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Data as the Decider

I have discussed before how data providers can leverage their central, neutral market positions to collect highly valuable data that otherwise couldn’t be collected. Examples abound of data providers that have convinced companies to provide them with their information crown jewels – sales data, pricing data and the like – in return for getting it back (on a paid or unpaid basis) in aggregate, anonymized form. Fundamentally, the companies realize that their data, no matter how sensitive they consider it to be, has even more value to them when combined with or compared to a larger set of similar data. These situations are wonderful opportunities for data publishers, and they are cropping up more and more as companies get better about organizing their internal data and then become more sophisticated about how to optimize it.

But there is a level above this enviable market position. It’s when data actually starts to drive commercial transactions. I have worked with companies whose data products actually drive the bonus compensation of salespeople and managers across entire industries. I have seen data products that are used to set valuations of companies for sale. And of course, there are industry giants such a Nielsen, with its well-known television ratings that drive billions in ad dollars.

The commonality among this rarified group of data providers is that their data is survey-driven. These companies leverage not only their neutrality and impartiality, but they are gathering data that no individual organization could easily or credibly collect on its own. In many cases, these data companies are gathering customer and user experiences and actions.

Yes, for the right kind of opportunity, a simple survey can be turned into an extraordinarily valuable data product. Again, the key drivers of such opportunities are: 1) a need to gather customer/subscriber/user opinions/ratings/activities; 2) the information is difficult for industry players to gather themselves; and 3) the need for trust and objectivity in the collected data.

It may sound hard and complicated, but in the right situations, a well-executed survey can be the path to a very valuable data franchise.

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Good Databases Are More Than Just Good Data

We can look to the UK for a case study of how a government agency, after several tries, couldn’t build a user-friendly data product, creating a giant opportunity for a for-profit data company.

The story begins with a regulatory agency called the Financial Conduct Authority (FCA) that among other duties, registers and regulates financial advisors and advisory firms. The FCA has a searchable database on its website, but like so many government websites, it is optimized for one purpose: checking the registration status of a known individual or firm. As a tool to assist you in identifying an advisor to help you with your investments, it’s pretty useless.

In recognition of this shortcoming, the FCA called on a quasi-governmental organization called the Money Advice Service (MAS) to help build a better adviser database, and MAS accepted the challenge. I took a look at this website when it first launched, and though I saw some design issues, it had potential.

But even though MAS nominally had the freedom to build a creative database with almost any business model behind it, its need to avoid controversy ultimately resulted in a very limited and timid product. And when, unsurprisingly, there wasn’t a lot of revenue to be had with such a product, MAS buried the database three levels down on its website and moved on to greener pastures.

With two free databases of financial advisers out there, you think there wouldn’t be much opportunity left for anyone. However, a company called Unbiased saw things differently, and said there was indeed an opportunity … for the right product.

Unbiased has been a big hit in the marketplace, and the way it differentiated itself from the free government services with the same basic listing data holds lessons for us all

  • Greater visibility – Unbiased wants to be found because its business model depends on driving lots of traffic to its participating advisers
  • Deeper data – ratings, discount offers and detailed profiles
  • Strong user interface – clean, inviting design and both parametric search and a custom matching service         

If you have ever wondered how you could compete against a free, government online database, Unbiased provides the answer: data presentation can be as valuable as the underlying data itself, particularly if you are serving a consumer market. And aggressive promotion of your online database will let you run circles around government agency databases, that are generally hard to find in addition to being hard to use. 

Inferring Intent

Today’s Gartner blogpost points to some interesting limitations and opportunities surrounding intent data. Let’s start at the beginning by defining what it is.

Simply put, intent data is an indication that an individual or organization is actively interested in purchasing a specific product or service. You may already be familiar with sales triggers. One classic sales trigger is so-called “new move” data. It’s valuable to know when a company moves offices because it is highly likely that the company will likely make lots of new purchases such as office furniture and the like. Think of intent data as a more sophisticated cousin of the sales trigger.

Media companies are in a great position to generate sales intent data, because much intent data is generated by watching what a person reads and does online. If a reader looks at five articles on 3-D printers in a short period of time, those actions can be viewed as indicating an intention to purchase a 3-D printer. Intent data can get a lot more sophisticated than that, but this gives you the general idea.

You might think that if a sales organization has intent data available to it, that’s probably all the data it needs. After all, intent data is like mind-reading: it’s identifying people who are likely to be purchasing a product before they purchase it. What could be better?

Well, as the Gartner blogpost points out, many companies are filtering sales leads based on intent data with something called “fit analysis.” This is an automated attempt to evaluate if the company is a likely buyer. If your company typically sells to larger, multi-office organizations, a fit analysis will filter out smaller, single location companies because they represent lower grade prospects.

Further, the Gartner blogpost notes that companies selling highly specialized products or brand-new technologies often can’t get enough intent-based sales leads or they get leads that are weak because the intent indicators aren’t sufficiently granular. Finally, some sales departments don’t like intent-based sales leads because they identify prospects too early in the sales process. As you can see, sales leads based on intention are still fairly rudimentary, and there is lots of opportunity to refine them.

But what’s most worthy of note is that Gartner believes that most intent-based sales lead data is focused on the technology industry. But there is no reason that it should. Technology sellers just happen to be free-spending early adopters. I have long preached the virtues of what I call “inferential data,” a term that includes both intent and sales trigger data. I firmly believe that many data publishers have opportunities in this area, and if they happen to be part of larger media companies, they are even greater. In fact, data publishers are natural providers of fit analytics as well. If you look at your data creatively and read between the lines you can make some very lucrative connections. 

Data Democratization: A Timely Trend That Empowers Users

“Democratization” is the latest trend in data. While it is rapidly acquiring multiple definitions, the one I find most useful suggests that there is a growing opportunity to open up complex datasets to people who could benefit from them, but haven’t traditionally used them.

With this definition, data democratization usually involves some combination of pricing and user interface design. Reduced pricing is meant to make a data product more broadly accessible, and user interface design is about making the data incredibly easy to use. Putting these two together, those employing a data democratization strategy believe they can significantly expand their markets. In addition, a powerfully simple user interface should result in reduced support costs by enabling less sophisticated data users to start getting the answers they need directly, by themselves.

The best opportunities for data democratization? Look for data silos.  The data provider combines several datasets, doing all the complex normalization and matching that is required. The user interface then lets users painlessly do what amounts to cross-tabulation and filtering with all the complexity carefully hidden. Results are usually in the form of highly visual data presentations.

Data democratization is not “dumbing down” data. Indeed, a democratized data product often has all the power of much more complex and expensive business intelligence (BI) software. The nuance is making the user interface more accessible and less scary, and reducing the price point so that the product isn’t a major purchase decision.

You can see an analogy of sorts with what happened with computers, moving from centralized, expensive installations operated by a few with specialized skills to the amazing desktop computing capabilities we all enjoy today. Whether data democratization is an opportunity of the same scale and profundity as the computer revolution is unclear, but it certainly bears close watching because this is a strategy with a powerful first-mover advantage.

To see a great example of data democratization, check out one of this year’s Models of Excellence, Franklin Trust Ratings.

Better yet, meet the founder behind it. John Morrow, at this year’s Business Information and Media Summit, Nov. 13 – 15 in Ft. Lauderdale. There will be lots of other data trendsetters there too! 

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.