projecteverest

Experiment Results

[Experiment Results]: Utility Test Health Amisen April 2019

by
Morgan Allan
Morgan Allan | 1 month ago | in ROA **TRAINING**

Lean Phase: Solution – Utility Test

 

Assumptions:

Customer segment places high value on the following functions:

  • ability to be able to easily access all their medical health data in one area and identify personal tendencies towards certain illnesses (susceptibility).

  • ability to be able to get personalised action plan that assists with treating illness via the pre-stored symptoms database.

  • ability to be able to get personalised action plan that assists with treating illness via the pre-stored symptoms database.

  • ability to be alerted quickly about contagious diseases spiking in geographical area and how to mitigate risk of infection.

  • ability to be quickly and thoroughly diagnosed when being seen by doctor.

 

Time Box:

3 hours

 

Success Metric

  • Scaled interest in solution value (1-7) - value of 5 or over is considered success.

  • Number of people who are identified as successful result.

  • Reach - number of people

  • Conversion rate = (no. of people considered successful interest)/(total reach)

 

Criteria:

Green Light- Proceed to channels

Success Point – Average of 5/7 satisfaction.

Orange Light – Re-evaluate the functionalities and their values to the customer.

Failure Point – Average of 3/7 satisfaction.

Red Light – Re-assess platform (mobile application) that utility tests are being based around.



MVP details:

  • Personal data - ability to be able to easily access all their medical health data in one area and identify personal tendencies towards certain illnesses (susceptibility).

    • Demonstration: Patient receiving tailored medical advice taking into account diabetes and medical history.

  • Diagnosis -  ability to be able to get personalised action plan that assists with treating illness via the pre-stored symptoms database.

    • Patient puts in their symptoms, product diagnoses likely causes of symptoms.

  • Geographic Warnings - ability to be alerted quickly about contagious diseases spiking in geographical area and how to mitigate risk of infection.

    • Patient receiving “there is a large presence of the flu in your area. Recommended mitigations are here....”.

  • Health behaviours - ability to know common and high-risk diseases and mitigate risk of infection.

    • List of behaviours such as washing hands, eating properly listed with their respective risk mitigation outcomes.

  • Medical data - ability to be quickly and thoroughly diagnosed when being seen by doctor.

    • Medical summary of patient, pointing key times of medical episodes which would improve doctor’s speed to help.

 

Data results for customer functionality satisfaction (SCALE: 1-7).

Function Label

Result 1

Result 2

Result 3

Result 4

Result 5

Result 6

Average

Personal Data/tailoring

5

5

6

5

5

7

5.5

Diagnosis

7

6

5

6

6

6

6

Geographic Warnings

6

7

7

6

7

6

6.5

Health Behaviours

6

4

5

5

4

6

5

Medical Data

2

6

6

4

3

4

4.2





Conclusions:



Priority

Function

Success Metric

Average Scaled Satisfaction (1-7)

1

Geographic Warnings

Green light

6.5

2

Diagnosis

Green light

6

3

Personal Data/Tailored Solution

Green light

5.5

4

Health Behaviours

Green light

5

5

Medical Data

Orange light

4.2

 

Next move:

Black Label write up for solutions.

Move to Channels.

edited on 28th April 2019, 08:04 by Morgan Allan

Wade Tink 1 month ago

Status label added: Experiment Results

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