Upping the Data Ante

Step back a bit from the fray and you’ll see an interesting evolution in the world of data: from providing lists of people or entities that might be prospects, to lists of people or entities that should be prospects, based on something they have done (think sales triggers). Now we’re beginning to move squarely into what used to be the realm of science fiction: identifying prospects before they have done anything at all.

We’re blazing new trails here, and pre-prospecting (for lack of a better name) depends heavily on lots of input data and Big Data analytics. The 800-pound gorilla in this space right now is a company called InsideSales that calls its analytical secret sauce “Neuralytics.”

All hype, you say? Well some level of hype is a given these days, but the company has raised over $139 million to date, and Salesforce.com in particular has fallen hard for the company’s pitch, and actually led its most current funding round, that also included Microsoft.

I don’t have any inside knowledge of what InsideSales is up to, but from the tantalizing tidbits that have surfaced in the press, it seems to be a combination of obvious inputs such as social media feeds, plus less intuitive things such as weather patterns and sports team scores. I can only guess that you’re a somewhat better prospect if it’s sunny out and your team won last night, but perhaps these data are being used in a more subtle and sophisticated way.

The other hint I picked up is that InsideSales depends on “email and phone records” to perform its analytical alchemy. Needless to say, these tend not to be public records, so to deliver the holy grail of sales prospecting, InsideSales apparently depends on the holy grail of input data as well!

I’m not dismissing InsideSales, primarily because I am doing some big league speculating here. But I will say there are data sources available today that get us a long way towards the notion of pre-prospecting. What excites me the most is what is going on today with online ad re-targeting. Ad re-targeting is based on what might be described as networked cookies. Visit a site, and a common cookie is placed on your computer. As you move to other sites that are part of the network, ads can be displayed based on sites you’ve previously visited. More importantly, your travels around the Internet can be centrally stored, creating a wealth of information about you, your interests, your habits and much more. While not easy, it is a straightforward leap to start learning about not only what interests you but also what are the early signs that you are beginning to contemplate a purchase.

Privacy isn’t the issue in re-targeting (at least for now), because nobody needs to know who you are for re-targeting to work. But as your movements around the Internet are recorded and analyzed, it is entirely possible that we’ll someday know when you’re thinking about buying something, and perhaps even a little before.

The next generation of sales insights likely isn’t all that far away, so now is a good time to do some pre-pondering on what it might mean to you and your business.

How Many Ways Can You Monetize Data?

I watch the real estate sales vertical with great interest. There’s a lot of data, and money here, which in turn means a lot of innovation and competition. Companies like Trulia, Zillow (which are poised to merge shortly), Move (which operates the Realtor.com site) and a host of fascinating and scrappy regional players such as PropertyShark makes for endless creativity and impressive user experiences. The first thing you notice about all the online real estate information services is that none of them is trying to disintermediate real estate brokers. Indeed, these services typically have business models that depend on agents for revenue. Thus what has happened in this very unusual market is that customers have taken on the primary work of discovery (formerly a big part of the agent’s job), even though agents haven’t reduced their commissions to reflect this.

The second thing you notice is the wealth of structured data that is available for parametric searching. Search by zip code, price range, bedrooms, lot size, and much, much more. In fact, such powerful searching is table stakes now. Map integration? Done. Alerts? Done. Rich multimedia? Done. So what’s left to innovate?

Zillow burst onto the scene (beautifully timed to coincide with our late, great real estate boom a few years back) with its audacious system that put a price valuation on every home in the country. That brought it tremendous visibility, but also introduced consumers to the power of predictive analytics.

Trulia later upped the ante by overlaying neighborhood crime statistics on its database. Not to be outdone, its competitors overlaid school district boundaries to map the schools nearest to each home. Trulia then upped the ante again, licensing data from our Model of Excellence winner GreatSchools.org, that showed the relative quality of each school. And that’s where the market seems to be headed today – qualitative assessments of neighborhoods, along with more predictive analysis.

As you might expect, qualitative assessment starts with Census demographic overlays. Real estate site Movoto.com is already there, with zip-level income, education and ethnicity. Some other sites are hesitating because of the vagaries of real estate anti-discrimination laws. But that is not an impediment to third-party data providers such as Onboard Informatics, which provides a raft of local data, including an innovative “lifestyle search engine.” Other sites like neighborhoodscout.com provide sophisticated demographic views of local areas. And we’d be remiss not to acknowledge diedinhouse.com for those who need to know if former home occupants left on their own power or not.

But what’s most fascinating is that this lifestyle analysis of neighborhoods has even been elevated to a personalized, consultative model. The New York Times recently profiled a New York area firm called Suburban Jungle that helps homebuyers target areas based both on demographics and deep market knowledge. Suburban Jungle doesn’t sell real estate; it refers its clients to real estate agents in exchange for a fee-share, another great example of how many different ways data can be monetized.

Deals of Excellence

It’s been a banner few weeks for deal making for companies honored as Models of Excellence by InfoCommerce Group.

In the mega-deal category, we have 2006 honoree real estate data powerhouse Zillow, entering into a $3.5 billion deal to acquire arch-rival Trulia. This will of course put pressure on market leader Home.com, which operates the Realtor.com website. The whole real estate vertical has been one to watch from a data perspective. Zillow was not only an early innovator in the area of map-based user interfaces, it also blew more than a few minds by not only aggregating property data on almost every home in the country, but creating a home price estimate for every home as well. If this merger goes through, expect even more extreme innovation as these two giants battle it out for audience and advertising.

In the smaller (but hardly small) category, we have the $175 million acquisition of 2009 Model of Excellence honoree Bizo by 2004 Model of Excellence honoree LinkedIn. From a strategic standpoint, I’d rate this acquisition as nothing short of brilliant. At a high level, you are putting together “who” (the LinkedIn database, with “where,” (the Bizo B2B ad network). The potential opportunities are endless.

And while we’re in the world of high finance, a shout-out to 2010 Model of Excellence honoree SmartZip also seem in order, as they’ve just closed on a new $12 million financing round.

Where do these and other Models of Excellence companies meet each year to get their deals on? InfoCommerce Group’s DataContent gathering, now part of an even bigger show, the Business Information & Media Summit. See you in Miami!

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.

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.