Monetizing the Middle

Regular readers know that I am focused (fixated?) on a concept I call “central market position.” I use this term to describe companies (typically media and data companies) that occupy a trusted, established and neutral position in the markets they serve. Central market position is important because it can be monetized.

Traditional data publishers collect data themselves, whether via manual or automated means. They scrub it, organize it and otherwise add value to it, then turn around and sell it This is a solid, established and successful model, but companies with central market position have a much larger opportunity.

With central market position, you have the potential to do things that nobody else can, things that would otherwise be viewed as impossible. You can, for example, ask all the companies in your industry to share their customer lists with you, their sales data, employee information, their prospect lists – practically anything. How is this possible?

Well, two conditions must exist. First, this privileged information will only be provided if it is directly used to solve a major need or problem in the marketplace. Second, the intermediary who will be handling the information has to be established, trusted and neutral. The natural intermediary is a company that has a central market position.

Consider a product called PeerMonitor, a Thomson Reuters product. PeerMonitor literally hooks into the accounting software of participating law firms and sucks out all their billing information, right down to line item detail. Why would any law firm allow this? Because the need to know the going market rate for, say, a bankruptcy attorney in Atlanta far outweighs the reflexive need to protect information like this.

Consider also a company called SQAD in the media world. Advertising agencies electronically submit their purchase orders to SQAD. Are they giving away key company secrets by doing this? Yes, but it’s worth it because the data that comes back to these agencies – namely the real prices being paid for television and radio advertising – is more than worth it. And SQAD, as discussed, is a central, trusted neutral player that normalizes, de-identifies and aggregates the data in such a way that companies can give away their secrets without giving away their secrets. It works for everyone involved. Another interesting company is MDBuyline. Here, participating hospitals submit price quotes for medical devices and other hospital equipment. MDBuyline aggregates the data so that all participating hospitals can see the true going rates for medical equipment, not the meaningless list price. Again, the benefit is sufficiently large to justify supplying confidential information to a third party.

What you need to do is recognize your central market position, and start identifying market needs you can address as the central collector and aggregator of critical industry data that would otherwise never be shared. Trust me, the opportunities are endless. 

Getting Inside the Head of a Sales Prospect

B2B prospect identification and targeting has come a long way in the last few years. Things that once seemed impossible are now taken for granted. We can now identify with some precision when someone in a company is actively in the market for a new product. We can take this purchase interest information and bump it against company firmographic data to help qualify and score this individual as a lead. We can easily review the business contacts of this person to see if we know people in common. We can view the work history of this person, and even order a deep background report based on public domain data. We can order an organization chart for this person’s company to understand where he or she fits in the hierarchy of the business, as well as to identify other possible purchase influencers. Pretty impressive, right?

But what if we could go further? What if we could get something close to a psychological profile of the prospect to better understand how to interact with that person to advance the sales conversation. You probably won’t be surprised to hear that there is a company working on it.

The company is called CaliberMind. By mining public domain data, email exchanges with the prospect and even recorded telephone conversations with the prospect (prior consent to recording is required by Caliber Mind), CaliberMind can provide a salesperson with deep and unique insights into the personality and motivations of the prospect, along with recommendations on how to engage them most productively.

Yes, there is an inherently creepy aspect to this, but CaliberMind stresses it only works with public information and freely shared information between the salesperson and prospect. What it does, besides mining these information nuggets, is interprets them in order to build a deep profile of the prospect and specific tips on how to accelerate the sales process. Not surprisingly, the company was founded by former intelligence agents.

This is cutting-edge stuff from a young company, but in many respects it seems to be the logical culmination of the various selling tools that have been introduced over the past few years. CaliberMind is leveraging both increased computing power and the explosion of public domain information to help inform and accelerate the B2B sales selling process. CaliberMind also represents just one more piece of evidence that data opportunities are everywhere – and that the tools needed to collect, process and apply data continue to get more and more powerful.

Can You Over-Monetize?

To avoid accusations of commercial blasphemy, I am going to pose this as a question, not a statement: can you over-monetize a data product?

Consider the online real estate listings databases. There are lots of them, all engaged in a fierce battle to the death. They make their money selling listing upgrades to real estate agents, a hotly competitive and demanding group. The product they are selling is homes that can easily cost $1 million and more, with very sizable commission dollars at stake. In such a high ticket and fiercely competitive market, would you want to junk up the user experience with irrelevant advertising, and annoy your real estate agent customers by distracting users from the listings they are paying to enhance? The answer appears to be yes.

Several of these sites have now been designed to display programmatic ads. With all it takes to attract a live buyer to your site, do you really want to risk that buyer clicking on an ad for a local car dealer and leaving your site entirely? Do you want to intersperse listings of homes with ads for mortgages when your primary source of revenue is real estate agents who badly want your site visitors to look at their listings?

You know the saying: real estate is all about “location, location, location.” Does it make sense then that when a potential buyer clicks on a map icon to see where a home is located, she is presented with a map cluttered with logos indicating the location of nearby State Farm insurance offices? Does anyone buy a house based on proximity to an insurance agent? Doubtless someone thought this was a clever marketing gambit, but it distracts, confuses and possibly annoys the potential buyer.

