Data Marketplaces: Almost There

There has been much excitement about the recent launch of the Salesforce Data Studio, a new data-sharing platform within the Salesforce Marketing Cloud.

The idea of the Data Studio is simple: marketers can, on a fully automated basis, identify, order and integrate datasets that others are offering for sale. In its early implementation, the Data Studio seems mostly like a cool way for marketers to buy email lists. But the vision is much bigger and more interesting: to allow marketers to augment and overlay existing email lists with more data so that they become smarter about their lists, target their efforts more effectively, and get better results.

Data Studio at time of launch is heavy on audience data, mostly from larger publishers, but there’s no reason any data publisher couldn’t participate as well, especially if the Data Studio wants to exploit its full potential.

Interestingly, Salesforce is not the only big player that has an interest in data marketplaces. The Amazon Web Services Marketplace sells software through its marketplace – again, a totally automated buying experience – but it also offers a selection of public domain datasets for free. It’s a small jump then for Amazon to start selling databases on behalf of others.

As you can see, neither of these two marketplaces is quite ready for prime time as far as becoming a meaningful sales channel for data publishers, but they’re tantalizingly close. Keep an eye on these marketplaces: they could become very important to data publishers very quickly.

Top Level Domains/Low-Level Trustmarks

If you’re not immediately familiar with the term top level domain (TLD), think of “.com” and “.net” and “.edu” – they are all top-level domains, along with hundreds of others, and by the way, they are not limited to three characters anymore.

In the early days of the Internet, domain names were free for the asking, and I stocked up on quite a few for no other reason than a gut feeling they had some value. I did ultimately sell a lot of them, including several Fortune 500 companies who bought their corporate names back from me. By the time I realized there might be a bigger opportunity here, the rules of the game changed and big companies that had previously shown up with checkbooks now showed up with lawyers. Ah, well!

But for all my domain name hoarding, I couldn’t ever get domains names with the “.edu” TLD because they were reserved for schools. Similarly, “.net” was reserved for Internet Service Providers back then, and “.org” was reserved for non-profits. These distinctions were widely understood back then, and even today, I hear people telling me some organization “must” be a non-profit because it has a “.org” domain name. Old naming conventions die hard. More importantly, people are hungry for trustmarks.

But TLDs were never great trustmarks, for two reasons. First, validating an organization’s credentials before handing out a domain name is hard and expensive work. Second, domain names don’t sell for a lot, so you can only make money with volume. The pickier you are, the less money you make.

Despite this, the non-profit sector is now pushing the “.ngo” TLD. Think of it as a do-over of the “.org” TLD, because the operator of the domain is trying to limit sales to non-profit entities with the explicit hope that the TLD will become a trustmark over time. Similarly, the AICPA, the big association of certified public accountants, is in a fierce battle to control the forthcoming “.cpa” TLD, again with the hope it can restrict its use to certified public accountants and build it into a trustmark.

My view is that TLDs make for poor trustmarks. The economics make it hard to enforce standards, and there are too many sleazy operators in the business that drag down the credibility of TLDs across the board. The need for online trustmarks remains high. Who better than data companies to seize the opportunity?

Bigger Is Not Always Better

One key dynamic of the data business is that the strongest businesses serve single, tightly-defined markets, typically a single vertical market. The result is that the market opportunity tends to be smaller, but it is much easier to stay close to and defend.

The problem for data publishers attempting to build products with horizontal coverage across multiple markets, or who want to play in large consumer markets, comes down to a very simple reality: it’s hard to be everything to everybody.

It’s instructive to look at some of the reasons why it’s so hard to achieve long-term success with broad-based data products:

Lowest common denominator: In order to operate efficiently, broad-based data publishers typically have to collect fairly standardized and fairly shallow data across multiple vertical markets. This creates an opportunity for other data publishers to “slice and dice” these publishers, peeling off the largest and most profitable vertical sub-markets, and serving the same need with deeper and more tailored data.

Greater incentive for competitors: If you achieve any level of success with a horizontal, broad-based data product, you’ve not only identified a big market need, you’ve identified a big market opportunity as well. That means it may well be worth it for a competitor to invest significantly to steal market share or push you out entirely. Contrast this with successful vertical market data publishers, where the small scale of the market is one of their best protections. Competitors typically can’t financially justify trying to push their way into small vertical markets.

Turning an ocean liner: In addition to being a juicy competitive target, an established broad-based data publisher typically succeeds because it has built an operation that over time becomes very difficult to change for technical and business reasons. That means it will be at the mercy of such forces as new technology, shifts in user preferences and new business models, and just a few competitive successes can break the momentum and market dominance of the incumbent data provider. Moreover, the incumbent data responder is only able to react slowly, if it can react at all.

Too cool for school: While some broad-based data publishers become exposed because they can’t react quickly, others expose themselves by innovating so aggressively they get ahead of their markets and their customers. In a relentless quest to stay relevant and ahead of the competition, these publishers roll out features and functionality that their customer often don’t understand or even want, adding complexity to the user experience while muddying the core value proposition.

Platform envy: Perhaps encouraged by the spectacular success of Amazon, it’s easy to take the view that your data product can become a data platform, a way to distribute all kinds of data, products, whatever. That’s a big leap technologically, and while platforms are enticing to publishers, they almost inherently mean diffused focus, thus opening opportunities for competitors to enter the market with more focused products.

The most successful data publishers and products I see these days tend to serve one market and serve it extremely well. As long as these businesses stay close to their customers, evolve their products regularly and prudently, and offer good customer support and fair pricing, they can be enormously profitable while remaining largely immune to competition. That’s why in the data business at least, bigger isn’t always better.

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!