Big Data For News Publishers

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Big Data For News Publishers

The glass half empty: Has news publishing lost its mojo?

06 July 2014 · By John M. Lervik

Publishers are reputedly pessimistic by nature. But if nearly two out of three of them participating in a recent online publishing profitability survey are correct, there is solid justification for their negative outlook — but also a path to regain momentum.

In the survey, more than 65% of nearly 400 U.S. publishing executives said they believe the publishing industry is in for a long, slow slog – at least two to five years or more – before ad sales will start showing some real traction again.

That’s a rather dismal outlook on one’s business.

The survey, conducted by Cxense in conjunction with Editor & Publisher magazine, took the pulse on profitability in publishing. Perhaps most sobering of all the results was that more than one-third of respondents don’t believe ...

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5 tips for selling subscriptions in a digital news universe

02 June 2014 · By John M. Lervik

For some stargazers, a view of the heavens with the naked eye suffices. But others – admittedly a smaller group – are more curious.

They want to identify the planets, glimpse Saturn’s rings, count the moons around Jupiter, or view our moon’s seas and craters – all of which require a good lens.   

The digital media business works in much the same way. Most readers surf the vast Web seeing what catches their eye, while a subset pays for favoured publication subscriptions where they can home-in on the content that most interests them.

This reality is not sustainable. After all, it costs money to create and manage a publisher site and its content. With revenue-generation a major priority, various subscription models are being tested at publications around the globe. But most have been slow to catch hold.   

So how do we convince site visitors that being able to home-in with a subscription “lens” is worth paying for?

It won’t be easy.

The Digital News Report from The Reuters Institute for the Study of Journalism shows ...

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Hire the best data team members, equip them with the best tools ... and trust

18 May 2014 · By Greg Doufas

It’s been lost to the vagaries of crowd-sourced curation, but for a while my favourite definition of the word “capability” came from Wikipedia, where it was succinctly described as “the sum of expertise and capacity.”

Your data analytics technology stack is an enabler of your analytical capability – it alone is not your analytics capability. I’m not just saying this for philosophical reasons.

The fact is Big Data technology and software solutions are evolving so quickly that analytics leaders (regardless of  industry) simply cannot depend on any past or current standard as a means to define their capability.   

It brings to mind one of the most commonly asked questions I get these days: What technology are you using, or what analytics toolset do you recommend for doing the hardcore stuff? 

Obviously, it’s far too circumstantial a question to answer intelligently without a much deeper conversation. But the first thing I usually ask ...

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Big Data lessons from the bank industry … and toothbrushes

24 April 2014 · By Patrick Glenisson

Editor’s note: Patrick Glenisson, manager/marketing analytics at Belgium’s KBC bank, is a guest blogger for Big Data for News Publishers this week.

When I attended last year’s edition of a Big Data in Retail Financial Services conference, everyone — speakers and participants alike — seemed to agree on one thing: We collectively hated the term “Big Data.”

Why? It’s too technical. It’s often misused by vendors. It creates confusion (especially with senior managers). It inflates expectations. And is often reminiscent of other failed “Big” IT implementations (data-warehousing, business intelligence, CRM …). 

Moreover, for banks, collecting, storing and using customer data is not a new thing: Credit risk scoring is common practice since the ‘90s. And most banks have undertaken one or multiple customer segmentation exercises, and have been building up a portfolio or marketing response models through their in-house analytics units. 

So, nothing new under the sun, right?

An opportunity to grab 

Yet a lot has change during the past five years or so: 

  • Customers are more than ever in the driver’s seat, visibly impacting loyalty and churn rates.  
  • Government regulation is impacting margins across a variety of sectors.
  • Schumpeter doesn’t sleep lately as non-traditional players keep disrupting business models.
  • And finally, the economic downturn weighs on consumer spending behaviour.

Also banks cannot expect anymore to gain (or keep) market share by just pushing a good product in the market. Rather, the battle for every customer is raging. More than ever before, this requires us to bring customer-centric thinking in the way we change, plan, and operate our business. 

