Are You Using the Most Advanced Data to Target Consumers?

The way we use data to target audiences is constantly evolving. The first phase in targeting was fairly simple in that we relied on only a few simple demographics, like age and gender, to segment consumers. Then audience groups were formed. More advanced and specific, audience groups were, and still are, based on consumers’ shared interests. The newest chapter in data targeting, utilizing real-time insights, merges information about demographics and audience groups with real-time activity. But that’s just the tip of the iceberg. Real-time data isn’t just information about your consumers’ spending habits in the last month. True, real-time insights let you know what your target customers are searching for the moment they shop online.


In the mid-20th century, marketers focused on only a few consumer demographics when developing marketing campaigns. While factors like age and gender were more important sixty years ago when people sourced their news and entertainment from the same place, the traditional methods for obtaining consumer data are not as relevant anymore. McKinsey’s John Forsythe demonstrates the problems associated with using only a few, superficial demographics by citing the differences between Prince Charles, Queen Elizabeth’s son and her heir apparent, and Ozzy Osbourne, lead singer of heavy metal band Black Sabbath. While both men are British and the same age, a marketer obviously wouldn’t market to them the same way.


Marketing and brand expert Adam Paulisick also believes that simple demographics don’t provide enough information to properly target consumers.


“Segmenting consumers by age and gender or other demographics is inefficient at best, even for more traditional marketing campaigns because there are no hard and fast rules anymore for what a man or a women will intuitively buy (with few exceptions).”


While we might not know the “hard and fast rules” that drive what a consumer buys, we can know the next best thing: what product they are shopping for the moment they shop. Real-time data takes into account everything we used to know about consumers based on demographics and audience groups and merges it with live activity.


Keith Sayewitz, Chief Marketing Officer and Head of Sales at BDEX, a market-driven exchange platform that provides users with real-time data, explains the value of real-time analytics for marketers.


“For years a company depended on simple demographics to identify a certain consumer, like ‘soccer moms.’ Then audience groups were formed, so we discovered those soccer moms were interested in fitness. But now, with real-time data, we learn which of those soccer moms are in the market for a treadmill or are switching to vegan cuisine. This information is incredibly powerful because it allows for truly advanced targeting. We know that this customer is likely to buy a treadmill because she is in the market for one at this exact moment.”


Marketers can then create specific ads for the desired consumer, increase the probability for conversion, and, therefore, create more sales. The insights provided by real-time data are essential to brands, retailers, and agencies who want to stay up-to-date on consumer activities and truly understand their customers’ needs.


BDEX, the first ever Data Exchange Platform (DXP), is currently the only source for true, real-time data. For more information about BDEX’s unique services, click here.

Image Credit: NEC Corporation of America

Consider These 3 Factors When Selling Data

Big Data’s incredible economic and social influences are evidenced in the variety of industries it’s revolutionizing. For example, healthcare providers can better “predict epidemics, cure disease, improve quality of life and avoid preventable deaths” (Forbes). Brands can better serve their existing customers while attracting new ones, and retailers can predict what trends will resonate with their shoppers.

However, those new to the data monetization side of the Big Data industry may feel a little overwhelmed since there are thousands of companies ready and willing to utilize their data. Before you take the plunge and decide where and how you should sell your data, consider these important data factors: location, price, and privacy.

Where You Sell Your Data Matters

You’re probably wondering, “Where do I sell my data?” After all, the “personal data economy is fairly new.” While you can sell data to a variety of websites, the process can be time-consuming, as tech blogger Chris Hoffman points out. And if you’re selling a limited amount of information, weighing the amount of time spent selling versus the value of the actual data is important.

But as the data monetization industry grows, more and more options become available. Data marketplaces, or online stores where people can buy and/or sell data, are alternatives to the traditional DMP. Data marketplaces allow a wider range of businesses to take advantage of data monetization. Some marketplaces, like BDEX’s, don’t even require revenue sharing.

The Price Must Be Right

Determining the value of your data is perhaps the most difficult part of monetizing data. If you set the price too high, buyers will choose other providers, but if the price is too low, your chances of creating a decent margin are squashed. In a marketplace environment, data sellers can determine the price of their data based on that of the competition. BDEX even shows their data sellers the optimal price point of their data so they raise or decrease the price when necessary.

