Buying Guides That Do Stuff

It’s been very interesting to watch the transition of buying guides from print to online. Print buying guides were a pretty good business, although in fact few of them were very good products. That’s because most buying guides were what I call shallow information products: they would typically list a product and the names and addresses of companies that (hopefully) made or sold the product. After that, users were on their own. This stripped-down format was in part practical, because even this limited information was hard to obtain. It was in part by design, because it encouraged companies to buy advertising next to their listings to provide additional information. There’s no room on the web for shallow information products anymore. Search engines have gotten good enough that you can find at least a few manufacturers or sellers of just about anything with very little effort. And company websites now typically contain a wealth of product information, in part because it is so cheap and efficient to do so. Overall, this leaves little room for buying guides to add value, at least in their traditional format.

So is the buying guide model dead? If you are talking about the traditional shallow information model, the answer is yes (something that the big yellow page publishers, incredibly, have still not figured out). But what is emerging in its place are a number of exciting new products that mix and match such features as:

• User ratings and reviews (and some now validate users and even confirm that they have purchased the product they are reviewing)
• Links to third-party professional reviews
• Downloadable CAD drawings
• Photo portfolios showing product applications and/or the product in use
• Strong parametric search
• Side-by-side comparison of selected products
• Guided search where instead of traditional searches, users answer a questionnaire instead
• Shared online areas where users can post products for review by co-workers
• Ability to request product samples from the manufacturer
• Integrated ordering capabilities
• Warehousing and shipping of product on behalf of manufacturers
• Product specification data, warranty data, installation instructions, manuals
• Real-time inventory information
• Real-time pricing information

In short, the list is long. And what results is a true destination purchasing research site and, increasingly, a central marketplace. Find exactly what you need and order it. That’s been the holy grail of buying guides for decades, and it’s finally becoming a reality.

The other piece of the puzzle is advertising. Because publishers are now building these true destination sites, they can also develop substantial traffic simply because they are offering utility and value. And advertisers respect these highly qualified or often quite large audiences because they are truly “in the market,” and what advertiser doesn’t want visibility when the buying decision is being made. It is, as we like to say, “data that does stuff.”

So while the approach is different, what we see with buying guides is exactly the same as what we see with other forms of data, and exemplifies infocommerce: creating a high value proposition with better, deeper data and tools to act on it.

Lessons From the Data Brokers

Despite its name and author, the new report entitled “Data Brokers: A Call for Transparency and Accountability” from the Federal Trade Commission makes a fascinating read. First of all, what’s a data broker? I believe this label originated within the government to describe aggregators of information about consumers, excluding credit bureaus. As you might expect, this definition includes an eclectic mix of companies. For example, the nine randomly selected companies asked to provide background information for this report are: Acxiom, Corelogic, Datalogix, eBureau, ID Analytics, Intelius, PeekYou, Rapleaf and Recorded Future. The operating scale of these businesses is impressive: one reported that it maintains over 700 billion aggregated data elements, another holds information on 1.4 billion consumer transactions, and still another adds 3 billion new records monthly to its database. And the money is substantial too, with just these nine companies generating over $427 million annually from the sale of consumer data.

So are there lessons for data publishers in the activities of these large providers of personal data? Absolutely. Here’s what I see:

Let’s start with creativity. There seems little doubt these consumer data companies are a number of years ahead of most B2B companies in extracting maximum value from their data. They have developed market segmentation systems, ratings and scores, and powerful analytical tools. They understand the value of historical data to discern patterns and trends, and have rolled out lucrative new products based on data others might discard. Also, these companies truly understand and apply the concept of inferential data. Someone with a pickup truck and a fishing license can be categorized as an outdoor enthusiast, for example.

Perhaps most powerfully, these companies are active in helping marketers to bridge the online-offline divide. They are routinely matching online website registration data to the deep offline data they collect, creating much richer audience profiles. These companies will even embed some of their data in tracking cookies for ad targeting purposes. And of course there is intense marketer interest in understanding online-offline buying behavior.

