Challenge: Describe your challenge, including all relevant information.    I work for a department store in the area of data and analytics. Our main goal is to support the  business areas to increase revenue. We provide quality data and business insights to all the  organization. We work together with the business divisions to implement initiatives as a result of  the business insight we found on data. This year my team is working with e-commerce and  commercial in order to create automatic recommendations for clothing in the online channel.  The idea is, if a customer add a piece of clothing in the shopping cart, clothes that combine in  color and design should be shown in the same window. For this, we needed to create an AI  component, which understands all the colors and designs from pictures of the clothes we have  in stock.     This project is risky for several reasons. First, we have never built a recommender system, so  we didn’t know how difficult it could be to created and implemented. Second, we don’t know  where in the customer journey, the users would like to see the recommendations or if they even  want to see them. Finally, the business wasn’t all on board with this project, they believe that if  we show the wrong recommendation, we will detriment conversion rates rather than improve it,  so we need to convince them that our recommendations will be good.    Selection: In your own words, briefly describe the tool you selected (e.g., what it is and why you  selected it for your challenge -- including any appropriate video lecture references).    I choose the learning launch tool to overcome the main challenges described above. Due to all  the uncertainty regarding this project, we thought that rapidly testing and iterating will be the  best shot we have to be successful.     Application: Describe how you applied the tool you selected to your challenge (e.g., what you  did and how the tool was applied effectively or ineffectively).    For our first learning launch, we decided to test simple recommendations (just random products)  in the web page. Our goal was to test where, in the customer journey, the user was comfortable  to receive a product recommendation. We separated the online traffic in three groups, the first  two groups were about 10 percent of the total population and the last group the rest. The first  group showed a recommendation right after the customer selects a piece of clothing. The  second group showed a recommendation before the customer pays for all the products  contained in the shopping cart. The last group didn’t show any recommendation. We assumed  our recommendations were accurate and customers would like to see them in our web page.  We were measuring the test with conversion rates or quantity of products sold.     For the second learning launch, we decided to test more advanced recommendations from our  AI system in the web page, but this time the recommendation was shown ones the piece of  clothing is selected. Our goal was to test if the advance recommendations were accurate. We  separated the online traffic in two groups, the first group was about 10 percent of the total  population and the last group the rest.The first group showed an advanced recommendation

from our AI system. The second group showed a simple recommendation (just random  products). We assumed that customers would like to see recommendations in our web page.  We implemented a small survey at the end of the online process. We were measuring the test  with conversion rates or quantity of products sold and the user’s feedback.      Insight: Describe the insight you gained from applying the tool you selected to your challenge  (e.g., how an insight affected your thinking about the challenge and about design thinking more  broadly).    We found 3 key business insights from these experiments. First, from the first learning launch,  we learned that customers were more kind to add a product to their shopping cart, if the  recommendation was right after they selected a product into the cart. We learned this by looking  at the conversion rates and quantity of products sold. So, we incorporated this change for the  next test. Second, from the second experiment, we learned that customers liked our advanced  recommendations more than showing them just random products. Our new suggestions were  made based on colors and design of the same piece of clothing they already picked in real time.  We learned this by analyzing conversion rates and customer’s feedback.  Finally, we learned that by applying the learning launch tool, we were able to quickly change  and applied those changes into real products that turns out into higher sales. We were able to  test, what works and what doesn’t, in a control environment and after analyzing results quickly  scale to the entire population. All of these, help us to convince the business that initial was  sceptical about the results of this project.      Approach: Describe what you might do differently next time -- applying the same tool you  selected or a different one -- and the reason(s) why.    I think learning launch is an extremely powerful tool that helps organizations with very  challenging projects full of uncertainty. It manages uncertainty, by applying small experiments  that makes all the process simpler and faster. From now on my team is applying this tool for  every innovative project.

