Hello, what would be the best practices for region...
# ask-stanley-ai
t
Hello, what would be the best practices for regional prices for different countries in Euro ? For instance, we sell some items in EURO but at different prices for each EU countries: would it be to create new fake currency ( but the issue is that there no automatic update, what's the impact of that?) OR would it be to duplicate the different price levels per EU countries ? Or is there a best practice ? thanks a lot in advance.
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AI generated answer (using internal Slack threads):
****** Disclaimer: This response was generated by an artificial intelligence assistant based on previous conversations from multiple Slack channels, and may contain inaccuracies or mistakes. ****** Based on the conversations, it seems that the best practice for handling regional prices for different countries in Euro would be to use Price Levels. You can set up your item with the desired pricing in the specific currency. However, it's important to note that there is no automatic conversion with exchange rates in NetSuite. You would need to set the price manually either in the UI or via a CSV import. For instance, if you have some items in USD for US America, and in USD for EMEA countries with different pricing, you can create different price levels for each region. NetSuite allows you to have up to 1000 price levels. If you need to update prices in different currencies, you can do this through a CSV import. This method can be used for mass updates, making it easier to manage prices across different regions and currencies. Remember, these are general best practices and may need to be adjusted based on your specific business needs and NetSuite setup.
Slack thread 1: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.84885782
Slack thread 2: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.820492327
Slack thread 3: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.807624
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