Sharing in Private

While there are many, many B2C ratings and review sites where consumers rate and otherwise report their experiences with businesses, there are relatively few B2B sites where businesses rate other businesses. There are multiple reasons for this, but prime among them is that while businesses tend to have a strong interest in using this kind of information, they typically don’t want to supply this kind of information. In short, they see competitive advantage in keeping their vendor experiences confidential.

One fascinating example of this in the legal market is a company called Courtroom Insight. Originally founded with the simple and reasonable idea of creating a website where lawyers could rate expert witnesses (experts hired by lawyers to testify in court), the company hit this exact wall: lawyers didn’t want to tell other lawyers about which experts they did and didn’t like.

Rather than close up shop, though, Courtroom Insights pivoted, in an interesting way. It discovered that large law firms were very sloppy about keeping records of their own expert witnesses. So, Courtroom Insights built a database of expert witness from public sources and licensed data. It then went to large law firms an offered them an expert witness management database. Not only could lawyers search for expert witnesses and verify their credentials, it could flag those experts they used, along with private notes that could be shared freely within the law firm, but not externally.

This pivot created a nice business for Courtroom Insights but it wasn’t done. Since all of its large law firm clients were sharing the same database, but also individually flagging the experts they were using, could Courtroom Insights convince them to share that information among themselves? Recently, they offered this “who’s using who” data to its clients on a voluntary, opt-in basis. And it worked. While not every client opted in, enough did so that Courtroom Insights could make another level of valuable information available.

While this is just my personal prediction, I think Courtroom Insights will ultimately be able to offer the expert witness ratings that it originally tried to provide. How? By using the protected space of its system to let lawyers trade this high-value information with each other. It will probably start small: perhaps lawyers could click a simple “thumbs up/thumbs down” icon next to each expert that could be shared. But I also suspect that if Courtroom Insights can crack the initial resistance to share information, the floodgates will open, because lawyers will realize they are communicating only with other lawyers, and because the benefits of “give to get” information exchange becomes so compelling.

The Courtroom Insights story provides a fine example of the power of what we call the Closed Data Pool in our Business Information Framework. Sometimes data that nobody will share publicly can in fact be shared among a restricted group of participants, with of course, a trusted, neutral data publisher making it all happen.


Meet Three of the Best…

We created the Infocommerce Models of Excellence program back in 2003 to recognize data products that provided good examples of innovation in business models, markets served, content innovation and technology.

The point is that they offer fresh ideas and approaches that are applicable across many market verticals, and that’s why they’re worthy of attention and study.

The three honorees this year, in no particular order are:

Franklin Trust Ratings, a healthcare data provider that has not only done an impressive job integrating nearly a dozen public domain databases, but has an innovative business model as well. Franklin Trust founder John Morrow is building his business around the idea of “democratization of data.” In simple terms, this means that that he is consciously building an end-user analytics tool, using a powerful but simple user interface with pricing that makes it accessible to all. In an era of five- and six-figure analytics-driven data products built for trained data analysts, Franklin Trust has built an analytics product that can be used by anyone, and that anyone can afford.

Savio has developed a talent marketplace for the marketing research industry. Talent marketplaces aren’t a new concept. Indeed, it can be argued that they helped jumpstart the growth of the gig economy. Savio has bigger ambitions, however, seeking to tap into the growing shift to automated vendor discovery and procurement, as well as to start to evolve its existing online buying guide product to a truly transactional meeting place for buyers and sellers. It’s almost inevitable that all buying guides will have to move in this direction. Savio is blazing the way.

LexisNexis Risk Solutions is one of the largest and best-known data players out there, but that’s not inhibiting it on the innovation front. Consider its new Active Insights product for the insurance industry. How many millions (billions?) of dollars have been spent building out cutting-edge lead generation technology. Whatever the number is, it’s huge. How much has been spent on similar technology for custom retention? It’s a tiny amount by comparison.

What Active Insight does is simple yet brilliant. It flips lead gen technology on its head and applies it to customer retention. An insurance company supplies its custom file to LexisNexis Risk Solutions, which monitors them in real-time for trigger events. Did a customer just put her house up for sale? LexisNexis Risk Solutions spots the real estate listing and tells the insurance agent to get in touch with the customer ASAP to help her seamlessly transfer her coverage to her new home. We have the technology. This is just a great new way to put it to use.

All three of our 2017 honorees will be presenting at the annual BIMS conference next week in Ft. Lauderdale just. Their creators are people you’ll want to meet to put their experience and innovation to work for you in your industry. See you there.


Subscription Package Pricing: The Right Choice Makes All the Difference

The rush to adopt the subscription model to all kinds of businesses has become a frenzy. After all, what business wouldn’t want to make its revenue more dependable and automatic? But the subscription model needs to be fully understood and properly executed to reap its benefits. Let me explain.

When I recently made the move from a PC to a Mac, I knew I would have to buy some software over again. I dutifully went to the Adobe site to get the Mac version of Adobe Acrobat. Imagine my surprise when I discovered Adobe only sells software by subscription, in this case $12.99 per month, forever. Sorry, I just don’t make that many PDFs. Perhaps Adobe made a conscious decision to lose some of its customers as it shifted itself to a recurring revenue model, but forcing your customer base to buy on a subscription basis is a risky one.

