Design Thinking for Innovation Reflection     Adigun Okoro April 2020 

 Testing a Niche Market 

 A Learning Launch Reflection 

 The Challenge 

 Background ; West African diets are traditionally high in carbohydrates .   Modernization is   increasingly making the foods more highly refined and abundant , causing a steady increase in dietary   problems , particularly obesity and type 2 diabetes .   There is a growing awareness of healthy eating   in the affected communities .   A nutritionist has developed a series of substitutes for the problematic   traditional foods and needs a compelling proof of concept to raise capital for the business .   The   formulas have been tested , tasted and do n’t require separate FDA approval as all ingredients are   approved .   Production either requires the huge outlay of building a factory or outsourcing to an   existing factory with milling and mixing capabilities . 

 The challenge was to establish the commercial viability of this series of products .   To do this , I   worked with the nutritionist to test the assumptions that people of West African descent , who are   interested in healthy diets , would buy this new and unique product just because of the health   promises it made and that they would do so at prices that would be profitable . 

 Selection 

 I chose the Learning Launch tool for this challenge as it seemed to best fit the bill .   A Learning Launch   experiment to produces results fast and cheap and as we learned from the Frank in the video , to test   key customer execution and defensibility assumptions was ideal for the challenge . 

 Application 

 With the tool selected , I analyzed the assumptions and distilled them down to a simple “ must be   true ” which was “ people will buy this product online at this price . ”   There were some researched   factors , the cost of production and minimum order sizes were taken from outsourcing quotations .    The cost of advertising was estimated from quotations by a selection of advertising firms , the   advertisers chosen anticipated an average 0.5 % response   The production and advertising costs were   added to anticipated admin costs and used to calculate production costs which was then multiplied   by a safety margin and marked up for profit giving us pricing for the experiment . 

 A simple webpage was set up , with the product available to buy and an initial mailing list of 1000   potential customers were emailed with an advert and a link to the sale page .   The mailing list   thousand were randomly selected from 20,000 + existing ( opted - in ) subscribers to the nutritionist ’s   blog .   I played the role of a sceptic as everyone else involved was very excited and I wished to   mitigate the confirmation bias . After discussion , the sample customers were estimated to be ten   times more likely to buy than the unqualified leads expected from outsourced advertising .   This led   to an expected response of 5 % ( 10 X the 0.5 % provided by advertisers ) to prove the hypothesis . 

 Upon landing on the sale page , customers were able to select items , review the shopping cart and   click ‘ Buy Now ’ .   As the product was n’t actually available to buy , and we did n’t want to take money   during the experiment , once the customers committed and reached the checkout , they were shown   a message saying that the product was out of stock and if the customers wished , they would be   notified and sent a discount voucher when it was available to buy . 

 Insight 

 The Learning Launch was a great success , the results of the experiment were very conclusive .   There   were 132 ‘ sales ’ from the 1000 prospects approached , representing a 13.2 % percent response , far   above the 5 % predetermined as a success marker .   As the sole success criterion was already   predetermined , further data analysis was unnecessary but the high response led us to project   returns on the full available ( opted - in ) mailing list of blog subscribers and this projection added   conviction to the decision to go ahead with the next phase of using outsourced manufacturing   production and starting online sales while building a case for scaling up to a purpose - built factory . 

 From my point of view , the assumption that the blog subscribers were ten times more likely to buy   than other members of the demographic , as well as the original projected 0.5 % provided by the   advertising firms as an average response rate were nothing more than guesses and as such   represented a significant risk , but the relatively low cost of the Learning Launch phase 2 mitigates   this risk very well .   There was a financial decision made as the blog subscriber list was free and there   would have been a cost to using commercial advertising at this stage .   In the event that the   experiment yielded borderline results , we had pencilled in plans for a phase 1.5 ; running the   experiment again with a small investment in advertising .   As it turned out the results were   comfortably above the cut - off and this phase was scrapped in favour of going straight to phase 2 . 

 Approach 

 Despite the success of the experiment , I still think it would have been better to use a sample from a   more neutral market rather than the preselected mailing list that we used .   I had been concerned   about the fallout from leading customers to a checkout page only to tell them that they could n’t   actually complete the purchase , but I was pleasantly surprised that they were not upset and 100 % of   them elected to be informed when it was available . 

