Data’s Value Resides In Its Use, So Should Its Price

This article was written by Jennifer Belissent and originally appeared on the Snowflake Blog here: https://www.snowflake.com/blog/data-value-resides-in-use-so-should-its-price/

 

Is FOMO setting in? As the data economy finally heats up, will the fear of being left behind in the market drive more companies to explore how to turn their data into products and services?

Companies across many different industries are already doing so successfully. Manufacturers like GE Aviation or Siemens Mobility offer data services to help customers improve efficiencies and reduce operating costs. Telecom operators like Telefonica and Singtel offer location data services to help retailers understand catchment zones and customer frequency. Retailers offer data services to suppliers to help predict customer demand and improve product offerings. These new data products take a variety of forms, from raw data for developers or data scientists to more derived insights that deliver value directly into a decision-making process or workflow. Embedded insights can be automated into processes like logistics routing or prospect prioritization. The opportunities to deliver customer value are endless and becoming more obvious.

But when taking data to market, a nagging question often rears its head: What’s our data worth? Research across business and academia has explored the question of how to assign a value to data, often resorting to complex theoretical formulas. These don’t work. The bottom line is that your data is worth what a potential customer is willing to pay for it. Taking data to market in the form of a product or service forces the question of price, and ultimately delivers the answer on value.

FOUR ALTERNATIVE DATA PRICING MODELS

It isn’t as simple as it sounds. The pricing exercise includes two components: the business model and the actual amount charged. Let’s first explore some of the alternative models and the trade-offs of each. The following table offers a number of options:

Let’s take a closer look at usage-based pricing.

THE RISE OF USAGE-BASED PRICING

Usage-based pricing is not new. Most of the things we buy are usage-based. We pay for our water and electricity bills on a monthly basis. Most of us, unless we are building reserves for Armageddon, buy food and other necessities on an ongoing basis. We’re all accustomed to the concept of usage- or consumption-based business models. And, most of us are horrified at the thought of wasting money on something that we don’t use.

In the tech world, however, products and services were (and often still are) purchased with big capital outlays, and vast amounts remain unused. “Shelfware”—or as security software is often called, “scareware”—is a common source of “leakage” in CIO budgets, even in today’s SaaS era. According to Gartner, in 2020 only 6% of firms reported that none of their subscriptions went unused, and 40% reported that more than a quarter remained “on the shelf.”

For more and more vendors, usage-based pricing models offer a chance to differentiate by demonstrating value. Adoption is snowballing. According to TechCrunch, 45% of SaaS companies offered usage-based pricing in 2021, up from 34% just a year earlier. Another 11% plan to test the pricing model within a year, and another 23% in two years or more. That means almost three-quarters of SaaS companies will eventually be offering usage-based pricing. But not all companies are going all in at once: a half offer pay-as-you-go pricing and the other half offer usage-based tiers.

As it turns out, the majority of customers want usage-based pricing. People only want to pay for the technology they use, and they detest waste. IDC’s research shows that 61% of organizations worldwide agree/strongly agree that their strategy is to aggressively shift to consumption-driven digital infrastructure purchasing models. The research indicates, however, that preferences differ: Faster-growing companies often prefer usage-based pricing, whereas larger enterprises prefer the predictability of subscription pricing. However, is this due more to inertia than it is to true consumer preference?

Shifting to consumption-based pricing doesn’t mean you can’t predict your costs—it just means you have to predict them differently. In fact, you have to actually predict them. The same is true for vendors. They have to forecast their revenues through true predictions. As Snowflake’s CFO Mike Scarpelli explained, “Our finance professionals don’t turn to spreadsheets to forecast our revenue. Instead, they deploy modern technologies, such as machine learning and AI.” Yes, it might be a little harder for both the consumers and providers, but “no pain, no gain,” right?

USAGE-BASED PRICING FOR DATA

As companies strive to differentiate themselves, they hunt for fresh insights into how to tweak their strategies, their products and services, and their customer experiences. Demand for external data sources is growing. And, that brings all the challenges of any procurement process. Potential data consumers ask themselves: Will we derive value from the purchase? Have we done our due diligence? Will we get what we pay for? Will this data be shelfware?

Snowflake’s Data Marketplace can offer peace of mind when purchasing data.  Our “try before you buy” offer allows data teams to test the impact of data on an analytic model before purchasing. But for many data assets, it’s the usage-based pricing that allows buyers to sleep better at night. They will only pay for what they actually consume. Together these reduce the cost of acquisition, the common barrier to entry.

Yet, it’s more than just about acquisition cost. A lower initial price point means not only less risk but less time spent evaluating the data to reduce the potential risk. And, that gets data scientists back to their real jobs more quickly. It also means that data teams can test more diverse data sets to find real insights to drive value for their customers.

It’s also about greater flexibility, extensibility, and observability. A usage-based approach means that companies can dial their use of data up or down depending on their needs, and not pay for more than they use. This flexibility better reflects dynamic business environments. As data use spreads across business units and functional teams, a usage-based approach can extend out to accommodate new demands. In addition, a usage-based model allows data and analytics leaders to track and monitor how much data is being used, by whom, and for what purposes. This observability allows for greater knowledge sharing and collaboration across projects, and for better accountability and value attribution. A usage-based approach is value-driven, directly linking the price paid with the value generated—or not generated.

And, when the price paid is based on data use, the incentives of the providers and the consumers of data align. For the provider, it’s not about signing a deal and walking away; it’s about developing an ongoing relationship to ensure the customer uses and derives value from the data. More data use delivers more value to both parties, a win-win scenario.

NOW IS THE TIME TO SWITCH TO USAGE-BASED PRICING

Now is the perfect time to make the switch to usage-based pricing, particularly for data products and services. Why? Because we can and because we should. As Mike Scarpelli pointed out in his blog post, one of the biggest challenges for technology providers was the systems. Most companies, other than telecom and utilities operators, didn’t have the ability to monitor and bill for use. In the past, setting up an additional user in the billing system cost more than offering additional access to a new data set or document. Now, Snowflake’s Data Marketplace allows for monitoring and billing by query.

And, with the growing adoption of automation, AI, and data-enabled applications, the consumption of data becomes more continuous. The use of data is no longer tied to a business analyst or a data scientist or to a specific report. Data use is linked to the execution of a business process or an embedded analytic or AI model. Selling “seats” doesn’t make sense anymore. Selling a whole data set at a fixed price doesn’t reflect the value derived from its use. The now familiar adage “data plus use equals value” is best reflected in a usage-based pricing model.

Here we’ve focused on pricing models, and in particular the merits of usage-based pricing. It’s not the only model, but as a newer option it’s certainly worth exploring. In the next blog post, we’ll explain how to determine the actual price. Stay tuned.