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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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

When Real-Time Data is Actual Real-Time Data

Many data platforms claim they can provide their customers with real-time insights, but their definition of “real-time” is often debatable. True, real-time data is not only obtained in real time but offers brands, retailers, and agencies up-to-the-second information on their customers’ behavior. After all, if you acquire information about an online shopper one minute but the information is 30 days old, the data’s value is greatly diminished. But if that same online shopper searches for sandals on one website one minute, and the marketer learns that information a minute later, the data is priceless. “Organizations can reap a lot of benefits by accessing real-time analytics purely because of their close relevance to market realities” (Techopedia).

Mobile Data

Real-time data can be learned a variety of ways, perhaps most obviously, through mobile IDs. Every Apple device has a UDID, or Unique Device Identifier. “Originally, the UDID was intended as a sort of serial number for Apple devices. But, as the industry began to explode, app developers turned to the UDID to help track and target mobile users.” Apple later denied app developers access to users’ UDIDs and created a data set called IDFA, or Identifier for Advertisers. Unlike the UDID, the IDFA is not easily linked to devices or users. In fact, users can even opt of advertising tracking altogether if they wish.

Despite mobile advertising regulations, mobile ID tracking provides valuable data for marketers, especially when tracking real-time behavior. With over 100 million mobile device IDs tied to AAIDs and IDFAs, the BDEX DXP has some of the most comprehensive and diverse mobile data on the market.

Cross-Device Matching

Ninety percent of today’s consumers bounce back and forth between devices when making purchases. When you consider that 65% of the revenue generated online comes from purchases that are made across multiple channels, you have little choice but to target users with ads regardless of the device they’re using to access these channels” (Shopify). To target the same consumers across multiple channels, however, marketers must link those consumer’s various IDs. Not surprisingly, connecting the ID “dots” is easier said than done.

There are over 80 million email-to-mobile ID and email-to-cookie ID matches available in the BDEX marketplace. Thanks to a plethora of data provided by more than 75 sources and custom BDEX identifier tags, marketers can link their consumers’ information across multiple sites and platforms and use that data to advertise via mobile, email, display, or any other channel.

Moving Beyond Interest and Intent

While interest and intent data is certainly valuable, when you add a real-time element to the equation, the information learned is not only a predictor of what a consumer may buy but an indicator of what he/she will buy. With millions of new data points received daily across thousands of categories, the BDEX Data Exchange Platform offers the most comprehensive and time-relevant data on the market. For more information about our platform, visit our website. Want to get in touch? Email info@bdex.com.

Image via Flickr/Jean-Pierre Bovin

Desperate for the Right Insights: How Data Exchange Can Solve Your Procurement Issues

Big data procurement is a pain point for analytical marketers. Chief Marketer reports that “getting a 360-degree view of the customer” is a primary struggle for today’s marketing leadership. While it’s clear that integrating a wide array of data insights is the right solution, many marketers are simply unable to obtain the right big data assets via traditional procurement methods, such as data management platforms (DMPs), internal assets or leading third-party vendors.

 

The most sophisticated marketers understand there’s more to customers insights than “RFM – recency, frequency and monetary value.” To effectively maximize conversion potential, subject matter expert Karl Wirth recommends including insights on relationships, persona and intent. You must understand how your prospects are researching across platforms, individual motivational factors and pain points, and the context that surrounds each of these qualifiers. It’s abundantly clear that big data means big opportunities for marketers, but only if they’re able to procure sufficiently recent and comprehensive insights.

 

What Comprehensive Big Data Procurement Looks Like

While analytics experts have multiple ways of categorizing the types of data that provide marketers with comprehensive understanding of their target customers, marketer Jim Robert’s definitions are among the most intuitive:

  1. Identity
    A consumer’s identity includes basic demographic characteristics, such as age, gender, and race. It also includes geographic details on the area of residence, and insights such as employment, job title, and income.
  2. Quantitative
    Quantitative data is most likely to be first-party insights stored within an organization’s DMP based on their interactions with a customer, but can also be sourced via data exchange with third-party vendors. This includes data on transaction history and communications with the brand. It will also include online activities across desktop and mobile devices, including historical engagement with branded content or company’s sales teams.
  3. Descriptive
    Descriptive data offers a more comprehensive view of an individual’s life than pure identity data. It can include parenthood status, including the number of children and whether an individual owns pets. It can detail whether someone owns or rents their home, their education level, and work history.
  4. Qualitative
    Many marketers are familiar with the concept of “attitudinal data,” but qualitative insights actually encompass much more. A consumer’s opinions, brand preferences, and motivations may be included among these insights. Qualitative profiling can also lead to an understanding of brand preferences, consumer pain points, and individual priorities.

