Can Struggling Newspapers Look To Big Data for Revenue?

Advertising still isn’t bringing in the revenue that will be needed to sustain most digital publications, but there’s another way for these brands to capitalize on an influx of readers.

In Frederic Filloux’s Monday Note, he suggests that for newspapers and other legacy print media to reverse their woeful fortunes in garnering digital revenue, they should take a page from social media and start mining.

The data, of course. Filloux describes how Facebook’s combines its data extraction with predictive algorithms to tailor the advertising a user sees, then posits that there’s no reason a newspaper’s online edition couldn’t use the same formula:

Applied to news contents, the same techniques could help refine what is known about readers. For instance, a website could detect someone’s job changes by matching his reading patterns against millions of other monthly site visits. Based on this, if Mrs. Laura Smith is spotted with a 70% probability to have been: promoted as a marketing manager in a San Diego-based biotech startup (five items), she can be served with targeted advertising especially if she has also appears to be a active hiker (sixth item). More importantly, over time, the website could slightly tailor itself: of course, Mrs Smith will see more biotech stories in the business section than the average reader, but the Art & Leisure section will select more contents likely to fit her taste, the Travel section will look more like an outdoor magazine than a guide for compulsive urbanites. Progressively, the content Mrs. Smith gets will become both more useful and engaging.

Additionally, Filloux suggests, when income indicators rise, that same customer could be ripe for an upgrade to becoming a subscriber or premium user of the site, allowing it to accrue even more revenue through fees.

Clear that cache, people, or don’t be so damn predictable.