Everyone into the (data) Pool

There’s a quiet revolution going on in agriculture, much of it riding under the label of “precision agriculture.” What this means is that farms are finding they can use data both to increase their productivity and their crop yields.

To provide just one vivid example, unmanned tractors now routinely plow fields, guided by GPS and information on how deep to dig in which sections of the field for optimal results. Seeds are being planted variably as well. Instead of just dumping seeds in the earth and hoping for the best, precision machinery, guided by soil data, now determines what seeds are planted and where, almost on an inch-by-inch basis.

It’s a big opportunity, with big dollars attached to it, and everyone is jockeying to collect and own this data. The seed companies want to own it. The farm equipment companies want to own it. Even farm supply stores – the folks who sell farmers their fertilizer and other supplies want to own it. In fact, everyone is clamoring to own the data, except perhaps the farmer.

Why not? Because a farmer’s own soil data is effectively a sample size of one. Not too valuable. Value is added when it  is aggregated to data from other farmers to find patterns and establish benchmarks. It’s a natural opportunity for someone to enable farmers to share their data to mutual benefit. This is a content model we call the “closed data pool,” where a carefully selected group agrees to contribute its data, and pay to receive back the insights gleaned from the aggregated dataset.

One great example of this model is Farmers Business Network. Farmers pool their data and pay $500 per year to access the benchmarks and insights it generates. Farmers Business Network is staffed with data scientists to make sense of the data. Very importantly, Farmers Business Network is a neutral player: it doesn’t sell seeds or tractors. Its business model is transparent, and farmers can get data insights without being tied to a particular vendor. Farmers Business Network makes its case brilliantly in its promotional video, which is well worth watching: https://www.youtube.com/watch?v=IS4KIrcRMMU

Market neutrality and a high level of trust are essential to building content using the closed data pool model. But it’s a powerful, sticky model that benefits every player involved. Many data publishers and other media companies are well positioned to create products using this model because they already have the neutral market position and market trust. Closed data pools are worth a closer look. Google certainly agrees: it just invested $15 million into Farmers Business Network.

When You Centralize Data, You Too Become Central

One of the ways that bricks and clicks are starting to merge is through a technology called beacons. It’s all the rage in retail right now. Acme places specialized transmitters in each of its stores. When a customer with an Acme app on his or her phone enters the store, the transmitter can push real-time, targeted promotional messages to that customer. Even better, the customer doesn’t have to access the app – it’s designed to wake up and alert the customer.
Cool stuff, and what better time to target customers then when they are inches from your cash register. Yet, not every promotional message generates a sale. Despite your best efforts, the customer leaves your store. Now what?

This is the interesting area where a start-up called Unacast is playing. It wants to marry the data you have on the customer who just left your store to online ad re-targeting platforms, so you can continue to advertise to these customers, in the hope of making the sale. Again, cool stuff.

But Unacast is taking this a step further. It is going around to all the manufacturers of these beacon transmitters and positioning itself as a central back-end data repository for this valuable shopping data. As a central repository, Unacast can watch where else the customer is going to gain both marketing and segmentation insights. Did the customer go to a competitor? Better re-target with your best deal then. Does the customer go to discount stores or high-end retailers? A retailer can not only learn a lot more about its customers, but is better able to serve them highly customized advertising messages as well.

It’s a data bonanza that will yield endless benefits, and Unacast is moving fast to lock up this opportunity. That’s important because there’s typically only room for one central clearinghouse in a market.

This is a model you might apply to your own vertical. If you are seeing numerous companies collecting similar pots of proprietary data, chances are there is both a need and an opportunity to be the central repository. Why you? Why not? You’re established, know the data business and you’re a neutral player. Central clearinghouse opportunities typically go to the fleet of foot, especially now because the value of data is much more broadly appreciated. Do you have your running shoes on?

Shine a Light on Your Hidden Data

If you watch the technology around sales and marketing closely, you’ll know that beacon technology is all the rage. Stores can purchase beacon broadcasting equipment, and when shoppers enter their stores with beacon-enabled apps, the apps will respond to the beacon signals – even if not in use. Stores see nirvana in pushing sale offers and the like to customers who are already on the premises. And of course, it is expected that some mainstream apps (Twitter is often cited, though this is unconfirmed) will become beacon-enabled as well.

Beacons represent a concrete manifestation of the larger frenzy surrounding geolocation. Everyone wants to know where consumers are at any given moment, as epitomized by big players such as Foursquare, which has evolved from its gimmicky “check ins” to become more of a location-driven discovery service.

That’s why I was so intrigued by Foursquare’s most recent product announcement called Pinpoint. Shifting its focus from where people are now, Pinpoint is going to mine valuable insights around where people have been and let companies use it for precise ad targeting.

Details about Pinpoint are scarce right now, but Foursquare is smart to start mining its historical data. At the lowest level, it means that Foursquare can help, say, Starbucks target lots of Starbucks customers. Useful, but not too sophisticated. If Pinpoint can roll up businesses by type (such as pet food stores), it starts to get a lot more interesting. But the real home run would be to be able to divine purchase intent. If someone visits three car dealers in a short period of time, you suddenly have an amazingly valuable sales lead. And mining insights like this is now practical with Big Data tools.

But the real insight here is that your history data isn’t just ancient history: it provides the multiple data points you need to find patterns and trends. Knowing that a company replaces its CEO every 18 months or so is a hugely valuable insight that you can identify simply by comparing your current data to your historical data. At a minimum, you’ve got a powerful sales lead for recruiters. But that level of volatility might be a signal of a company with problems, thus creating useful insights in a business or competitive intelligence context. We’ve all heard about the predictive powerful of social media sentiment analysis. You may have equally valuable insights lurking in your own data. All you need to do is shine a light on them.

How Starbucks in Mall of America looks to Foursquare

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.

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.