Quantifying the 'Return on Data' for Consumer-Service Exchanges
Consumers should evaluate the utility they receive from digital services against the personal data they provide to ensure a fair exchange.
eYLS (Yale Law School) · 2019
Key Findings
- 01The 'Return on Data' (ROD) is a critical but underexplored metric for evaluating data-for-service transactions.
- 02Current legal frameworks primarily focus on data protection rather than the balance of utility and data provided.
- 03Consumers lack a clear method to compare the value proposition of different data exchange offers.
Application
Design takeaway
Design products and services where the perceived utility for the user significantly outweighs the personal data requested, and make this value proposition clear.
How to apply
When designing a new digital service, map out the specific user utilities provided and the personal data required, then consider how to maximize utility while minimizing data collection, or clearly communicate the value exchange.
Project actions
- 01When designing a digital product, consider how you will communicate the value exchange to users.
- 02Explore ways to quantify user utility and data input for your specific design project.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Introduces a novel and relevant metric for evaluating data exchanges.
- +Highlights a critical gap in current consumer protection discourse.
Limitations
Quantifying 'utility' and 'data value' can be subjective and difficult to measure precisely in a design project.
Reliability & validity
The theoretical nature of the ROD framework means its reliability and validity would need to be established through empirical testing of user perceptions and behaviors.
Think critically
To what extent can 'utility' and 'data value' be objectively measured, and how might subjective perceptions influence the perceived 'Return on Data'?
Design Principles
"Strive for a transparent and favorable 'Return on Data' for users in all data-driven service designs."
Understanding the 'Return on Data' (ROD) allows designers and businesses to create more transparent and equitable data-for-service agreements. This framework can inform product development and marketing strategies by highlighting the perceived value exchange for users.
What This Means for Your Design
Think about what you get (like a cool app feature) versus what you give away (like your personal info) when using online services. Is it a fair trade?
How to use in your project
- 1.Use the ROD concept to justify design decisions related to data privacy and user benefit.
- 2.Analyze competitor services using the ROD framework to identify design opportunities.
Add to My Project
Quick Cite
(2019). Return on Data: Personalizing Consumer Guidance in Data Exchanges. eYLS (Yale Law School). Retrieved from https://designdex.org/study/2c78e45b-e8c1-4821-94dd-f3793352adb8/quantifying-the-return-on-data-for-consumer-service-exchanges
Paragraph starter
The 'Return on Data' (ROD) framework, conceptualized as the ratio of utility gained to data provided (ROD = U/D), offers a valuable lens for evaluating the fairness of digital service exchanges. This concept highlights the need for designers to ensure that the benefits users receive are commensurate with the personal information they share, fostering greater transparency and user trust in the design of data-driven products and services.
Source
eYLS (Yale Law School)
Return on Data: Personalizing Consumer Guidance in Data Exchanges
journal · 2019
View sourceQuestions about this research
- What does the research say about quantifying the 'return on data' for consumer-service exchanges?
- Design products and services where the perceived utility for the user significantly outweighs the personal data requested, and make this value proposition clear. Evidence: eYLS (Yale Law School) (2019).
- Why does "Quantifying the 'Return on Data' for Consumer-Service Exchanges" matter for design?
- Understanding the 'Return on Data' (ROD) allows designers and businesses to create more transparent and equitable data-for-service agreements. This framework can inform product development and marketing strategies by highlighting the perceived value exchange for users.
- How can designers apply this research?
- Design products and services where the perceived utility for the user significantly outweighs the personal data requested, and make this value proposition clear.
- What were the main findings?
- The 'Return on Data' (ROD) is a critical but underexplored metric for evaluating data-for-service transactions.. Current legal frameworks primarily focus on data protection rather than the balance of utility and data provided.. Consumers lack a clear method to compare the value proposition of different data exchange offers.
- What research method was used?
- Conceptual framework development and theoretical analysis.
- How strong is the evidence?
- Evidence strength is rated Moderate effect, based on a 2019 journal from eYLS (Yale Law School).
- What should I do differently in my next project?
- When designing a new digital service, map out the specific user utilities provided and the personal data required, then consider how to maximize utility while minimizing data collection, or clearly communicate the value exchange.
- What are the limitations?
- The paper is theoretical and does not provide empirical data on how consumers perceive utility or quantify data value.
- Is there evidence that return data affects design outcomes?
- The study introduces a 'Return on Data' (ROD) metric to measure the value consumers receive from services relative to the personal data they share, highlighting a gap in current consumer protection and comparison tools. Understanding the 'Return on Data' (ROD) allows designers and businesses to create more transparent Source: eYLS (Yale Law School) (2019).
- Where does this data rod research apply?
- Digital services and data exchange It sits within innovation & markets research on designdex.org.
Related research topics
return data design research · evidence on return data · does return data improve design outcomes · data rod studies for designers · return data and data rod findings · innovation & markets research evidence