3 Ways 3rd Party Data Leads Marketing Teams Astray

Data quality is among the most common pain points associated with marketing initiatives. For teams engaged in email marketing, programmatic marketing, or other big data-driven projects, quality issues can significantly reduce results. If your organization’s efforts to produce targeted, real-time messaging are generating poor lift, it could be important to look towards your third-party data vendor as a potential source of the problem.

In best case scenarios, third-party data can allow marketing teams to develop 360-degree understanding of their target customers. However, directing dollars towards the wrong third-party vendor can actually damage efforts to programmatically generate advertising messages. If your vendor’s insights are out-of-date, generated through poor data logic or clustering technique or inaccurate, your results could be worse than if you were solely reliant on first-party insights in your data management platform (DMP). In this blog, you’ll learn the differences between data types, and how the wrong vendor can lead your team astray.

Understanding the Classes of Big Data
While sources and volume can vary significantly, there are a few terms commonly used to describe the origin of data that may be applied to a big data-driven marketing campaign. Understanding the following classifications can allow marketers to understand sources of risk in their marketing campaigns, and make the right choices about data acquisition at a large scale.

1st Party Data: These insights are generated by your company’s web, mobile, and transactional records. Typically, these insights are the most accurate, and are housed in a data management platform (DMP), which is typically integrated with a CRM.

3rd Party Data: These insights are obtained through an external data provider. The data is generally anonymized, and may be matched with your contacts in a data management platform. Vendor sources can vary significantly, but purchasing from a large-scale vendor can result in insights that are out-of-date and suffer from quality issues.

2nd-Party Data: These insights are among the most rare. 2nd-party data could originate from long-term data sharing agreements between organizations to continually combine and match profiles.

For many big data campaigns, the single biggest source of risk is 3rd-party data. When completing audience profiles with old or inaccurate insights, your audience profiles could be significantly diluted. Sources of risk in 3rd-party data quality can originate from the following factors:

1. Sourcing Methods
Third-party data vendors often have “mountains of information” available, according to Dunn & Bradstreet (D&B). However, their sourcing methods can be a bit of a mystery, even to some external representatives of the organization.

In one case study, a 3rd-party data vendors classification of “new parents” proved 10-20% inaccurate, per D&B, because it was based on individuals who’d recently purchased a certain magazine subscription. In other cases, vendor’s sourcing is based solely on online browsing cookies.
Regardless, your marketing results could be questionable if you’re not able to quickly establish each of the following with a prospective data vendor:

● Where does the data come from?
● Does the data represent online and offline behaviors?
● Do you rely on multiple data points to build audience groups?

2. Quality Assurance Methods
Quality assurance represents a major source of effort for data science teams. While purchasing third-party insights that are cleansed can provide convenience for marketing teams, your vendor’s quality standards need to be impeccable to yield gains.

Understanding your vendor’s approach to data verification, elimination of old data assets, and comparison is crucial. The best indication of data quality is results. Proof of recent conversions is the most objective way to measure third-party data assets.

3. Refreshing Methods
Generally, most data vendors “refresh” their data assets on a periodic basis, by pulling new insights into their data management platform. For vendors that source from a variety of sources, these “refreshes” may occur very occasionally, such as every several months.

In a world where consumers have access to immediate purchases via mobile devices, recent data is crucial. Insights that accurately reflected your audience’s behavior three months ago are not accurate today. Unless your vendor’s data is updated in real-time, it’s out of data.

BDEX: A New Approach to Real-Time Data Exchange
BDEX offers a first-of-it’s kind marketplace for real-time big data exchange. Instead of having to rely on third-party vendors to aggregate data from a variety of sources, brands are able to purchase insights directly from the source as they are generated. With objective, third-party scoring of conversions, prospective customers can gain peace of mind that the data is sufficiently high-quality to generate lift.
For more information on purchasing data via BDEX, 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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5 Big Data Trends Marketers Need to Know for 2016

As marketing executives look towards 2016, shaping big data-focused strategies for customer acquisition is a key priority at many organizations. While 90% of organizations have a medium to high investment in big data, only 50% are “routinely” applying big data to regularly engage customers. Some 44% report a “lack of consistency” in omni-channel marketing campaigns. At companies with the technological basis for big data-driven marketing but a lack of consistency, targeting prospects and customers across multiple channels is likely to be an area of focus.

Tech Journalist Craig Zawada predicts that in 2016, big data will shift from a marketing priority to a “main vehicle” for driving growth in sales and revenue. As organizations increasingly adopt sophisticated personalization and targeting strategies based on first and third-party insights, organizations will likely have to choose between jumping on board these trends or risking falling behind. Join us as we review ten rising trends that data-focused marketers should pay attention to in 2016.

1. Analytics Projects Will Stop Falling Short

For many organizations, big data projects have been shaped by available data assets to date. Analysts at Tamr believe this trend will change significantly in 2016, as organizations “liberate” themselves from “artificial constraints” set by poor data asset availability or poorly-formulated business questions. Increased automation, better processes, and improved assets can allow organizations to stop participating in analytics projects that consistently “fall short” of targets.

