Variable pricing is a well-known pricing strategy that changes the price for the same product or service based on factors such as time, date, sale location and level of demand. Implemented properly, variable pricing is a powerful tool to optimize revenue.

The downside to variable pricing is that it has a bad reputation. For example, when prices go up at times of peak demand (which often translates into times of peak need), that’s variable pricing. Generally speaking, when you notice variable pricing, it’s because you’re on the wrong end of the variance.

Variable pricing lends itself nicely to data products. But rather than thinking about a traditional variable pricing strategy, consider pricing based on intensity of usage.

Intensity of usage means tying the price of your data product to how intensely a customer uses it – the greater the use, the greater the price. Intensity pricing is not an attempt to support multiple prices for the same product, but rather an attempt to tie pricing to the value derived from use of the product, with intensity of usage a proxy for value derived from the product.

For data producers, intensity-based pricing can take many forms. Here are just a few examples to fuel your thinking:

1.         Multi-user pricing. Yes, licensing multiple users and seats to large organizations is hardly a new idea. But it’s still a complex, mysterious thing to many data producers who shy away from it, leaving money on the table and probably encouraging widespread password sharing at the same time. The key to multi-user pricing is not to try and extract more from larger organizations simply because “they can afford it,” (a contentious and unsustainable approach), but to tie pricing to actual levels of usage as much as possible.

2.         Modularize data product functionality. Not every user makes use of all your features and functionality. Think about identifying those usage patterns and then re-casting your data product into modules: the more modules you use, the more you pay. We all know the selling power of those grayed-out, extra cost items on the main dashboard!

3.         Limit or meter exports. Many sales-oriented data products command high prices in part because of the contact information that they offer, such as email addresses. Unfortunately, many subscribers still view data products like these as glorified mailing lists to be used for giant email blasts. This is a high intensity use that should be priced at a premium. A growing number of data producers limit the number of records that can be downloaded in list format, charging a premium for additional records to reflect this high-intensity type of usage. It’s similarly possible to limit and then up-charge certain types of high-value reports and other results that provide value beyond the raw data itself.

4.         Modularize the dataset. Just as few users will use all the features available to them in a data product, many will not use all the datamade available to them. For example, it’s not uncommon for data producers to charge more for access to historical data because not everyone will use it, and those who do use it value it highly. Consider whether you have a similar opportunity to segment your dataset.

While your first consideration should be revenue enhancement, also keep in mind that an intensity-based pricing approach helps protect your data from abuse, permits lower entry-level price points, creates up-sell opportunities, and properly positions your data as valuable and important.

There are competitive considerations as well. When you are selling an over-stuffed data product in order to justify a high price, the easiest strategy for a competitor is to build a slimmed-down version of your product at a much lower price – Disruption 101. You simply don’t want to be selling a prix fixe product in an increasingly a la carte world (look at the cable companies and their inability to sustain bundled pricing even with near-monopoly positions).