Take Action on Actionable Data

Actionable information has long been a  cornerstone of infocommerce, and recent stats from MediaRadar, a 2011 Model of Excellence company which will be featured again when DataContent moves to Miami as part of the all-new Business & Information Media Summit, provides a fine example, and in a way that hits home for many of us. Built on an understanding of advertising sales workflow, MediaRadar makes full use of its database to yield a highly valuable product. At core, MediaRadar provides sales leads to media companies by tracking who is advertising where. At a higher value, it offers benchmarking to its clients by allowing them to easily see how they are doing versus the competition. At the highest value, it creates an analytical layer, tapping into its data at an aggregate level to find trends and insights. Consider these recently released MediaRadar insights on email advertising:

• As annoying as everyone says email is, advertisers like it. Over a 12 month period, MediaRadar identified 19,915 distinct B2B advertisers who bought an email advertising program
• A third of advertisers buy only email advertising
• Those advertisers who buy via email rarely buy the full range of media options. For example, 54% of  advertisers buy print advertising with email ads,  and 47% of advertisers buy other digital advertising along with email ads
• In a remarkable trend, 44% of email advertising now being sent by B2B media companies are dedicated email blasts, and the trend appears to be increasing
• A lot of email-only advertisers fly under radar and are hard to identify
• Response rates vary significantly by market
• 30% of e-newsletters carry only a single ad; 66% carry from 1 to 3 ads, and 17% carry 5 or more ads

Most data products have this multi-dimensional potential, which can often open up new revenue and even new markets. Is your dataset working as hard for you as it can?

P.S. – Speaking of data and analytics, I would like to ask you to take just a few minutes to complete our new email benchmarking survey. It’s quick, and you’ll not only get a warm feeling of satisfaction from helping to make the entire industry smarter, you’ll also get a free executive summary and key data charts.

Everything Has Its Price

An excerpt from a new book by former Time Inc. executive Walter Isaacson makes a point that is still not fully appreciated by everyone in the content business:

“At Time Inc., we initially planned to charge a small fee or subscription, but Madison Avenue ad buyers were so enthralled by the new medium that they flocked to our building offering to buy the banner ads we had developed for our sites. Thus we and other journalism enterprises decided that it was best to make our content free and garner as many eyeballs as we could for eager advertisers”

Isaacson confirms an absolutely critical insight: it’s not that “information wants to be free.” The reality is that many of the largest content companies chose to make information free. And with no history to provide a guide, and a sense of a giant gold rush and land grab underway, other content producers followed suit. Soon enough, pretty much all content on the web was free, and guess what: users decided they liked things that way, so much so that any content producer brave enough to offer paid content experienced derision from other content producers and almost militant pushback from users.

All this led to the sorry state of affairs where advertisers have moved much of their advertising dollars elsewhere, and users have been fully conditioned to expect their content for free. Intriguingly, what saved most data publishers from this fate was the fact they typically had little in the way of advertising revenues. Thus, offering free online content was clearly nothing more than an express lane to bankruptcy, and this gave them the backbone to continue to charge for their content. And they are all better off for it.

Even today, it remains true that you can make more money faster selling advertising than selling subscriptions. And that’s why many media companies, with their executives steeped in advertising sales culture, still can’t get fully comfortable with the notion of paid content. Subscription-based businesses are desirable, durable and diversified in terms of the customer base, but these businesses build slowly. Indeed, almost all the characteristics that make subscription-based businesses attractive as businesses make them unattractive to those who grew up selling advertising. It’s truly a cultural issue.

All this leads me to think that the emergence of the freemium and metered models is critical to the future of many content publishers. More and more websites are sporting “plus” and “pro” versions that offer different and supplemental content on a paid basis. The publisher keeps a portion of its content for free, the better to aid discovery and get the user hooked. And a portion of the audience will pay to get even more of that content.

