Data Trends for Investment Professionals

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Data Monetization: Pricing Your Data Product

Pricing Your Data Product

You’ve built your data product and are ready to put it on the market. You’ve done your due diligence, making sure that your data is clean, predictive and completely scrubbed of sensitive information. You’ve created great relationships with hedge funds and consultants. Everything seems to be in order until you make that first call with a potential client. They ask, “How much do you charge for your data?” In other words, is there any logic to the way you are pricing your data product.

What do you charge, indeed: Is your asking price too high, too low or a Goldilocks-fabled just right? You don’t want to alienate your prospect with an outrageous demand but you also want them to take you seriously. The industry is nascent and pricing is not widely understood; in many cases, it is as much art as it is science. However, there are a few useful guidelines to keep in mind.

Here are some factors that determine the commercial value of a dataset:

Data Edge

Your customers will look to gain an edge from your data. It needs to be either faster or more accurate than what they are using or it must offer a unique insight previously unavailable to them. This seems simple and obvious but one thing that often gets overlooked is just how accurate Wall Street’s estimates actually are. These estimates come from analysts who have been honing their craft for decades. It’s not sufficient to make an accurate prediction; you have to do it faster and better than Wall Street can.

Monetization Strategy

Customers should be able to convert the edge offered by your data into trading or investment profits, via a clear and straightforward monetization strategy. The more direct the connection between your data and a profitable trading strategy, the more valuable your data.

Deep Market

The best monetization opportunities are found in large, liquid markets. Data that predicts the behavior of small or illiquid securities is inherently less valuable. For example, your data may be predictive of penny stock movements. But very few Wall Street professionals will be interested in trading penny stocks, which means your data would not likely find a hedge fund audience.

Uniqueness and Replicability

The more unique your data, the more valuable it can be. Are there others who can replicate either the data you have or the signal you are likely to produce? Are other versions of your data available? Are there proxies that achieve the same purpose? If the answer to all these questions is no, then you are likely to have a very valuable data asset.

Exclusive Access

If everyone in the market has access to a given dataset, it won’t have much value. For this reason, it’s important to restrict access to a few select customers. Exclusive distribution partnerships help create a sense of value for clients.

Table Stakes Potential

New and alternative datasets typically begin life as exclusive assets available only to a select few, at a very high price. Gradually, knowledge of the data diffuses through the market and its value diminishes. However, a few datasets survive and become “table stakes”: they are no longer exclusive but become required purchasing by everyone in the market. At that point, participants who don’t have these datasets are at a disadvantage. The most valuable datasets are those that have the potential to become “table stakes”.

Assets Under Management (AUM)

The single strongest indicator of how much a client will pay for a dataset is how big the client is. This is not just because larger clients can afford to pay more (though that is certainly the case); it is also because larger clients can derive more profit from the same data.

Assume a given alternative dataset is expected to generate 1% in “excess” returns for a client. That dataset would be worth more to a hedge fund managing $5 billion (where 1% = $50 million) than it would be to a fund managing just $100 million (where 1% = $1 million).

Note that we are talking here about “excess” returns: these are the returns that accrue from using the alternative data, above and beyond the returns that the manager would have made without that data.

Return on Data Investment

Given the risks and uncertainty involved in all investing, hedge funds will expect a 10× to 20× return on a data investment. So if a fund expects to make $1 to $2 million in excess trading profits by using your data, they should be willing to pay you $100,000 for that data.

Of course, the same dataset can be sold multiple times, to multiple funds, at no extra cost to you. (The cost of duplicating a data asset is zero). However, this imperative must be balanced against the need to keep your data closely held and hence expensive.

Tiering

One way to maximize income while remaining narrow is to build a variety of data products from the original raw data asset. You can slice it and dice it differently, depending on the profile of the firms you are targeting: fundamental versus quantitative, small versus large, hedge fund versus investment bank, fast versus slow access and so on. This allows you to sell essentially the same data product multiple times without diminishing its alpha.

Exclusivity

In some cases a potential client may ask you for exclusivity — i.e., you can only sell your data to them and no one else. This is a fascinating quirk of the hedge fund world — the fewer the people who have access to a dataset, the more an individual firm can profit from it. Consider offers of exclusivity if the bid is near your expected total revenue from selling the dataset to multiple customers.

The price you charge for your data asset is one of the most important business decisions that will determine the success of your new data venture. Price your data product correctly and you may well be creating the foundation for a lucrative new revenue stream. We hope that this post has helped clarify some points on pricing your data product.

You are now equipped with the basics to determine whether you want to move forward with this potential new revenue stream. You may choose to move forward by leveraging your internal resources. Or you can consider partnering with a data marketplace like Quandl, a fast and effective pathway to transforming your data assets into recurring revenue.

If this blog series has piqued your interest and you think your data could become a new revenue stream, consider downloading our white paper on data monetization for more information.

 

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