Pricing strategy is a core component of any business. While it is important, implementing a beneficial pricing strategy is not a small feat, especially for business that sell unique products or services. Selling intangible assets that have time based price degradation, are non-depletable and are prone to overcrowding (we define “overcrowding” as value decay when many users are exploiting an asset), further complicates pricing strategy formulation. Data is such an asset when used in trading.

In fact, the value of data can decrease overtime and hence, the same pricing strategy may not work even just one hour into the future. It is a non-depletable asset as it can be replicated without loss. It is prone to overcrowding as the more traders act on the same information, the less the value of it. This poses quite a challenge given that data plays an important role in the 21st century across all businesses and an appropriate pricing mechanism is still missing.

In the recent years investment managers have started to notice the possibility of using a new source of data called alternative data to give them competitive advantage (Denev, Mining alpha through alternative data, 2019)  in addition to their traditional usage of financial data. While this shows that there is a huge growth opportunity for data vendors, the pricing problem for alternative data still stands and is even more pressing. How can data vendors price their products appropriately to a fair transaction to both the buyer and the seller of these complex data assets? Currently, data is priced in ad-hoc way and sometimes differentially according to the size of the data buyer. For more information see (Denev, The Book on Alternative Data, 2020).

We discuss here a potential solution to this problem based on auctions. Auctions, in their many forms are a long-standing approach for achieving optimal pricing for sales while restricting the number of buyers thus avoiding overcrowding. In today’s fast-paced information driven environment, the auction process needs new features to adapt and optimize revenue generation for intangible assets such as data.


A data pricing strategy

We conducted a study research to assess the market potential of a new solution that will assist data vendors in generating a robust pricing strategy for non-tangible products. Our solution, which specially targets, is based on a customised version of the Vickrey Auction (Vickrey, 1961). There is an increasing number of successful implementations based on a hybrid Vickrey auction mechanism. One example is the Vickrey-Clarke-Groves (VCG) mechanism which is utilised by Facebook to sell their ads platform and by Google for their contextual ads platform (Varian & Harris, 2014).

A customised Vickrey Auction could help data vendors significantly given its property to encourage bidders (in this case prospective data buyers) to reveal how much they are willing to pay for the data products. The approach eliminates the need for the expensive market research (money and time wise) to determine the right pricing strategy for a new data product. It also overcomes the risk of a market research not being an accurate reflection of reality, which occurs when potential data buyers hide their true valuation of the data products and hence, give a wrong signal. Additionally, data vendors can restrict the number of licenses to avoid overcrowding.

How does the TSA work?

Our Two-Stage Auctioning (TSA) system is primarily intended for any data products. Data vendors firstly determine the number of winners. Data buyers then submit two bids: 1) the first bid is used for rights to use the data whereas 2) the second bid is used for exclusivity rights over the data. The auction is done in two stages: in the first stage, data vendors choose the ‘n’ number of winners. The winners then proceed to the second stage of the auction where one of them may trigger an exclusivity right over the data only if certain conditions are met. If there is no winner in the second stage, the data is shared among the initial ‘n’ number of winners. A detailed version of the TSA system can be found in our white paper.

Could exclusivity add risk?

In an era where data is prevalent and has unprecedented applications, regulations that revolve around data rights and usage, such as the GDPR, are important and much needed. In the past, data related scandals were widespread and damaging to the reputation of several institutions and companies. One example is the early audio streams of Bank of England press conferences (Delphine Strauss, 2019). An investigation revealed that a third-party supplier was providing the back-up audio feed from the press conferences to high-speed traders a fraction of seconds before the video feed went live. In a very competitive financial industry, an early access to information (even though it’s only in fractions of seconds) can make a great difference. Another example is the current antitrust allegations against Amazon. The company is under investigation for allegedly using data on third party sellers that use Amazon’s platform to gain advantage in the market (Lee D. , 2020). A different scenario can be found around the exclusivity provisions over clinical data in the pharmaceutical industry (Lee, Khan, & Ming, 2016). Firms that develop original products are granted exclusive rights over their clinical data for a period of time (usually years) and hence gain significant advantage.

The examples above illustrate how data exclusivity rights can be a sensitive matter. It can lead to predatory and non-competitive behaviour, but it can also bring incentive for innovation. In the light of all this, where does the proposed here solution stand? Our intention has always been to help our clients to get a sense of the value of their data products. The solution is meant to serve as the backbone of developing a good pricing strategy for data vendors. Furthermore, in order to protect any data vendors from any risk, the solution could be applied only to newly created products, meaning data products that have not yet been rolled out to the public, without disrupting any existing pricing arrangements.

Advisory services can be provided in this space to assess risks associated with exclusivity rights on a case-by-case basis and determine whether or not exclusivity have any negative impact.

Conclusion

Despite the potential for growth, data vendors still struggle to find the “right” price for their non-tangible products such as data. In this blog we provided an approach to this challenge: our analysis suggests that by applying the auctioning model that we developed, data vendors can identify different groups of bidders and facilitate transactions at optimal values. This model considers any potential impact associated to exclusivity rights.

For more information, please refer to our technical paper “Auction Pricing Strategy for Data Vendors”. In that paper we provide an in-depth description of our auctioning model and conduct simulations of different real-life scenarios using Ebay data.  


Bibliography


Bradshaw, T. (2019, October 30). Facebook agrees to pay fine over Cambridge Analytica scandal. Retrieved from ft.com: https://www.ft.com/content/790270b2-fb16-11e9-98fd-4d6c20050229
Delphine Strauss, C. G. (2019, December). FCA probes early audio streams of Bank of England press conferences. Retrieved from ft.com: https://www.ft.com/content/e5e1daea-21f6-11ea-92da-f0c92e957a96
Denev, A. (2019, December 10). Mining alpha through alternative data. Retrieved from uk financial service insight: https://ukfinancialservicesinsights.deloitte.com/post/102fvpo/mining-alpha-through-alternative-data
Denev, A. (2020). The Book on Alternative Data. New York: John Wiley & Sons Inc.
Guardian, T. (2019, December). Watchdog investigates Bank of England security breach. Retrieved from The Guardian: https://www.theguardian.com/business/2019/dec/19/hedge-funds-hacked-into-bank-of-england-briefings
Lee, D. (2020, May 16). Amazon pledges to work with Congress on antitrust concern. Retrieved from ft.com: ft.com/content/a14c3969-52a6-476e-90af-a61770db15a4
Lee, K., Khan, T., & Ming, L. (2016). Review of data exclusivity provisions in the Trans-Pacific Partnership Agreement. Research in Social and Administrative Pharmacy.
Nuttall, C. (2019, July 5). Adtech industry faces grim reaper over data harvesting. Retrieved from ft.com: https://www.ft.com/content/c14acf56-9f75-11e9-9c06-a4640c9feebb
Varian, H. R., & Harris, C. (2014). The VCG Auction in Theory and Practice. American Economic Review.
Vickrey, W. (1961). Counterspeculation, Auctions and Competitive Sealed Tenders. The Journal of Finance.