The idea of customer centricity is not new either. Ten years ago (and since then continously), high-end consultancy firms were preaching this as the new normal. It took an economic turndown and some dramatic examples of digital disruption to create a burning platform for customer-led strategies at board level.

This is where customer analytics or business intelligence professionals have a unique opportunity to stand up. Without rushing to build a 360° customer database (another loaded term), they should aim at answering a list of key questions that can bring clarity in the link between customer data and business results.

Often the underlying raw data is available in-house. Sometimes it’s scattered across silos, sometimes its quality varies, sometimes it requires cross-silo thinking, but .... it’s there.

The toothbrush test

Over the past three years, my team at KBC and I have been working on leveraging what we had. In the years before that, we had done a great job of building a customer data mart, along with a portfolio of models that predict various cross- and up-sell opportunities at customer level.

But we were stalling on:

  1. The systematic adoption of these models by marketers.

  2. The integration of customer intelligence in sales support tools.

In other words, we had been trying to push our analytical products rather than considering our (internal) customers’ real needs. Push marketing, but on the inside. Trapped in silo-thinking, we too had forgotten the ultimate customer-centric mantra: “Be relevant.” 

This is where Google inspired me with its so-called the “toothbrush test.”

In one article describing Google’s product strategy, its head of M&A said: “We ask ourselves, ‘Is this something people use once or twice a day and does it solve a problem?’ That sounds like a toothbrush, at least for those of us who want to keep our teeth.” 

And that’s exactly what data professionals should be asking themselves: How can data, big or not, be made relevant for employees and customers on a daily basis? 

Linking marketing and sales

Evolutions in the business intelligence landscape (see, for example, Gartner Magic Quadrant for BI & Analytics 2014) now provide cost-effective options to rapidly deploy dashboards and interactive views on considerable sizes of data.  

We used one of these solutions to unlock the value of our data and data products to a broader audience of users — all while keeping in mind that answering business questions requires regular interactions and several iterations. Hence, an agile approach. 

For example, a regional director came to us with the hypothesis that the sales targets for his region were not in accordance with local customer potential. Our task was to confirm or reject this thesis, and provide him with insight into how his region’s potential compares to that of his peers. 

We linked our full customer database containing dozens of socio-demographic indicators, with our list of predictive scores and a list of sales performance indicators at regional level.  

In less than a week, we prototyped several interactive views through a visual analytics dashboard, published it on a central portal, and iterated two times with him over the results. By the Friday of that week, the results were on the agenda of performance review meeting.

He found the ammo he was looking for — the data supported his gut feeling. 

This approach also allowed us to standardise frequently asked queries and generate self-service dashboards — hereby freeing up time from our analysts that were in heavy demand. 

This, in turn, has created opportunities to start looking at business questions requiring more advanced data crunching, but also to spend more time guiding our internal customers through proper interpretation of customer data and analytical results.

It was a first step in a journey that is still ongoing within KBC. 

Think big, start small

Is this Big Data? Yes and no.

No, because it’s a far cry from the digital-driven business models displayed by the Amazons, LinkedIns, or Twitters of this world. But, as customer centricity is becoming an imperative, it is crucial to start gaining a company-wide understanding of the business impact of tiny groups of customers, and ultimately of every individual customer. 

Data, big or small, plays a critical role in developing this understanding. Just make sure that you start looking at what you already have. 






Content recommendations shouldn’t be an audience engagement afterthought

17 April 2014 · By John M. Lervik

If there’s a war in publishing, it’s the ongoing battle to increase reader engagement.

Most publishers enter the fray by engaging with a content recommendations vendor that promises to keep visitors on their sites longer, where presumably they will engage with more content.

But before ceding control over the content being promoted on their Web sites, publishers should ask themselves some important questions:

  • Is your content recommendations vendor driving your business or theirs?

  • Are you in control?

  • Who gains insights about your readers in this relationship?

  • Can the industry afford to commoditise content distribution in a similar way to how ad distribution has developed?

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In looming battle for identity, news publishers must claim their audience through more intelligent data

16 March 2014 · By Greg Doufas

Who are you? What do you like? And how will I know if we meet again?

