Making Music

I’ve been impressed and entranced by the music service Pandora since I first ran across it several online lifetimes ago in 2007.

Two things particularly impressed me about Pandora. First, unlike services such as Spotify that allow you to access music you already know about, Pandora was the first large scale attempt to offer music discovery. Enter the artist or tracks you like most, and Pandora would find more music that was similar. Normally you would expect to learn that Pandora is powered by cutting-edge algorithms.

In fact, Pandora is powered by humans. Music school graduates. Many dozens of them, all methodically classifying individual songs against a master taxonomy of over 400 characteristics. It’s an expensive approach, but it’s organized and returns consistently high quality results. And while Pandora continues to struggle from a profitability standpoint, nobody argues with the quality of its service.

But what if you could create a Pandora-like service without the high labor costs? That’s what a company called 8Tracks set out to do.

Rather than having a paid staff categorize music, 8 Tracks went the social media route. Everyone was invited in essence to become a DJ, and upload their own song lists to the 8Tracks site. These playlists were organized via tags, so users could discover music based on mood or musical style, for example. If users like particular playlists, they can follow the people who uploaded them in order to see all their new playlists right away.

8Tracks is unquestionably providing a music discovery service, just like Pandora. But it’s a fundamentally different experience. Pandora is dependable, seamless and efficient. 8Tracks is hit-and-miss, time-consuming and requires lots of user interaction.

There’s room for both services in the vast music market and indeed, both services have many enthusiastic adherents. Yet by looking at both services side-by-side, you can see the strengths and weaknesses of user-generated content very clearly.

Music is entertainment. There’s no risk or consequence if you don’t discover a certain song by a certain artist. But when you move into the realm of business information, that dynamic changes. Suddenly, getting the right answer starts to matter a lot. That’s where user-generated content can come up short. Users generate whatever content they want, whenever the want, for as long as they want. You have little control. User-generated content works best where there is a massive volume of content (think Yelp or TripAdvisor) and the correct answers will win out, or in situations where there is no alternative information source, making your content the best that is available. But when the quality of your content matters, social approaches to content creation can yield decidedly off-key results.

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.

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

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.

The Power of Predictive Prospecting

Out of all data products, the single largest group is what we call "opportunity finders," databases used by customers to identify sales prospects. These databases, many of which originated as print directories, have followed the normal trajectory of data publishing: moving from being a mile wide and an inch deep to adding tremendous amounts of depth. As publishers add more information to each listing (e.g., revenue, number of employees, year founded, line of business) they enable their users to engage in much more sophisticated targeting of sales prospects. In those situations where a company is looking to sell into a very specific market segment and the data exists to isolate those prospects, it's pretty much mission accomplished for the data publisher. For example, if you sell a product that is only of interest to banks with more than ten branch offices, you can probably find a database that will quickly help you to identify a manageable list of qualified prospects for your product. But there are an awful lot of situations that aren't so neat and tidy. For example, some companies have huge target markets such as "all companies with revenues under $5 million." Some companies literally target everybody. And an awful lot of companies are seeking highly defined target markets for which data doesn't exist (e.g., all private companies whose are considering starting a 401(k) plan).

Until recently, what this meant is that companies were required to slog through a huge number of semi-qualified prospects. Using expensive telesales and field sales teams, they would eventually identify some good prospects, but the work to do so was expensive, slow and not a lot of fun. Could there be a better way?

What we're seeing now are remarkable advances in lead scoring and predictive sales software. The premise is simple: by bringing to bear a lot of information and a lot of smarts about what data points might identify a good prospect, we are getting better a separating strong prospects from weak prospects. Some of the companies leading the way in this area are Lattice Engines (a DataContent 2012 presenter), Context Relevant and Infer.

The potential opportunity for data publishers is to move more aggressively into lead scoring for your customers. Imagine (possibly in combination with one of these firms) to allow your customers to enter parameters about their sales targets, then let them search your data to receive not only the raw information but a predictive score as well to indicate the quality of the prospect.

It's all part of the continued push to data publishers to surround their data with more powerful tools. And is there a tool more powerful that you can offer your customers than one that can help pinpoint where their next sales are most likely to come from?