4 Ways Third-Party Data Can Generate Lift in Your Marketing Results

Marketers may be hesitant to invest in third-party big data insights due to poor reputation. Digiday blasted the products of many big data vendors as “cheap, plentiful, [and] inaccurate, citing cases 30-35% inaccuracy rates discovered through validation testing. However, even the most outspoken critics of third-party data admit that not all vendors are equal, and marketers can drive desired results if they don’t trade “accuracy for scale.”

With the right vendor, third party big data can be a crucial tool for generating lift in marketing results. The proof is in the meteoric growth of programmatic advertising, in which results are largely dependent on data quality and scale. Perhaps more importantly, marketers must remember that third-party data purchased from outside parties isn’t a new concept.

Marketing teams have bought insights for decades as a tool for tailoring print advertising and direct mail campaigns. While the best advertising formats and data scale have changed, the importance of outside perspective hasn’t. Join as we review reasons why validated, high-quality third party data assets are crucial to marketing results.

1. Third-Party Data Can Be First-Party Data
Third-party that generates poor marketing results or contains vast inaccuracies is usually far-removed from it’s source. It was purchased from the organization who originally collected it months prior, scrubbed, categorized, and distributed.  However, with BDEX’s data marketplace, your team can purchase data from first-party sources in real-time. Instead of relying on aging insights or questionable segments, you can combine your data with another organization’s first party insights, resulting in far broader contacts and understanding.

2. Third-Party Data Introduces You to New Contacts
While emails, mobile, and programmatic advertising are important tools for customer retention, marketers are in the business of acquiring new customers. The goal of a marketing department is to attract people who resemble your most qualified customers.  Third-party data can function much like the contact lists or leads marketers may have purchased in the past. With exclusive partnerships, you can gain access to the email addresses of pre-qualified consumers who are actively looking for your product or services.

3. Your Data isn’t Validated
Third-party data assets from trusted vendors can reveal uncomfortable truths about your organization’s data quality. The most commonly reported data management challenge is resolving quality problems “before they become an issue.” Even if your organization has above-average data management practices, there are likely inaccuracies in your contact profiles. By reconciling your insights in a data management platform against a third-party vendors, you can perform validation and cleansing actions needed to maintain accurate information.

4. Your Touch Points aren’t the Full Picture
Even if your organization engages in extensive first-party data collection practices, you’re probably not getting the full picture. Your insights are limited to what you’re able to collect through Cookies, user-generated web content, and customer touch points.  If you’re in the finance industry, you may not know that your customer is expecting a child. If you’re in real estate, you may not know that a client is actively planning for retirement. In order to understand your consumers on an individual level, third-party insights are typically necessary.

Ideally, third-party data has the potential to elevate your team’s insights through validation and the addition of well-rounded insights. Instead of relying exclusively on your own touch points, you can gain insights from other organization’s data collection practices.

BDEX is a first-of-its-kind marketplace, offering marketing teams the ability to connect directly with first-party data vendors in a variety of industries. Buyers gain the ability to access objectively-scored, real-time insights, which can be downloaded directly into your data management platform (DMP) to immediately begin generating marketing lift.

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5 Incredibly Costly Big Data Marketing Mistakes

Low-quality big data assets can lead to incredibly costly marketing mistakes. Research by Experian indicates that low data quality has a direct impact on revenue for 88% of modern organizations. Average losses are approximately 12% of revenue. For organizations who are shifting towards data-driven marketing and customer experiences, low-quality data can lead to costly mistakes.

How Bad is the Average Marketing Big Data?
Per eConsultancy, 22% of information on contacts, leads, and customers contains inaccuracies. Perhaps most concerning, the average organization’s quality index is headed in the wrong direction. Twelve months ago, the average inaccuracy rate was just 17%. Incorrect data can have a real impact on your team’s ability to build segments, understand behavioral triggers and preferences.

In contrast, organizations with a high degree of data accuracy are more likely to appreciate:
● Efficiency
● Cost-Savings
● Customer Satisfaction
● Informed Decision-Making
● Protection of Brand Reputation

Poor-quality or old customer data can lead to a series of costly marketing mistakes. Join us as we review some devastating errors that can be directly attributed to inaccurate customer data.

1. Low Advertising Conversions
Low conversion rates on programmatic advertising is a symptom, not an issue. Poor click-throughs and conversions can be attributed to a lack of mobile advertising, poor segmentation, irrelevant data, or other factors. However, far too many marketing teams fail to take appropriate action in response to low advertising conversions. Instead of working to improve the breadth or quality of data, they continue generating ads. Before running more ad campaigns, marketing teams should take appropriate action to ensure they can achieve better returns.