If you want to talk about some of the most serious challenges facing the industry, these are the sorts of questions you are going need to come to terms with and understand the importance of. At their core, they are questions that hinge on the idea of identity.

By identity, I mean the ability to recognise, verify, and most importantly, establish a persistent one-to-one relationship with the reader, regardless of platform.

Unless you’re one of the few publishers that has implemented a “hard” registration wall as a gateway to your Web site/platform, chances are the majority of your readers are anonymous. Syndicated data and research studies are providing a more accurate picture of the uniqueness of your audience, but just at the aggregate level. They still lack the depth, dimension, and measurement necessary for a truly customer-centric view.

Simply put, regardless of how much behavioural or transactional data we collect, we often do not truly know who our readers are in a lasting and meaningful way. To be clear, I’m not just talking about having names in a database; I’m talking about a relationship based on trust, in which an identity is established in exchange for value.

This lack of direct connection is the sort of thing that should keep you up at night. I realise this is not exactly a new concept or an emerging phenomenon. But the consequences are now greater than just an inability to measure audiences accurately.  

Before exploring further, let’s take a step back.

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Publishers: Take back your company’s Big Data revenue potential from tech middlemen

03 February 2014 · By John M. Lervik

When the digital revolution swept over the publishing landscape, profits for publishers largely got swept away.

With publishers anxious to find ways to make money through the digital landscape as their print profits dwindled, third-party middlemen swooped in to lend a helping hand, taking care of all that “technical stuff” on publishers’ behalf.

Those same third parties also kept publishers in the dark about how they were exploiting the publishers’ most valuable asset: digital data.

These intermediaries, some of which are publishers’ direct competitors, may not have had nefarious intentions, but they knew the value of the data goldmine publishers have always been sitting on.

But unlike publishers, they had the technical maturity and know-how to manipulate Big Data to achieve their own profitability goals. Exacerbating the issue was that the publishing industry was slow to embrace the digital age and missed out on some of the early breaks and benefits.

The lesson learned is that ceding control over data turned publishers into passengers on the digital journey, and the middlemen “took them for a ride.”

So here’s a question for publishers: Do you want to be passengers or drivers?

The ride has hit some bumps, but it’s far from over. Publishers can retake the wheel and start driving their own revenue generation with a little creativity and by taking back control of data.

A 2012 Pew Research Center report cited statistics from an eMarketer survey stating that tech companies are taking up to 68% of online ad dollars. The 2013 version of the report showed that while the middleman’s share of digital ad revenue remained stable, the digital ad market is growing at a much faster pace than any other advertising segment.

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5 key strategy questions for today’s Big Data practitioners

29 December 2013 · By Greg Doufas

I remember how, as a young data analyst years ago, I used to love describing my analytical approach and methodology when dealing with business stakeholders.

Even when presenting my findings to senior management, I would proudly spend half the time articulating the complexity of the work required to achieve the results we would soon be discussing.

Reflecting back, it’s obvious some of that was simply my way of gaining credibility and authority within the organisation. It was a common issue not exclusive to me or my industry at the time.

There was, however, something larger at play.

Data analytics teams were becoming a must-have for any organisation, and it was often hard to figure out where and how these teams fit within an organisation. Business leaders were being challenged to evolve through better use of data and data-based technologies. They knew they had to; the benefits were becoming clearer. They just didn’t understand how.

These new data people were seen as an odd bunch — outsiders who lacked context, sometimes a threat. Let’s face it: The change wasn’t always embraced with open arms.

For all these reasons, it was easy to see how keen analytics professionals could often feel pressured to explain themselves far beyond what would seem logical, sometimes even aggressive in their assertions.

Eventually, through my own maturation, as well as some astute advice from wise mentors, I broke the habit and learned to trust in my own expertise and reputation. I learned to cut to the chase and focus on the insight and its value to the business. Validation of the awesomeness of the work (and its creator) could come later.

