Automotive Telematics Case Study
Client Data Profile
• Nasdaq listed company in the Automotive Telematics sector.
• Client has positioned itself as a data, analytics and software-as-a-service provider that analyses connected and electric vehicle data to create real-time insights.
• The business model is B2B for fields of use (FOU) of telematics data, insurance data, vehicle diagnostics data as well as subscriptions to global car manufacturers, local governments, government authorities etc.
Objectives
• The client sought a data valuation to reinforce the financial strength of the business model.
• To provide a value for off balance sheet data assets in order to leverage their data assets as collateral within a financing transaction.
• The client intended to receive funding of $100 million for recapitalization and utilise the data valuation as quantifiable evidence of data driven success.
• The client additionally sought to understand the use of the data valuation in relation to data funding and leverage for business growth.
Our approach
Methodologies Utilised
A mathematical hybrid of:
Hierarchical Consumption Method: This was a cost-inclusive, consumption-based method, which relies on data ownership, a repository or platform with consumable data and consumers to consume the data. Use cases were clustered to accurately represent consumption.
Cost Method: Determining the price point and replacement value of data, with client specific financials and statistics, in addition to extra research to ensure the values align with industry standards
Rationale for models selection
Hierarchical clustering was selected due to the existence of an owned database, defensible data acquisition, maintenance costs and consumers of data. Additionally, multiple use cases that can be fitted into clusters to further aid the accuracy of the valuation.
The Cost Method was selected because the data was seen to be revenue generating, owned and with a market value. The client also had purchasers of data and quantifiable enhancement to their data to determine a price point.
Insights & Recommendations
• Data value by use case cluster
• Data value as an intangible asset
• Pricing validation and replacement value
• New KPI’s based on data asset value for statutory reporting
• Recommendations on data asset backed lending for collateralization and leverage
• Advanced data value forecasts, up to 5 years into the future