2. Prescriptive Analytics

Traditional business analysis thought dictates three stages to analytics adoption within an enterprise:

  • Descriptive Analytics: an attempt to answer “what happened”
  • Predictive Analytics: using modeling to predict “what could happen”
  • Prescriptive Analytics: the application of simulation to make business decisions

Zawada predicts that in 2016, many organizations will reach final maturity stages and begin to apply prescriptive decision making to offer creation, targeting, and other segment-based marketing analysis. When prescriptive analytics is in full swing, an organization’s potential for marketing response can increase significantly.
3. Geo-Targeted, Programmatic Advertising

Programmatic ad targeting has introduced a new era of automation for marketers competing in the Adtech space. Consumer adoption of mobile technologies has introduced the potential for location-based targeting, which must be automated in real-time. AdTech writer Beth Principi writes that “combining location-based [big data] and programmatic” will likely have a dramatic impact on outcomes for marketers in 2016. Gaining access to location-based streams of insight on consumers will be critical for organizations who hope to take advantage of this trend.
4. Static Dashboards Will Die

CMO trust in static data dashboards is rapidly falling, according to recent research by Strata and Hadoop. As marketing executives realize the incredible potential of real-time big data, only 12% are willing to rely on static dashboard reporting for decision-making.

This statistic represents a larger shift in the way organizations are thinking about data analysis and big data. The growing preference for dynamic reporting indicates that marketers are beginning to value real-time data as a tool for the most accurate insights. Subject matter expert Bob Gourley is in agreement, writing that organizations will begin using big data to “enhance agility and…market-dominating strategies” in the year to come.
5. Mobile Takes Over

Mobile is beginning to crush social media as a digital channel for marketers. Marketing writer Michael Della Penna reports that app downloads have begun to exceed new social media followers for major brands, which creates immense potential for targeted loyalty experiences and personalization driven by big data. For companies with existing customer apps or new projects in development, big data strategies are likely to focus on:

  • Building existing customer loyalty
  • Delivering targeted offers for convenient purchase
  • Manage billing and electronic payments

App-based integration of location-based “beacon technology” will be adopted by 85% of the leading retailers in the year to come. Organizations with the ability to integrate location and multi-channel insights will uncover new potential for highly-personalized marketing messages, which offer precisely what customers need in the moment.

For organizations to remain competitive in the year to come, integrating high-quality, multi-channel data sources on consumer transactions, location, and web usage will be critical. As companies increasingly enhance their personalization efforts, organizations that drive results are likely to integrate 3rd and 1st party data sources from a Data Exchange Platform (DXP) for the most effective customer insights. Without access to the right kinds of big data, efforts to drive revenue through personalized mobile or programmatic, geo-targeted advertising are likely to be meaningless.

For more information on Big Data Exchange’s marketplace for real-time data insights, click here.

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Do You Know Your Buyers? 3rd-Party Data Insights for Existing Customer Conversions

Marketers have long understood that it’s easier to sell to your existing customers. However, research increasingly indicates that effective personalization is necessary for repeat sales. 74% of online customers get “frustrated” by product recommendations that are irrelevant to their needs and interests. Data consistently indicates a lift in sales when personalization segments and affinity analytics are effectively applied to online marketing. However, for brands who lack Amazon’s envious average customer lifetime value from effective personalization, the problem could be your insights.


Some 52% of marketers consider personalization fundamental to successful marketing, but only 20% of marketers use behavioral triggers. The ability to generate personalized offers, deals, and recommendations when an existing customer begins searching can ensure you’re best able to capture repeat sales in micro-moments of need.

As marketers shift towards more sophisticated applications of analytics for personalized marketing, the need for better segmentation and real-time analytics is clear. Developing a strategy for earning repeat spend from your existing contacts can have a significant impact on your revenue. Generating effective product recommendations and offers that mirror your prospect’s real needs in the moment requires access to transactional, quantitative, and qualitative insights from a variety of sources


What Does Effective Personalization Look Like?


There’s no question that creating personalized customer experiences can allow brands to increase retention and loyalty among repeat buyers. Adobe refers to “increasing data availability” as “disruptive,” for brands with the savvy to know their existing buyers better than ever before. Per Echidna, the status quo for effective customer personalization involves:

  • Optimal timing of personalized product recommendations and offers
  • Sophisticated adaptation based on demographics, “behavioral, and transactional” insights
  • Multidimensional recommendations, across categories, brands, products, and offers
  • Multi-channel personalization, for desktops, smartphones, and mobile devices


Brands must look beyond their first-party transactional insights to develop comprehensive understanding of their customers. By leveraging a variety of big data insights from social media, third-party transactions, and demographics, it’s possible to gain better insight into your customer’s latest preferences and purchases. Best-of-class marketers understand the importance of generating offers and recommendations based on searches your customers performed on their mobile device minutes ago, not a purchase made on your website last year.


Big Data Insights for Effective Personalization


Despite the critical importance of big data-driven personalization for digital marketing, adoption lags across industries. 26% of organizations have yet to adopt any real-time insights to personalize web experiences, and 39% of marketers believe their data is too old to produce actionable insights. While the average organization has yet to tap into 95% of their data assets, improving your utilization of internal transactional insights probably isn’t the key to better personalization.


