Creating Products That Fit: Using Real-Time Data for Product Development

Technology has had a significant impact on the concept of customer loyalty. Shopping for alternatives based on price or convenience requires just a few taps on a smartphone screen. Brand awareness is no longer enough to build lasting customer relationships. Brand preference might not be sufficient, either. Organizations must focus on creating customer experiences and products that are tailored to customers’ needs at exactly the right time.

In order to avoid customer defection, brands must make the buying process convenient. Marketers must anticipate customer needs based on desktop browsing history, mobile usage, and behavioral triggers. The use of real-time big data to programmatically generate custom offers and products is gaining traction. Join us as we review the success of companies who have already embraced big data as a tool for building meaningful customer relationships, and effective strategies for the future.

How Big Data is Shaping Real-Time Product Development in Finance

In one case study by IBM, a major bank utilized customer financial data, transactions, preferences, and social media posts to develop customized offers for “Peter,” a recent homebuyer and cooking enthusiast. Based on absence of risk and Peter’s Facebook post about wanting to purchase a “restaurant-style six burner gas stove,” the bank was able to extend a mobile offer to increase his credit limit before another credit card company captured his business.

In a world where purchases can be made with one-click and price and rates comparisons are as easy as a Google search, customer happiness doesn’t always trump convenience. Your customers may be happy enough with your brand to accept offers via mobile push notification, but they are less likely to call your institution or visit a branch to request the same thing. Effective marketing and product development today is about creating products and offers that seamlessly fit into your customer’s lives. This can only be accomplished with extensive big data resources, including high-quality 3rd party insights.

If the financial institution profiled by IBM lacked access to immediate customer insights, they could have lost Peter’s business. He may have decided to proceed with purchasing a restaurant-style stove and accepted the first acceptable line of credit offered. Without real-time data, marketers are unable to extend offers at the right moment. Streaming analytics isn’t just going to benefit marketing teams of the future. It’s going to be a necessity for brands that want to remain competitive.

Streaming Analytics for Customer Retention and Loyalty

Big data allows marketers to think in more personalized terms than traditional segments. Instead of offering a line of credit increase to all recent home buyers, they’re able to make offers to customers who present an appropriate level of risk, have expressed intent, and appropriate preferences. Big data is critical to analyze the hundreds or thousands of data points attributed to each of your buyers on a daily basis, which can change their qualification to buy on a minute-to-minute basis.

The combination of mobile technology and streaming analytics allows product development and programmatic advertising in real-time. Relevant messaging is needed for marketers to maintain relationships with customers. Examples of real-time marketing that are only possible with big data include:

● Location-Based Advertising: Retailers and other organizations are beginning to programmatically generate location-based offers and recommendations. Essence digital agency xAd’s first successful location-based campaign resulted in a 53% engagement rate.
● Co-Branding: By developing networks of partnership with brands who offer complementary products or services, marketers can exchange customer referrals by generating advertising that’s relevant and useful to customers.
● Value-Creation Tools for Customers: As mobile devices and internet of things (IoT) technologies generate an increasing amount of data points, marketers are able to develop big data-driven tools that offer value to customers. Customers of data-focused brands can now compare their home energy utilization to other customers, or learn how their spending stacks up to their peers.

Customer retention marketing strategies must be proactive in order to provide the convenient buying experiences today’s consumers expect. Forbes reports that 70% of large companies are currently relying on 3rd-party data sources, and 100% of big brands will purchase external insights by the year 2019. Integrating 3rd-party big data is no longer a competitive advantage. In many industries, it’s a necessity to keep up.

To learn more about BDEX’s first and only true real-time Big Data Exchange Platform, contact us today.

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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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