The segments of the business are interesting as well. Data brokers, in the view of the FTC, break into three types: those who primarily sell marketing data, those who sell risk management data and those who sell people search products (the “background check” and “find anyone online” products that are proliferating these days). Again, we see a progressive industry. It has morphed from the obvious marketing application for its data to risk management. Risk management in the B2C world largely means using data to pre-screen purchases. For example, a risk management product might alert an online vendor that the buyer has had merchandise shipped to an unusually large number of different addresses, a potential indication of fraud. So what’s largely the same dataset has been spun into an entirely new market, and very successfully we might add: for the nine companies in the FTC study, risk management revenues are rapidly approaching marketing revenues. Are there untapped B2B opportunities in risk management? I believe there are.

We also see that with the people search products, the industry has morphed from selling its data to businesses, to selling its data to consumers. This is a sweet pivot that many data publishers aspire to, because with little more than a different user interface, you’ve got a product of interest to a new and vast market.

Another intriguing insight from the report is that none of the companies surveyed obtained all their data from the original source. All were licensing substantial portions of their data from each other. And that makes sense because it allows these companies to move faster, and not have to develop the large staffs necessary to be expert in so many different data sources. Why build your own capability to pull in home ownership data from the source when you can license it from someone who gathers it as a primary business activity? And these licensing agreements, not surprisingly, have grown complex, with some even containing clauses preventing “reverse engineering” of licensed data elements as a way to keep other data brokers from getting too clever with licensed data.

What this report offers is an inside peek into the operations of some very savvy, large and successful consumer data providers, and there you’ll see the future of the business data industry as well.

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?

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.

Everything Has Its Price

An excerpt from a new book by former Time Inc. executive Walter Isaacson makes a point that is still not fully appreciated by everyone in the content business:

“At Time Inc., we initially planned to charge a small fee or subscription, but Madison Avenue ad buyers were so enthralled by the new medium that they flocked to our building offering to buy the banner ads we had developed for our sites. Thus we and other journalism enterprises decided that it was best to make our content free and garner as many eyeballs as we could for eager advertisers”

Isaacson confirms an absolutely critical insight: it’s not that “information wants to be free.” The reality is that many of the largest content companies chose to make information free. And with no history to provide a guide, and a sense of a giant gold rush and land grab underway, other content producers followed suit. Soon enough, pretty much all content on the web was free, and guess what: users decided they liked things that way, so much so that any content producer brave enough to offer paid content experienced derision from other content producers and almost militant pushback from users.

All this led to the sorry state of affairs where advertisers have moved much of their advertising dollars elsewhere, and users have been fully conditioned to expect their content for free. Intriguingly, what saved most data publishers from this fate was the fact they typically had little in the way of advertising revenues. Thus, offering free online content was clearly nothing more than an express lane to bankruptcy, and this gave them the backbone to continue to charge for their content. And they are all better off for it.

Even today, it remains true that you can make more money faster selling advertising than selling subscriptions. And that’s why many media companies, with their executives steeped in advertising sales culture, still can’t get fully comfortable with the notion of paid content. Subscription-based businesses are desirable, durable and diversified in terms of the customer base, but these businesses build slowly. Indeed, almost all the characteristics that make subscription-based businesses attractive as businesses make them unattractive to those who grew up selling advertising. It’s truly a cultural issue.

All this leads me to think that the emergence of the freemium and metered models is critical to the future of many content publishers. More and more websites are sporting “plus” and “pro” versions that offer different and supplemental content on a paid basis. The publisher keeps a portion of its content for free, the better to aid discovery and get the user hooked. And a portion of the audience will pay to get even more of that content.

Just as we trained users to expect all content for free, we now must begin the slow but essential process of training them that going forward only some content will be free. You can also argue that this shift simultaneously weans both users and publishing executives off of free content. There are still plenty of eyeballs to sell while at the same time the publishers begin to diversify their revenue streams.

And for those data publishers that have always charged for their content online, I will say just two words: carry on.