Big data in publishing
There is still a treasure trove of information out there!
- Publishers are throwing away significant potential by failing to make use of data
- Big data can help companies to pull off the structural change towards a digital organisation
Munich, 21 November 2014 – German publishers are not fully exploiting the potential of big data – and as such, they could end up missing the boat on the urgently needed transformation towards a digital organisation. This is one of the findings from a recent in-depth survey of 15 German publishers – small and large publishing houses and specialist publishers alike – conducted by goetzpartners, a leading consultancy for strategy, M&A and transformation. “We took the industry’s pulse and discovered that players still have a lot of catching up to do, even though some publishers, particularly the large ones, have at least begun thinking about the issue,” says Marc Ziegler, Head of Digital Business at goetzpartners and lead author of the study.
The volume of data is growing at an almost exponential rate: According to experts, there will be about seven devices connected to the Internet for each person in 2020. One of the consequences of this is that the volume of data for companies to process will more than double year on year. Only about 0.5 percent of the data that’s available is currently being used. But there are big data tools out there that can close this gap.
“We see the use of big data technologies first and foremost as an additional source of income, and only secondarily as a means to cut costs,” says Dr. Alexander Henschel, Managing Director at goetzpartners and co-author of the study. Publishers that make consistent use of these technologies to improve processes and expand their portfolio are in a position to generate a higher proportion of their revenues with digital business – in some cases more than 40 percent – and achieve a higher-than-average total return, which can be as high as 30 percent. Ziegler explains, “In order to do that, media companies need to make use of their data long term as well, for instance by offering database or database-related services that constantly generate new revenues in the form of monthly subscription fees or royalties.”
But the publishing houses first need to do their homework. They have at their disposal data from a huge range of sources – such as traffic analyses, CRM data, real-time information from social networks – in unparalleled frequency. However, this does not become relevant information until it has been processed analytically and the results visualised. Publishers will need to make these two big data "disciplines" into their core competence in the future. Otherwise they will not be able to make use of the opportunity that this structural change brings in these times of falling revenues in the traditional print business that cannot yet be offset to companies’ satisfaction by the digital business.
“This applies, above all, to publishers who are unable or unwilling to monetarise their digital content direct,” emphasises Marc Ziegler. Using real-time analyses, they would be able to find out things like how the content of an article should be structured, which multimedia elements need to be combined in what form, and how a page should be laid out to turn readers into users and for an article to spread virally on Twitter or Facebook. This additional reach that publishers generated could then be used to increase their advertising revenues. Predictive analytics can even be used in advance of this stage to help create the “right” content, in other words content that readers are demonstrably interested in – or are going to be interested in. And this can then be automatically prepared for the different devices and displayed to specific target groups as appropriate. How different headlines and pictures or the positioning of an article on the page influence people’s reading behaviour can also be analysed today in real time. “This survey and the fact that we’ve long been watching developments in the international markets have enabled us to identify 25 use cases where big data technologies can be employed for profit,” explains Marc Ziegler. But, he says, it’s important first to identify the ones that are relevant and then to begin with a pilot project. “Experience shows that once the first successes start to come in, the level of acceptance rises within the company and transformation can then be pulled off on a large scale,” adds Henschel.
The complete study can be downloaded HERE