2. Inconsistent Brand Experiences
Without accurate or up-to-date data, your brand communications could send the message that you don’t know your customers. You may generate programmatic advertising for products your customers already own. You could send an email blast for baby products as their children are approaching preschool age.  Marketers need to actively combat a brand experience that’s inconsistent with a customer’s needs and activities. If you miss the mark repeatedly, you’ll struggle to build customer loyalty and sales.

3. Poor Email Deliverability
The average return on investment (ROI) for email marketing at mid-sized organizations is 246%. However, organizations have the potential to significantly exceed these benchmarks with appropriate timing, segmentation, and other big data-driven activities.  Email communications to outdated contact lists have the potential for a high bounce rate, or percentage of emails that are undeliverable. Email segmentations that are vastly inaccurate could also increase your risk of being pinged as spam. In the mind of a consumer, spam is simply “unsolicited bulk email.” If your messaging is irrelevant or feels too much like a mass communication, it’s likely unwelcome.

4. Mobile Neglect
Far too many big data marketing strategies are focused on desktop advertising, email receipt, and experiences. In reality, consumer behavior demands mobile marketing. As of 2015, adults now spend more time engaged with mobile devices than desktops, laptops, and other connected devices combined.  There’s a good chance that, at least 50% of the time, your desktop-optimized advertising is consumed on mobile devices. This can lead to poor user experience (UX) and returns on investment.

5. Poor Verification Methodologies
All too often, major brands go viral for all the wrong reasons. Poor data verification can lead to mistakes that are embarrassing, insulting, or even hurtful to their loyal customers. OfficeMax sent coupons addressed to “Mike Seay, daughter killed in car crash.” The addendum to the customer’s name was unfortunately true. The company ultimately issued a public apology to the customer.   Manual data verification processes are rarely effective in the big data age. Fortunately, using a data management platform (DMP) or another tool to perform quality checking against 3rd party data can eliminate much of the risk of similar mistakes.

If your organization’s data quality is average or below average, you’re at risk for many of these expensive marketing mistakes. By taking the appropriate internal steps to improve your quality standards, you can improve the ROI and impact of your marketing efforts.

BDEX offers high-quality, real-time big data assets from trusted 3rd party vendors to safeguard against low-return marketing investments. By downloading the right data resources directly into your DMP, you can improve the accuracy of your customer records, gain deeper insight into your buyers, and build better segments.For more information on becoming a BDEX buyer or seller, click here.

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What Does a Big Data-Driven Customer Experience Look Like?

Your customers expect you to understand their needs. 80% of modern consumers expect personalized experiences from their favorite brands. Despite increased budget for big data marketing initiatives, 43% of marketers feel they’re getting almost “no benefit” from their existing data assets. These two statistics illustrate a clear disconnect between what customers want, and what marketing teams are able to deliver.

The savviest marketing teams aren’t just deriving value from their internal, or first party, data assets, they’re obtaining high-quality, real-time insights from 3rd-party data vendors to develop a 360-degree view of their customers. In order to capture and retain today’s complex digital consumers, a big data-driven customer strategy is a must.

What Does a Big Data-Driven Marketing Strategy Entail?
Every time your customers swipe on a mobile device screen or post a status update to social media, they leave a trail of data on their preferences and behaviors. Each of these interactions offers the potential for your brand to gain insight into how to create personalized experiences for your customers.

By synthesizing first and third-party data insights in a data management platform (DMP), you can create a holistic view of your customer base. This allows you to understand patterns and stories that extend beyond your own touch points, and discover truths about how your customers interact with the world around them, by using these stories to create segments and understand your customers on an individual level. In this blog, we’ll discuss several of the best practices best-of-class organizations adopt when developing a marketing strategy that’s driven by big data insight.

1. Expand Your Data Collection
Transform your strategy from first-party data analysis to a program that’s focused on true cross-channel synthesis. By combining the broadest array of data sources possible, you can improve your strategic analysis and customer understanding.

2. Score Your Segments
By creating narrow segments of your existing customers, you can focus on your best clients. These are the individuals with the highest customer lifetime value (LTV), and who may be most likely to promote your brand on social media channels and other online forums. The creation of buyer persona profiles has traditionally been executed through qualitative research methods, such as focus groups. By allowing data to tell your story, you can eliminate organizational biases about what your best customers look like.

