Take Action on Actionable Data

Actionable information has long been a  cornerstone of infocommerce, and recent stats from MediaRadar, a 2011 Model of Excellence company which will be featured again when DataContent moves to Miami as part of the all-new Business & Information Media Summit, provides a fine example, and in a way that hits home for many of us. Built on an understanding of advertising sales workflow, MediaRadar makes full use of its database to yield a highly valuable product. At core, MediaRadar provides sales leads to media companies by tracking who is advertising where. At a higher value, it offers benchmarking to its clients by allowing them to easily see how they are doing versus the competition. At the highest value, it creates an analytical layer, tapping into its data at an aggregate level to find trends and insights. Consider these recently released MediaRadar insights on email advertising:

• As annoying as everyone says email is, advertisers like it. Over a 12 month period, MediaRadar identified 19,915 distinct B2B advertisers who bought an email advertising program
• A third of advertisers buy only email advertising
• Those advertisers who buy via email rarely buy the full range of media options. For example, 54% of  advertisers buy print advertising with email ads,  and 47% of advertisers buy other digital advertising along with email ads
• In a remarkable trend, 44% of email advertising now being sent by B2B media companies are dedicated email blasts, and the trend appears to be increasing
• A lot of email-only advertisers fly under radar and are hard to identify
• Response rates vary significantly by market
• 30% of e-newsletters carry only a single ad; 66% carry from 1 to 3 ads, and 17% carry 5 or more ads

Most data products have this multi-dimensional potential, which can often open up new revenue and even new markets. Is your dataset working as hard for you as it can?

P.S. – Speaking of data and analytics, I would like to ask you to take just a few minutes to complete our new email benchmarking survey. It’s quick, and you’ll not only get a warm feeling of satisfaction from helping to make the entire industry smarter, you’ll also get a free executive summary and key data charts.

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.

TripAdvisor and the Ignorance of Crowds?

We all know the many benefits of user-generated data, including ratings and recommendations. Underlying all of it is a general belief in “truth in numbers” - if enough people say it is so, then it must be so. But what happens when the crowd returns a result that is obviously and intuitively wrong? A small but perfect example of this occurred recently when TripAdvisor published its list of “Americas Top Cities for Pizza.”

I’ll spare you the suspense: the top-rated city for pizza in the United States is (drumroll, please) ... San Diego, California. Are you already checking flights to San Diego or are you shaking your head in disbelief? I am guessing the latter, and that’s exactly my point.

Something went wrong in this tally by TripAdvisor. Most likely the underlying methodology wasn’t sound. TripAdvisor merely rolled up ratings for individual pizzerias and restaurants around the country and ranked them by city. There’s a little problem with that: context. What TripAdvisor converted to a national ranking were individual ratings made in the context of specific geographies. That’s why you see comments for top-rated San Diego along the lines of “it’s just as good as New York.” These people clearly didn’t see themselves voting in a national poll.

Also odd is that TripAdvisor decided to go ahead and publicize the results of this analysis. Clearly, TripAdvisor saw the potential for buzz with its surprising findings. Yet the flip side of this is the creation of a credibility issue: if TripAdvisor thinks the nation’s best pizza is in San Diego, how can I trust the rest of its information?

A few lessons for the rest of us can be found here. First Big Data is only valuable if properly analyzed. Second, evaluating data supplied by a large and disparate crowd can be tricky. Third, always balance the potential for buzz in the marketplace against reputation risk. Say enough dumb things online and you will hurt your credibility.

TripAdvisor will no doubt say in its defense that it is simply reporting what its users are saying. But if your user base is saying something dumb, it’s probably not in your interest to amplify it. And in fact, the TripAdvisor user base isn’t dumb; their individually smart comments were aggregated in such a way that they yielded a dumb result. But most people aren’t going to dig that deep. They’ll simply say that’s one less reason to rely on TripAdvisor.

Drawing the Line: Customers as a Data Source

Today’s New York Post reports that Bloomberg was confronted by Goldman Sachs for allegedly allowing its journalists to tap into subscriber usage data. It is early into this event, and still unclear what the ultimate impact  on Bloomberg might be, but regardless of outcome, this remains an area of  acute importance to all data publishers. That’s because data publishers  often have access to potentially confidential and valuable information,  and the slightest misstep could put your whole business at risk by  destroying customer trust.

The Bloomberg case was actually pretty tame in many respects: a Bloomberg reporter called Goldman Sachs to inquire if a partner was still working there because he hadn’t logged into his Bloomberg terminal for a long period of time.

Login data provides one level of insight into the activity (or non-activity) of your subscribers, but that’s just the tip of the iceberg. If you know what job function a particular subscriber performs, and also what that subscriber is searching on, you could potentially get insights into new product development activity, sales strategy or even potential acquisition targets. You see where I am going, and hopefully you also see why you should never go there. Your subscribers, often unknowingly, are trusting you with a lot of potentially sensitive and valuable information. It’s your duty to guard it carefully.

I’m not suggesting that there is any issue with aggregate analysis of activity against your database to better understand what your subscriber base as a whole is interested in so that you might improve your product. But whenever you start associating specific search and view activity with specific subscribers, you need to be very careful.

Depending on the markets and the job functions you serve, you may even want to re-think if, say, your customer service people should be able to view a specific subscriber’s saved searches. And even something as innocuous as putting up a list of “most viewed companies this week” could inadvertently reveal too much if you operate in a tight vertical.

Too often these days, I am seeing people do things because they can, not because they should. Technology is often addictive in this way. But I urge you to look before you leap. Trust is easy to lose, hard to regain and essential to your success.

Sentimental Journey

Sentiment analysis represents a real opportunity for many data publishers to add new, high-value, proprietary and even real-time insight to their data products. But sentiment analysis has inherent strengths and weaknesses you need to appreciate when considering if there is a sentiment analysis opportunity for your data products.

A wonderful example of sentiment analysis at work is represented in a new service called the Twitter Political Index. Working with two respected political polling organizations, Twitter analyzes over 400 million tweets per day, to determine how people are feeling about the two presidential candidates. The key word here is "feeling," because that's what sentiment analysis is all about -- guessing how people feel about a topic. It's not easy, and it is particularly complex for tweets, which are short and often lack context. Moreover, most sentiment analysis tools go well beyond simply binary like/dislike assessments and try to gauge the degree of like or dislike. It's tricky stuff, but the potential applications are numerous and exciting.

This is often the point where many people will start questioning such issues as whether or not Twitter offers a representative sample, and what level of precision and confidence sentiment analysis can offer. These are valid questions, but be careful not to fall into the trap described by research industry veteran Ray Poynter, who notes that all new research methodologies are invariably measured against the standard of perfection. This implies that all current research metholodogies are perfect, which is far from the case. When using tools such as sentiment analysis, you need to consider the application, then pick the methodology, seeking the best fit possible.

That's why data publishers should be thinking about sentiment analysis. You don't need to analyze every tweet; indeed you don't necessarily need Twitter at all. Sentiment analysis can be applied to research reports, blog posts and press releases. And if you can help your customers better understand how the world currently views a company or product, for example, you can deliver a useful new layer of insight that differentiates you from the competition, makes your products more valuable, and can be acquired and implemented fairly quickly and economically.

And particularly with new research methodologies, I think it's useful to remember the saying, "don't let the perfect become the enemy of the good." Building powerful new data analysis tools is a long journey, one that both publisher and customer are taking together.