North Korea Sparks a Trip Down Memory Lane

The latest news from North Korea should make us grateful we are not in the business there.  On word that several North Korean phone directories had been smuggled outside of the country, the country’s leader, Kim Jung-un, ordered that ALL phone numbers in the country be changed … randomly and without warning!

Here’s some nostalgia to put this in perspective. Here in the US, it began with  something called the “fax machine.” This was a device that scanned documents and then transmitted them via phone lines to a distant location. Faxes were the email of their day, but to get the real-time delivery benefits of faxing, you needed a separate phone line for your fax machine so that it was always available to send and receive. This created a huge jump in demand for new phone lines, and thus, new phone numbers.

If fax machines weren’t enough, we also had the advent of mobile phones, each of which demanded its own phone number. Phone companies ran out of available phone numbers in existing area codes, and begin the seemingly endless process of introducing new area codes (73 in just the past ten years), creating endless amounts of new work for data publishers in the process.

Those of you in the trenches for all this fun may also recall that the phone companies initially favored the dreaded area code “splits,” where half the people in an existing area code would be assigned a new area code. After much complaining, particularly from businesses that had to change signage, stationery and more, the phone companies moved to “overlay” area codes, where all new phone number requests in an existing area code simply received numbers with the new area code.

That’s another quaint aspect of area codes in the old days – they used to define specific geographies. But with the growth of both toll-free numbers, VOIP phones and number portability, your phone number no longer necessarily ties you to any geographic area.

Of course, for all the angst and additional work these changes have caused, at least they were systematic.  And if you are looking for expansion opportunities in 2018, North Korea appears wide open. 

Transforming Data the Humin Way

Imagine launching a start-up that is touted as a pioneering “social operating system” a key player in the burgeoning area of “contextual computing” and even a “digital butler.” Let’s go even further, and imagine the burden of having to live up to the goal of “organizing the world” and most intriguing of all, building “a master contacts database for pretty much the entire world?” Well, if you can in fact imagine living up to expectations like this, you’ll probably want to apply for a job at a company called Humin.

On a more practical level, Humin (at least for now) is an app that grabs your contact list, calendar entries and social networks to build a master list. It then automatically contacts everyone on the list and asks them to confirm their details and provide additional information. Once all these data are confirmed and unduplicated, you get a contact list that can be searched by location, by connections (who knows who) and a lot of other ways that go far beyond the typical address book.

To live up to its contextual computing hype, Humin wants to move into push mode. Fly into Cincinnati, for example, and it will present you with a list of your contacts there. Humin will of course get smarter as it begins to find deeper meaning in both the data itself and how you use it. Privacy concerns? Not to worry. Humin hangs onto only the minimum amount of data needed to do its magic – all the most valuable data stays right on your phone.

Those of you who are students of data may see shocking similarities between to an earlier service called Plaxo. In its original incarnation, Plaxo grabbed your address book and would periodically query everyone in it via automated emails to confirm that their details were current. Even more cool, if you updated your own information, Plaxo pushed it out to all your contacts automatically. It was the original globally synchronized contact list. Ultimately, Plaxo went astray, jumping on the social media bandwagon in a failed attempt to challenge Facebook.

The lesson of Humin (beyond possible confirmation that all great data publishing ideas are derivative), is that while Humin may be loosely based on the Plaxo concept, it is moving aggressively to surround data with tools. Humin isn’t just organizing and tidying a giant pile of data and then asking the user to find value in it – it is innovating in multiple ways to do that thinking for the user, and to deliver the right data in the right format at the right time to offer maximum value. We at InfoCommerce Group call it “data that does stuff.” Surround good data with good tools, and you, too, can become master of the data publishing universe.

The Hidden Data in Invoices

The data business is one of creativity, and what could be more creative than asking companies to send you their invoices and other types of billing data so you can get them into a database and sell the aggregate results back to them? Now, why would something like this ever make sense? Well, in many industries, there is nothing more useful or important than pricing information. Yet the pricing information that many companies publish (if they publish it at all) is almost always the list price. And in many industries, the list price is close to meaningless, since every customer will have a special deal and varying discount. So how do you develop a database of what companies are really paying for specific products and services? Ask to see their invoices!

There are lots of spins on this intriguing model, so let’s take a look…

The question already on your mind quite likely is, “Why would any company let me look at its invoices?” The simple answer is what I often refer to as “strength in numbers.” A company will happily give up its individual data (properly secured and anonymized) in exchange for access to the aggregate results. And they’ll pay for that access, and that’s exactly the play here.

A great example of collecting, normalizing and reporting out information drawn directly from the internal systems of advertising agencies can be found in a company called SQAD. Its NetCosts product collects data for media purchases from advertising agencies worldwide, generating what may be the only honest look at what broadcasters are charging for media buys, and even what they have charged into the future. You can immediately see how valuable this information can be.

Thomson West has a product called Peer Monitor that does the same thing in a slightly different way: rather than work with the recipients of invoices, it works with the senders of invoices, in this case law firms, to collect similar data to be used in similar ways.

If it sounds like a lot of work, it is, or probably was. That’s because SQAD now receives most of its purchase data digitally, through interfaces with client systems. And while those interfaces were doubtless painful to build, at the same time SQAD has built an almost impregnable franchise, because as long as SQAD doesn’t get greedy, nobody can justify the time, cost and pain to try to compete with them. The same holds true for Peer Monitor.

