Reflection – Learning Launch

Challenge     In my country, youths constitute approximately 45% of our overall population. My employer, a  telecommunications company is challenged with the demography of its subscriber Base. A minute  fraction of their subscribers is below Age thirty, with the rest being above. Within our local  telecommunications industry, people under 30 consumed the highest volume of data for internet  connectivity. This reality stirs up a quest to attract young subscribers to our network. We are needed to  develop a product in 12 weeks. One that will appeal to the youth and make them prefer us to our  competitors; consequently, growing a sustainable customer base, increasing our revenue, and delivering  on annual KPIs.    Selection    Our team have had weeks of interaction with potential subscribers within our target age range. They  developed personas from the diverse characters they interacted with. Based on these personas, we  tailored a product that is expected to suit their interests. We have decided to use the Learning launch  tool since it enables us to run fast cheap experiments to confirm or disconfirm our assumptions.  Experimental Learning Launch enables us to search for the truth. The truth enables us to identify void in  our assumptions. We can use truths to pivot, which means to change our idea, amend it, and keep going  until we develop at a finished result.      Application     Our initial learning launch focused on a sample group of 30 young people. We provided free weekly  subscription of an experimental data offer that also enabled their lifestyles. The offer was valid for seven  days (7days) only. They were able to earn coupons per volume of data consumed within the one week.  The accumulated coupons could be used to earn game, concert and/or movie tickets, meals, and drinks.  We noticed that about 39% of the sample group voluntarily opted to extend their subscription to  continue using our offers. We compared the expected outcome to our assumptions and figured there  was more margins to cover.    After keenly analyzing all data from the first Launch, our next iteration focused on personas that were  not initially considered. We recruited a group of 33 young people to test our new experimental offers.  We again provided free week subscription for data offers that also enabled their lifestyles. In addition to  accumulating coupons that could be used to earn game, concert and/or movie tickets, meals, and  drinks, the coupons were also being used to earn discounts on purchases made at partner stores. We  noticed that 61% of the sample group voluntarily opted to extend their subscription.

Insight     Using the Learning Launch tool, our team was able to roll out and test the offers fast and cheap. In Our  initial iteration we did not assume that youth are low income earners. The learning launch made us  identify element that we skipped or ignored when we were developing the prototype. We were able to  use the useful information from the first learning launch to modify the design of the second learning  launch. Based on the performance of the second learning launch, we were convinced to rolled out the  offer on a larger scale to attract young subscriber.     Approach    The dynamics of customer behavior changes over time. In future product designs, we would use the  Learning Launch tool when designing a product. This enables the design thinking process to connect  directly with stakeholders to co-create the product by experimenting the different stage of production  and gathering relevant truths to improve subsequent launches. Provided that our assumptions will not  always be correct, our team will be able to use analyses of behaviors, traits, and patterns from  experiments to create personas that would correctly represent our target group.  Today, through learning launch and our customer-centricity, my employer has earned a youthful  subscriber base with hopes of significant growth in revenue from data usage.

