This week I spoke to a colorizer from Great Britain who provided some very interesting insights. I also created the first draft of the colors data set.

Conversation notes from Jordan on 8th March, 2019

Jordan J. Lloyd: Director at Dynamichrome

http://dynamichrome.com/

  • Potentially has a use for independent designers for doing any branding work
  • New data set to help refine your palette
  • Big difference between using 99 designers and having a designer to engage in the branding
  • A designer will know that colors and font type will need to represent the company values
  • Looking into space of retail is fascinating because they try to choose colors which will force customers to buy the product
  • If all the elements are considered well, your brand will really stand out
  • Look into existing brands and see how it has changed over the years
  • Look into the category of energy drinks – catered to a specific younger demographic – speed, danger
  • If you are sampling and can effectively generate a data set based on industries and sub industries – likely group industries together
  • Generate brand colors associated with the industry, mention the color scheme best suited
  • Simpler it is, the better
  • Work like adobe color
  • Type form can answer some questions
  • Ask for industry, sub industry, keywords or hashtags associated with the brand, generate a number of real color schemes existing in that area, then give option to refine and customize
  • Final result should be asc (swatch extension), svg, png, jpg, hex code
  • Tell which big brands use this color scheme, helps to associate
  • Include interesting facts, basic color psychology, contextualize the info
  • Cultural preferences should be taken into account, take into account different regional attitudes
  • Column for universal attitudes, another column for regional attitudes
  • General consumer, Satisfy curiosity, creative agencies, marketing people, retail scientists
  • Keep it as simple as possible, browser based
  • Input a couple of drop downs and keywords – straight to result
  • Reach out to communities based around color on dribble and behance

Resources to look into:

https://brandcolors.net/

https://www.underconsideration.com/brandnew/

https://www.quora.com/When-old-pictures-are-colorized-how-do-they-know-decide-which-colours-to-use

https://dribbble.com/stories/2019/02/15/7-useful-chrome-extensions-for-designers

https://dribbble.com/stories/2019/01/25/6-handy-color-palette-picking-tools

https://www.color-hex.com/

https://www.colorhexa.com/

Notes from Mark on 6th March, 2019

Is there a need for this tool?
Are there any other tools you prefer? How would you compare and contrast them?
Will it provide real value?
Is it possible to make it?
Look for quotes or statements to include in the paper, expert advise
What approach should I follow?
Give them the bigger picture and then go into specifics of the functionalities
Is the final result enough? What else would you expect?

Data set creation

After this, I proceeded with the creation of the data set. I created ten color buckets – red, orange, yellow, green, blue, purple, pink, brown, white and black and made a list of ten words corresponding to each color. These words were associated with the energy and emotions of each colors. After going through a bunch of resources and references, I prioritized the words which were reoccurring and mapped them to each color. This resulted in the following:

Here the words in the first column are the ones that were repeated most often from different sources and references. After this, I created a set of ten shades for each color bucket and mapped one unique word to each shade resulting in a data set of 100 colors and 100 words as seen below:

Next, I plan to create the first prototype using the data set as the base and create an end to end scenario to validate the results and the working of the tool.


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