Another company I looked at has a neat online product where you input raw data and it makes very impressive, high-end charts that you can download. I felt I could make regular use of this product, and was willing to pay some modest amount per month for it. But the company only offered three subscription options: a “free” plan that was so limited it wasn’t much more than a product demo; $14.99 per month for a “pro” version that still had annoying limitations (for example, the company’s name would appear in every slide), and an “organization” version for $1,000 per month – finally, all the features, but at a heady price. In short, these plans provided no option for a serious by low-volume commercial user. Sorry, no sale.

Poorly conceived subscription plans are everywhere. Here are four things to consider as you plan your subscription packages:

  • Free plans are meant to build loyalty and usage among low-volume users, some of whom will eventually move up to a paid plan with you. If you cripple your free offering to the point where nobody can get any real value out of it, you’ve shot yourself in the foot. A free plan is not the same as a product demo. It’s used to attract users and grow them over time into customers.
  • To maximize revenue, design a plan for serious but low-volume users. There are lots of people who want access to all your product features but won’t use your product every day. A plan that offers a low monthly fee but only offers half your features is not the same thing.
  • Limiting features in your mid-priced subscription plans in order to “force” users to buy your premium plan often will backfire. If I am a single user, I will never by a 5-user plan for a lot more money to get the features I want
  • Carefully consider price differentials between plans. I have seen products that offered three price points: free with limited functionality, $999 per year and $10,000 per year. Three sizes will rarely fit all user profiles.

The subscription model is a great model. But its success lies in how you choose to implement it. 

Blockchain: The Next Big Thing

We all lived through the heights of the social media craze when every new product needed a social aspect in order to succeed (success is defined as getting funding). My personal favorite was the backyard grill thermometer that posted the temperatures of what you were cooking to Facebook and Twitter. (Okay, there was a little more to it than that, but not much).

But as an Internet fad, social is starting to cycle down, meaning that another Internet fad needs to take its place. My nomination: blockchain.

You have doubtless heard of blockchain, although the odds are you don’t know exactly what it is or what it does. Most people don’t. My understanding of it is sketchy. But when it comes to the Internet, complexity is a benefit because everyone salutes when they hear about a new service using blockchain, without being able to ask any tough questions about how or why.

A great example of this is a restaurant review site called Munchee. Munchee plans to disrupt sites such as Yelp and Zagat in part by using blockchain technology. Think about that for a while. Or better yet, don’t think about it. You’ll get a headache.

Munchee has a few interesting twists to it. First, it’s meant to be more granular than sites like Yelp, by focusing on the individual dishes a restaurant serves, based on the belief that all dishes served by a particular restaurant are unlikely to be of equal quality. You might doubt the need, but it’s a plausible idea.

Munchee also wants to correct for sample bias in reviews. It’s well understood that people are more likely to post a review when they are dissatisfied. Munchee wants to get around this problem be rewarding all reviews with tokens that can be redeemed at restaurants or even sold to other Munchee participants for cash. If you are getting paid for every review, the reasoning goes, you’re as likely to create a positive review as a negative one. Again, an interesting idea.

To get even more accuracy, Munchee wants all reviews to be peer-reviewed by other Munchee users. Munchee intends to recruit peer reviewers by using (buzzword alert) machine learning to find the other Munchee users best qualified to pass judgment on the review. Still again, the notion of peer review is an interesting one.

So where exactly does blockchain come in? Does it, for example, somehow definitively tie the reviewer to the restaurant, in order to eliminate false reviews? Well, no. Instead, those award tokens that Munchee offers are actually crypto-tokens that are tied to the Ethereum blockchain. That’s it.

Munchee actually has some fresh approaches to review platforms, but it apparently couldn’t resist the temptation to bolt on a tenuous blockchain application to sound even cooler and more cutting-edge. Unfortunately, that works to obscure the more basic ideas it has that are likely to be where the real value is created. We all need to be careful not to fall into the trap of rushing to adopt new technologies just because they get a buzz around them. You’ll only end up confusing your customers … and yourself … about the true ways you offer value.


Data Marketplaces: Almost There

There has been much excitement about the recent launch of the Salesforce Data Studio, a new data-sharing platform within the Salesforce Marketing Cloud.

The idea of the Data Studio is simple: marketers can, on a fully automated basis, identify, order and integrate datasets that others are offering for sale. In its early implementation, the Data Studio seems mostly like a cool way for marketers to buy email lists. But the vision is much bigger and more interesting: to allow marketers to augment and overlay existing email lists with more data so that they become smarter about their lists, target their efforts more effectively, and get better results.

Data Studio at time of launch is heavy on audience data, mostly from larger publishers, but there’s no reason any data publisher couldn’t participate as well, especially if the Data Studio wants to exploit its full potential.

Interestingly, Salesforce is not the only big player that has an interest in data marketplaces. The Amazon Web Services Marketplace sells software through its marketplace – again, a totally automated buying experience – but it also offers a selection of public domain datasets for free. It’s a small jump then for Amazon to start selling databases on behalf of others.

As you can see, neither of these two marketplaces is quite ready for prime time as far as becoming a meaningful sales channel for data publishers, but they’re tantalizingly close. Keep an eye on these marketplaces: they could become very important to data publishers very quickly.