3. Focus on Customer Experience
When you have identified your best customers, it is critical to discover ways you can improve your client experience. You can discover insights on how your customers interact with brands through the inclusion of 3rd-party data. Are they mobile shoppers, or heavily-engaged app users? Tailor your engagement strategy to your client’s existing behavior patterns.

4. Get Personal
The best marketers know that big data has the potential to move your strategy from segments to true personalization. Use your big data insights to discover behavioral triggers, and tailor personalized marketing efforts to meet your client’s needs for relevant email marketing and programmatic advertising.

5. Measure and Optimize
With your programmatic advertising and email marketing metrics, your brand has the potential to move towards continual improvement cycling in your marketing program. Never stop collecting data, analyzing, and improving your efforts to deliver a best-of-class customer experience.


Are you ready to make the shift towards customer-focused, Real Time big data-driven marketing? Contact BDEX today for more information on high-quality, real-time big data assets from trusted 3rd-party sources.

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Creating Products That Fit: Using Real-Time Data for Product Development

Technology has had a significant impact on the concept of customer loyalty. Shopping for alternatives based on price or convenience requires just a few taps on a smartphone screen. Brand awareness is no longer enough to build lasting customer relationships. Brand preference might not be sufficient, either. Organizations must focus on creating customer experiences and products that are tailored to customers’ needs at exactly the right time.

In order to avoid customer defection, brands must make the buying process convenient. Marketers must anticipate customer needs based on desktop browsing history, mobile usage, and behavioral triggers. The use of real-time big data to programmatically generate custom offers and products is gaining traction. Join us as we review the success of companies who have already embraced big data as a tool for building meaningful customer relationships, and effective strategies for the future.

How Big Data is Shaping Real-Time Product Development in Finance

In one case study by IBM, a major bank utilized customer financial data, transactions, preferences, and social media posts to develop customized offers for “Peter,” a recent homebuyer and cooking enthusiast. Based on absence of risk and Peter’s Facebook post about wanting to purchase a “restaurant-style six burner gas stove,” the bank was able to extend a mobile offer to increase his credit limit before another credit card company captured his business.

In a world where purchases can be made with one-click and price and rates comparisons are as easy as a Google search, customer happiness doesn’t always trump convenience. Your customers may be happy enough with your brand to accept offers via mobile push notification, but they are less likely to call your institution or visit a branch to request the same thing. Effective marketing and product development today is about creating products and offers that seamlessly fit into your customer’s lives. This can only be accomplished with extensive big data resources, including high-quality 3rd party insights.

If the financial institution profiled by IBM lacked access to immediate customer insights, they could have lost Peter’s business. He may have decided to proceed with purchasing a restaurant-style stove and accepted the first acceptable line of credit offered. Without real-time data, marketers are unable to extend offers at the right moment. Streaming analytics isn’t just going to benefit marketing teams of the future. It’s going to be a necessity for brands that want to remain competitive.

Streaming Analytics for Customer Retention and Loyalty

Big data allows marketers to think in more personalized terms than traditional segments. Instead of offering a line of credit increase to all recent home buyers, they’re able to make offers to customers who present an appropriate level of risk, have expressed intent, and appropriate preferences. Big data is critical to analyze the hundreds or thousands of data points attributed to each of your buyers on a daily basis, which can change their qualification to buy on a minute-to-minute basis.

The combination of mobile technology and streaming analytics allows product development and programmatic advertising in real-time. Relevant messaging is needed for marketers to maintain relationships with customers. Examples of real-time marketing that are only possible with big data include:

● Location-Based Advertising: Retailers and other organizations are beginning to programmatically generate location-based offers and recommendations. Essence digital agency xAd’s first successful location-based campaign resulted in a 53% engagement rate.
● Co-Branding: By developing networks of partnership with brands who offer complementary products or services, marketers can exchange customer referrals by generating advertising that’s relevant and useful to customers.
● Value-Creation Tools for Customers: As mobile devices and internet of things (IoT) technologies generate an increasing amount of data points, marketers are able to develop big data-driven tools that offer value to customers. Customers of data-focused brands can now compare their home energy utilization to other customers, or learn how their spending stacks up to their peers.

Customer retention marketing strategies must be proactive in order to provide the convenient buying experiences today’s consumers expect. Forbes reports that 70% of large companies are currently relying on 3rd-party data sources, and 100% of big brands will purchase external insights by the year 2019. Integrating 3rd-party big data is no longer a competitive advantage. In many industries, it’s a necessity to keep up.