Winning over hearts and minds proved much more valuable than preaching how effective our statistical methods were. Those who prospered in our field understood tangible business results and realised benefits were the only factors worth obsessing over, regardless of the people, politics, and process at play.

Now in this relatively new era of all things Big Data, I’ve started feeling as if those bad old habits have begun manifesting themselves again, and in more pronounced ways.

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7 tips for keeping Big Data in perspective

29 September 2013 · By Dirk Milbou

In a previous post, Big Data ambitions were compared to the Gold Rush, when those who provided tools to dig the gold were the ones who earned money in the first place.

This reminds me of the 1990s, when many companies invested in huge customer relationship management (CRM) systems that fell under the umbrella term “database marketing.” These systems often were driven by information and communication technology (ICT) or operational departments, and stressed “database management” over “marketing.”

At that time, information was mainly collected via surveys (declared behaviour) and did not give the business people a great deal of insight. It was difficult to effectively track proven behaviour and, therefore, create uplifts for marketers. So they lost interest.

Since the digital revolution, publishers are in a better position than ever before to collect plenty of information about consumers and their habits, needs, and wants. This daily, interactive contact with large audiences generates “big” data. So “big” systems are needed to capture this proven behaviour.

For many companies, Big Data equals Hadoop, the system that collects all that big data, regardless of why they need it and how they plan to use it. When gathered this way, however, Big Data becomes simply “Big Useless Data” that creates noise and obscures the signal in our data. The noise is increasing faster than the signal.

Then again, just like in the ’90s, right-brained editorial and business staff will quickly lose interest in it.

No wonder, then, that a new analyst report indicates that enterprises are deriving far less value from Big Data than they expect, or even than they invest.

Dramatically less, according to preliminary findings from Wikibon research, which found 46% of Big Data practitioners were only partially successful with their projects. They hear it’s a big deal and throw money at it without really understanding what they’re hoping to achieve. Two percent even had to write off their investments as complete failures.

Just like the CRM-bubble in the ’90s….

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Unlocking the revenue potential behind Big Data for publishers

09 September 2013 · By Dirk Milbou

We all know there is more data available than ever. No need to repeat the exponential growth of data: from penta- to zeta- into yottabites, which seems to be the latest geek term on it.

Historically, publishers were always in the forefront of collecting data, thanks to the penetration of their newspapers and their frequent contact with readers.

But since the evolution from print to digital and mobile — and thus the growing interactivity with large audiences — publishers are more than ever very well placed to collect lots of information about consumers, their habits, their needs, and their wants.

The catch-all term for it is “Big Data,” where “big” stands for an interminable volume of data, a limitless velocity of the (real-time) data-streams, and an endless variety of consumer information. Not only socially desirable “declared behaviour,” like in the old days, but also daily proven behaviour and interests that publishers can derive from reading and buying habits.

But where exactly is the potential behind big data for publishers? And how do we unlock it?

Data is said to be the new oil. This is certainly true for companies that provide other companies with specialised data solutions. It’s like the Gold Rush, when those who provided tools to dig the gold were the ones who earned money in the first place.

It seems also to be true for companies such as Google and Facebook and Apple, the new technology driven kids on the block, which have less system legacies and business models mainly based on “data.” 

But where is the added value for publishers? Where is the added value for the consumer and advertiser?

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About this blog

The aim of the Big Data For News Publishers blog is to provide thought leadership to turn data-oil into a gold mine of opportunities. The blog will highlight case studies of how Big Data is used to bring more editorial relevancy, better audience engagement, and more value to advertising clients.


Meet the bloggers

Greg Doufas
Vice President
Data and Audience Intelligence
Globe and Mail
Toronto, Ontario, Canada
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John M. Lervik
Founder
Cxense
Oslo, Norway
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Dirk Milbou
CRM Manager
De Persgroep Publishing NV
Asse, Belgium
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July 2014 ( 1 )
June 2014 ( 1 )
May 2014 ( 1 )
April 2014 ( 2 )
March 2014 ( 1 )
February 2014 ( 1 )
December 2013 ( 1 )
September 2013 ( 2 )
August 2013 ( 1 )



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