Automotive Case Study
Client Data Profile
• Client has over 70 million data points spanning parts and services, invoices and financial information, CRM and vehicle information.
• A B2B and B2C SaaS services for retail of new and pre-owned Trailer industry parts, servicing, Commercial and Personal Financing products.
Objectives
• The client had a data valuation completed to reinforce financial strength of existing data driven assets to enable them to leverage their data assets as collateral within a financing transaction.
• The client was additionally looking to understand the use of the data valuation in relation to data funding and leverage for business growth.
• To understand the value of the client’s customer list/CRM data in order to add value to their balance sheet.
Our approach
Day 0-5
Framing Activities
• DVP considered the data value to be directly proportional to its impact, where impact is created from the transformation of data from descriptive to prescriptive
• DVP completed a thorough analysis of data repositories and with an in-depth review of financial information, historic and future value
• Analysis of data operations, acquisition, licensing, subscription, cost improvement and more were considered in the data valuation
• Checking essential assumptions on data ownership of the data
Day 6-15
Collateral Activities
• Select mathematically driven valuation methodology based on prior information, data analysis, ownership of data repository and themes
• Consideration of replacement value of data
• Performing customer list valuation
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Day 15-30
Valuation Activities
• Insights on data value and recommendations on how to leverage data value were obtained
• Valuation calculation and creation including CRM/customer list
• Data decay rate and data quality weighting determined
• Reports and fine tuning to finalise valuation
• Additional intangible asset value obtained
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Methodologies Utilised
A mathematical hybrid of:
Customer Lifetime Value Method: Determining the customer list value using relative customer value, duration and costs associated with storing customer information.
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
Due to the existence of an owned data repository, replacement costs for data that can be leveraged and the existence of customer information in a CRM database.
Insights & Recommendations
• 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
• Additional intangible asset value