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Leveraging customer data to drive business strategy

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The shift to digital news publishing provides access to a vast amount of data that can provide an understanding of customers and how they interact with online content. These data can help publishers increase customer engagement, increase advertising yield and optimize their mix of content. Yet while many publishers realize the opportunity these provide, they often are unable to realize a return on their big-data investment. A significant gap exists between having the data on hand and actually putting it to good use.

So what causes this gap and what is the solution? Is it possible to economically turn customer data into actionable insights that result in increased revenue and a stronger business?

The challenge with digital data
At the heart of this digital data challenge are the silos that exist within publishers. Typically, the advertising function is separate from the audience group and neither work closely with editorial. Taking a holistic view of digital revenue, with data from all of these functions, is essential because actions taken by one part of the business will often affect the others.  For instance, limiting access to online content for subscription sales has immediate effects on advertising inventory and the associated revenue.  Managing these two revenue streams independently makes both less effective than a coordinated, dynamic strategy.

When it comes to data, each function often operates independently, using their own tools to manage digital business operations. Combining the data from these tools, at a level of detail necessary for meaningful analytics, is often impossible due to inconsistent definitions or a lack of common fields for merging data files. In addition, data capture tools commonly used by these groups, such as Omniture or Google Analytics,  are often expensive and require highly specialized skills not often available to publishers. These tools can yield extensive raw data that requires specialized hardware, software and expertise to extract meaningful insights. To compound these challenges, in the process of transforming their businesses, many news media organizations have lost the staff with knowledge of how those tools were implemented and how to use them.

What is the solution?
Publishers need tools that capture the right data, at the right time and price, to support analytics that will generate real, actionable insights.  These tools should combine data from online activity with information about their print subscribers so a publisher can get a complete view of their audience.  The tools should combine advertising impressions and revenue with online traffic log files so tradeoffs between audience and advertising revenue are measured and understood.  The data from these tools should be at a level of granularity, without sampling, sufficient for analytics and application to each customer, both subscribers and advertisers.

Mather Economics has worked with hundreds of publishers on print and digital analytics, and we have found digital data to be an opportunity and a constraint.  These data yield tremendous insight into customer behavior, but combining data from different systems can take a long time and require compromises in the level of detail and precision from the analytics.  Furthermore, the resources required to capture, merge, query and analyze these data are expensive and hard to manage effectively.  To solve this challenge, we created ListenerTM, a web-based tool that gives publishers a holistic view of customers and revenue – all in one place.

Listener brings together data from several sources into one data file when a page is loaded into the browser, eliminating the need to later combine these data with limited accuracy. This allows publishers to capture data on individual customer activity, even if they are anonymous, and data on advertising impressions delivered to those customers in one file.  Revenue from delivered impressions can be added to this data file by ad ID, so publishers can determine which actions will maximize digital revenue from both advertising and audience streams. Data from other sources also can be added to the Listener file as well, such as mobile activity, print subscription account history, demographics and product bundles. Listener will also capture data on other events taking place on a page, such as a meter count or payment module, supporting analytics on price sensitivity and offer conversion rates.

All of this occurs behind the scenes without impacting the user's online experience, allowing a publisher to model the behavior of customers and to use those insights to reduce churn, drive advertising revenue and strategically price and bundle subscriptions. With these insights, publishers can better monetize online content and maximize revenues without negatively impacting customer engagement or sacrificing print revenue streams.

With the right data and analytical tool set, it is possible for publishers to invest less time and money to unlock the value in data for making better business decisions, monetizing digital content, increasing customer loyalty and driving advertising revenue.  We believe that the industry is positioned to support product innovation that will reverse the revenue declines of recent years and that digital data will be the driving force behind future growth.

Matt Lindsay, Ph.D. has more than 20 years of experience in helping businesses improve performance and grow revenue through economic modeling. In consulting roles over the past 15 years, he has shared this expertise and developed pricing strategies and predictive models for clients, including the Intercontinental Exchange, Gannett, The Home Depot, NRG Energy, Tribune, IHG, McClatchy, the Everglades Foundation, the Walton Foundation, Dow Jones and The New York Times.

Connect with Mather Economics at www.mathereconomics.com.  Contact Matt Lindsay at (770) 993-4111 or matt@mathereconomics.com.

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