Big Data

Monetizing the “Long Tail” with Big Data

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) March 19, 2014

In a recent meeting, a client started talking about strategies for “monetizing the long tail.” I found this very provocative, so I started to think about where and how one would leverage Big Data to make money from the long tail.

Let’s start with a definition of what is meant by the “long tail”

A long tail distribution exists when a large percentage of your customers (50%+) are located away from the “head” or the central part of the distribution curve. This runs counter to the Pareto Rule where 20% of your customers represent 80% of your revenue and/or profits. A long-tail distribution arises with the addition of many variables, dimensions or characteristics in the segmentation of your population, which increases the magnitude of the distribution skew.

A long-tail business strategy enables organizations to realize net-new profit opportunities from selling small volumes of lower-demand products to many customers, instead of the traditional marketing model of only selling large volumes of popular items to the mass market.

The term “long tail” gained popularity as a retailing strategy enabled by the web for selling a large number of unique items with relatively small quantities sold of each. Chris Anderson popularized the concept in his book “The Long Tail: Why the Future of Business Is Selling Less of More” (see Figure 1).

Monetizing the Long Tail with Big Data

Figure 1: Long Tail Distribution

Figure 1 is an example of the long tail distribution with the area on the left (in green) called the “Head” and the area on the right (in yellow) called the “Tail”. In a growing number of markets, the area in yellow (the tail) is greater than the area in green (the head). The growth of the “tail” is being driven by the increased level of personalization and product stratification and proliferation in a number of product categories.

Big Data And The Long Tail

Using traditional marketing approaches, organizations have not been able to monetize the “tail” due to certain economic factors (revenue, costs, and profits). With traditional marketing, it costs more to reach the tail customers than the revenue or profits that would be generated by them (see Figure 2). As a result, most marketing organizations settle on targeting just a small number of large, generic gender/age/geography/education/network customer segments (for example, females ages 45 to 55 with college education and medium-to-high net worth).

Big Data and the Long Tail

Figure 2: Traditional Marketing Model

However, Big Data has the potential to change the marketing economic model. Big Data enables organizations to monetize the tail by reducing the cost to target and serve the tail segments (see Figure 3).

Big Data-enabled Marketing Model

Figure 3: Big Data-enabled Marketing Model

Big Data can change the marketing economic model in the following ways:

  • More granular customer profiling models (e.g., developing analytic models at the individual customer and product categories levels) that allows marketing organizations to better match the messaging, offers, and product promotions to the exact needs of the individual customer
  • Dramatically lowering the costs to target and serve by building consumer-grade, third-platform (mobile) apps via self-service web and mobile apps that provide an easier-to-learn, easier-to-use, and easier-to-remember customer experience
  • Delivery of highly personalized, finely targeted recommendations and offers leveraging improved insight into customer and product propensities, behaviors, tendencies, and preferences
  • Expanded use of low-cost communication media that can be personalized (emails, texts, display ads, social media) to deliver relevant messages and offers; reduction or elimination of non-personalized, high-cost, mass communications channels (direct mail, call centers, advertising, radio, TV, newspaper advertising)
  • Leverage graph analytics to uncover new customer network relationships (e.g., strength and direction of people relationships) that can be used to improve use of social media to build advocacy and referrals that drive low-cost, loyalty-based customer engagement and sales
  • Leverage of real-time analysis and location-based services to improve the timeliness of marketing messages and product offers—delivering offers at the time that the customer is ready to use or respond to that offer

Summary

As organizations get more proficient at capturing customer engagement and product usage data and leveraging deep Big Data Analytics to uncover new customer and product insights, the potential exists to further reduce the marketing cost curve. Marketing organizations can create an improvement cycle where customers are willing to share more data about themselves to organizations that perfect the science of converting that new data into offers and recommendations that benefit that customer, thereby driving down marketing costs even more (see Figure 4).

Customer Marketing Improvement Cycle

Figure 4: Customer Marketing Improvement Cycle

Big Data can enable organizations to successfully monetize the long tail of customers that could not be monetized using traditional marketing methods. Again and again, we’re seeing Big Data changing the economics of the business by enabling organizations to optimize key business processes and uncover new monetization opportunities.

 

A Reminder To Cast Your Vote3 17 14 Bill Cube

I’ll be representing EMC in Big Data for theCube/SiliconAngle’s #CUBEmadness tournament, so be sure to vote here. Voting for the first round starts on Thursday, March 20th  and ends on Monday, March 24th.

 

 

 

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting the strategy and defining the Big Data service offerings and capabilities for Dell EMC Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power the organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill was ranked as #4 Big Data Influencer by Onalytica.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored Dell EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a masters degree in Business Administration from the University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

Read More

Join the Conversation

Our Team becomes stronger with every person who adds to the conversation. So please join the conversation. Comment on our posts and share!

Leave a Reply

Your email address will not be published. Required fields are marked *