A Plug and Play Publishing Platform?

Dun & Bradstreet Credibility Corporation, an independent company with such an extensive relationship with Dun & Bradstreet that it was even granted use of the vaunted D&B name, has been targeting smaller businesses with not only traditional D&B credit products, but a beta offering of what might be called a “next generation credit rating,” a so-called credibility score that examines the company from a number of different non-financial perspectives, yielding a letter grade and presumably an online trust mark that companies can use to build confidence with both suppliers and customers. It’s a clever and ambitious concept. And there are some serious resources behind this venture: Boston-based private equity firm Great Hill Partners is backing the venture with in excess of $100 million. In an apparently related development, D&B Credibility recently announced the launch of the “Credibility Review Business Marketplace,” an innovative move to partner with publishers to extend the reach of its credibility ratings, by turning B2B data publishers into a sales channel. D&B Credibility indicates a number of publishers have already signed onto this program.

I’m still waiting to get full details on this program from the company, leaving me free to speculate wildly, a favorite pastime. Here’s what I picture:

D&B Credibility has licensed access to the full D&B business database, and this provides a content backbone to the initiative. When it emerges from beta, D&B Credibility will presumably move to aggressively sell credibility scores to smaller businesses. Each sale yields a richly detailed business profile (part of the score involves “transparency,” so participating companies are obliged to supply all sorts of useful information – smart!) that the participating company is highly motivated to keep current (yielding high leverage user-generated content). These enhanced listings are added to the basic listings in the content backbone.

To accelerate adoption of the credibility scores, D&B Credibility will partner with publishers on an intriguing offer: a self-maintaining database offering a growing number of credibility scores, that the publisher can access for free in exchange for selling credibility scores (and anything else it wants) to companies in its vertical market.

As I envision it, publishers would simply flag the companies they want to appear in their vertical market buying guides, getting in effect a customized view of the larger database. The publisher codes each company against its own vertical market taxonomy, and presto-whammo, it’s got a high quality database that costs almost nothing to build or maintain. All it has to do is sell the credibility scores and other advertising to companies that it has flagged. For trade magazine publishers in particular, selling ads is a true core competency, where database development and maintenance is not.

What’s in this for D&B Credibility? It gets a revenue cut from every credibility score a publisher sells. It gets all the company information being collected (everything goes into its backbone database), and it gets valuable help in building momentum and acceptance for its scores.

Is this a good deal for publishers? When it comes to vertical market buying guides, the majority of publishers have unevenly maintained databases with limited company information. This approach not only goes a long way to solving the twin issues of data quality and data depth, it also provides the ability to sell a new and useful offering – a B2B trust mark.

Fascinating stuff, and well worth watching as the product rolls out from beta.

LinkedIn: The B2B iTunes Store?

Regular readers know that I am a big fan of LinkedIn. My interest, of course, has been in its database, which may be the most important biographical database ever created. But LinkedIn can’t rest on its laurels. That’s because it depends on user generated content, and the biggest threat to LinkedIn is if users start questioning its value. LinkedIn is already jokingly referred to by many as “the boring social network.” And while LinkedIn now benefits from lots of buzz and momentum, it needs to remain fresh in the eyes of users and give them a reason to interact with LinkedIn as frequently as possible, and to continue to deliver back some tangible value as well. LinkedIn thinks it can address all three of these requirements with content. As Deep Nishar, the company’s SVP of Products and User Experience puts it:

“We believe LinkedIn can be the definitive professional publishing platform – where all professionals come to consume content and where publishers come to share their content.”

So is LinkedIn positioning itself to become sort of an iTunes for professional content?

As of now, it’s hard to see how LinkedIn as a publishing platform will evolve – and the company itself may well not have a full vision. But I have already seen conference companies with their entire businesses based on LinkedIn groups. It’s entirely possible we will see new trade publications where the entire audience is composed of LinkedIn members, delivered via LinkedIn, with LinkedIn potentially supplying those members by somehow matching them to relevant publications.

But with this intriguing vision comes lots of equally intriguing – and potentially worrisome - questions. Will publishers be able to distribute advertising via the LinkedIn platform? Who will own the audience? Will LinkedIn seek transactional revenue of some sort from publishers? The more you look at it, the more you see the potential for a B2B ITunes Store, with all the attendant issues.

We may be at the early stages of a truly transformational shift in business and professional publishing as LinkedIn begins leveraging its massive audience of business people to move into content distribution. Exactly how it chooses to do so could have profound implications for B2B publishers.

Cleaning Up by Cleaning Up

 

Meet Equilar, a $20+ million data publisher that sells publicly available SEC data. Yup, get it free from the SEC, or buy it through Equilar. How does that work?

Well, as data publishers well know, an approach like this usually doesn't work, unless you find a way to add value. And Equilar does this, in spades. You see, Equilar deals in executive compensation benchmarking data, where making it comparable and getting the data right is the basis for an incredible business, and getting it wrong is the basis for going out of business. That’s the challenge and opportunity that exists in many public datasets today, and there is plenty of opportunity still to be mined by savvy companies such as Equilar that look for highly focused data needs and meet them well.

