Makeup Retail Progress

Target Stakeholder:

Female User Persona:

  • Knows how to use products
  • Sticks to what she knows and rebuys
  • Is curious about what others products might be out there but doesn’t feel confident buying them
  • Asking store clerks is intimidating because she feels pressured to buy something – doesn’t want to buy something that looks good in the bottle and wrong on her skin

Male User Persona:

  • Needs a skin regimen but has no idea where to begin
  • Is overwhelmed by the amount of products on the market – are they all worth it or is the price just jacked up because companies can
  • Feels uncomfortable walking up to a clerk in a makeup store to ask about products,
  • feels as though the stores are geared for women, wants confidence in showing he belongs in the store

 

Concept Sketches:

 

Storyboard:

 

Potential Items:

  • Mini Arduino Mini ($10) / Uno ($25)
    • Bluetooth chip ($20)
    • Camera ($25)
    • Battery ($10)
  • PLA Filament ($12) /PETG Filament ($25)

Readings #2

Intelligence on Tap:

Lars Eric Holmquist talks about machine learning in relation to designers in this article. Some of the interesting things he brings up is thinking about machine learning as a new material that designers can use. While it is still challenging for most people to understand the limitations of the “material” it is no doubt evolving and changing. As he mentioned, Facebook, Google, and Amazon are already been implementing this tech and storing data for the machines. Data is another interesting thing Holmquist talks about in how it is such a valuable resource more machine learning. After working with a ML system it makes a lot of sense because the machine lives and dies on how much data it has to learn from. It will be interesting to see how data will be handled ethically in the future for ML.

 

UX Design Innovation:

In this article the interviews with designers that they conducted was an interesting part of the article for me. The designers were asked about the difficulties that they have had in understanding ML and its capabilities. The UX designers said that the main issue that they had with ML was not understanding its possibilities. Some of them would get lectured that they cannot just sprinkle magic around have the system do everything but it also seems that ML can do amazing things beyond what they are doing. Knowing where that upper limit is or how to get there is something that only time can tell right now but I am interested in seeing where all the technology can go. Knowing ML’s limitations as a designer will help us further the reaches of what it can do and maybe even take it even further than originally thought.

 

Machines Learning Culture:

Body Tracking and the culture of machine learning is something I have been coming more and more accustomed to as of late. While working on the neurorehabilitation research work I have been seeing computer vision in action and now am getting to the point where I will get to see ML in action too. Machines have been getting better and better at reading movements and people and what it can do with that ability can do a lot of good for the world. It starts to allow for a trained machine specialist to watch and help you with certain things such as rehabilitation and it is an exciting time to be able to see all of this work out.

Week One Reading

The reading this week really focused on breaking down and understanding the experiences of uses. Sengers focuses on his article about breaking down Fun into quantifiable means which in the end doesn’t work. What did work well was the Chinese Dragons example which was a simple system that responded to the user. Working to design for experience rather than design an experience is a much stronger way of thinking that Wright talks about and that the dragons example shows. Trying to make people feel a certain way is difficult and not always enjoyable for the user, but being flexible and adjusting to the user might be a better way to make a better experience.