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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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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