Customer Privacy is Essential

Sharing data should be a mutually beneficial experience for all involved, including the consumers. To ensure that your consumers’ information is protected, you should encrypt the data or hire a third party to do it for you. You should also be sure that the website or marketplace that buys your data is doing their part to protect the data as well. Data sellers who take advantage of the BDEX marketplace can rest assured that their customers’ information is anonymized and protected.

BDEX is changing data monetization. Sellers can enable activation and monitor their data, while buyers can access tremendous scale and even integrate the BDEX DXP into their own DSP. When they utilize BDEX’s data monetization services, data sellers have complete control of what data they sell and its individual price point. For more information, email us at info@bdex.com.

Image Credit: Flickr/http://401kcalculator.org

Why You Should Consider DaaS

You’ve probably heard the term SaaS. SaaS, or Software as a Service, combines the services of a software provider with a self-service approach. For a monthly or yearly subscription fee, customers can utilize software themselves, no hardware required. As the Internet became faster and as virtualization and big data tools developed, SaaS became more available, setting a precedent for other user-friendly tech products and services.

One of the byproducts of SaaS is DaaS, or Data as a Service. “In the last few years many businesses have sprung up offering cloud-based Big Data services to help other companies and organizations solve their data dilemmas,” says Big Data expert and writer Bernard Marr. And with more and more businesses utilizing data, it only makes sense that companies are offering “data on demand.”

But not all DaaS services are created equal. With BDEX’s data services, businesses can get the combined benefits of a data marketplace and data services without the upfront cost. For a fixed monthly fee, retailers, brands, and marketers can receive services like:

  • Email Retargeting

This service gives businesses the ability to automatically email users that have visited their website, even if a visitor leaves and never registers with the site. It is an opportunity to reach an otherwise completely lost website visitor.

  • Access to In-Market and Real-Time Data

Companies receive up-to-the-minute, real-time data about consumers looking for a product or service. This data can enhance current marketing and lead generation efforts.

  • Geofencing

Businesses can better target prospective buyers within a pre-defined geographic area.

  • Direct Mail Targeting

While digital marketing tends to be the primary focus in the use of online data, combining digital with more traditional forms of advertising, like direct mail targeting, is now possible.

With over 5,500 data categories, including finance and retail, the BDEX DXP has data for businesses big and small. Email us at info@bdex.com to learn more.

Image via Pixabay

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 Predictive Marketing Models Yield Terrible Results

When Whole Foods announced their plans for a line of grocery stores geared towards millennials, the announcement was deemed “offensive” and stock prices dropped. According to Marketing Mag’s Katie Martell, the Harvard Business Review, and a number of other outlets, there’s nothing inherently wrong with the plan. However, there is something wrong with Whole Food’s predictive marketing models.

Harvard Business Review’s Robyn Bolton writes that members of Generation X and Baby Boomers also want access to “lower-priced, organic, and natural foods.” While Whole Food’s product line announcement was frequently deemed “offensive,” Bolton believes the real problem is with Whole Food’s marketing models. Simply being in a demographic doesn’t predict certain preferences or behaviors. Organizations who make similar mistakes might not receive as much criticism as Whole Foods. However, they’re unlikely to achieve great results.

The advent of big data provides marketers with a new ability to build vivid customer segments. Behavior, beliefs, and intent can yield models that are much richer than demographic generalizations. However, many attempts to build predictive marketing models fall flat in terms of outcomes. Join us as we review the most common issues behind poor segmentation and predictive modeling results in marketing.

1. Poor Behavioral Data
Ultimately, the purpose of predictive marketing models is to predict how consumers will behave in the future. Without extensive or accurate knowledge of how your segments have behaved in the past, this is difficult to accurately model.

Infogroup research indicates that just 33% of marketers believe they collect enough behavioral data on customers. Only 21% are “very confident” about accuracy in profiling. If your predictive models rely on your transactional data or self-reported sources, you may not have accuracy.

2. Limited Context
Perhaps you understand who your customers are, but do you understand why they’re buying from you? Arjuna Solutions writes that predictive models need certain insight into how your segments “behave in the marketplace.”

Contextual insights are a critical part of marketing models in an age where consumer identity and segments are more and more fragmented. Fast Company’s Dan Herman advocates the idea of building models based on context and purchase motivation.