 I really liked using this tool and I ’m looking forward to doing more work with it .   The single biggest   takeaway for me is that it is virtually impossible to predict customer behaviour but extremely easy to   record it when it is happening .   This makes a Learning Launch an excellent tool for design . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: Adigun Okoro
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 Reflection     Adigun Okoro April 2020 

 Testing a Niche Market 

 A Learning Launch Reflection 

 The Challenge 

 Background ; West African diets are traditionally high in carbohydrates .   Modernization is   increasingly making the foods more highly refined and abundant , causing a steady increase in dietary   problems , particularly obesity and type 2 diabetes .   There is a growing awareness of healthy eating   in the affected communities .   A nutritionist has developed a series of substitutes for the problematic   traditional foods and needs a compelling proof of concept to raise capital for the business .   The   formulas have been tested , tasted and do n’t require separate FDA approval as all ingredients are   approved .   Production either requires the huge outlay of building a factory or outsourcing to an   existing factory with milling and mixing capabilities . 

 The challenge was to establish the commercial viability of this series of products .   To do this , I   worked with the nutritionist to test the assumptions that people of West African descent , who are   interested in healthy diets , would buy this new and unique product just because of the health   promises it made and that they would do so at prices that would be profitable . 

 Selection 

 I chose the Learning Launch tool for this challenge as it seemed to best fit the bill .   A Learning Launch   experiment to produces results fast and cheap and as we learned from the Frank in the video , to test   key customer execution and defensibility assumptions was ideal for the challenge . 

 Application 

 With the tool selected , I analyzed the assumptions and distilled them down to a simple “ must be   true ” which was “ people will buy this product online at this price . ”   There were some researched   factors , the cost of production and minimum order sizes were taken from outsourcing quotations .    The cost of advertising was estimated from quotations by a selection of advertising firms , the   advertisers chosen anticipated an average 0.5 % response   The production and advertising costs were   added to anticipated admin costs and used to calculate production costs which was then multiplied   by a safety margin and marked up for profit giving us pricing for the experiment . 

 A simple webpage was set up , with the product available to buy and an initial mailing list of 1000   potential customers were emailed with an advert and a link to the sale page .   The mailing list   thousand were randomly selected from 20,000 + existing ( opted - in ) subscribers to the nutritionist ’s   blog .   I played the role of a sceptic as everyone else involved was very excited and I wished to   mitigate the confirmation bias . After discussion , the sample customers were estimated to be ten   times more likely to buy than the unqualified leads expected from outsourced advertising .   This led   to an expected response of 5 % ( 10 X the 0.5 % provided by advertisers ) to prove the hypothesis . 

 Upon landing on the sale page , customers were able to select items , review the shopping cart and   click ‘ Buy Now ’ .   As the product was n’t actually available to buy , and we did n’t want to take money   during the experiment , once the customers committed and reached the checkout , they were shown   a message saying that the product was out of stock and if the customers wished , they would be   notified and sent a discount voucher when it was available to buy . 

 Insight 

 The Learning Launch was a great success , the results of the experiment were very conclusive .   There   were 132 ‘ sales ’ from the 1000 prospects approached , representing a 13.2 % percent response , far   above the 5 % predetermined as a success marker .   As the sole success criterion was already   predetermined , further data analysis was unnecessary but the high response led us to project   returns on the full available ( opted - in ) mailing list of blog subscribers and this projection added   conviction to the decision to go ahead with the next phase of using outsourced manufacturing   production and starting online sales while building a case for scaling up to a purpose - built factory . 

 From my point of view , the assumption that the blog subscribers were ten times more likely to buy   than other members of the demographic , as well as the original projected 0.5 % provided by the   advertising firms as an average response rate were nothing more than guesses and as such   represented a significant risk , but the relatively low cost of the Learning Launch phase 2 mitigates   this risk very well .   There was a financial decision made as the blog subscriber list was free and there   would have been a cost to using commercial advertising at this stage .   In the event that the   experiment yielded borderline results , we had pencilled in plans for a phase 1.5 ; running the   experiment again with a small investment in advertising .   As it turned out the results were   comfortably above the cut - off and this phase was scrapped in favour of going straight to phase 2 . 

 Approach 

 Despite the success of the experiment , I still think it would have been better to use a sample from a   more neutral market rather than the preselected mailing list that we used .   I had been concerned   about the fallout from leading customers to a checkout page only to tell them that they could n’t   actually complete the purchase , but I was pleasantly surprised that they were not upset and 100 % of   them elected to be informed when it was available . 

 I really liked using this tool and I ’m looking forward to doing more work with it .   The single biggest   takeaway for me is that it is virtually impossible to predict customer behaviour but extremely easy to   record it when it is happening .   This makes a Learning Launch an excellent tool for design .
### Output: 