All Customers Are Not Created Equally in Value

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Mark Zilling, MeritDirect
Mark Zilling, MeritDirect

What does Big Data allow marketers to do with segmentation that was not possible before?

The potential ways to use Big Data for customer segmentation are only limited by the imagination of the direct marketer.

Big Data takes many forms and can be aggregated from numerous sources. For business direct marketers, the early foundations of Big Data occurred when businesses combined disparate large data sources into common platforms. Combining multiple mass market and specialty compilers, membership databases, and thousands of unique customer files from publishers, direct merchants, seminars, associations, and others, creates a single platform to launch all direct marketing communication. This approach has provided a common perspective for marketing communication throughout the customer lifecycle for the past decade.

Over the past few years, behavioral information has introduced yet another level of data sitting between the static demographics of compiled data and the proven buying characteristics of customer files. Today new technology provides the opportunity to link online activities, like email responses or website visits, to these large aggregated data sources, further enhancing the behavioral category known colloquially as “hand raisers” or those displaying interest in specific products or services.

Together these data sources integrate to create a Big Data platform that changes the landscape for marketers focusing on customer segmentation. Here are three ways marketers can use Big Data to improve segmentation.

Identifying probability of high-value, long-term customers at first order

Marketers typically focus on new customers with the intent to convert the highest number possible into repeat purchasers. While this is a noble endeavor, it's simply not practical.

All new customers are not equal in regards to their future potential and the traditional tools are not adequate to segment greater potential customers from those of lesser value. In fact, new customers all have the same recency and frequency, leaving only the size of first purchase (monetary) and the product purchased as the characteristics on which to base segmentation decisions. By integrating Big Data, additional attributes can contribute to reorder models that have proven highly predictive in not only determining which customers are more likely to repeat purchase, but also how much they're likely to  purchase and what product or category it will most likely be in the next 12 months. This information is useful in determining what frequency and channel of communication to use in converting the site, and what offer or messaging strategy to employ. It can also help define which customers to send to the sales force well before they've self-selected or before a competitor has a chance to pry them away.

Identifying higher-potential customers while they're prospects saves dollars

Big Data also can identify prospects who are not only more likely to respond and become a new customer, but of those prospects who are likely to become higher-value customers.

This allows for a segmentation strategy that either avoids lower-value potential customers before spending money to acquire them, or tailors the acquisition and subsequent retention strategy to a lower-cost approach, creating a balance for the lower potential revenue and an acceptable ROI.

Linking website visits to marketing media recaptures leads lost to e-commerce

It's the intent of every successful marketer's website to capture a visitor first and foremost with an order. If not an order, a registration provides future marketing reach across most channels. Unfortunately, on average, eight or nine out of every 10 visitors doesn't place an order or register on the site. Marketers use cookies to tag a higher percentage of visitors, but cookies are only useful when visitors return to the website or when attempting to reach them via targeted display network platforms. Recent technological advances now provide the ability to link these website visitors to a business entity's physical location. While the technology does not guarantee the website visitor at the contact level, linking to their business and then targeting appropriate contacts at those locations based on current contact strategies has shown improved performance in new customer acquisition efforts as high as 15 to 25%.

These are three new ways Big Data can help to identify and segment customers along multiple points of the lifecycle. The opportunities are as endless as the data itself.


Mark Zilling, MeritDirect

While it's common to see computer science engineers among the ranks of direct marketers these days, Mark graduated from Duke with a degree in biomedical engineering. Jobs are now rife in that specialty, but it was not so special when Mark hit the streets looking for work in the mid-1980s. His father had a friend in Germany who owned a company and had a job available, so off he went on a European adventure. It was a direct mail company and the rest, as they say, is direct marketing history. For the past 25 years the EVP and partner at MeritDirect has brought his considerable intellectual power to B2B database marketing, directing the company's advanced analytics and strategic services group. Currently, when not coaching his daughter's soccer team, he ponders ways to merge online and offl ine data to improve Web conversion rates.

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