Instead of “40-something home buyers,” a mortgage company using more context could discover they are trying to reach “40-something primary home buyers,” “40-something vacation home buyers,” and “40-something residential real estate investors.” By identifying the reason for the purpose, marketers can better understand goals, pain points, and probable behavior patterns.

3. Small Sample Sizes
The vast majority of marketing professionals have some background in research methods and statistics. However, many organizations are trying to market with models that are based on a tiny segment of the population.

In the age of big data, companies can’t compete if their marketing models are based on first-party customer data. Their models may be inaccurate if they’re using focus groups, surveys, or other dated research models. Obtaining recent, accurate, 3rd-party data is the best way to understand how populations behave with confidence.

4. Flawed Data Logic
Marketing models built internally and externally may fail because they’re based on terrible data logic. Affinity algorithms can reveal incredible patterns, such as the famous example of when Target’s algorithm predicted that a teen was pregnant before her parents knew. In other cases, affinity algorithms don’t hold up. Visiting a high-end furniture store within the space of six months does not necessarily make a woman a “trendy” food consumer.

As MIT research highlights, clustering and other methods of discovering patterns are “inherently unsupervised.” This can reveal surprising truths, but it can also output garbage if your inputs are inaccurate or your sample is too small.

For organizations struggling to develop accurate predictive marketing models, the problem could be related to their big data. Small sample sizes, old data, or a lack of 3rd-party insights for context can all result in predictive models that don’t reveal the future. 


For more information on how BDEX’S first-of-a-kind data marketplace can revolutionize the accuracy of your predictive marketing models, click here.

 

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Why You Shouldn’t Limit Retargeting to Lost Customers

According to retailnext.net, at least four trillion dollars’ worth of merchandise was abandoned in online shopping carts in 2014 and over half of that amount is likely recoverable. So how do you turn those losses into gains? By retargeting—but not just any type of retargeting.

Modern consumers know they have the world at their fingertips—literally. They browse online stores, compare prices, read reviews, and more on their desktops, laptops, tablets, and smart phones. In short, consumers hold the power; they can either choose to boost a brand’s sales or purchase a different, and possibly cheaper, option. That’s why retargeting should be an essential step in every marketer’s strategy.

What is Retargeting?

Founder at AdProfs, a digital advertising consultancy in Canada, Ratko Vidakovic describes retargeting as a way “to show ads directly to visitors after they’ve left a site or landing page, providing multiple shots at the conversion.” Once a consumer visits a particular website, a piece of code is added to the landing page. A cookie is then placed in the shopper’s browser and she is added to a specific audience list and targeted with ads customized to meet her needs and interests.

Retargeting is not a new concept, however. Brands, retailers, and agencies have been taking advantage of this marketing tool for years. But why limit retargeting to only your lost customers? By taking advantage of the breadth of information in a data marketplace, like BDEX’s, you can retarget customers who not only visited your website but those who are shopping for your products or services elsewhere.

Why Retarget?


“It’s not uncommon to see amazing CTRs [click-through rates] with retargeting, anywhere from 0.30-0.95% – which is 3-10x higher than the industry average,” according to Vidakovic. In short, retargeting is a proven way to increase conversions and is more effective than regular targeting alone.


By courting consumers who have previously purchased or currently show interest in a product or service, marketers have a much better chance of increasing conversion rates. Retargeting potential consumers who have shopped for the same or similar items on other sites or apps increases the chances of boosting sales even more.


How Can I Retarget Better?


Most DMPs offer retargeting solutions in some form or another, but with an ever-changing marketplace to consider and new technological innovations being introduced constantly, taking advantage of the most advanced retargeting solutions can be difficult. A DXP like BDEX tracks millions of users across every channel, making it easy to target campaigns geared toward particular consumers. For example, if your business sells garden equipment, you can retarget ads to people who have either been to your website or recently bought garden equipment on another website or are in the market for new garden equipment. When you add a real-time element from outside your system to the scenario, you can find the most recent, relevant data possible. You can retarget with even greater specificity, leading to more sales.


Retarget better with BDEX. With over 20 billion new data signals on U.S. consumers every month, the BDEX marketplace is the most comprehensive data source of its kind. Whether you want to target—or retarget—consumers, the BDEX Data Exchange Platform offers in-depth insights perfect for any marketing campaign. To learn more about BDEX’s unique services, click here.

Image credit: Flickr/Bernard Goldbach

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