Learning Launch With Toys 

 Challenge and Selection 

 I am often employed to help companies at the launch phase of their products . One of the biggest   challenges is that by the time I am brought in , companies have already invested a lot of time and money   to craft their “ new ” product or experience . Usually they are looking for us to help them “ fine tune ”    things like customer segments , marketplaces , geographies , key differentiators to highlight , etc . By the   way , the video did a great job of explaining the problem with “ fine tuning ” thinks that do n’t work . 

 And because they have almost always done prior survey research to confirm customer interest in the   new product , it can be very difficult on many occasions to report findings that do n’t match their   expectations on certain dimensions . That said , I was very cautious to try not to fall into the confirmation   bias trap as described in the video . 

 Application 

 I was fortunate to have the chance to inject what I learned in the Learning Launch tool in a current   project which I just finished . Specifically , a major toy manufacturer employed my team ( which I lead ) , to   explore the launch of more interactive retail experiences in the toy section of a major retailer . Their   operating hypothesis and customer assumption was that these more interactive , playful experiences   would cause children and parents alike to be more likely to spend time in the toy aisle and purchase   more toys . 

 Just as mentioned in the Learning Launch video , we wanted to do this fast and cheap . So to test this   hypothesis quickly , we identified a subset of six stores . More specifically , we chose three geographic   markets , looking for two stores in each market with almost identical toy sales figures for the   manufacturer . We then began by conducting rapid ethnographic interviews in homes and in stores with   both parents and children . 

 Next , we employed a well - known design firm to create rapid prototypes of ( hypothetically ) more   engaging experiences based on analysis and videotapes of our interviews . We then deployed the design   firm to reconfigure three of the stores ( one in each market ) based on conversations with key client   stakeholders , the design firm and my team . The client was really impressed with the design team ’s work   and everything was progressing quickly and positively . 

 Insights 

 After the stores were reconfigured , we analyzed sales figures from the toy departments as well as video   footage from store cameras for all six stores over a two week period . Much to our surprise , the control   group actually came out ahead in terms of overall sales by a magnitude of approximately   15 % . As   mentioned in the video do n’t be afraid to be wrong . 

 Our tentative finding – which seemed to be confirmed by video analysis – is that when you engage   children in play , they become distracted and are less likely to “ shop ” and “ beg their moms for toys . ” I   say tentative because this was a “ quick and dirty ” project based on rapid prototyping and should be   validated on a larger sample . 

 But an accidental finding to emerge from the interviews was that when parents drag their kids along on   other occasions – things like grabbing prescriptions after work – kids would ( naturally ) beg for toys .   Interestingly , because parents are n’t focused on toys ( with the exception of Christmas and birthdays ) ,   they would often say things like “ OK , just do n’t anything more than   $ XX , where XX was usually $ 10 , $ 15   or $ 20 . 

 This really hit home , because as noted in the video , while all ideas not necessarily good , there are   always new ideas to surface . 

 Outcome and Approach 

 When I presented the findings , I tried to take an empathetic approach . I started by noting that we   learned from our ethnographic interviews that when children accompany their parents to stores they   tend to emulate their parents behavior . They basically become “ junior shoppers . ” But if you engage   them with toys –   such as the iconic train tables at Toys“R”Us – they quit shopping and start playing . It is   obviously not easy to tell a client that their idea was “ wrong , ” so we had worked with the design team   to be able to suggest newer aesthetic details without including “ engaging experiences . ” 

 Perhaps more importantly , we surfaced new ideas in the form of $ 10 , $ 15 and $ 20 purchases on random   occasions . The client is now in the process od testing “ $ 10 toy bins ” in areas such as the pharmacy , etc .. 


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 With Toys 

 Challenge and Selection 

 I am often employed to help companies at the launch phase of their products . One of the biggest   challenges is that by the time I am brought in , companies have already invested a lot of time and money   to craft their “ new ” product or experience . Usually they are looking for us to help them “ fine tune ”    things like customer segments , marketplaces , geographies , key differentiators to highlight , etc . By the   way , the video did a great job of explaining the problem with “ fine tuning ” thinks that do n’t work . 

 And because they have almost always done prior survey research to confirm customer interest in the   new product , it can be very difficult on many occasions to report findings that do n’t match their   expectations on certain dimensions . That said , I was very cautious to try not to fall into the confirmation   bias trap as described in the video . 

 Application 

 I was fortunate to have the chance to inject what I learned in the Learning Launch tool in a current   project which I just finished . Specifically , a major toy manufacturer employed my team ( which I lead ) , to   explore the launch of more interactive retail experiences in the toy section of a major retailer . Their   operating hypothesis and customer assumption was that these more interactive , playful experiences   would cause children and parents alike to be more likely to spend time in the toy aisle and purchase   more toys . 

 Just as mentioned in the Learning Launch video , we wanted to do this fast and cheap . So to test this   hypothesis quickly , we identified a subset of six stores . More specifically , we chose three geographic   markets , looking for two stores in each market with almost identical toy sales figures for the   manufacturer . We then began by conducting rapid ethnographic interviews in homes and in stores with   both parents and children . 

 Next , we employed a well - known design firm to create rapid prototypes of ( hypothetically ) more   engaging experiences based on analysis and videotapes of our interviews . We then deployed the design   firm to reconfigure three of the stores ( one in each market ) based on conversations with key client   stakeholders , the design firm and my team . The client was really impressed with the design team ’s work   and everything was progressing quickly and positively . 

 Insights 

 After the stores were reconfigured , we analyzed sales figures from the toy departments as well as video   footage from store cameras for all six stores over a two week period . Much to our surprise , the control   group actually came out ahead in terms of overall sales by a magnitude of approximately   15 % . As   mentioned in the video do n’t be afraid to be wrong . 

 Our tentative finding – which seemed to be confirmed by video analysis – is that when you engage   children in play , they become distracted and are less likely to “ shop ” and “ beg their moms for toys . ” I   say tentative because this was a “ quick and dirty ” project based on rapid prototyping and should be   validated on a larger sample . 

 But an accidental finding to emerge from the interviews was that when parents drag their kids along on   other occasions – things like grabbing prescriptions after work – kids would ( naturally ) beg for toys .   Interestingly , because parents are n’t focused on toys ( with the exception of Christmas and birthdays ) ,   they would often say things like “ OK , just do n’t anything more than   $ XX , where XX was usually $ 10 , $ 15   or $ 20 . 

 This really hit home , because as noted in the video , while all ideas not necessarily good , there are   always new ideas to surface . 

 Outcome and Approach 

 When I presented the findings , I tried to take an empathetic approach . I started by noting that we   learned from our ethnographic interviews that when children accompany their parents to stores they   tend to emulate their parents behavior . They basically become “ junior shoppers . ” But if you engage   them with toys –   such as the iconic train tables at Toys“R”Us – they quit shopping and start playing . It is   obviously not easy to tell a client that their idea was “ wrong , ” so we had worked with the design team   to be able to suggest newer aesthetic details without including “ engaging experiences . ” 

 Perhaps more importantly , we surfaced new ideas in the form of $ 10 , $ 15 and $ 20 purchases on random   occasions . The client is now in the process od testing “ $ 10 toy bins ” in areas such as the pharmacy , etc ..
### Output: 