and dollars involved in this digital transformation make it a high-risk/high-reward undertaking. CMOs who succeed stand to create powerful data marketing platforms, turbo charge their real-time personalization efforts and gain a step in the MarTech arms race. Those who fail, risk squandering millions of dollars in resources creating what the industry has lovingly come to call a “Frankenstein stack.”

Nearly all – 96 percent – of respondents to the Marketo 2017 Marketing Benchmark Report agreed that the task of finding and integrating MarTech solutions can distract CMOs from their primary mission of helping acquire and keep more customers.

Such is the price we pay when it comes to learning how to implement new technologies, and indications are that global brands are still in the early days of their digital transformations. CMOs, it would appear, will have to keep tabs on multi-million-dollar, multi-year IT integration projects on top of their day jobs for years to come.

Where BDEX comes in

The good news is that thanks to BDEX, CMOs and the brands they represent don’t have to miss a beat during this tumultuous period. They can rely on BDEX to fulfill all their U.S. consumer data needs whether they are halfway through their digital transformation, have completed it or plan to forego it and continue relying on agencies and other vendors for marketing data.

BDEX can say this because of how we have integrated an unparalleled network of third-party data providers with an extremely robust ID graph and uniform taxonomy to power human connectivity.

The BDEX ID Graph

These all come together via the BDEX ID Graph, which tags all data signals coming in from our partners in real-time using a uniform taxonomy that spans more than 5,500 tags and 500 industries. This enables us and our customers to query nearly a trillion real-time data signals from more than 70 data providers using the same taxonomy.

On top of this, the BDEX ID Graph constantly reviews logins, app downloads, credit card purchases, and other authenticated real-time signals to make sure the incoming data signals are matched to the right Customer ID.

We do this by building one of the industry’s largest cross-referenced libraries of email addresses, mobile device identifiers, browser cookie IDs and U.S. Postal addresses. Our library spans 800 million mobile-to-email ID and cookie-to-email matches. If someone upgrades from an iPhone 8 to a Samsung 10 or switches from a Google Pixelbook to a Microsoft Surface, the BDEX ID Graph detects it the moment the user downloads an app or logs into an authenticated account tracked by any one of our data partners.

Our use of authenticated identifiers enables 100 percent deterministic cross-device matching, which means you can have 100 percent confidence our ID graph is assigning billions of incoming data signals to the appropriate Customer ID.


Thanks to the BDEX ID Graph and our uniform taxonomy, the BDEX Data Exchange Platform (DXP) presents 290 million consumer identities, 1.3 billion email addresses and more than 900 billion data signals in a highly searchable database.

You can peak under the hood and get a sense of how broad and deep our data is using Explore BDEX Taxonomies, our free and highly intuitive taxonomy tool.

API or batch-mode access

Thanks to our uniform taxonomy, querying this massive database is quick and easy. You can match IDs instantly (10ms response time) using a real-time API query or upload ID lists via batch mode and have IDs returned in hours.

Custom segment building

Either way, you can harness real-time data signals to build or enhance custom audience segments for programmatic advertising, personalization and other projects.

The BDEX DXP provides access to hundreds of pre-built audiences uploaded by our partners. You can use these to fast-track your own audience-building efforts or use them as is until you whip your own MarTech stack into shape.

Whether your organization’s MarTech stack ends up humming like a Lamborghini or shrieking like Frankenstein, or your organization opts to continue relying on agencies and other external vendors, it’s going to need access to third-party data. You’ll be hard-pressed to find a better one-stop shop for that than BDEX.

To learn more about how BDEX helps marketers maximize ROI, visit the BDEX solutions page.

10 Reasons Programmatic Keeps Rolling on

10 Reasons Programmatic Keeps Rolling

The expansion of programmatic advertising into the TV, out-of-home and other channels will only increase the need for reliable, real-time, third-party data.

It’s only natural that programmatic advertising has become the driving force behind digital display ads and is now spreading rapidly to out-of-home, television and other channels.