 

While you can gain a basic understanding of customers by procuring just identity and quantitative insights, it won’t be a truly comprehensive understanding of how your customers operate. You won’t understand why they make the decisions they do, or how they’re most likely to research based on education level.

 

Each additional type of data insight can change a consumer’s goodness-of-fit with a marketer’s target market. While consumer identity factors may dictate that they can afford to purchase a product, descriptive and qualitative factors may affect their priorities or reveal that budgets are most likely directed elsewhere.

 

DXP: Simple Procurement of Comprehensive Insights

For marketers struggling to build comprehensive profiles and filter targeted advertising opportunities towards the most qualified customers, the Data Exchange Platform can represent the solution. Instead of relying on limited or aging insights in a DMP environment, marketers can procure big data via a wide range of third-party resources all in one place via the DXP. No other platform can give marketers the breadth of data availability like the DXP due to it’s inherent access to so many data providers at once and it’s ability to merge data points from multiple sources across a single data taxonomy. This facilitates the first steps towards a true, 360-degree understanding of who brands are trying to connect with.

 

It goes without saying that better understanding leads to better conversions and sales. By ensuring their messaging lands in front of genuinely qualified prospects exactly when they’re motivated to buy and actively searching, conversion rates can finally exceed organizational targets. Instead of struggling to drive sales with data that reveals only part of the picture, marketers are given the opportunity to finally achieve the understanding they need.

 

For more insights on customer understanding through big data analytics, we recommend our blog: Re-Imagining the Consumer Needs Through Data.

 

image credit: nec corp via flickr/cc

 

 

How Big Data Can Drive Competitive Intelligence

Companies increasingly mine their own customer data for insights into the market. But what about data that tracks your competitors’ activities – in an ethical but profitable way?

Oh, sure. Coca-Cola isn’t likely to just send Pepsico a terabyte of sales data for kicks. But what if Pepsi knew how often people buy Coke at the supermarket, and what else is likely to be in their shopping cart at the same time?

It’s time for businesses to start a competitive intelligence process — while lowering the cost of analyzing information. In short, you need to continuously gather data online, from social media, website changes, news sources, and posted documents online, looking for clever bits of gold in the digital stream.

You’ll need to blend baseline of competitive intelligence (to prevent surprises in your own business) with proprietary data sets obtained on the market (to create surprises –you’re your rivals).

Indeed, a universe of third-party data can add context to internal marketing data and can provide strategic insight into the vulnerabilities of competitors. Consider these three market trends:

1. Vendors like Acxiom, KBM Group, Bluekai and Datalogix have been increasingly vacuuming up that marketing data for resale. A study from the Tata Group looking at big data trends estimated that half of firms producing big data sets sell their digital data today. The average sale earned those companies $22 million in 2012.

2. Of the industries producing big data byproducts for sale, telecommunications firms and tech businesses tend to be the most prolific vendors as well as the most prolific users of external data, according to Tata. Insurance companies tend to make the most money from selling their data, however.

3. Manufacturing firms and energy companies tend to sell the least amount of big data, while consumer goods and media companies tend to use external data the least, despite the obvious value. That’s changing though, because it’s becoming easier to find buyers.

“Third-party data is barely used, and it should be used more,” said Keith Sayewitz, chief marketing officer and head of sales for the Big Data Exchange in Seattle, a Seattle startup that works like a stock market to trade Big Data sets. “Imagine, if you’re a brick-and-mortar retailer, and every consumer walked in with a sign showing you what they had been shopping for in the last month.”

The practice of selling data to the marketplace appears to be much more prevalent in Asia than in Europe or the United States, according to Tata. That may reflect regulatory considerations. U.S. data brokers generally ensure that big data sets have been stripped of individually-identifiable consumer information, both to ensure regulatory compliance and to prevent the inevitable public backlash. But it’s telling that China’s southwestern province of Guizhou is establishing an exchange,GBDex, to provide data cleaning, modeling, and data platform development. Alibaba is a partner in the exchange in Guiyang.

A small firm with a progressive attitude toward analytics may be able to carve out a competitive advantage against a much bigger rival simply by understanding their niche in the market better.

It’s data judo, using the weight of data of a larger rival to one’s own advantage … if you’re not bogging down your marketing staff with lower-value tasks like vetting and cleaning information.

Gathering external data is rarely a core enterprise function for most firms looking at competitive intelligence. External data isn’t proprietary. Market research should center on the highest-value tasks – data analysis and presentation of results.

As long as you have access to solid information – both from internal and external sources – the clever analysis of that data is what creates competitive advantages.

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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