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

The Award for Outstanding Performance Goes to Internet Movie Database

We awarded the Internet Movie database a Model of Excellence  in 2003, and it is still a standout in terms of innovation and best practices.  

The Internet Movie Database (often called by its acronym IMDB) originally started in the UK as a non-profit undertaking, and it may well be the earliest and most successful example of crowdsourcing – well over a decade before the term was even coined. Very simply, the IMDB was a site for movie buffs worldwide to build an enormously detailed database of every movie ever made. And we are talking about a serious level of detail. Want to know who was the hairstylist for the co-star of an obscure French drama from the 1950s? Well, IMDB was the go-to source. What also made IMDB interesting was that from its inception it was a true database, and despite the inherently unruly nature of crowdsourcing, there were enough committed volunteers to take on the unsexy work of removing duplicate entries and normalizing the data.

In 1998, IMDB was quietly acquired by Amazon and turned into a for-profit company. There are some great best practices to be observed here. Taking over and commercializing a site built by tens of thousands of unpaid, die-hard movie fans was a risky proposition. The backlash could have killed the business in short order. But Amazon left IMDB alone, infusing it with editorial resources so the database got bigger and better every year. Better data, less work and all free. Not much here to get upset about!

But Amazon (surprise!) wasn’t in this to be charitable. First, it started marketing to the substantial audience of IMBD users with links to its site. Like the movie? Great. Amazon can sell you a copy.
Amazon’s next move was sell sponsorships to movie studios eager to promote upcoming releases. From there, Amazon launched a subscription-based Pro version of the database that offered enhanced searching and even deeper content to movie industry professionals for research purposes. The core site remained free, meaning Amazon was a pioneer with the freemium model, well before that term had become popular. 

Is Amazon now resting on its laurels? Absolutely not. To support both its Kindle and Amazon Prime offerings, Amazon has launched a service called X-Ray, powered by IMDB. Amazon also selectively licenses this new data capability. What X-Ray does is link movies to the IMDB database, so users can visually identify actors in the film, find movie trivia, explore the movie soundtrack and much more, right while watching the movie.  It’s not all software magic, by the way. Amazon is doing a lot of the necessary linkages manually, but it already has thousands of movies coded. Also of interest, it’s touting its “X-Ray Enabled” badge that if it plays its card right, could someday become a differentiator for new movie releases.

Endless innovation. Strong support of its core e-commerce platform. Deft handling of often prickly enthusiast community. Endless monetization. This is where data is going!

User Interface Design: No Small Matter

In advance of big changes to the way pensions are managed, the UK government set up a quasi-independent service called Money Advice Service (MAS). MAS has the worthy goal of trying to improve financial literacy, particularly among those about to retire.

As part of its program, MAS set up an online directory of financial advisors, just launched in beta. Given its high profile and semi-official status, the MAS directory has come under a lot of scrutiny, particularly from the financial advisors it lists, all of which are keen to be highly visible in this important new directory that anticipates very heavy use. But let’s look at it from a user’s perspective to see some important lessons on how not to create an online directory.

Sample Directory Listing

The directory database itself is quite mundane. It presents such information as advisor name, contact details, certifications (if any), and the types of services it provides (from a fixed list of categories). But here’s how a seemingly basic directory quickly becomes complicated.

First, it encountered the issue of business locations. It’s easy to list ABC Advisors at its headquarters address in London. But what if ABC Advisors has 400 branch offices scattered around the country? Do they each get individual listings? Even more confusing, how do you properly represent advisory firms that have independent advisors, many of whom work from home? What about advisory firms that are affiliated with other advisory firms? You may think all of this is annoying, but not a huge deal. But it becomes a huge deal when the user interface is location-centric.

As it happens, the MAS directory is location-centric. It uses a postal code to do a search to return results based on proximity. But depending on how you handle the entity issues described above, ABC Advisors might appear 100 times in results of a specific search (with each of its offices or advisors appearing as a separate listing), or not at all (because only the headquarters location was listed and it wasn’t anywhere nearby). This can be very confusing to users (who often see the multiple records as annoying duplicates and the absence of major companies as questionable data quality). And if you are selling paid participation or paid enhancements in the directory, this can cause an advertiser revolt.

The MAS directory also lets you search by specialty service. Here, results are not returned by proximity, and because there is no secondary sort on distance, the first search result may list a firm 500 miles away, while a firm 1 mile away appears on page three of search results.

Perhaps the biggest issue of all is that searches tend to return hundreds of listings, and the thin dataset gives the user very little information or tools to differentiate or compare them. Apparently, the plan is to add fees and charges in the near future to build out the database. In the meantime, users struggle with a marginally useful directory. Governments can get away with this.  But those of us in the business know how to do it a lot better – or at least we should. User interface design starts with the design of the database itself, which is in turn informed by the user needs and problems you are trying to address. Shortcuts in the design phase mean expensive additional work later, and can potentially endanger the success of your data product.