Top executives at publicly-traded companies need to justify their compensation to a number of different constituencies. The best way to do this is to benchmark their compensation against peer companies. But with the complexity of executive compensation plans these days, that’s easier said than done. Equilar saw the need and set out to create a flexible, normalized database of executive compensation data points for benchmarking purposes.

Equilar has done such a good job meeting this need in the marketplace that it faces a problem many of us think we’d like to have – its flagship product has essentially captured all of its core market, and now needs to look elsewhere to find continued growth.

So how does a company that has executed so brilliantly come at a challenge like this? How does it look at opportunities? What does it see as the challenges? Take the opportunity to hear the answers directly from Equilar CEO David Chun, when he provides a company case study at our upcoming Subscription Site Summit this May 8-9 in New York City. There’s limited seating, so sign up today to meet David and other CEO’s from subscription content companies and industry experts that will have you filling notebook after notebook with actionable insights you can use to clean up in your market.

Does Correlation Trump Causation?

A new book called Big Data: A Revolution That Will Transform How We Live, Work and Think, written by Viktor Mayer-Schonberger of Oxford and Kenneth Cukier of The Economist, raises some intriguing and provocative issues for data publishers.  Among  them is this one:

 “…society will need to shed some of its obsession for causality in exchange for simple correlation: not knowing why but only what.”

The underlying thinking as I understand it is that Big Data, because it can analyze and yield insight from millions or even billions of data points, is both incredibly powerful and uncannily accurate, in large part because of the massive sample sizes involved.

But are all Big Data insights created equal?

Without a doubt, some insights from Big Data analytics yields useful and low-risk results. If Big Data, for example, were to determine that from a price perspective, the best time to purchase an airline ticket is 11 days prior to departure, I have both useful information and not a care in the world about causation. Ironically, in this example, Big Data would be used to outsmart airline Big Data analytics that are trying to optimize revenues through variable pricing.

But riding solely on correlation often creates situations where heavy-handed or even ridiculous steps would be necessary to act on Big Data insights. Consider a vexing issue such as alcoholism. What if we learned through Big Data analytics that left-handed males who played tennis and drove red cars had an unusually high propensity to become alcoholics? Correlation identifies the problem, but it doesn’t provide much of a solution. Do we ban alcohol for this entire group? Do we tell left-handed males that they can either play tennis or drive a red car, but not both? Does breaking the correlative pattern actually work to prevent the correlated result? Things can get strange and confusing very quickly when you rely entirely on correlation.

Am I calling into question the value of Big Data analytics? Not at all. The ability to powerfully analyze massive data sets will be beneficial to all of us, in many different ways. But to suggest that Big Data correlations can largely supplant causation research plays into the Big Data hype by suggesting it is a pat, “plug and play” solution to all problems. Big Data can very usefully shape and define causal research, but there are numerous situations where it can’t simply replace it.

The lesson here is that while you should embrace Big Data and its big potential, remain objective and ask tough questions to separate Big Data from Big Hype because lately, the two have been tightly correlated.

Walking Around Money

A young company called Placed is deep into Big Data analytics, but with a twist: it marries customer data with its own proprietary data to yield insights into customer behavior. Essentially, Placed wants to provide context around how customers use the mobile applications of its clients, for example, when do they use the app and where do they use it?

The “where” part of the analysis is what’s interesting. Placed could simply spit back to its clients that its customers are in certain ZIP codes or other dry demographics – interesting, like so many analytics reports are, but not particularly useful.

Instead Placed marries customer location with its own proprietary database of places – named stores, major buildings, points of interest. By connecting the two, Placed can tell its clients where mobile use of its app is occurring. For example, if a client’s customers utilize its mobile app in a competitor’s store, it might suggest competitive price comparisons. Knowing its customers frequent Starbucks and nightclubs might influence the clients’ marketing strategy or advertising campaign design. Knowing that the app is used most often when someone is walking (yes, Placed can tell you that) can be important for user interface design – you get the idea.

And therein lies an important insight. There are an endless number of companies offering Big Data analytics capabilities. But almost all of them expect their customers to bring both the problem and the data. That’s a sure recipe for commoditization, and as analytics software evolve, it’s also certain that the companies with the biggest analytics needs will decide to do the work themselves.

Solution? Big Data analytics players should bring proprietary data to the party. Placed is a perfect case study. It differentiates itself by providing answers others can’t. It adds value to its analytics by integrating proprietary and licensed data with customer data and its own optimized analytical tools. As I discussed in my presentation at DataContent 2012, there are lots of ways publishers can profit from the Big Data revolution -- even if they don't have big data themselves.

In a market where companies like Placed can make money by tracking people walking around, it behooves data publishers to walk around to some of these Big Data analytics players and suggest data partnerships that will help them stand out from the crowd.