Learning Launch Approach in Severe Asthma Promotional Space 

 Challenge : 

 One of our marketing team came to my analytics team with the following request .   The marketing   team had just launched an interactive Facebook Asthma Assessment via Messenger Chatbot to help   patients determine their level of asthma and link them to the relevant product information   dependent on their severity levels .   The team now wanted our analytics team to put our stamp of   approval on this costly new promotion . 

 BACKGROUND : Our company markets a severe asthma biologic and wants to increase awareness .    Our market research shows that many severe asthmatics do n’t identify as having severe asthma .    Almost 80 % of eligible patients are not currently treated .    This is about 265 K patients in the US ( see   chart below ) . 

 Facebook was chosen because of its market leadership in user healthcare research and because it   would be a new partnership for our company .   “ Facebook is the undisputed leader of the pack with   close to 700 - million registered users , including nearly half of the American population . But more   important than size alone , Facebook is fast becoming a primary resource for users looking for health   information . ”   SOURCE : https://www.healthcaresuccess.com/blog/medical-marketing-advertising/facebook- fundamentals.html 

 Selection : 

 Since the marketing team had already done most of the storytelling aspects of this promotion   ( example below ) and the promotion was already live , my team decided to bring in the learning   launch approach to ensure we get the most out of the live promotional experience .   This also   allowed us to redefine the promotion as an experiment . 

 The learning launch approach has you define a customer and a specific need that 's causing the pain   and explain how your idea meets that need better than other options .   Then the focus is on a specific   hypothesis you are testing and determine measurable facts that can either confirm or disconfirm the   hypothesis .   In other words , we were going to structure this promotion for fast learning and   modification and be very clear about success / failure metrics . 

 Application : 

 We had two 1/2 day sessions using the learning launch approach to determine our hypothesis and   measurement of this promotion .   The framework allowed for a very frank discussion and alignment   on what good truly looks like .   We also agreed to only run the promotion for 6 weeks for an ability to   quickly refine or make a GO / NO GO decision . 

 Below is some of the output from our session .   The first session focused on the customer need and   the value proposition we are trying to address with this pilot ( i.e. our hypothesis ) .   The second   session focused on measuring the hypothesis . 

 Defined the Customer and their Need :   Women above the age of 45 who had untreated severe   asthma and do not realize that their asthma is considered severe .   These are customers that are very   interactive and build relationships on Facebook . 

 Hypothesis / Value Proposition :   Our Facebook Chatbot Interactive Asthma Assessment would allow   asthmatics to interactively evaluate their type of asthma utilizing a familiar / comfortable platform 

 and determine if they may need a conversation with their provider about medications beyond just an   inhaler . 

 •   PRO : More interactive promotional approach   o   Feasibility of chat bots in a regulated environment   o   Facebook Messenger as a vehicle to drive qualified traffic   o   Automation as a customer engagement vehicle     •   CON :   Is a chatbot promotion truly full - filling this patient need ?   We have no way to link to   patient intent and satisfaction after interaction .   This needs to be addressed in future pilots ,   if we decided to go forward after the initial 6 weeks . 

 Measurable Facts : 

 •   Facebook targeting is sophisticated and will serve up chatbot to appropriate Asthmatic   customers who are in engaging .     o   Set Goal CTR   o   Set Goal for % Severe Asthmatics determined by the Assessment   •   What is the high value actions we expect patients to take to show high engagement at the   end of assessment ?   o   Customer Relationship registrations on brand website      We have baseline weekly registrations on website from other promotions   o   Time Spent on Website   •   Cost of Patient Leads as compared to other digital patient engagements .   We set a   benchmark for cost per lead . 

 Insight : 

 The learning launch approach allowed us to refocus the Facebook promotion as a learning   experiment with less judgement and less time and money spent .   We were able to align on the   promotion purpose and measurement , which made the subsequent conversations on performance   much easier .   Any low metrics were not seen as a failure but as a key learning that will improve   future experiments . 

 We also ended up coming up with a variety of alternative promotions for us to try in the future to   better meet the customer need .   If we had done the learning launch approach before the pilot went   live , we probably would have moved forward with a different experimental design . 

 Approach : 

 The learning launch tool was the right tool for this situation .   It allowed us to ensure we get the most   learning out of the new promotion in the least amount of time .   In the future the marketing teams   have committed to having these learning launch sessions before a new marketing approach is rolled   out .   That way we can better assess the various hyphothesis / options as a team and make an   informed decision rather than coming in after the fact . 