To learn more about BDEX’s first and only true real-time Big Data Exchange Platform, contact us today.


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When Your Audience Doesn’t Reflect Reality: Big Data Audience Building

Marketers understand that you simply can’t build audience groups on pure demographic factors. After all, Prince Charles of England and rocker Ozzy Osbourne are both British males of the same approximate age. However, it’s safe to say that a marketing message tailored for Ozzy wouldn’t necessarily convert the heir apparent, Prince Charles. Consumer preferences, motivations, and needs play a critical role in purchase decisions.

It’s clear that audience groups must be more sophisticated than demographics. Even deep demographic factors like income or family status don’t tell the full story. As Harvard Business Review’s (HBR) highlights, the sorts of audience groups that convert are rarely “created.” Instead, they’re “uncovered” through data analysis that incorporates behavioral clues from cookies, web analytics, user-generated content, and other big data sources.

Why Your Audience Groups aren’t Converting

Despite the fact that marketers understand what’s required to build audience groups, too few brands have segments that reflect reality. Information Week recently wrote about some of the “perils” of big data analysis biases, which can include:
● Selection Bias
● Inclusion of Outliers
● Overfitting and Underfitting
● Confirmation Bias
The term “data scientist” is ultimately accurate. To accurately understand patterns in reality, marketing teams must leverage enormous amounts of data to control against faulty results. If your big data audience segments are based on false positives from too-small or incomplete data sets, you could be suffering as a result. In one anonymous case study detailed by Information Week, a brand’s profit margin decreased significantly as a result of audience groups’ creation that didn’t control for bias.

Do You Trust Your Audience Analysis Methods?
Many marketers have developed some level of big data fluency. They understand some common analysis methods used to develop audience groups, such as clustering or linear analysis. Undergraduate studies of statistics has leant familiarity with concepts like sample size and statistical significance. An abundance of easy-to-use analytics tools allows marketers without extensive technology backgrounds to perform complex analyses in a point-and-click environment. However, a lack of big data resources has forced many marketing teams to rely on pre-formed audience groups from 3rd party vendors that are questionable in accuracy.

One large-scale study by HBR indicated that some 85% of product launches fail because of poor segmentation methods. Ineffective segmentation can have a significant impact on your brand’s profitability and outcomes. If you’re reliant on pre-packaged audience groups that you’ve purchased from a 3rd-party vendor, it’s likely time to refresh your segments. Join us as we review a new approach to building audience groups that convert.

1. Form Segment Hypotheses
Big data analysis for the purpose of segmentation is inherently scientific. The first step is to develop hypotheses about your segments. Based on what you know about your segment, you can develop a framework for analysis.
To avoid the risk of confirmation bias, your hypothesis should be based on known variables and goals. It could resemble the following statement:
Individuals who are seeking a mortgage for a second home are often 30-50 years
old with an income of $100,000 or more per annum.”
A correctly-formed hypothesis serves to narrow your analysis, while still providing room to discover behavioral and motivational insights.

2. Obtain and Combine Data
By participating in BDEX’s  Data Exchange Platform, marketers can gain immediate access to billions of data points in real-time. Marketers have the ability to set their own budget, and access insights on web behavior, preferences, and transaction history on consumers that match their existing contacts. Depending on your campaign goals and objectives, you can also opt to obtain contact information for additional prospects that match your goals and objectives. By connecting BDEX’s marketplace with your data management platform (DMP) tool, you can gain immediate access to fresh data insights.

3. Analyze
Effective marketing segmentation today has little resemblance to the mass marketing messages of yesterday. By obtaining third-party insights, you can gain a comprehensive understanding of how your contacts behave. This can lead to an understanding that your buyers prefer self-guided product research, are likely to have two children, or other rich factors that reveal segmentation without bias.
By allowing big data to form your segments without bias, you can avoid the risk of inaccurate results. BDEX’s open marketplace forum allows analysis with minimal risk of bias, due to the sheer volume of available insights.

4. Launch Advertising
Once you have developed rich, up-to-date and accurate market segments, you can launch advertising to connect with your audience groups. Instead of relying on months-old segments created by a third-party vendor, your marketing team has the power to continually test, iterate, and improve your audience groups.

For more insights on the power of real-time targeting for marketing initiatives with BDEX, click here!

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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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