A Media Operator is a media company for those building digital media companies.

Creating Data Co-Ops To Generate SaaS-Like Revenue

Research and data as a business model have been areas that I've been spending time thinking a lot about. While harder to execute, I believe it can generate higher value subscription dollars when compared to news and content-driven subscriptions.

I wrote about this concept of Linear Commerce for B2B a couple of years ago:

At the core of it is identifying what type of information your readers need beyond just straight reporting and then trying to build that for them. While I zero in on data, it doesn’t have to be that. It could be in-depth research where analysts dig into specific topics, spend time with it, and provide something with more information than anything else available.

In the piece, I dig into how FreightWaves has done this with a couple of different products. But something that's not discussed in that piece is that the data business is treated separately from the core media business. When I interviewed FreightWaves' CEO, Craig Fuller, he said this:

These companies trade at very high multiples because they’re data-driven businesses. They provide fundamental data to traders or businesses to make better decisions. Those businesses become immensely valuable, and that’s what our VCs are focused on. What I’ve tried to do is professionalize the management team on our data business. We brought in professional management that manages the sales organization and runs it like a traditional SaaS process. You hire salespeople, business development executives, customer success executives. You build the metrics around SaaS, and that business is run as almost a separate unit.

Even as founder, while I get involved in some of the product discussions, I don’t spend a lot of time managing the day-to-day of our data product, not because I don’t want to, because I’m not good at it. The team that’s involved in managing it is very good at organizational processes and organizing the sales motion that we need around our data product. That’s where a lot of the management infrastructure has gone.

That made a lot of sense and I think explains why Craig has gone on to raise a large sum of venture capital. He's building a much different business versus what others are doing with their media-first businesses.

But let's say you do want to get into this business and generate SaaS-like revenue. How could you?

One concept that I've spent some time thinking about is known as a data cooperative thanks to a well-timed tweet by CB Insight's Anand Sanwal. As a New Yorker, when I hear co-op, I immediately think of real estate where owners of apartments are actually owners of the buildings where your stake is equal to the size of your apartment. A data co-op is similar, but has some key differences.

Essentially, a data co-op is a centralized entity that aggregates data from individuals and then those same individuals get access to the data. This white paper on Lexis Nexis does a nice job explaining the concept broadly and then specifically for the insurance industry:

Every insurance company has its own data: quotes, policies, claims, fraud experience, market trends and more. But when that information is isolated inside each company, each carrier can only achieve a view of its own business. In contrast, when carriers augment their book with market data, they can view their portfolio in the context of the market—as well as achieve new insights that aren’t otherwise possible. 

A contributory database is a collection of data provided by market members to a central repository that is then shared among the contributors. The ideal contributory database adds value by normalizing, standardizing, aggregating and linking contributed data with other information. This enables carriers to drive revenue growth and profitability, improve operational efficiency, drive innovation, access new tools, and protect themselves against security concerns.

Every business wants to know what their competitors are doing. Attempting to aggregate that information independently is timely. And this is the opportunity for operators.

Let's say you ran an insurance publication. You could create the platform that aggregates this information in a structured way. Therefore, every insurance provider answers the same questions at the same time. The data is then anonymized and then made available to everyone involved.

So, where does the money come from?

First and foremost, the data contributors. Let's say I launch a property insurance company called Jacob's Insurance. Yes, my data has value. However, what I really want is everyone else's data. I get nothing by holding back my data, but I get something valuable by purchasing access to the aggregate data. And so, if you have 100 insurance companies that provide data to the co-op, I would argue that you likely have 100 paying customers. Perhaps you have to give them a discount, but you're still providing a service by aggregating the information and should be compensated.

Second, the executives that read your publication are the major customers. While they might not be contributing any information, they are likely going to be very interested in the data that you have aggregated. Would the reinsurers of property insurance companies want to understand the data? Absolutely. Are there investors? Absolutely. Would government want to know? Absolutely.

In both cases, the amount of money you're generating per person is significantly greater than a content-based subscription even if the total addressable market is smaller.

What I like about this model is that the revenue is also much stickier. If you design the data collection around time, you'll be able to track contributor's changing responses. Because the data changes, paying subscribers will never want to churn. As CEO of Jacob's Insurance, I'm going to want to understand what my competitors are doing in three years the same as I will today. And so, this becomes a must have part of running my business.

Another appealing aspect of these co-ops is that your costs for acquiring the data are much lower. Because you are playing aggregator and the data is important to all contributors, you can get it much more easily. And since you likely have readers with the data, it's easier to ask them for the information, structure it, and then sell it.

Marketing becomes easier because you can use some of the data in stories that then promote the paid product. When I spoke with Craig at FreightWaves, he said that they will often write stories that use data from Sonar because it helps readers understand how they should be using it. In some cases, you might be able to generate new subscriber dollars without any marketing spend. Back to my interview with Craig, he said:

Our investors get two numbers, they get the CAC number, which is what gross customer acquisition costs in a traditional metric, and then they get what we call NetCAC, which is a term that we invented. Which is if you took all of the margins in media, which are quite large, and you put it against the traditional customer acquisition costs, what is your NetCAC? And that’s actually negative now.

What it means is we’re able to grow our business without basically having to use capital to acquire new customers. It’s created this immensely valuable flywheel. To describe what I do as I spend most of my time driving the evangelism of the business through our media outlet. Making sure that the media business is focused on evangelizing the core components of our SaaS business, that maybe we have a new product or new data set we’re trying talk about. Making sure it’s embedded into the communication stream of our media business.

That's powerful. A truly diversified media business can generate revenue from their readers and users in various ways. This just becomes another channel for monetization.

I had a boss once who said that he thought the most successful b2b media companies were three-legged stools. The media and events businesses were the first two legs. And data/research was the third. Being able to grow this type of business is certainly harder, but if you can figure out what type of data your readers would both want and provide, you might be able to build a co-op that gets them excited.

Thanks for reading. I'd love to hear about ways you've monetized data at your company. Join the AMO Slack and let us know! Have a great weekend.