However, simply integrating third-party data insights into your personalization strategy won’t necessarily yield improved results. In a case study of Kraft, the brand achieved approximately 50% accuracy with third-party data assets. Further analysis by Kraft’s digital marketers revealed the issue was with their data vendor’s segments. Their vendor classified women as “trendy” if they visited a high-end furniture store website within the previous six months.  The experience lead Kraft VP Bob Rupczynski to conclude “There’s just so many problems with the quality and the recency…third-party data has a problem.”


Will Integrating Third-Party Data Insights Improve My Personalization Efforts?


For many brands, taking steps to integrate third-party data insights can provide much richer grounds for personalization. However, not all data vendors are created equal. To avoid similar results to Kraft’s by purchasing from a vendor with significant quality and recency issues, look for a Data Exchange Platform (DXP) to provide real-time, quality scored data and consider the following criteria before integrating any third-party insights into your marketing campaigns:

  • Are we renting this data, or is it an owned asset? Marketers typically maintain better control over segmentation and classification of owned assets.
  • Can these insights be integrated? The right data vendor will allow you to combine third-party assets with your own CRM insights conveniently in a data management platform (DMP).
  • What don’t we know about our customers? Chances are, your own data assets lack understanding of your customer’s interactions with other brands, demographics, and preferences. Seek out data vendors that allow you to fill in the gaps.


The BDEX Data Exchange Platform (DXP) allows marketers to access the right insights to improve personalization instantly across hundreds of quality scored data sources. Instead of fighting to generate relevant offers from six month-old web browsing history, you’ll gain access to minutes-old data from third-party vendors. Once purchased, marketers own the knowledge assets for seamless integration in personalization strategies.


For more information on the importance of personalization, we recommend our recent blog Why Personalized Mobile Marketing isn’t Possible Without Big Data.


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Why Personalized Mobile Marketing Isn’t Possible Without Big Data

As marketers look towards 2016, building an improved mobile strategy will be a key priority for many teams. Mobile technology writer Emma Sarran Webster predicts rising consumer adoption of wearable technologies and mobile videos will lead to new delivery methods over the next twelve months. As teams navigate the new landscape of mobile advertising, the biggest pain points of 2015 are also likely to persist throughout 2016. According to VentureBeat, contemporary teams are most likely to struggle with:

  • A “hypercompetitive” market due to sky-high mobile adoption rates
  • App and platform abandonment on mobile devices
  • Creating seamless, disruption-free mobile experiences

Creating engaging, high-return mobile marketing experiences requires accurate personalization. Currently, only 13% of marketing teams are delivering segmented mobile experiences, compared to 43% of desktop campaigns.

As more brands adopt mobile marketing, data-driven personalization is necessary for results in an increasingly competitive landscape. Offering effective product recommendations, results for on-site search, and other customization requires knowledge of your customer segments across platforms. Without big data insights, sufficient mobile marketing personalization is impossible.


Capture the Big Picture of Your Customer’s Behavior


Few modern consumers rely solely on a smartphone or tablet to use social media, perform searches, and make purchases. If your customers are anything like the average consumer, they use a mix of desktop and mobile devices, depending on the situation, task, convenience and device proximity. According to Direct Marketing news, the best mobile marketers capture big data trails from each of these platforms to generate truly-comprehensive, multi-channel understanding of their clients.


Most sophisticated marketing teams already have a centralized database of customer insights from transactional, social, and web analytic sources, which is typically housed in a data management platform (DMP). However, if your attempts at segmentation or personalization have been met with mediocre results, it’s not due to your competitors or the quality of your tools. Most likely, the culprit is your insights.


Mobile technology has opened up a wealth of new insights to marketers who are savvy enough to acquire the right third-party data. Studies reveal that 91% of adult smartphone users keep their devices within arm’s reach at all times. Location-enabled smartphones generate a continual stream of geo-targeted insights that marketers can use to pinpoint consumer habits with precision. If your mobile targeting is based on exclusively first-party insights or dated third-party offerings, your personalization efforts will be inaccurate.


Real-Time Data Exchange for Mobile Personalization


In a case study of travel retailer Orbitz, the brand successfully introduced “device differentiation,” the practice of delivering price and format-sensitive search results to consumers. Implementing this improvement in segmentation required the analysis of 1.5 petabytes of consumer behavioral insights from multiple devices.


Some 68% of marketing teams complain they “lack enough data” or their existing data assets aren’t usable. Participating in a real-time Data Exchange Platform such as BDEX can provide your organization with the right assets to achieve similar success in personalization to Orbitz and other major brands who have achieved milestones in personalized mobile marketing campaigns.


By connecting directly with third-party sellers of data assets, mobile marketing teams can set budgets and buy insights that are seconds old to generate marketing offers in real-time. As mobile marketing grows more and more competitive, big data is a critical means to success.


For more insights on real-time marketing, check out our recent blog: Is Your Big Data Dated? The Importance of Truly Real-Time Email Marketing.


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