Just as we trained users to expect all content for free, we now must begin the slow but essential process of training them that going forward only some content will be free. You can also argue that this shift simultaneously weans both users and publishing executives off of free content. There are still plenty of eyeballs to sell while at the same time the publishers begin to diversify their revenue streams.

And for those data publishers that have always charged for their content online, I will say just two words: carry on.

Smarter Data is Right Inside the Box

Most of us are at least somewhat familiar with the concept of the “sales trigger,” something I lump into a larger category I call “inferential data.” If you’re not familiar with the concept, what we are talking about is taking a fact, for example that a company has just moved, and drawing inferences from that fact. We can infer from a recent company move that the company in question is likely to imminently be in the market for a host of new vendors for a whole range of mundane but important office requirements. So if we learn about this company move right after it happens (or, ideally, right before it happens), we have an event that will trigger a number of sales opportunities, hence the name “sales trigger.” But as I noted above, sales triggers in my view are a subset of inferential data. I say that because sales triggers tend to be rather basic and obvious, while true inferential data can get extremely nuanced and powerful, especially when you start analyzing multiple facts and drawing conclusions from them. Tech-savvy folks refer to these multiple input streams as “signals.”

Let’s go back to our example above. The company has moved. That means they likely need a new coffee service and cleaning service, among others. That’s fine as far as it goes. But let’s go deeper. Let’s take the company’s old address and new address, and bounce them against a commercial property database. If the company is moving from $20/square foot space to $50/square foot space, chances are this company is doing well. At a minimum, this makes for a more interesting prospect for coffee service vendors. But it can also be the basis for assigning a company a “high growth” flag, making it interesting to a much broader range of vendors, many of whom will pay a premium to learn about such companies.

Or perhaps we know this company has changed addresses three times in five years. We could infer from this either extremely high growth or extreme financial distress. Since this relocation signal doesn’t give us enough clarity, we need to marry it with other signals such as number of employees during the same period, or the cost of the space or amount of square feet leased. Of course, signals go far beyond real estate. If the company had a new product launch or acquisition during that period, these signals would suggest the address changes signify rapid growth.

You can see the potential power in inferential data, as well as the complexity. That’s because in the business of signals, the more the better. Pretty soon, you’re in the world of Big Data, and you’ll also need the analytical horsepower to make sense of all these data signals, and to test your assumptions. It’s not a small job to get it right.

That’s why I was excited to learn a company called – what else – Infer. Infer collects and interprets signals to help score sales leads. And it sells this service to anyone who wants to integrate it with their existing applications. It’s essentially SaaS for lead scoring. Intriguingly, Infer licenses data from numerous data providers to get critical signals it needs.

Inferential data makes any data it is added to smarter, which in turn makes that data more valuable. Many publishers have latent inferential data they can make use of, but for others, watch out for those “signals in a box” products from what I suspect will be a growing number of vendors in this space. It’s the smart thing to do.

Finding Business in Discovery

I have spent a lot of time recently with the website Artsy.com. That’s in part because I find it fascinating, and in part because it is trying to accomplish so many things at once. At one level, it’s a fine art discovery service. Artsy has partnered with art galleries nationwide to allow them to post images and descriptions of artwork for sale. That’s a great convenience for someone looking to buy art, but it’s not a new idea. A number of services are already offering similar services.

Where Artsy separates itself from the pack is with its “Art Genome Project.” Essentially, Artsy is categorizing artwork against a taxonomy much as Pandora has done for music. And that’s where the magic happens. If you find a piece of art you like, you can easily explore other artwork with similar characteristics. That’s no small feat when you consider that Artsy already catalogs over 125,000 pieces of art from over 25,000 artists – and it’s still only scratching the surface of what’s available.

Artsy is what might be characterized as a next generation discovery tool. Certainly, there’s value in aggregating artwork from galleries all around the country. But the big breakthrough here is being able to point to a single piece of art and say “I’d like to be able to see art in a similar style by different artists.” That’s a powerful step forward on discovery that benefits both the art buyer and the artist. If you’ve got a market where there are huge numbers of manufacturers, little standardization and prodigious output (music, art and wine are great examples), there’s a next generation discovery opportunity waiting for you.