The photo slideshows that are the critical core of each home listing are now increasingly cluttered with advertising. If I was a real estate agent paying to upgrade a listing only to find it was chock full of ads, I’d be furious. I want prospects looking at pictures for the home I am selling, not distracted or annoyed by irrelevant advertising from third parties.

A lot of this comes down to the degradation of the user experience. But in some cases, it’s an even bigger issue: it’s a problem of the data publisher forgetting who they are serving and in some cases, why they are even in business. A little bit of incremental revenue can sometimes have a very high cost attached. And the guiding rule of all things online remains the same: just because you can, doesn’t mean you should.

Get it Right ... Or Else!

Why is data getting so much attention these days? Why is it such a good business? Why is it so profitable? Well, there are numerous reasons, but the one I’d like to highlight today is that increasingly, data matters.

What do I mean by that? Simply that data, to a degree you don’t see with other forms of content, gets relied on to make serious decisions, some of which have significant, business, economic and personal impact. Some people (many of them rich data publishers) have understood this for a long time. For others, this insight is a new one. And one consequence of data’s growing importance is that it is increasingly the focus of lawmakers. Consider just a few examples:

In a true "only in Hollywood" moment, the state of California now has a law that says data providers cannot publish the ages of people in the entertainment industry. Yes, actors have long been skittish about putting their ages out there, but in the old days, they simply lied about their ages. Now, they have the force of law behind them. The ostensible purpose of this law is to help prevent age discrimination, however, the law also specifically includes everyone in the videogame industry as well, so go figure.

Across the pond, UK financial regulators have taken Morningstar, the mutual funds data company to court. Its offense? A number of the funds to which it gave high ratings ended up under-performing relative to their benchmarks. Apparently your predictions are now required to always be accurate. Of course, if Morningstar could identify top-performing funds with 100% accuracy, my strong recommendation to Morningstar would be to get out of the data business and into the investing business, pronto.

We also have the example of health insurance company physician directories. Every health plan publishes a directory of participating physicians, and in many cases, these directories are woefully inaccurate. Examples abound of plan directories with physicians who have left the plan, moved offices (sometimes hundreds of miles away), retired and even died. This would be just another everyday annoyance except for the fact that many people select their health plans, and spend thousands of dollars, based on the network of physicians a health plan claims to offer. The federal government has stepped in on this one, and not to be outdone, California (surprise!) has its own legislation covering physician directories.

These examples are just the tip of the iceberg. Consider all the various laws around credit data, for example.

Back to my original point, all these rules and laws simply illustrate that data at its core is all about helping people to identify, select, assess and decide. And as databases proliferate, so does their influence and impact. There is power in data, which is why, increasingly, data producers are being held to higher standards of quality and accuracy. While painful for some, in the aggregate, good data is good for all of us.

Professionally Rating Professionals

Online rating systems are ubiquitous and powerful. But not everything is easy to rate. Restaurants and hotels are pretty easy, in part because the people rating them are consumers who are simply supplying their impressions and opinions. In these scenarios, everyone who rates and comments is equally (un)informed. People know what they know and think what they think and rate accordingly. The value tends not to be in the individual comments (trust me, I’ve read more than my fair share!) but rather in the aggregate view. If a majority of people hate something, you stand very little chance of figuring out exactly what is causing the hate, but at least you know something is wrong. There is still good utility in that.

This could explain why it’s been so much harder for ratings site for professionals to take hold. There urgent need for consumers to get better insight into lawyers, doctors and other professionals is huge. But there are two major complicating factors it: knowledge asymmetry and personal relationships.

First, how does a consumer rate the knowledge and quality of a lawyer? They really can’t, which is why a lot of legal ratings devolve to “great lawyer” (read: she won my case) or “lousy lawyer (read: she lost my case). You also never see nuance such as “she did a great job despite my lousy case.” Services like Avvo have built a name rating lawyers based on public domain data. It’s a start , but it doesn’t provide the color and insight of crowdsourced consumer reviews.

Doctors are a special case. Beyond the knowledge asymmetry, many people feel a personal connection to their physician. That creates a “my doctor can do no wrong” mentality that doesn’t leave much room for ratings services. Moreover, physicians have proven to have very thin skins when it comes to being rated: doctors have been much more active in trying to get patients to sign legal agreements prohibiting their patients from rating them, much more so than lawyers.

One interesting workaround is to ask doctors to rate other doctors and lawyers to rate other lawyers. It’s not a new concept: in fact, Martindale-Hubbell has been rating lawyers this way for 100 years. More recently, Castle-Connelly has built a name with a similar service for doctors. What’s fascinating, though, is the Martindale system, originally built for lawyers to use in picking other lawyers, was something of a dud when offered to consumers. It seems that because consumers didn’t trust lawyers generally, they certainly weren’t going to trust a system where lawyers rated other lawyers. The unexpected outcome of the Castle-Connelly rating system is that doctors tended to give the highest ratings to academic and research physicians, top doctors to be sure, but ones that typically didn’t see patients.

We’re making strides, but there’s still no clear solution to this fascinating problem and massive business opportunity, one that’s been resistant to a technological solution precisely because of the human element.