Design Thinking for Innovation Final Project 1/26/2017 

 Fabio Sameh 

 Challenge and Selection : The challenge I would like to review has to do with recruiting potential students to a graduate   advertising program where I work .   We are a small program , only 200 students , and we have a   different way of funding as compared to most graduate programs at public institutions .   Our   operating funds are directly related to the students we can recruit to our program , meaning if we   do n’t get 100 students each year , we might not be able to make certain purchases , hold events   or hire new staff or faculty .   Interestingly enough , while our students are highly successful after   graduation ( we have a 99 % placement rate after 6 months and hold a recruiter fair where almost   300 recruiters come to interview and hire 100 students ) we always had a difﬁcult time getting   enough qualiﬁed recruits each year to ﬁll the tracks .   Many times we would only get 190   completed applications where our goal was at least 300 . 

 Clearly there was a disconnect and we were certainly missing something important .   In this   case , we used a form of visualization to try and ﬁgure out what was going on .   We needed a   global view of our data and see the “ what is ” before we moved to the next steps ( of course I did   not know these steps in the names that this course has given them at the time ) . 

 Application : As mentioned above , we can get plenty of data from our application process and qualitative   data from our current students as well .   We pulled data from the start of the application   download process where students tell us about themselves , how they heard about us , where   they lived , etc . 

 We also did a poll of our current students gathering other data that included questions of the   application and acceptance process and their impression of the number of students that apply   versus the 100 that get in .   We also asked the current student how they found our school and   who were the inﬂuencers in their decision to attend . 

 And we pulled data from the faculty who reviewed applications and accepted students .   Then   things got interesting .   What we found out was that in some ways , we were the major stumbling   block to our own recruiting efforts .   Much like the Good Kitchen example , we could not just “ ﬁx   the menu ” but needed to look at our entire process . 

 Insight : As we went to a white board and laid out that data , we saw interesting areas that had   “ gravitational pull ” or areas that showed commonality among responses .   Our ﬁrst and major   insight from our data is that our program , while ranked # 1 in the nation by business periodicals , 

 was not as well known to potential students as we thought .   We were not telling our own story   well enough .   Our “ front - end ” story needed a lot of work . 

 Orbiting that insight closely was where our prospects went for information on graduate schools   for marketing and branding .   We were off base and needed to recalibrate our marketing and PR   efforts .   We also saw a pattern of data that told us our application , rigorous to be sure , was   actually preventing prospects from applying !   We found that there were almost 2000   applications requests and downloads but only around 200 actually completed the process .    Why ?   The data also made us question if our application was too rigorous and pushing qualiﬁed   applicants away from attending the school .   And ﬁnally another area of “ data gravity ” was our   approval process .   We saw that our faculty took too much time to accept students , sometimes   late into May .   Many applicants by this time either had taken full time jobs or could not get the   ﬁnancial and other resources together to be at our school by the last week in July . 

 By using a form of visualization ( via a whiteboard , markers , sticky notes , etc ) , we were able to   see the “ what is ” in our difﬁculty getting the desired number of qualiﬁed applicants and how   many of these problems were inter - connected and of our own doing .   We had met the enemy   and they were us ! 

 Approach : Reﬂecting back on how we did our own “ what is ” analysis and used a form of visualization , I   would have liked to have known more about the next steps in the process learned in this class   ( what if , what wows , etc . ) .   I feel we jumped to learning launch products too quickly without the   proper foundation .   For example , the school hired a ﬁrm to optimize our search results via   Google , but we did not have the completed story yet ready to tell based on those searches .   But   with that said , even using one of the tools learned in this class on a real world problem helped   tremendously . 

 Right now , I have been asked to look at our online education presence and come up with ideas   that might enhance and advance these efforts .   This is a perfect opportunity to utilize the skills   learned in the class . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: Fabio Sameh
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: Design Thinking for Innovation Final Project 1/26/2017 

 Fabio Sameh 

 Challenge and Selection : The challenge I would like to review has to do with recruiting potential students to a graduate   advertising program where I work .   We are a small program , only 200 students , and we have a   different way of funding as compared to most graduate programs at public institutions .   Our   operating funds are directly related to the students we can recruit to our program , meaning if we   do n’t get 100 students each year , we might not be able to make certain purchases , hold events   or hire new staff or faculty .   Interestingly enough , while our students are highly successful after   graduation ( we have a 99 % placement rate after 6 months and hold a recruiter fair where almost   300 recruiters come to interview and hire 100 students ) we always had a difﬁcult time getting   enough qualiﬁed recruits each year to ﬁll the tracks .   Many times we would only get 190   completed applications where our goal was at least 300 . 

 Clearly there was a disconnect and we were certainly missing something important .   In this   case , we used a form of visualization to try and ﬁgure out what was going on .   We needed a   global view of our data and see the “ what is ” before we moved to the next steps ( of course I did   not know these steps in the names that this course has given them at the time ) . 

 Application : As mentioned above , we can get plenty of data from our application process and qualitative   data from our current students as well .   We pulled data from the start of the application   download process where students tell us about themselves , how they heard about us , where   they lived , etc . 

 We also did a poll of our current students gathering other data that included questions of the   application and acceptance process and their impression of the number of students that apply   versus the 100 that get in .   We also asked the current student how they found our school and   who were the inﬂuencers in their decision to attend . 

 And we pulled data from the faculty who reviewed applications and accepted students .   Then   things got interesting .   What we found out was that in some ways , we were the major stumbling   block to our own recruiting efforts .   Much like the Good Kitchen example , we could not just “ ﬁx   the menu ” but needed to look at our entire process . 

 Insight : As we went to a white board and laid out that data , we saw interesting areas that had   “ gravitational pull ” or areas that showed commonality among responses .   Our ﬁrst and major   insight from our data is that our program , while ranked # 1 in the nation by business periodicals , 

 was not as well known to potential students as we thought .   We were not telling our own story   well enough .   Our “ front - end ” story needed a lot of work . 

 Orbiting that insight closely was where our prospects went for information on graduate schools   for marketing and branding .   We were off base and needed to recalibrate our marketing and PR   efforts .   We also saw a pattern of data that told us our application , rigorous to be sure , was   actually preventing prospects from applying !   We found that there were almost 2000   applications requests and downloads but only around 200 actually completed the process .    Why ?   The data also made us question if our application was too rigorous and pushing qualiﬁed   applicants away from attending the school .   And ﬁnally another area of “ data gravity ” was our   approval process .   We saw that our faculty took too much time to accept students , sometimes   late into May .   Many applicants by this time either had taken full time jobs or could not get the   ﬁnancial and other resources together to be at our school by the last week in July . 

 By using a form of visualization ( via a whiteboard , markers , sticky notes , etc ) , we were able to   see the “ what is ” in our difﬁculty getting the desired number of qualiﬁed applicants and how   many of these problems were inter - connected and of our own doing .   We had met the enemy   and they were us ! 

 Approach : Reﬂecting back on how we did our own “ what is ” analysis and used a form of visualization , I   would have liked to have known more about the next steps in the process learned in this class   ( what if , what wows , etc . ) .   I feel we jumped to learning launch products too quickly without the   proper foundation .   For example , the school hired a ﬁrm to optimize our search results via   Google , but we did not have the completed story yet ready to tell based on those searches .   But   with that said , even using one of the tools learned in this class on a real world problem helped   tremendously . 

 Right now , I have been asked to look at our online education presence and come up with ideas   that might enhance and advance these efforts .   This is a perfect opportunity to utilize the skills   learned in the class .
### Output: 