In order to gauge how your fans are spending (and for the system to calculate an average fan value), you need to import sales data. This can be historical data from merch, physical music, ticketing, Patreon/subscriptions data and more. Correctly formatting the data for importing means you'll have the most accurate reflection of fan spend across the database, and you'll know what you're selling, how much, and to whom (this is key for identifying your top spenders and super-serving them).
TOP TIP: Connect your e-commerce store via a web hook so that transaction data comes in and populates the database and fan records in real time, saving regular manual imports. For connecting e-commerce, see here.
This article is focused specifically on the importing of sales data; for importing basic mailing lists, see here.
Preparing/formatting your data
It's important that the data is prepared in a certain way before attempting to import it. The key thing to remember is to freeze your column headers and make sure as a minimum you have these data points:
Name
Email
Transaction Description
Transaction Date
Transaction Value
Transaction Volume
Currency (Must be it's own column and 3 characters, GBP, USD, AUD etc)
Note: Data generated for demo purposes only.
It's a bonus if you have anything else like country or address - the system may already have that information for certain fans, and if it does, only new data will be pulled in, nothing gets overwritten. If it's ticketing data, then of course venue name and type of ticket is all relevant and valuable data to have.
Lastly, download your sheet as a .csv file.
Importing the data
Navigate to Fans, Import Fans, then Multiple Fans.
Here you need to choose a "tag" that will be attributed to every fan record on this import.
You also need to specify the type of transaction i.e. Merch, Ticketing, Physical Music, Digital Music, Subscriptions etc.
For the most accurate imports, separate the above types into individual sheets and import them separately. That way, the type of purchases fans are making will reflect accordingly in your analytics.
Next, map the line items to the relevant open stage field e.g. full name, email, transaction volume, value etc. (it's crucial these are mapped correctly).
If there are fields that don't match an Openstage field you can choose "tag" and anything in that column will be added as a tag to those fans instead. But in most cases you should find a match for each line item column.
When all fields show in gold below, then you've successfully mapped them.
Hit import. (You can navigate away at this point and come back later to hit refresh, and even if you lose connection, or close the window, the import will continue).
Records will go through the below stages.
Hit refresh at regular intervals to see the progression of the import.
Any fan records with errors will appear at the bottom, and in most cases it's due to misspellings of emails or phone numbers at point of entry.
Once fan records are validated, hit validate # fans and again, you can refresh this at regular intervals as it progresses.
When "Records ready to upload" hits 0, all your validated records are in the system.
It may take a short while for this to reflect on your dashboard, fan overview and in your analytics tab.
TOP TIP: If you've distributed unique codes through the system and then receive the sales data, you can import this and map the unique codes to the field "unique codes" to see how many were used.
Once in, fan records populate with this transaction information and you can analyse transaction spend across your entire database.
Video Walkthrough