Learning Launch 

 Challenge : A short while ago , as the principle UX consultant in an organization , I was asked to lead a “ tiger team ” to   revamp the CRM system for a large Telco Service Provider . From the business perspective , some of the key issues were   obvious : Many unintegrated applications ( 23 to be exact ) , steep learning curve , many unresolved customer calls , too   many call transfers and a high average handling time for common tasks . However , it soon became clear from initial SUS   scores and ad - hoc interviews that users were frustrated with the system . It was unnecessarily complicated and did not   approximate the mental model of actual users in the real world . 

 Accordingly , we kicked of the project with discovery and fact - finding – What is … We received from the client ,   operational statistics for a two - month period , previous UX studies , screen shots and user guides of the existing system ,   system architecture , business processes , business requirements and the data model . Together with our due diligence ,   we visited 5 client call centers where we observed users going about their tasks in passive observation or “ side - by- sides ” . We interviewed 8 different users and toured the call centers . The challenge was to present a solution for 3 agent   types where the delta between the existing system and our projected solution would be compelling 

 Selection : Once the gathered information was elicited and processed , we brainstormed our findings with a diverse   audience from the client ’s business , IT and our team . This was necessary to identify actionable insight , prioritize   elements of our solution , but most significantly , to guide us to the next step of the improvement process : what are our   key assumptions / hypothesis and how will we validate these assumptions ? We chose the Launch Process tool to test the   validity of our prioritized key assumptions . More specifically , we established measurable UX goals to validate , including :   a ) for all frequent tasks , it must be possible to navigate from anywhere in the system to anywhere else in the system   with no more than two user actions b ) All actions must be in context and What You See Is What You Need – no more and   no less c ) First call resolution + call deflection will increase by 30 % and average handling time per frequent task will   decrease by 30 % d ) User satisfaction ( SUS ) will increase from 56 % to 85 % 

 Application : The test we designed to validate our key assumptions ( in the form of goals ) was based on a mock - up of the   12 most frequent flows and scheduled a “ walk through ” to a large audience of diverse stakeholders in a collaborative   brainstorm / design review . We began by stating our assumptions behind our proposed ( baseline ) solution that needed to   be confirmed or disconfirmed – for example , for every user step , we asked , “ Can this be further simplified ” , “ What is   wrong with the existing way of doing things ” “ Is there a better way of accomplishing this task ” , “ What are the   dependencies and constraints ” “ What is the learning curve for this task ” . 

 Insight : Shortly following the testing of my hypothesis / assumptions , I became aware that I may be getting caught up in   “ experimenter ’s bias ” ( similar to confirmation bias , but in the reverse : to design a test that will confirm the   assumptions ) . To optimize an existing system in terms of system integration , task completion rate and task time is not   that difficult , but from a user ’s perspective , that was not their focal pain point . It turns out that the cause behind so   much of users ’ frustration was that “ they were leading the system instead of the system leading them ” . Indeed , the   most focal heuristic as far as users were concerned was “ Can the system do more to alleviate the cognitive load on the   user ” . As a result , we fleshed out all frequent tasks ( per persona ) to explore inferences where the system can do more   on behalf of users and lead users to make the correct choices in context . This had an impact not only on the UI and   interaction design , but on system integration , automation , navigation , etc . 

 Approach : While this project that continued for 18 months and involved over 300 people was wildly successful ( savings   of > $ 250 M in the first year after rollout and increase of user satisfaction from SUS 56 % to SUS 90 % ) , there were lessons   to be learned . My major learned lesson was to have brainstormed a lot more with the client at the discovery phase   ( What Is ) that would have better informed the assumptions that I tested – for example , “ in the context of constraints ,   the system will lead users to make the correct choices in context and perform anything and everything that users can not   perform themselves ” 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Learning Launch 

 Challenge : A short while ago , as the principle UX consultant in an organization , I was asked to lead a “ tiger team ” to   revamp the CRM system for a large Telco Service Provider . From the business perspective , some of the key issues were   obvious : Many unintegrated applications ( 23 to be exact ) , steep learning curve , many unresolved customer calls , too   many call transfers and a high average handling time for common tasks . However , it soon became clear from initial SUS   scores and ad - hoc interviews that users were frustrated with the system . It was unnecessarily complicated and did not   approximate the mental model of actual users in the real world . 

 Accordingly , we kicked of the project with discovery and fact - finding – What is … We received from the client ,   operational statistics for a two - month period , previous UX studies , screen shots and user guides of the existing system ,   system architecture , business processes , business requirements and the data model . Together with our due diligence ,   we visited 5 client call centers where we observed users going about their tasks in passive observation or “ side - by- sides ” . We interviewed 8 different users and toured the call centers . The challenge was to present a solution for 3 agent   types where the delta between the existing system and our projected solution would be compelling 

 Selection : Once the gathered information was elicited and processed , we brainstormed our findings with a diverse   audience from the client ’s business , IT and our team . This was necessary to identify actionable insight , prioritize   elements of our solution , but most significantly , to guide us to the next step of the improvement process : what are our   key assumptions / hypothesis and how will we validate these assumptions ? We chose the Launch Process tool to test the   validity of our prioritized key assumptions . More specifically , we established measurable UX goals to validate , including :   a ) for all frequent tasks , it must be possible to navigate from anywhere in the system to anywhere else in the system   with no more than two user actions b ) All actions must be in context and What You See Is What You Need – no more and   no less c ) First call resolution + call deflection will increase by 30 % and average handling time per frequent task will   decrease by 30 % d ) User satisfaction ( SUS ) will increase from 56 % to 85 % 

 Application : The test we designed to validate our key assumptions ( in the form of goals ) was based on a mock - up of the   12 most frequent flows and scheduled a “ walk through ” to a large audience of diverse stakeholders in a collaborative   brainstorm / design review . We began by stating our assumptions behind our proposed ( baseline ) solution that needed to   be confirmed or disconfirmed – for example , for every user step , we asked , “ Can this be further simplified ” , “ What is   wrong with the existing way of doing things ” “ Is there a better way of accomplishing this task ” , “ What are the   dependencies and constraints ” “ What is the learning curve for this task ” . 

 Insight : Shortly following the testing of my hypothesis / assumptions , I became aware that I may be getting caught up in   “ experimenter ’s bias ” ( similar to confirmation bias , but in the reverse : to design a test that will confirm the   assumptions ) . To optimize an existing system in terms of system integration , task completion rate and task time is not   that difficult , but from a user ’s perspective , that was not their focal pain point . It turns out that the cause behind so   much of users ’ frustration was that “ they were leading the system instead of the system leading them ” . Indeed , the   most focal heuristic as far as users were concerned was “ Can the system do more to alleviate the cognitive load on the   user ” . As a result , we fleshed out all frequent tasks ( per persona ) to explore inferences where the system can do more   on behalf of users and lead users to make the correct choices in context . This had an impact not only on the UI and   interaction design , but on system integration , automation , navigation , etc . 

 Approach : While this project that continued for 18 months and involved over 300 people was wildly successful ( savings   of > $ 250 M in the first year after rollout and increase of user satisfaction from SUS 56 % to SUS 90 % ) , there were lessons   to be learned . My major learned lesson was to have brainstormed a lot more with the client at the discovery phase   ( What Is ) that would have better informed the assumptions that I tested – for example , “ in the context of constraints ,   the system will lead users to make the correct choices in context and perform anything and everything that users can not   perform themselves ”
### Output: 