After all, the ability to put your message in front of a person based on what they are doing that moment has long been the Holy Grail of marketing. We may not be there yet, but we are dramatically closer than five years ago and getting closer every day thanks to the MarTech boom.

Not too long ago, online display advertising remained a labor-intensive process that required a request for proposal (RFP), negotiations back and forth between representatives, and manual insertion orders. Programmatic advertising simplifies the process through an automated process that enables computers to use data to decide which ads to buy and how much to pay for them based on real-time data signals generated by millions of connected devices. Generally, brands or their representatives use what is called a demand-side platform (DSP) to choose which ad impressions to buy and how much they should pay, and publishers use a supply-side platform (SSP) to offer ad space.

Programmatic technology was developed as a way for publishers to sell their limitless online ad space but has rapidly expanded its share of ad spending as buyers and sellers recognized the value in such an efficient and productive system.

Today, approximately four out of five digital display advertising dollars are spent on programmatic advertising. By 2020, it’s forecast that 86.3 percent of digital display ads will be served programmatically. That includes everything from online display ads, to messages on electronic signs and billboards, audible search results and video ads.

In some way, programmatic advertising is just getting started, according to Digilant, which recently published a Top 10 Programmatic Media Buying Trends infographic for 2019. Below we share their list with some annotations of our own.

Voice Marketing: The rise of smart speakers has created a new frontier for relaying brand messages. With over 56 million smart speakers sold in 2018, advertisers who are interested in being the front runner of this new medium are already re-defining their digital strategy.

Programmatic Television: With more people viewing television via smartphones, laptops and smart TVs, programmatic technology is poised to transform TV advertising. According to eMarketer, U.S. advertisers are on track to spend $3.8 million on programmatic television ads in 2019 – a number that will continue to rise as agencies and brands become more acquainted with the process and familiar with the results. Marketers can take advantage of this valuable insight today through BDEX, which has data on the viewing habits of tens of millions of consumers.

Audio Advertising: Weekly podcast listeners listen to podcasts for six hours a week on average and comprise 17 percent of the U.S. population, according to With 100 percent deterministic cross-device matching from BDEX, you can capture what your target customers are listening to add resolution to your customer profiles and or target them via programmatic advertising.

Social Media Shopping: Making purchases directly through Instagram and Snapchat is a growing trend. Twenty percent of Snapchat users are more likely to make purchases after engaging with a shoppable ad seen on the app.

Digital Out of Home Ads: Electronic billboards, signs, and display boards have been steadily increasing in popularity due in part to the ease of automated ad buys. Targeted Digital Out of Home advertising can help drive ticket sales and store visits, increase app downloads and online engagement, and raise awareness of products, events, and services.

Artificial Intelligence in Media Buying: Using algorithms derived from data and online behavior can help optimize your media buying strategy. Four in 10 advertisers are currently utilizing AI to help target prospective consumers more effectively in a way that increases their return on investment.

Fewer Platforms: Agencies have been consolidating media and DSP partners for ease of use and reduction of the ad tech tax. Using programmatic advertising streamlines media buys in a way that benefits brands and ad sellers alike.

Cost Transparency: Having a full understanding of the cost breakdown is important to ad buyers. Agencies are pushing for more transparency when it comes to fees, which makes it easier to evaluate the risks and rewards of committing to an ad buy.

Adding In-House Talent: More CMOs plans to bring programmatic skills in-house – a sure sign they will be upping their spend.

More In-House Agencies: According to Forrester’s “In-House Agency Forum” survey, more brands will be bringing their media in-house, with 64 percent of respondents stating that they will be using in-house agencies in 2019.

The role of third-party data

Marketers that don’t commit to programmatic advertising risk falling further and further behind a trend that is destined to enhance personalization, improve the customer’s brand experience and increase marketing ROI. Yet many are still resisting the technology because of bad experiences they have had with third-party data and/or because they lack the substantial in-house resources needed to vet, cross reference and use it. These concerns are exactly what inspired the creation of the BDEX Data Exchange Platform, or BDEX DXP.

