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.

Score Big with Rankings

We’re all familiar with the growing influence of user-generated rating sites such as Yelp and TripAdvisor. The power of these sites to determine which businesses thrive, while others struggle to stay in business, is well documented. Without a doubt, there is power in ratings and rankings. But you could be excused for thinking that this is all a very B2C phenomenon: consumers, retailers, restaurants and the like. After all, this is where all the noise and press reporting has been focused. But there are strong B2B opportunities in the world of ratings and rankings. And these opportunities don’t need to be at the scale of a Yelp or a TripAdvisor. Indeed, a simple list of the top players in a market can be absurdly influential, and where there is power and influence, there is usually also opportunity.

Consider this one compelling example. Bloomberg reports that two companies, Goldman Sachs and Morgan Stanley, were willing to forego millions of dollars in fees just to get credit as having worked on several large M&A deals. This “credit” in turn pushed the companies higher on a listing (often referred to as a league table) of the companies handling the most M&A transactions, and published by a third-party company called Dealogic.

Step back and consider, even savor, this for a moment. Two prestigious, successful and extremely savvy companies that hardly need more publicity or name recognition, are willing to trade millions of dollars in fees to push themselves higher in a list that ranks transaction activity. Clearly what’s going on is that these companies feel that the bragging rights and marketing value of ranking highly on this list will be worth many more millions that those they walked away from.

Now you may be noting that Dealogic, the transaction platform and data company behind this league table, didn’t see any of the millions of dollars. But monetization isn’t always direct. And in the case of the league table in particular, it shouldn’t be.

But let’s tally up the benefits to Dealogic. It certainly needs name recognition more than the big name companies in its ranking, and it gets that recognition in spades as the producer of this important list that drives deal activity. Secondly, the league table is inherently a highly summarized product. Dealogic can easily sell the underlying data at a premium price to those who want to do more granular analysis. Third, the league table has a halo effect on other Dealogic products. As a producer of critical industry data, every Wall Street player will be receptive to hear about all the other products and services that Dealogic offers. Indeed, many of these Wall Street players will be regularly reaching out to Dealogic to make sure they are properly reflected in these league tables. As a neutral producer of this relatively small dataset, Dealogic has built strong market authority and credibility, and is able to reach and sell to the biggest names on Wall Street more as an equal than an obscure vendor.

The power of rankings and ratings is undeniable. But the really important lesson here is that the rankings don’t have to be elaborate, and the market doesn’t have to be huge for them to yield outsized benefit.

The Hidden Data in Invoices

The data business is one of creativity, and what could be more creative than asking companies to send you their invoices and other types of billing data so you can get them into a database and sell the aggregate results back to them? Now, why would something like this ever make sense? Well, in many industries, there is nothing more useful or important than pricing information. Yet the pricing information that many companies publish (if they publish it at all) is almost always the list price. And in many industries, the list price is close to meaningless, since every customer will have a special deal and varying discount. So how do you develop a database of what companies are really paying for specific products and services? Ask to see their invoices!

There are lots of spins on this intriguing model, so let’s take a look…

The question already on your mind quite likely is, “Why would any company let me look at its invoices?” The simple answer is what I often refer to as “strength in numbers.” A company will happily give up its individual data (properly secured and anonymized) in exchange for access to the aggregate results. And they’ll pay for that access, and that’s exactly the play here.

A great example of collecting, normalizing and reporting out information drawn directly from the internal systems of advertising agencies can be found in a company called SQAD. Its NetCosts product collects data for media purchases from advertising agencies worldwide, generating what may be the only honest look at what broadcasters are charging for media buys, and even what they have charged into the future. You can immediately see how valuable this information can be.

Thomson West has a product called Peer Monitor that does the same thing in a slightly different way: rather than work with the recipients of invoices, it works with the senders of invoices, in this case law firms, to collect similar data to be used in similar ways.

If it sounds like a lot of work, it is, or probably was. That’s because SQAD now receives most of its purchase data digitally, through interfaces with client systems. And while those interfaces were doubtless painful to build, at the same time SQAD has built an almost impregnable franchise, because as long as SQAD doesn’t get greedy, nobody can justify the time, cost and pain to try to compete with them. The same holds true for Peer Monitor.

There’s also what might be called the pre-order model. Here, your objective is to gather RFPs and proposals to get a true look at what companies are proposing to charge for their products. One advantage of picking up information at this stage is that there is often a lot more detail, allowing you to collect even more granular product price data. A great example of this model is MD Buyline, a company that collects price quotes and proposals on medical equipment to build a high-accuracy pricing database.

Lots of variant models, but the objective is the same: gather data on actual prices being charged in the marketplace, the more granular the better. Your job is to aggregate, normalize, and report back to the marketplace, while protecting the anonymity of those who participate.

It’s important to note that the need for pricing data isn’t equally compelling in every market. The dynamic seems to be a reasonably large pool of both buyers and sellers, and a solid tradition of haggling over price. It won’t work for everyone, but it’s certainly worth considering if it would work in your market.