InfoCommerce Group Blog


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:

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.



There's No Substitute for Structured Data

Cloud-based contact management software provider Nimble recently introduced a new feature called its “Smart Contacts App.” Load the app to a supported browser, and if you see the name of a person or company that interests you, whether reading a news story or in Facebook or Twitter, just highlight the name and Nimble constructs a full profile on the fly. In addition to basic background information, Nimble also searches a number of social networks to find matching accounts. The goal is to build the richest possible profile of the person or organization, and it’s all real-time. With one more click, you can load the profile into your Nimble contact manager.

This isn’t an entirely new concept, but it’s slickly executed. After putting a magnifying glass up to the various screen captures provided by Nimble, what I think I see is that a lot of the magic depends on LinkedIn. And guess what? LinkedIn is a data product. Nimble’s ability to associate social media accounts is impressive, but still imperfect. Indeed, it asks the user to explicitly confirm every social media account match. Nimble also does a nice job integrating with email so that it can pop up a profile of anyone who sends you an email. Microsoft has offered this for a while now, but this is part of a bigger push by Nimble to have its customers do all their work in Nimble so all prospect and customer data resides in one place, all tightly linked and readily accessible.

I draw two insights from all this:

  •  The push to tightly integrate sales prospecting data is serious and intense. The idea of any contact manager (and this includes Salesforce) having a button that says “click to view profile” is quickly getting dated. That means data has to be more tightly integrated into these systems to a degree we haven’t yet seen, and that means software companies will need to license more data from data publishers to get this level of deep integration.
  • For all its sizzle, this new offering from Nimble isn’t creating data; it’s assembling data from other data sources. To be valuable, Nimble needs data that is accurate, rich and most importantly, structured. You can’t assemble that out of thin air. And that unique characteristic – structure – is what makes data so powerful and so valuable.



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?



Is Your Data "Datanyzed"?

A new product by a cool young company called Datanyze is capitalizing on some well-established infocommerce best practices. Here’s how they did it.

The core business of Datanyze is identifying what SaaS software companies are using (sometimes called a company’s “technology stack”). To do this, Datanyze interrogates millions of company websites on a daily basis, looking for telltale clues as to the specific software they are employing online, and apparently a lot of categories of software can be divined this way. Datanyze aggregates and normalizes these data, then overlays company firmographic data (Alexa website rank, contact information, revenue estimates) to create a complete company profile.

Datanyze links directly to the Salesforce accounts of its customers, so it can add and update prospects on a real-time basis. At a basic level, the use case for this product is straightforward: a marketing automation platform like Eloqua could use it to find companies using a competitor or no marketing automation at all. But wait, there’s more!

Datanyze’s new product essentially flips this service. Now, Datanyze clients can have Datanyze analyze their existing best customers, and Datanyze will build a profile of these customers that can be used to predictively rank all their prospects, current and future. Here are the best practices to note:

  • The transition of Datanyze from a data provider to an analytics provider, something that’s happening industry-wide
  • The shift from passive (we supply the data, you figure out what to do with it), to active (here are top-rated prospects we’ve identified for you), and the associated increase in value being delivered by the data provider
  • The tight integration with Salesforce means that Datanyze customers just need to say “yes” and Datanyze can get to work – no IT involvement, no data manipulation, no delays
  • Datanyze is pouring leads into critical, core systems of its customers, a strong example of workflow integration
  • The use of inferential data. Boil down a lot of the analytical nuance, and Datanyze has discovered that companies that buy expensive SaaS software are better prospects for other kinds of expensive SaaS software. Datanyze doesn’t know these companies have big budgets; but it does know that these companies use software that implies they have big budgets

Datanyze offers a concrete example of how data companies are evolving from generating mountains of moderate value data to much more precise, filtered and valuable answers. Are you still selling data dumps or analytics and answers?

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

How Starbucks in Mall of America looks to Foursquare