Top sales days

In this article, we'll look at how to get: 

  • The top sales day ever (both number of sales and revenue)

  • The top sales day each month (both number of sales and revenue)

1️⃣ Identify your top sales date ever

First, by the number of sales:

  1. Find the table with your sales data - this might be a transactions table or similar. Check the table has completed orders only (look for a 'cancelled' column!)
  2. Count the number of orders / transactions with the 'day' as the dimension.
  3. Sort by descending to see which day had the most orders!

Done! The top result in the table is the one you're after. Check out a step-by-step in the video below.

Second, by revenue:

  1. Start from your sales table, go to the column with the revenue or price per transaction. Click the header and choose summarize -> sum of and then add the day dimension.
  2. Sort by descending to get your highest revenue day.

Check out a video walk-through of these here:

2️⃣ Identify your top sales day in each month

This one's a little trickier, but certainly possible 🤓

  1. Start with your sales / transactions table

  2. Summarize by counting the number of transactions with the dimension as the day the transaction took place.

  3. Click the plus button next to "rows" and click summarize as new column.

  4. Click max and then the column with the count of transactions, and use the dimensions of the date of the transaction, sorted by month. Hit done. A new column will be added.

  5. Add a filter. Filter by the count of transactions and then hit the button, followed by "Show other columns" and then select the column 'max_count_of_transactions' (the column that was added in step 4).

  6. This will give you the top sales days based on numbers of transactions. To get the best days for revenue you can do the same steps, but use a sum function instead of count. You can check out a walk-through video here:

All done! 🚀

You now have a table containing each month's top sales day 😀

Here's a screenshot of what the steps should look like:


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