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 Approach in Severe Asthma Promotional Space 

 Challenge : 

 One of our marketing team came to my analytics team with the following request .   The marketing   team had just launched an interactive Facebook Asthma Assessment via Messenger Chatbot to help   patients determine their level of asthma and link them to the relevant product information   dependent on their severity levels .   The team now wanted our analytics team to put our stamp of   approval on this costly new promotion . 

 BACKGROUND : Our company markets a severe asthma biologic and wants to increase awareness .    Our market research shows that many severe asthmatics do n’t identify as having severe asthma .    Almost 80 % of eligible patients are not currently treated .    This is about 265 K patients in the US ( see   chart below ) . 

 Facebook was chosen because of its market leadership in user healthcare research and because it   would be a new partnership for our company .   “ Facebook is the undisputed leader of the pack with   close to 700 - million registered users , including nearly half of the American population . But more   important than size alone , Facebook is fast becoming a primary resource for users looking for health   information . ”   SOURCE : https://www.healthcaresuccess.com/blog/medical-marketing-advertising/facebook- fundamentals.html 

 Selection : 

 Since the marketing team had already done most of the storytelling aspects of this promotion   ( example below ) and the promotion was already live , my team decided to bring in the learning   launch approach to ensure we get the most out of the live promotional experience .   This also   allowed us to redefine the promotion as an experiment . 

 The learning launch approach has you define a customer and a specific need that 's causing the pain   and explain how your idea meets that need better than other options .   Then the focus is on a specific   hypothesis you are testing and determine measurable facts that can either confirm or disconfirm the   hypothesis .   In other words , we were going to structure this promotion for fast learning and   modification and be very clear about success / failure metrics . 

 Application : 

 We had two 1/2 day sessions using the learning launch approach to determine our hypothesis and   measurement of this promotion .   The framework allowed for a very frank discussion and alignment   on what good truly looks like .   We also agreed to only run the promotion for 6 weeks for an ability to   quickly refine or make a GO / NO GO decision . 

 Below is some of the output from our session .   The first session focused on the customer need and   the value proposition we are trying to address with this pilot ( i.e. our hypothesis ) .   The second   session focused on measuring the hypothesis . 

 Defined the Customer and their Need :   Women above the age of 45 who had untreated severe   asthma and do not realize that their asthma is considered severe .   These are customers that are very   interactive and build relationships on Facebook . 

 Hypothesis / Value Proposition :   Our Facebook Chatbot Interactive Asthma Assessment would allow   asthmatics to interactively evaluate their type of asthma utilizing a familiar / comfortable platform 

 and determine if they may need a conversation with their provider about medications beyond just an   inhaler . 

 •   PRO : More interactive promotional approach   o   Feasibility of chat bots in a regulated environment   o   Facebook Messenger as a vehicle to drive qualified traffic   o   Automation as a customer engagement vehicle     •   CON :   Is a chatbot promotion truly full - filling this patient need ?   We have no way to link to   patient intent and satisfaction after interaction .   This needs to be addressed in future pilots ,   if we decided to go forward after the initial 6 weeks . 

 Measurable Facts : 

 •   Facebook targeting is sophisticated and will serve up chatbot to appropriate Asthmatic   customers who are in engaging .     o   Set Goal CTR   o   Set Goal for % Severe Asthmatics determined by the Assessment   •   What is the high value actions we expect patients to take to show high engagement at the   end of assessment ?   o   Customer Relationship registrations on brand website      We have baseline weekly registrations on website from other promotions   o   Time Spent on Website   •   Cost of Patient Leads as compared to other digital patient engagements .   We set a   benchmark for cost per lead . 

 Insight : 

 The learning launch approach allowed us to refocus the Facebook promotion as a learning   experiment with less judgement and less time and money spent .   We were able to align on the   promotion purpose and measurement , which made the subsequent conversations on performance   much easier .   Any low metrics were not seen as a failure but as a key learning that will improve   future experiments . 

 We also ended up coming up with a variety of alternative promotions for us to try in the future to   better meet the customer need .   If we had done the learning launch approach before the pilot went   live , we probably would have moved forward with a different experimental design . 

 Approach : 

 The learning launch tool was the right tool for this situation .   It allowed us to ensure we get the most   learning out of the new promotion in the least amount of time .   In the future the marketing teams   have committed to having these learning launch sessions before a new marketing approach is rolled   out .   That way we can better assess the various hyphothesis / options as a team and make an   informed decision rather than coming in after the fact .
### Output: 