There’s also what might be called the pre-order model. Here, your objective is to gather RFPs and proposals to get a true look at what companies are proposing to charge for their products. One advantage of picking up information at this stage is that there is often a lot more detail, allowing you to collect even more granular product price data. A great example of this model is MD Buyline, a company that collects price quotes and proposals on medical equipment to build a high-accuracy pricing database.

Lots of variant models, but the objective is the same: gather data on actual prices being charged in the marketplace, the more granular the better. Your job is to aggregate, normalize, and report back to the marketplace, while protecting the anonymity of those who participate.

It’s important to note that the need for pricing data isn’t equally compelling in every market. The dynamic seems to be a reasonably large pool of both buyers and sellers, and a solid tradition of haggling over price. It won’t work for everyone, but it’s certainly worth considering if it would work in your market.

People Power

There was news this week about the formation of Bloomberg Beta, a new venture capital fund sponsored by data company Bloomberg LP. One of Bloomberg Beta’s early investments is a company called Newsle, that will alert you whenever someone you specify – a friend or colleague – is in the news. This is a tough nut to crack. Searching thousands of news sources and trying to determine if the John Smith mentioned in an article is the same John Smith of interest to you is a complex undertaking. But what really intrigued me is that those who have written about Newsle see another major problem that the company faces: lack of activity. Think about it. If you import your list of Facebook friends (something Newsle encourages you to do), the chances of any of them appearing in news stories is pretty low. That means most people will sign up for Newsle and nothing will happen, not because Newsle isn’t working, but because there is no news to report. It’s hard to establish the value of your service if you’re not delivering at least a little something every now and then.

That’s why in addition to your Facebook friends, Newsle also encourages you to import your LinkedIn contacts, and while you are at it, your address book as well. Somewhat incongruously, Newsle also encourages you to follow politicians and celebrities. The hope is the more people you track, the more likely you’ll get hits.

But what if Newsle flipped its model? Instead of serving individuals who for the most part have small lists of mostly boring contacts, why not hook up with commercial data publishers, many of whom have tens and even hundreds of thousands of contacts in their databases? Publishers could then send real-time alerts out to their subscribers who are interested in specific people or any activity relating to executives at a given company. In addition to sales intelligence, these news alerts could also provide a basis for making contact with a prospect. Plus, publishers could database these news events to build deep profiles on company executives that would have evergreen value.

This could be a great opportunity for Newsle to crack its volume problem, and for data publishers to add in high-value alerting services and historical data all in one fell swoop.

That’s powerful, people!

#hashtags

As Twitter continues to grow, so does the use of hashtags (those keywords and concepts in tweets preceded by the # symbol). But hashtags can help or hurt your goal of maximum visibility for your tweets. A couple tips based on observation and some quick research:

• Use them judiciously
• Use them authentically
• Their use/misuse reflects favorably or unfavorably upon your brand

Using one or two hashtags is about the most you'll want to use in a tweet, if their use is warranted at all. Tweets using a particular hashtag are classified together in a list. Within Twitter, clicking on the hashtag produces a list of all posts across the site. Using 3 or more hashtags means your tweet will appear on more lists, however the overall power and focus of your message is diluted and your content will look like gobbledygook. Remember, hashtags eat into your 140 maximum character count. The more you tag a tweet, the less you can say in a tweet.

Hashtags make content easily findable on Twitter, but for a relatively short period of time - which varies depending on a number of factors. This time-limited aspect means that hashtags are terrifically useful for current events - unusual weather events, breaking news, sports scores, product releases, conferences, etc.

Hashtags can be used as means of appearing on "trending" topics lists that exist to attract followers to content. For example, trending topics which appeared within the last week or so were: #thestorybehindmyscar, #mycatisajerk. As you will guess from these examples, a hashtag such as #softpaywalls (based on an actual recent InfoCommerce tweet), will never make a trending topics list. Which is to say, when it comes to social media for business, trending topics lists are generally irrelevant.

Hashtagging broad terms is not helpful either. Hashtags for generic words like #marketing, #money, #music, #internet, #ecommerce, etc. are nearly useless because the topics are so broad and so often referenced that they don't appreciably improve searchability. In this instance, hashtags mostly just gunk up a message and create the impression that the writer doesn't understand the concept of hashtags or the platform itself.

Sometimes hashtags are the rough equivalent to saying "like" or "ummm" when you speak. They seldom add to the readers understanding and reflect poorly on the writer.

Search engines being what they are - operating on ever-changing rules and keen on sniffing out baddies - are more likely to push heavily hashtagged content lower on search results pages because it has the look and feel of stuffing. "Stuffing" - in not terribly sophisticated or technical terms - is loading your message with unnecessary words in hopes one of them will trend and help your content be found. Generally, stuffing is something that: 1) people who don't know what they are doing do, and 2) something unscrupulous people do.

Finally, hashtagged posts are not necessarily read more often than posts without hashtags. Social media software provider Argyle Social researched hashtags and developed some interesting insights into their effectiveness. It is worth reading in full, but here are some key summary points:

• In 53% of the social properties analyzed, posts with hashtags actually performed worse than posts without hashtags.
• In 21%, there was no significant difference between posts with and without hashtags. And 26% of social properties had better performance in posts with hashtags.
• When averaging across the entire sample, posts with hashtags received 5% fewer clicks than posts without hashtags.

Argyle's summary conclusion of its research is one that I agree with as it relates to hashtags in any sense: Your followers are your primary source of attention. Tweets should be targeted at providing value to your existing followers, as they are the most likely to read and share your content.

-- Nancy Ciliberti