Big Data and Real Dollars in the Publishing Industry
Among all the industries to look for a positive case study in leveraging technology, few would choose publishing, especially when thinking of a “legacy” print newspaper. Invariably, articles addressing publishing speak to declining circulation volumes and advertising revenues, how nobody will ever pay for journalism, and that millennials are too interested in posting pictures of cats on Facebook. The reality is that publishers are quietly catching up and even surpassing other industries in their application of technology to drive revenue and sustain the publishing business model.
Let’s first start with the term: “legacy.” I take issue with using this term for newspapers, in part because one of the largest platforms for hosting online obituaries in the newspaper industry is legacy.com. I do not consider newspapers should be equated as such, but more seriously because the traditional newspaper is evolving with new ways to engage their audience and their local communities beyond just delivering a newspaper to a doorstep. Creating new apps for mobile and tablet devices, embracing social media (and even working with Facebook and Google directly to find common ground), using the latest in digital advertising targeting, deploying intelligent paywalls, letting data inform publication schedules, and communicating with customers on a 1:1 basis are just some of the ways media companies are embracing and applying new technologies
The perennial question publishers have struggled with is how to maintain precious advertising revenue from digital content, but at the same time justify a paid print product while also dabbling with paywalls to acquire new digital subscriptions. One would think there is only a choice between the two, but in fact, by leveraging a robust and granular data collection tool, a publisher can understand exactly where their most valuable advertising positions are and where the conversion probability to paid subscriptions is greatest. Gathering detailed event-level data on page views, users, ad impressions, and paywall events in a single internally-housed database, typically supported by familiar Big Data names like Hadoop, Hive, and Spark, lets publishers mine their data to decide which content should be paid vs. free. Once the technology is there, a business analyst can crunch the numbers to answer where both revenue streams are maximized. Let’s take for example two content types, Entertainment (assume high CPM’s and low user engagement) vs. Sports (assume low CPM’s and high engagement). In this case, the publisher might leave the Entertainment section completely free but implement a premium paid model for sports content, thereby deploying an intelligent or “dynamic” paywall and realizing the best of both worlds. Knowing what to leave free vs. paid ensures revenues aren’t cannibalized by competing departments.
“A publisher, using a DMP now knows that 20 percent of their engaged audience fits a similar profile and can find a buyer for their valuable inventory”
In addition to optimizing overall revenue through paywalls, many publishers have embraced Data Management Platforms (DMP’s) to raise the yield from each ad impression. These tools track users online to create profiles and behavioral segments, but also help to connect advertisers looking to target specific users with publishers looking to sell their unique audience. For example, an upscale automotive brand might want to make sure its online advertising reaches a specific market segment, such as those with more affluent and older demographics who live in a major metro area. A publisher, using a DMP now knows that 20 percent of their engaged audience fits a similar profile and can find a buyer for their valuable inventory. An advertiser is also willing to spend much more per ad impression to reach this market segment since the expected ROI from a targeted ad campaign is much greater than a mass marketing campaign. The result is the content producer realizes a much higher yield from their audience, the advertiser reaches a key market segment, and hopefully the user sees an ad they actually find interesting. Additionally, publishers with robust internal technology can create custom segments using their proprietary (first party) data. Audience segments enriched with unique local attributes are even more valuable for both advertisers and publishers. The proper infrastructure to build, maintain, match, and integrate a publisher’s first party data with DMP’s is critical to drive new revenue.
A process known as “entity resolution” (or “customer resolution”) is a key method to build and integrate a first party data with advertising DMP’s by taking “offline” information from customer databases and making it available for advertising targeting. Beyond advertising, Entity Resolution can also support 1:1 customer marketing efforts. Assembling a series of data sources tied together in a database using fuzzy matching techniques, publishers can understand how a print customer engages online, when was the last time they interacted with customer service, or when they clicked on a link in an email campaign. This type of customer-centric data collection informs marketing, pricing, targeting, and nearly any other action taken to engage a customer. By moving away from mass marketing and instead using data to target specific users and behavior with customized messaging, publishers have seen the effectiveness of retention and acquisition campaigns improve significantly. A more engaging customer experience means fewer dollars spent on acquiring new customers and stronger monthly revenues due to fewer customer stops. In addition to just developing the data infrastructure, analysts skilled in predictive modeling are required to unlock the data stored within the data infrastructure, and so the infrastructure must be built to accommodate this role.
Finally, the content itself can be pushed in a way to realize the highest level of engagement (which in turn drives audience, advertising, subscriptions, and retention) from digital readers. Real-time tools to recommend articles to online readers help to keep a user on the website, thereby continuing to generate ad revenue or perhaps engaging the user long enough to actually convert to a paying subscriber. Also, real-time tools used in the newsroom inform a digital editor when to publish the next sports article vs. when to swap an existing article from 5th to 1st on the homepage due to high interest. In this way, real-time technology informs the daily decisions and publication schedule, which in turn engages audience and drives revenue.
The case studies mentioned above are all due to publishers embracing Big Data and new technologies. As with any industry that has gone through dramatic transformation can attest, in order to survive, there must be an adoption of new and in some cases untested technologies. It is the role of the CIO to make sure the technology does more than just execute some function, but in fact can be used to make informed business decisions and drive revenue. For certain use cases (such as using data for predictive modeling) the technology actually supports a human analyst and needs to be built to accommodate them. Therefore, understanding the needs of an organization as a whole and implementing technology in a way to empower the business to drive real dollars must be paramount. Always understand how the technology drives revenue and start with the end in mind.
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