Thanks to more than 100 partners, BDEX analysts can search more than 900 billion data points on tens of millions of U.S. consumers across 19 categories of shoppers and more than 5,000 other categories to help marketers fine-tune their real-time marketing. The platform has been designed from the ground up to save marketers time and money while supercharging their campaigns.

Dozens of global brands, retailers and agencies have been using third-party data vetted, cross-referenced and categorized by BDEX to power their programmatic advertising for years.

Whether you are looking for help launching your first programmatic campaign or a more reliable source of real-time data for your next one, you owe it to yourself to contact BDEX today to see how we can take your marketing to the next level.

Thick Data: Why Marketers Must Understand Why People Behave the Way They Do

92% of companies are still dealing with obstacles to successful big data projects, according to global research by CA Technologies. Across industries, the adoption of big data initiatives is way up. Spending has increased, and the vast majority of companies using big data expect return on investment.

However, companies still cite a “lack of visibility into processes and information” as a primary big data pain point. Modeling customer segments accurately can be impossible for marketers who don’t understand why their customers decide to make purchases.

Many marketers applying big data to programmatic advertising or email marketing initiatives understand patterns. With sufficiently high-quality and recent insights, marketing departments can create segments and offers that reflect reality. However, experts are predicting that the next step for marketing will be the adoption of “thick data” for behavioral understanding.

What is Thick Data?
Data-driven marketing is the act of making educated guesses about human behavior, based on historical patterns and other analyses. Product development, offer creation, and email campaigns are, at best, well-informed guesswork about your customers. Thick data can represent the missing piece by explaining why humans act the way they do.

Harvard Business Review (HBR) defines thick data as a tool for developing “hypotheses” about “why people behave” in certain ways. While big data can indicate trends in behavior that allow marketers to form hypotheses, thick data can fill in the gaps and allow marketers to understand why their customers are likely to take certain actions.

While “thick data” is recently receiving a great deal of attention among big data thought leaders, it’s not a new concept. There’s little difference between “thick” data and “prescriptive analytics,” both of which represent advanced maturity in marketing big data. By shifting your focus from predictive big data to forming and testing hypotheses, marketers can better understand how their buyers will act in the future.

Where Does Thick Data Come From?
Historically, big data has been transactional, while thick data has been qualitative. For data-driven brands of years past, insights into consumer behavior were typically derived from behavioral observation, voice of the customer (VOC) or Net Promoter Score (NPS) surveying, focus groups, or other time-intensive research methods.

Today, insights into consumer behavior can come from a variety of sources. Thanks to social media, internet of things technologies and other drivers of big data, marketers can gain insight into why humans act the way they do with data sources such as:
● Online or Mobile Behavior
● User-generated social media content
● 3rd-party transactional data

Studies indicate that currently, 95% of brand research into consumer preferences is performed manually, using methods such as surveying or focus groups. However, in an era where consumers produce thousands of insights each day from mobile usage, online shopping and social media updates, the insights are easy to obtain.

How Thick Data Can Benefit Your Marketing Results
One of the most famous examples of thick data application belongs to Lego, who BIGfish reports was on the brink of financial collapse in the early 2000’s. After several failed repositioning attempts, the brand engaged in a “major qualitative research project” to understand why the “emotional needs of children” at play weren’t being met by Lego’s current offerings. After observing and analyzing countless hours of video recordings, Lego was able to successfully reposition their products and resurrect their status as an important toy brand.

While Lego’s use of thick data occurred in an age where analytics tools were far less sophisticated or widely available, the concept offers lessons to contemporary marketing teams. By applying attitudinal, social, and other preference-driven data to your marketing analyses, you can understand what your customers actually need. Yesterday’s focus groups have been replaced by the trail of qualitative insights consumers leave on their mobile devices, in apps, and at sensor beacons. For brands that are willing to listen, there’s remarkable potential for prescriptive analytics.

If your marketing goals for the year to come include a better understanding of your customers, integrating more qualitative and attitudinal big data insights can allow you to unleash the power of thick data. The BDEX marketplace allows brands to connect directly with 3rd-party data vendors, to gain real-time access to insights on why their buyers act the way they do. To learn more about BDEX’s innovative approach to real-time data exchange, click here.