But what about Amazon and Netflix, you may ask? These companies too have done a lot to improve discovery, but in a very different way. These companies don’t look so much at the product itself as who bought the product and in what combinations. This is a powerful and effective approach, but what Pandora and Artsy did was build taxonomies based on inherent product characteristics and then committed to manually classifying products against them, a significant exercise in metadata creation, but one that yields powerful, proprietary results.

The other interesting aspect of Artsy is one that I initially viewed as an overreach. Artsy includes not only artwork for sale, but artwork in museums and private collections. Certainly this makes artsy more attractive to art lovers, art students and others, but it seemed to confuse somewhat the art buying experience. This is largely explained by the fact that Artsy has a mission-driven aspect to it, but there may be a huge business opportunity here as well.

If Artsy is doing all the work of classifying and posting images of artwork held in museums and private collections, why not go one step further and become an international registry of artwork? Much of the value of art is tied to its provenance – its history of ownership. Artsy could become a central registry, collecting a small fee to re-register a piece of art every time it changed hands. It could then sell subscription-based access to this database to auction houses and galleries. Lots of details to be worked out to be sure, but this notion is only a small jump from Artsy’s current ambitions.

That’s what makes the data business so fascinating and lucrative: there are infinite opportunities to make money. All it takes is some creativity, and in this case, re-framing an existing business model.

Data Is Not a Zero Sum Game

Back in ancient times, when print directories walked the earth, one of the most surprising things I learned was that people were willing to pay meaningful amounts of money for information that wasn’t very good. And this wasn’t reluctant willingness, these buyers were just short of cheerful. How many businesses exist where your customer tells you your product stinks, and in the same breath excuses you because what you are doing “is such hard work?” One more reason to love the data business! But, you may be thinking, those days disappeared with print directories. I’m not so sure about that though. What I am seeing is a fascinating bifurcation of the market. On the one hand, you have laser-focused data products with pristine datasets that command enormous prices. On the other hand, you have these massive databases, often consisting of harvested data that have a lot of similar characteristics to the old print directories. The primary difference is one of scale.

Think about the number of new data products with one million, ten million or even 100 million records in them. At such a scale, they are almost certainly relying heavily if not exclusively on technology. And that means records will be misclassified, missing key fields, or a confusing jumble because the source content couldn’t be normalized properly. And let’s not forget that companies harvesting website data inherently know nothing about the estimated 30% of all companies with placeholder websites or no websites at all. Yet what you hear from paid subscribers to these databases is that familiar refrain, “it’s not great, but it’s good enough for what I’m doing.”

At the same time, we are seeing a number of much smaller, deeper and more precise data products entering the market as well. And these products tend to offer analysis and workflow capabilities, and often feed high stakes business decisions and high ticket selling.

Are we poised for a shake-out? Will there be winners and losers? I think there will be room for everybody. Having the most data doesn’t make you an automatic winner. Having the deepest data doesn’t knock out all your competitors. It all comes down to your intended market, and how you bring your data to market.

There still seems to be a large and active value segment of the market, those who will be happy with “good enough” data in exchange for a reasonable price. At the same time, there are customers who will pay remarkably high prices for data they can depend on, because it’s driving some critical business activity. And to the extent you differentiate your data through your user interface and data manipulation tools, you can often define still another market that wants to powerfully interact with your data.

My take-away is that the data business is increasingly not about winners and losers. Multiple companies with largely similar data can exist and succeed by having differing price points, levels of coverage and degrees of accuracy. The front-end you provide to your data can be customized to appeal to specific market segments as well.

It’s hard to definitively assess your competitors in the infinitely malleable world of data, but at the same time it’s increasingly clear that this is not even close to being a winner-takes-all business. This does not imply that you can be sloppy about your business; indeed it makes it all the more important you deeply understand your customers, how they are using your data, and where you fit into the market.