Using a personalized approach to collecting information to improve outcomes, Digital Empathy transforms the data collection process to prioritize the needs of different populations.
Why is Digital Empathy important?
Organizations depend on accurate, comprehensive and actionable data to perform. Digital Empathy improves the breadth and quality of data by transforming transactional relationships into deeper ongoing trusting relationships.
Whereas generic data collection approaches focus on the needs of the data collector, Digital Empathy prioritizes:
- People being engaged
- People that use the data
- People that make strategic decisions
Challenges with the Current System
Generic Data Collection Challenges
A one-size-fits-all approach often results in low uptake and poor quality data.
Especially when collecting sensitive and specific data.
Sensitive topics like sexual health, drugs and alcohol, or mental health.
Low Literacy Levels
Resulting in poor understanding of information requested.
Socio-cultural language barriers, heavy use of jargon.
For cognitive, physical, or other disabilities.
Particularly with hard-to-reach populations.
Principles of Digital Empathy
There are four principles of Digital Empathy: Engage, Entrust, Encourage, and Empower. Through frictionless, satisfying, effective design that balances efficacy with efficiency, each design element with Tickit contributes to a Digitally Empathetic experience for beneficiaries.
Digitally Empathetic design helps people feel safe and comfortable enough to share honest and reliable information, resulting in higher fidelity data for the organization.
The Return on Investment
The Benefits of Digital Empathy
Tickit’s inclusive digital solutions help improve data quality, response completion rates, efficiency, and satisfaction while decreasing risks of missing information through surveys, assessments, and educational tools. Here are some benefits and returns reported by our clients from using Digital Empathy:
Higher Response Rates
of students identified with risk factors not previously known.*