Direct Mail Advertising: It’s Still Alive, and Data Can Help You Deliver on bdex.com

Direct Mail Advertising: It’s Still Alive, and Data Can Help You Deliver

Learn why direct mail isn’t dead and how it’s still the preferred marketing channel for many industries

With everything going digital these days, you may hear that books and newspapers are dead, and that no one gets anything in the mail anymore. So it might surprise you to learn that direct-mail marketing is still alive and thriving.

There’s something to be said about a potential customer receiving an addressed piece of promotional material – it can be more personal, is definitely more tangible, and can actually provide your business greater return on investment.

So, incorporating direct mail into a marketing strategy can be a solid way to round out a multiple-channel approach to reaching more leads and scoring customers. Just as with common digital marketing strategies, having high-quality data is one of the most important factors in direct-mail outreach.

Here’s why direct mail isn’t dead, how data drives these methods, and how BDEX can provide the most important data you need to help you deliver.

Personalized marketing works

Today’s consumer cares about personalized messages and connecting to their favorite brands. Data from the United States Postal Service (USPS) shows that 67 percent of people still feel that receiving something in the mail is more personal than via the internet. And, people consistently read their snail mail – USPS data also showed that 98 percent of consumers bring in their mail the same day they get it, and 72 percent bring it inside as soon as possible. So, getting a message in the hands of consumers this way means they’re going to see it.

What’s more, the customer’s desire for personalization could actually be because of the rise of digital: Everyone is now striving to feel real human connections. And as a marketer, the only way to make those human connections meaningful is to have the right data about consumers, right at the moment you need it. This is still a big factor in successful direct-mail marketing campaigns.

Where data fits in with direct mail

According to data from Data and Marketing Association (DMA), the median ROI for direct mail is 29 percent, which is better than paid search and online display. So, direct mail clearly isn’t dead and is still crucial to include in your strategy.

So how does data help marketers deliver in the realm of direct-mail marketing? Because they’re spending time, money, and resources on direct mail, and in different ways than with digital, it’s crucial that the information they have is both high-quality (meaning they will actually reach the right leads and customers) and up-to-date (meaning they have the up-to-the-minute customer contact data backing their outreach).

Also, remember that direct mail will be much more effective if paired with a digital marketing strategy. The customer data strategy you’ve been perfecting can only help this side of marketing as well. Direct mail can help increase both customer acquisition and retention and have information out in even more channels and avenues to foster human connectivity with the audience.

But, using data effectively in direct-mail marketing is a bit trickier than with digital. To get the biggest return on investment in this realm, marketers have to be sure they’re not using bad or old information. That means they should make sure they’re mailing to customers for whom they have accurate mailing addresses.

It can’t just be a gamble. Audiences must be better targeted with demographics, databases must be regularly scrubbed and updated, and you must ensure you’re gathering and using quality data.

Also, did you know that direct mail can be automated right along with your digital marketing strategy? For your overall plan to be effective, everything must be integrated and streamlined, and automation plays a big part in that success.

Let BDEX do the work

To get the highest-quality, most up-to-date data for your direct-mail marketing campaign, use our data from BDEX. Our Data Exchange Platform (DXP) allows you to use our simple Taxonomy Explorer tool, which holds data from almost 500 different industries and provides cross-device matching services to connect a customer’s various device IDs. We help you connect across channels, including direct mail, mobile, social media, and more. This means you’ll have the latest data about the right audience, right when you need it.

Now, we have over 1 trillion data points within the BDEX DXP and 700 million ID connections across channels.

To learn more about data from BDEX and how we can provide the data you need at the exact right moment, get in touch with our team or check out our taxonomy tool online today.

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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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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Understanding Your Target Consumer: The Case for Utilizing Traditional and Non-Traditional Data

Any digital marketer’s ultimate goal is to increase sales for a particular brand or retailer, but targeting the right consumer can be a difficult task. Traditional demographics, like gender, age and marital status, are not enough to properly assess a person’s buying potential. Consumers are diverse, their lifestyle and behavior not necessarily linked to their age or gender. And with a wealth of online and brick-and-mortar retailers to choose from, they demand more for their money and more from customer service. Vying for a customer’s business, however, becomes a lot easier when your marketing material is geared toward the right audience. By taking the time to analyze consumers’ behaviors and personalities, marketers can target consumers more effectively and efficiently.

Out with the Old… But Not Completely

It goes without saying that traditional demographics still have a role to play in contemporary marketing. After all, people who live near the beach will be in need of beach supplies whether or not their personality is similar to that of their neighbors. But not all demographics are as black and white as location. John Forsyth, partner at Forsythe Insights LLC, demonstrates the problem with traditional analytics by citing the obvious personality differences between Prince Charles and Ozzy Osbourne. Both men are British and the same age, but marketing to them in the same way is an obvious mistake, as one is the heir to the British throne and the other a rock star. In other words, their distinctive personalities and behavior alone are reason enough to target them separately.

In with the New

A consumer’s behavior can provide marketers a wealth of information, from shopping habits to favorite restaurants, but that’s only the tip of the iceberg. Data companies can offer marketers a wealth of information learned from multiple platforms, including social media, mobile, email, direct mail, and apps. By the year 2020, experts estimate “about 1.7 megabytes of new information will be created every second for every human being on the planet.” And while 2020 may be four years away, there is still an incredible breadth of data that can be tapped at this very moment.

A customer’s personality and values drive their economic decisions, and when marketers take the time to truly understand their audience, they can identify specific needs and locate look-alike audiences with similar characteristics. Advertisers and retailers can also give their respective brands informed suggestions about what products and services to offer their customers next.

The Next Phase in Targeting

”With many companies struggling with online – and offline, for that matter – conversion rates,” says writer Jessica Carter, “It’s crucial that they start to understand better what makes consumers tick (or click, as the case may be).” When marketers know what makes a customer “tick,” they can approach consumers from a variety of angles, from customized social media ads to personalized emails with coupons for a specific item.

When you add real-time targeting to the equation, the data becomes even more valuable. Brands can find consumers shopping for their products and services moments after they begin to browse online. But that’s only the half of it; marketers learn not only what consumers want at a specific moment, but they can influence where and when their audience gets the product or service.

While marketers may feel overwhelmed by the amount of data available, taking advantage of the newest innovations in consumer identification while incorporating the best of traditional targeting is key to creating exciting campaigns that will keep customer interest—and revenue—on the up and up.

BDEX provides brands, retailers, and agencies with extensive, quality data from a variety of sources in a true marketplace environment. With over 20 billion data points tied to mobile IDs and every channel added to the marketplace each month, buyers can understand consumers like never before and create compelling campaigns that return real results. Email us to learn more.

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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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