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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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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