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

3 Data Factors to Consider When Reaching Consumers

The importance of data to a business’s success isn’t a recent discovery. For decades, the fate of American television shows rested solely in the hands of The Nielsen Company, which still monitors citizens’ viewing habits via customer surveys and meter readings today. However, thanks to an incredible influx of information from a variety of sources, including cell phones, computers, and even sensor-equipped trains, brands have more access to analytics than ever before. Harvard Professor Gary King even referred to this stream of statistics as a “big data revolution” (Harvard Magazine). While the amount of information is impressive, King doesn’t believe the quantity is the “revolutionary” part.

“The big data revolution is that now we can do something with the data.”

But for many companies, knowing how to properly use acquired information is a major problem. When brands consider the following factors of big data, they are better equipped to reach consumers:

Timeliness of the Data

“In the rush to avoid being left behind, I also see that many companies risk becoming data rich but insight poor, says data expert and author Bernard Marr (Forbes). “They accumulate vast stores of data they have no idea what to do with, and no hope of learning anything useful from.”

One major issue with businesses storing data but not taking action is that the information goes bad quickly. Companies will keep the information hoping it will be of some use though it’s “no longer relevant, inaccurate or outdated,” says Marr. In other words, “time is of the essence.” BDEX is different from other data providers in that brands can access real-time analytics the moment consumers browse and shop online. By knowing what consumers want at a given time, businesses are better able to meet consumers’ needs.

Source of the Data

There is a common misconception that first-party data is superior to third-party data. While first-party data is owned by the company that obtained it, the data does not change as it’s transferred from party to party. That’s why data expert Kevin Tan believes companies should focus “on the quality and transparency of the data, not the party label.”

“Advertisers that choose to work with high-quality data providers can obtain third-party data that is timely and clear. Used together with first-party data, top quality third-party data enables brands to build a fuller picture of target audiences,” says Tan. (Exchange Wire)

In order to determine the quality of the information they receive, brands should also know where their data providers get their statistics. Some sources, like the US Census Bureau, may contain a broad range of data, while others, like market research surveys, may provide more specific information. By making use of data from a variety of sources, brands have the ability to assess their target audience and create better marketing campaigns.

Accuracy of the Data

You may be wondering, “How do I know what data is usable?” After all, the sheer number of data resources suggests that some of the data will either 1) not pertain to every business or 2) be incorrect. And while it does not serve your business to cater to every online consumer, know that the specificity of the information big data, especially third-party data, can provide is unparalleled. Information is collected by a variety of tools ranging from desktop cookies and e-tags to smart phone IDs.

“All this allows firms to glean what sites users have visited, what they have shopped for, what postcode they live in and so on. From this the firms can infer other personal details, such as their income, the size of their home and whether it is rented or owned.” (The Economist)

While the amount of data can be overwhelming, utilizing big data will not only help you reach your consumers but anticipate what they want next.

BDEX is the first ever Data Exchange Platform (DXP) offering real-time data in a marketplace environment.  All seller and consumer information is impartially scored to ensure that data is and high-quality and accurate. To learn more about BDEX’s unique services, or to become a BDEX buyer or seller, click here.

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.

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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