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When Your Predictive Marketing Models Yield Terrible Results

When Whole Foods announced their plans for a line of grocery stores geared towards millennials, the announcement was deemed “offensive” and stock prices dropped. According to Marketing Mag’s Katie Martell, the Harvard Business Review, and a number of other outlets, there’s nothing inherently wrong with the plan. However, there is something wrong with Whole Food’s predictive marketing models.

Harvard Business Review’s Robyn Bolton writes that members of Generation X and Baby Boomers also want access to “lower-priced, organic, and natural foods.” While Whole Food’s product line announcement was frequently deemed “offensive,” Bolton believes the real problem is with Whole Food’s marketing models. Simply being in a demographic doesn’t predict certain preferences or behaviors. Organizations who make similar mistakes might not receive as much criticism as Whole Foods. However, they’re unlikely to achieve great results.

The advent of big data provides marketers with a new ability to build vivid customer segments. Behavior, beliefs, and intent can yield models that are much richer than demographic generalizations. However, many attempts to build predictive marketing models fall flat in terms of outcomes. Join us as we review the most common issues behind poor segmentation and predictive modeling results in marketing.

1. Poor Behavioral Data
Ultimately, the purpose of predictive marketing models is to predict how consumers will behave in the future. Without extensive or accurate knowledge of how your segments have behaved in the past, this is difficult to accurately model.

Infogroup research indicates that just 33% of marketers believe they collect enough behavioral data on customers. Only 21% are “very confident” about accuracy in profiling. If your predictive models rely on your transactional data or self-reported sources, you may not have accuracy.

2. Limited Context
Perhaps you understand who your customers are, but do you understand why they’re buying from you? Arjuna Solutions writes that predictive models need certain insight into how your segments “behave in the marketplace.”

Contextual insights are a critical part of marketing models in an age where consumer identity and segments are more and more fragmented. Fast Company’s Dan Herman advocates the idea of building models based on context and purchase motivation.

Instead of “40-something home buyers,” a mortgage company using more context could discover they are trying to reach “40-something primary home buyers,” “40-something vacation home buyers,” and “40-something residential real estate investors.” By identifying the reason for the purpose, marketers can better understand goals, pain points, and probable behavior patterns.

3. Small Sample Sizes
The vast majority of marketing professionals have some background in research methods and statistics. However, many organizations are trying to market with models that are based on a tiny segment of the population.

In the age of big data, companies can’t compete if their marketing models are based on first-party customer data. Their models may be inaccurate if they’re using focus groups, surveys, or other dated research models. Obtaining recent, accurate, 3rd-party data is the best way to understand how populations behave with confidence.

4. Flawed Data Logic
Marketing models built internally and externally may fail because they’re based on terrible data logic. Affinity algorithms can reveal incredible patterns, such as the famous example of when Target’s algorithm predicted that a teen was pregnant before her parents knew. In other cases, affinity algorithms don’t hold up. Visiting a high-end furniture store within the space of six months does not necessarily make a woman a “trendy” food consumer.

As MIT research highlights, clustering and other methods of discovering patterns are “inherently unsupervised.” This can reveal surprising truths, but it can also output garbage if your inputs are inaccurate or your sample is too small.

For organizations struggling to develop accurate predictive marketing models, the problem could be related to their big data. Small sample sizes, old data, or a lack of 3rd-party insights for context can all result in predictive models that don’t reveal the future. 

For more information on how BDEX’S first-of-a-kind data marketplace can revolutionize the accuracy of your predictive marketing models, click here.


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New Year, New Data

With the new year comes resolutions, and though vowing to exercise more regularly or cut down on carbs is a worthy personal resolution, it’s important to make professional promises as well. The BDEX teams believes now is the time to take advantage of Big Data.

But it’s not just us. Experts believe 2017 will be a pivotal year for Big Data as well.

