Two events this week turn us back towards this perennial question. One is the purchase of Springer by BC Partners after a prolonged affair and a lover’s tiff which forced the price up a notch to $4.4 billion, but left 10% of the equity in the hands of the sellers. The other is the latest set of results from Wiley, covering the fourth quarter and thus the complete picture in 2013. While Wiley is the larger company, by virtue of its major presence in education markets, the two are very comparable in size terms in the science, technology and medical sectors. Both have STM units of plus or minus a billion dollars, and both have STM market shares of around 3% each. And they have another shared characteristic: neither of them is showing much by way of organic topline growth, and there are some very good reasons for this. Global recession and library budget cuts do not suggest growth, and nor do the consequent falls in book and journal purchasing. But both companies have gone digital to the extent that print declines are largely offset by eBook and eJournal supply, though often at lower revenues (and greater margin). This again does not indicate growth, but confirms a view of settled publishing environments in fairly stable markets with high margins: the impression that they like to give, and which analysts and investors like to believe. But underneath the surface, I believe that these markets are now boiling over with activity, and that both of these companies, and all of their peers, now face challenging growth targets if they are to deliver to private equity investors and shareholders real growth in returns in recovering economies, as well as investing in retooling for a digital data age.

In the first instance, digital transition from print is now over. Nothing more marks the point than the news this month that Elsevier the sector leader in journals, is to outsource its eJournal transaction completion to Atypon. This then is just a cost, and each player will seek opportunities to drive it as low as possible. Journal articles are getting commoditized and will be universally available before long, so this is not a growth area. Wiley’s growth in its Research sector – its new name for STM, was -1% in FY13. It is hard to imagine that Springer was more than low single digit, and indeed it is possible that the industry average is less than 2%. Given current constraints on price increases (between 5 and 9 % across the sector) and it is easy to imagine that we are suffering a market contraction. Yet private equity investors cannot do financial restructuring all the time and shareholders expect dividend growth as markets come back. So what are the growth strategies which will deliver that?

It seems to me that there are two current hopes for sustainable long term growth. Neither will be new to these two companies, since in a number of ways both of them are experimenting here. Both involve investment, but in both cases the investments will lead directly to productivity gains, and to the possibility of very rapid new product development. Neither is a long stretch beyond the current managerial capacity of these players, since both have strong and capable technology strength. The key question is whether they are flexible enough in managerial terms to embrace a future beyond the formats and business models on which they were reared and upon which they have grown comfortable in historical times. The two directions both rely upon the data they already hold, and data which they can obtain by alliance and joint venturing with third parties. They can be described as the development of workflow tools for the processes of research on the one hand, and the building of analytical tools and datasets/knowledge stores for researchers on the other. In order to play here the publishers will need a data platform which allows the cross-filing and searching of content-as-data, and ways of developing search in this context on both structured and unstructured files. They will be pushing the envelope on metadata development, imposing text enrichment disciplines to increase the value of their content, building extensive triples stores, and using their expertise as a draw for researchers to deposit experimental/evidential data with them, as well as publishing their articles. And, having decided their niches, they will be collaborating with other publisher data-holders, sourcing Open Data deposits and turning themselves into a part of the research value chain itself. When peer review gives way to PPPR (post-publication peer review) their grip on the “barrier to publication” cycle, in which the publisher-managed peer review is necessary for the researcher to enter the market, will be broken anyway.

So what will these new products and services look like? Well, both of these players already know something about that. Springer has successfully re-platformed on the widely-used MarkLogic system, which creates a completely different data-handling opportunity and is widely used in the sector. And Springer has form in the researcher workflow market through its recent purchase of Papers, the Dutch article production software (Mekentosj BV). Likewise, Wiley have made real strides in developing knowledge stores in support of their chemistry browser project and in response to the strength of their chemistry list (as noted here already). But these instances are swallows, not summer. There has to consistent and sustained development to create batteries of data services in chosen sectors, and the data enrichment must be widespread, not experimental. The workflow tools will include some acquisitions, but will reflect a great deal of home grown learning as many publishers discover, for the first time, what the eventual user (not the library intermediary) does for a living – and how he can be helped and supported by data-charged service modules which will become as essential to his view of research as, well, journals once were. The real issue, then, is not technology: it is the mindset to forge a new business out of the old, with end-users, not buyers, and with data, not pre-formatted reporting, at its core. It sounds like a choice, but it isn’t really. Growth in real terms is the key to survival. It is time to start thinking again about how we satisfy markets, and investors.

