Big Translations in Small Spaces

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Consumers are more likely to buy products if they can understand the labels and the instructions, but packaging space is too small to fit multiple languages, and translations are too difficult. Kamusi has a solution to this problem, making it possible to have accurate product information in tiny spaces for hundreds of languages at low cost.

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Big Translations in Small Spaces

  1. 1. Planet K Solving the Language Problem: How Global Companies Can Reach a World of Customers
  2. 2. Kamusi is the Swahili word for “dictionary”. Our mission: 1. Collect every word in every language 2. Share every word Planet K
  3. 3. Planet K is our idea to translate useful information that is frequently repeated, like , , and , into hundreds of languages. • Companies prepare their products once in their home language • Customers read the information in their mother tongues
  4. 4. • The company puts a QR code on their product • The customer reads the information on their mobile device
  5. 5. • The company shares BIG information in a small space • The customer learns better information about the product
  6. 6. • The company makes one label for hundreds of markets • The customer reads information about hundreds of products in their own language
  7. 7. There is a local company we thought we’d pitch the idea to. Nestle has: • More than 2000 brands • Nearly $100 billion sales • Operations in 200 countries Might it be helpful to them to have in the of their ? 2000 brands x 50 packages/brand x 100 terms/ package x 200 languages 2,000,000,000 words
  8. 8. We can show Nestle how Planet K could ease the challenge • “Milk” is “milk” on any package • “Milk” in another language will be that word on any package • 5000 repeating terms can be condensed to just five weeks of work per language But can we convince a BIG company to put QR codes on its products to offer low-income market customers more information? All brands x all packages x 5,000 unique terms (@25/hr= 200 hours/lang) x 200 languages 1,000,000 reusableterms
  9. 9. Guess what? They already do! This is a can of Nestle Milkmaid Sweetened Condensed Milk. It is sold in Sri Lanka. If you scan the QR code on your phone, the next page shows what you will see.
  10. 10. You see: • A crisp website • Instructions • Nutrition • Recipes • Social information In
  11. 11. Ethnologue estimates for Sri Lanka: • 20,000,000 people • 16,000,000 speak Sinhala • 4,000,000 speak Tamil • 90% are literate • 10% speak some English http://www.ethnologue.com/country/LK/languages
  12. 12. Ethnologue estimates for Sri Lanka: • 20,000,000 people • 16,000,000 speak Sinhala • 4,000,000 speak Tamil • 90% are literate • 10% speak some English http://www.ethnologue.com/country/LK/languages The Planet K concept: • Company gets repeated terms translated to the national have product information • Accessed through QR codes that the company has already invested in
  13. 13. These detailed translations , for two reasons: 1. Methods – they scrape digital sources for apparent matches. This somewhat works for French and German, but not much beyond 2. Data – most terms for most languages have never been collected, so they don’t exist in any way computers can use We ask for the words right from the people who speak them. Then we align them (because “truffle” could be a fungus or a chocolate, and you really don’t want to mix up ideas like that). We can get you . We demonstrate it for 4 languages below. We could do it for . * Some script rendering errors introduced by Adobe image editing software * Some script rendering errors introduced by Adobe image editing software
  14. 14. Any language Any product Any market Translated once Used everywhere * Some script rendering errors introduced by Adobe image editing software * Some script rendering errors introduced by Adobe image editing software
  15. 15. Any language Any product Any market Translated once Used everywhere

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