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.
Monetizing Your Unfair Advantage
In the news today was the announcement that BusinessWire, a press release distribution company owned by Warren Buffett’s Berkshire Hathaway, had decided to stop offering direct access to its press releases to high frequency traders. This follows on the heels of a decision by Thomson Reuters not to sell advance access to market-moving economic data that it publishes. I find myself concerned about these decisions. That’s in part because what these two companies were doing was actually quite different. And as you dive into the details, you start to see issues that a broader range of data publishers may ultimately have to confront.
The Thomson Reuters situation involves two indexes: Consumer Confidence and the ISM Manufacturing Index. These are both major indexes that can and do influence the stock market broadly. In both cases, Thomson Reuters had licensed the rights to publish them. Nobody argues that Thomson-Reuters should have the right to monetize these indexes. But it’s one particular aspect of this monetization that raised concerns. Thomson Reuters openly offered to sell access to these indexes either a few seconds or a few minutes before they were released to the public. That’s more than enough time for computerized trading systems to analyze the news and place buy or sell orders accordingly. And by the way, it’s all legal, and Thomson-Reuters wasn’t hiding any of these arrangements. But is it fair?
The BusinessWire case is even more innocuous. BusinessWire is in the business of pushing our press releases far and wide. To that end it offers direct electronic access to anyone who might benefit from it. Some smart traders figured out how to take that innocent feed, process it, and make buy and sell decisions on it very quickly. BusinessWire was just going about its business. Third parties figured out how to profit from their activities, with no help or encouragement from BusinessWire. And while press releases don’t sound that interesting, keep in mind it’s the way many public companies first announce big events such as acquisitions.
I’m not a lawyer, so there may be nuances to this I am missing, but I understand that public policy recognizes the value of a level playing field when it comes to the stock markets, in part to build confidence. And as an individual investor, providing advance peeks to savvy stock traders doesn’t feel right to me. But as an information professional, my view is why not? The entire B2B information industry largely exists to provide unfair advantage. In fact, I know data publishers who have seriously considered variants of “Your Unfair Advantage” as corporate tag lines.
Given the murkiness of the legal issues, I think it’s fair to conclude that both companies stopped these activities primarily for reputational reasons. And that’s important to think about. These two events are very different, but you’d never know that from a quick scan of the headlines they generate. Our products are complex, sophisticated and nuanced. Typically, they are used by a range of users in a range of ways. You can’t – and shouldn’t – police what users do with your data. But you should put some thought into how you position your data and its uses, especially if there is potential to use your data for stock trading. It’s too easy to get painted as the bad guy even if you’ve done nothing wrong.
The bottom line is that as data becomes more powerful and important, we’re all going to receive more scrutiny. And the complexity of our products works against us in the media. That’s why sensitivity to how we present our data products is going to become increasingly important. And if yours is one of the companies considering a tag line that includes the words “unfair advantage,” may I politely suggest a re-think?
Source Data’s True Worth
In my discussion of the Internet of Things (IoT) a few weeks back, I mentioned that there was a big push underway to put sensors in farm fields to collect and monitor soil conditions as a way to optimize fertilizer application, planting dates, etc. But who would be the owner of this information, which everyone in agriculture believes to be exceedingly valuable? Apparently, this is far from decided. An association of farmers, The Farm Bureau, recently testified in Congress that it believes that farmers should have control over this data, and indeed should be paid for providing access to it.
We’ve heard this notion advanced in many different contexts over the past few years. Many consumer advocates maintain that consumers should be compensated by third parties who are accessing their data and generating revenue from it.
Generally, this push for compensation centers on the notion of fairness, but others have suggested it could have motivational value as well: if you offer to pay consumers to voluntarily supply data, more consumers will supply data.
The notion of paying for data certainly makes logical sense, but does it work in practice? Usually not.
The first problem with paying to collect data on any scale is that it is expensive. More times than not, it’s just not an economical approach for the data publisher. And while the aggregate cost is large, the amount an individual typically receives is somewhere between small and tiny which really removes its motivational value.
The other issue (and I’ve seen this first-hand) is the perception of value. Offer someone $1 for their data, and they immediately assume it is worth $10. True, the data is valuable, but only once aggregated. Individual data points in fact aren’t worth very much at all. But try arguing this nuance to the marketplace. It’s hard.
I still get postal mail surveys with the famous “guilt dollar” enclosed. This is a form of paying for data, but it drives, as noted, off guilt, which means undependable results. Further, these payments are made to assure an adequate aggregate response: whether or not you in particular respond to the survey really doesn’t matter. It’s a different situation for, say, a data publisher trying to collect retail store sales data. Not having data from Wal-Mart really does matter.
Outside of the research world, I just haven’t seen many successful examples of data publishers paying to collect primary source data. When a data publisher does feel a need to provide an incentive, it’s almost always in the form of some limited access to the aggregated data. That makes sense because that’s when the data becomes most valuable: once aggregated. And supplying users with a taste of your valuable data often results in them purchasing more of it from you.
Read More
The Billion Prices Project
Last week, I discussed how the Internet of Things creates all sorts of potential opportunities to create highly valuable, highly granular data. The Billion Prices Project, which is based at MIT, provides another route to the same result. Summarized very simply, two MIT professors, Alberto Cavallo and Roberto Rigobon, collect data from hundreds of online retailers all over the world to build a massive database of product-level pricing data, updated daily. It’s an analytical goldmine that can be applied to solve a broad range of problems.
One obvious example is the measurement of inflation. Currently, the U.S. Government develops its Consumer Price Index inflation data the old fashioned way: mail, phone and field surveys. And inherently, this process is slow. Contrast that with the Billion Price Project that can measure inflation on a daily basis, and do so for a large number of countries.
But measuring inflation is just the beginning. The Billion Prices Project is exploring a range of intriguing questions, such as the premiums that are charged for organic foods and the impact of exchange rates on pricing. You’re really only limited by your specific business information needs – and your imagination.
The Billion Prices Project also offers some useful insights for data publishers. First, the underlying data is scraped from websites. The Billion Prices Project didn’t ask for it or pay for it. That means you can build huge datasets quickly and economically. Secondly, the dataset is significantly incomplete. For example, it entirely ignores the huge service sector of the economy. But’s it’s better than the existing dataset in many ways, and that’s what really matters.
When considering building a database, new web extraction technology gives you the ability to build massive, useful and high quality datasets quickly and economically. And as we have seen time after time, the old aphorism, “don’t let the perfect be the enemy of the good” still holds true. If you can do better than what’s currently available, you generally have an opportunity. Don’t focus on what you can’t get. Instead, focus on whether what you can get meaningfully advances the ball.