Phil Cotter’s comment on last week’s post here really got me going. Now that I know that suicide bombers max their credit cards before setting off to do the deed I somehow feel a gathering sympathy for the security services. So the starting point is 5 million up-to-the-limit cards? We need to funnel cash into predictive analytics urgently if anything we do is to show better results than airport security (to begin from a very low measure indeed). So I began to look for guidelines in the use and development of predictive analytics, thinking that while we wait for terrorist solutions we might at least get a better handle on marketing. I am surprized and impressed by how much good thinking there is available, so in the spirit of a series of blogs last year (Big Data: Six of the Best) here are some starting points on innovative analytics players who all have resonance for those of us who work in publishing, information and media markets. And a warning: the specialized media in these fields all seem to have lists of favoured start-ups enttitled “50 Best players in Data Analytics”, so I am guilty of scratching lightly at the start-up surface here.

In the same spirit of self-denial that drives me to abstain from a love of eating croissants for breakfast, I have also decided to stop using the expression “B** D***”. I am so depressed by publishers asking what it means, and then finding that, because of “definition creep” or “meaning drift”, I have defined it differently from everyone else, including my own last attempted definition, that I am going to cease the usage until the term dies a natural, or gets limited to one sphere of activity. So Data Analytics is my new string bag, and Predictive Analytics is the first field of relevant activity to be placed inside it. Or do I mean Predictive behaviour analytics?

I was very impressed by analysts studying our use of electricity ( Since the work throws up some lessons which we should bear in mind as we push predictive analytics into advertising and marketing. The thought that it was easier to influence human populations through peer pressure and an appeal to altruism, as against offers of “two for one”, cash bonuses and discounts is clearly true, yet our behaviour in marketing and advertising demonstrates that we behave as if the opposite was the case. The emphasis on knowing the industry context – all analytics are contextualised – and the thought that, even today, we tend to try to make the analysis work on insufficient data, are both notions that ring true for me. We need as well to develop some scientific rigour around this type of work, using good scientific method to develop and disprove working hypotheses. Discerning the signal from the noise, like “never stop improving”, are vital, as well as being hard to do. I ended this investigation thinking that even as the science was young, the attitudes of users as customers were even more immature. If we are to get good results we have to school ourselves to ask the right questions – and know which of our expectations are least likely to be met.

Which brings me to the people we should be asking. Amongst the sites and companies that I looked at, many were devoted from differing angles to marketing and advertising. But many took such differing approaches that you could imagine using several in different but aligned contexts. Take a look for example at DataSift ( It now claims some 70% accuracy (this is a high number) in sentiment tracking, creating an effective toolset for interpreting social data. Here is the answer to those many publishers in the last year who have asked me “what is social media data for, once you have harvested it?” Yet this is completely different from something like SumAll (, which is a marketeers toolset for data visualization, enabling users to detct and dsiplay the patterns that analysis creates in the data. Then again, marketing people will find MapR ( fascinating, as a set of tools to support pricing decisions and develop customer experience analytics. Over at Rocket Fuel Inc ( you can see artificial intelligence being applied to digital advertising. As a great believer in sponsorship, I found their Sponsorship Booster modelling impressive. This player in predictive modelling has venture capital support from a range of players, from Summit to Nokia.

When the data is flowing in real time, different analytical tools are called for, and MemSQL ( has customers as diverse as Zynga, and Credit Suisse and Morgan Stanley to prove it. Zoomdata ( is a wonderful contextualization environment allowing users to connect data, stream it, visualize it and give end-user access to it – on the fly. This is technology which really could have a transformative effect on the way that you interface your content to end users, and you can demo it on the Data Palette on the site. And finally, do you have enough of the right data? Or does some government office somewhere have data that could immensely improve your results? Check it on Enigma (, the self-styled “Google of Public Data”, a discovery tool which could change radically product offerings throughout the industry. Perhaps it is significent that the New York Times is an investor here.

So, for the publisher who has built the platform and integrated search, and perhaps begun to develop some custom tools, there is a very heartening message in all of this. A prolific tool set industry is growing up around you at enormous pace, and if these seven culled from the data industry long lists are anything to judge by, the move from commoditized data increasingly free on the network to higher levels of value add which preserve customer retention and enhance brand are well within our grasp.

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