Challenge

I work in the business intelligence department of my organization, and the main task of this  department is to analyze the key business processes that occur in our and complementary  markets. In fact, it all comes down to building profiles of consumers and the market as a whole,  forecasting our operating indicators, and taking into account risks. In addition, my last task in this  department was to create a unified system for predicting consumer behaviour based on various  data (both our private and public). Of course, internal resources (budget and time) limit the  project. The implementation period should not exceed 50 days, and the solution itself should  provide a high forecast accuracy. The core of the project is to find optimal indicators for  describing consumers and this is the most bottleneck in the development.

Selection

My team has already gone through several activities and design thinking tools, so we are ready  to try the learning launcher tool. A training launch is an experiment designed to quickly test an  idea and prototype. This is what you need in the face of a large number of indicator alternatives.  It is possible to understand which mathematical model is suitable for our company (for our  market). Alexander Funk made it clear in his video in the fourth week that it is a great tool for such  purposes.

Application

When we first started training, we focused on objective indicators, on all sorts of numbers and  time series. In particular, we wanted to understand what objective indicators would allow us to  best describe the situation (for example, the state of the stock market, operating indicators, etc.).  This tested our assumption that objective indicators cannot give a high forecast accuracy,  regardless of the algorithm used. Then we decided to involve the psychological research  department in our company. They helped us develop our models. In fact, we have built their  psychological portrait and the method of obtaining this portrait based on numerical and  qualitative indicators.

During the second launch of training, we focused on the implementation of this subjective  module of the forecasting system. In addition, this gave a result, having implemented this system  for several weeks; we saw that our self-learning system, which combined all the previous  developments, made it possible to predict real behaviour. This improved the efficiency of our  company by 40% and attracted many new customers.

Insight

Using a training launch tool, our team identified our assumptions and designed launches for  testing. In addition, we thought about the expected results, hoping to find that a flexible  approach even in the construction of rigorous mathematical models gave its result. Our actual  results initially did not differ from what we anticipated. However, after a few more months it  became noticeable that something was missing - a certain psychological market portrait. Moving  forward, our team will review this understanding and incorporate it into future launch projects.  Continued incorporating insights is especially important at this stage, as launches occur before  full deployment in the design thinking timeline.

Approach

Considering that our team has already gone through several activities and tools of design thinking  and is in this on the timeline, launching the training is a suitable tool. As mentioned earlier, our  team needs to analyze the information from our first two launches and incorporate it into future  launch designs. Future launch plans should reflect the knowledge gained, the progress made, and  move towards a convincing solution ready for full deployment.


Challenge I work in the business intelligence department of my organization , and the main task of this department is to analyze the key business processes that occur in our and complementary markets . In fact , it all comes down to building profiles of consumers and the market as a whole , forecasting our operating indicators , and taking into account risks . In addition , my last task in this department was to create a unified system for predicting consumer behaviour based on various data ( both our private and public ) . Of course , internal resources ( budget and time ) limit the project . The implementation period should not exceed 50 days , and the solution itself should provide a high forecast accuracy . The core of the project is to find optimal indicators for describing consumers and this is the most bottleneck in the development . Selection My team has already gone through several activities and design thinking tools , so we are ready to try the learning launcher tool . A training launch is an experiment designed to quickly test an idea and prototype . This is what you need in the face of a large number of indicator alternatives . It is possible to understand which mathematical model is suitable for our company ( for our market ) . Alexander Funk made it clear in his video in the fourth week that it is a great tool for such purposes . Application When we first started training , we focused on objective indicators , on all sorts of numbers and time series . In particular , we wanted to understand what objective indicators would allow us to best describe the situation ( for example , the state of the stock market , operating indicators , etc . ) . This tested our assumption that objective indicators can not give a high forecast accuracy , regardless of the algorithm used . Then we decided to involve the psychological research department in our company . They helped us develop our models . In fact , we have built their psychological portrait and the method of obtaining this portrait based on numerical and qualitative indicators . During the second launch of training , we focused on the implementation of this subjective module of the forecasting system . In addition , this gave a result , having implemented this system for several weeks ; we saw that our self - learning system , which combined all the previous developments , made it possible to predict real behaviour . This improved the efficiency of our company by 40 % and attracted many new customers . Insight Using a training launch tool , our team identified our assumptions and designed launches for testing . In addition , we thought about the expected results , hoping to find that a flexible approach even in the construction of rigorous mathematical models gave its result . Our actual results initially did not differ from what we anticipated . However , after a few more months it became noticeable that something was missing - a certain psychological market portrait . Moving forward , our team will review this understanding and incorporate it into future launch projects . Continued incorporating insights is especially important at this stage , as launches occur before full deployment in the design thinking timeline . Approach Considering that our team has already gone through several activities and tools of design thinking and is in this on the timeline , launching the training is a suitable tool . As mentioned earlier , our team needs to analyze the information from our first two launches and incorporate it into future launch designs . Future launch plans should reflect the knowledge gained , the progress made , and move towards a convincing solution ready for full deployment .