“2016 was an exciting year for big data, as finally, Big data is no longer a hype or a buzzword. This means that organisations are actually developing real world solutions and applications with big data analytics that have a big impact on their bottom line,” Mark van Rijmenam of Datafloq tells KDnuggets. As marketers take Big Data more seriously, the doors open for more Big Data projects and Big Data solutions.

The BDEX Data Exchange Platform (DXP) has steadily grown to offer our customers the most comprehensive collection of timely, third-party data on the market. We’re happy to announce that our marketplace is more robust than ever. Look at a few of our updated marketplace statistics below:


Shopping Cart Retargeting, or targeting customers who have shown interest in a product on a retailers’ website, is a common data service. The BDEX Shopping Cart Engine, or C2E service, takes this retargeting to the next level by linking retailers with the email addresses of customers who did not even register or purchase on the website. Retailers can connect with potential customers who have shown an interest in their products, whether or not they wanted to purchase merchandise at that particular time.


Want to learn more about Big Data or BDEX? Visit our website,, or email us at

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Cross-Channel Behaviors for Mobile Targeting: Big Data Insights for Effective Conversions

For consumers, mobile isn’t an isolated channel. 73% of adults who own three or more devices switch between platforms to perform a single task. Simultaneous or progressive use of a smartphone, tablet, and computer is a natural way for contemporary consumers to research products, make decisions, and complete purchases.


Despite the fact that mobile is part of a continuum, marketers all too often view a mobile strategy as a “silo” or a “singular tactic.” According to Adtech expert Chris Hansen, the disconnect between how marketing agencies and teams perceive mobile and how consumers use it is the single biggest source of pain and poor results in this industry.


Simply creating advertisements that will display on a smartphone or iPad isn’t enough to convert. Driving return on investment via mobile strategy requires marketers to recognize this technology’s role within the mixture of channels that consumers use on a daily basis. Effective mobile execution requires marketers to look beyond mobile-only data to understand their target audience’s behavior online and offline; across computers, tablets, and phones. Join us as we uncover why a cross-channel approach to mobile marketing is necessary to win — and the data points marketers need.


Why Non-Mobile Insights Matter


Consumers behave quite differently depending on the device they’re using. Convenience and ease-of-use may prompt a prospective customer to switch from their desktop computer to a tablet, but mobile experts are uncovering some fascinating patterns in content consumption across channels.


Research by Millennial Media indicates that consumers typically opt for smartphones to engage with streaming audio, games, and social media platforms. B2B educational assets and news media are more often accessed via desktops. It’s no secret that connected consumers are more likely to perform local searches on-the-go, often to discover local deals and brick-and-mortar purchase opportunities.


Few consumers are contained to just one or two devices on a daily basis. Accessing behavioral big data insights from each of the devices your target segments use on a daily basis is the only way to uncover true knowledge of their preferences, pain points, and proclivity to purchase. While mobile marketing is a standalone channel, marketers must access consumer data from a variety of devices to adequately develop an understanding of their buyers.


Real-Time Mobile Targeting


Local, social, and promotional activities are among the top actions performed by smartphone owners, which has inspired the coining of the phrase “SoLoMo” for social, local, and mobile among marketing specialists. The inherent portability of mobile devices means that owners are more likely to act quickly upon searches, requiring marketers to really refine their targeting to yield accurate conversions.


In order to profit from your ideal customer’s mobile searches, marketers must target based on comprehensive, cross-channel insights that are truly up-to-date. Mobile-only insights on customer segments that are months old simply won’t reflect the needs of your prospects today. By accessing a wide array of big data insights on your customers from multiple channels that’s just seconds old, mobile marketers can optimize their advertising and campaigns to the real-time needs of today’s connected consumers.


The BDEX Data Exchange Platform offers the first real-time data exchange, allowing mobile marketers to connect with objectively quality-scored data sets from vendors. By participating in the exchange, mobile buyers can gain insights into their segments behavior across multiple channels to perform the most accurate mobile targeting possible.


For more information on how BDEX can benefit contemporary marketing teams, check out Real-Time Segmentation: BDEX for the Email Marketer.



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