If you use Activity Stream, chances are you saw the Customer Tags graph on the Customer Overview dashboard:
Where do Customer Tags come from?
The customer tags are derived from an algorithm that analyzes purchases, customer records, partner databases, and also data from external databases, such as a gender categorization we set based on a customer's first name.
Each tag is associated with a customer when a certain criterion is met, and it is updated in real-time.
You may find some are more intuitive than others, so here's a simple explanation of each of them:
Purchase Tags:
- Classification Category Tags:
Customer who has attended more than 2 events labeled with a certain category - Ticket Variants:
Customer who has bought a ticket with a matching variant (e.g. student, retiree, or family) - Prefers Part Of Week:
The two categories are weekdays (Monday, Tuesday, Wednesday, and Thursday) and weekends (Friday, Saturday, and Sunday)
Customer who has attended at least 4 events
The ratio of purchases to one category is above 0.7 - Repeat Traveller:
Customer who has attended at least 2 events
The shortest trip is above 50 km
The average traveled distance is at least 200 km -
Returned Customer:
Customer who has at least one purchase within 6 months
Customer who has not bought a ticket for 3 years before the newest purchase -
Going Inactive:
Customer who is within a year of going inactive
-
Inactive:
If a customer has no purchases within 3 years of today
-
Recent Buyer:
Customer who has purchased within 90 days
-
Second Time Buyer:
Customer who has exactly two purchases
-
First Time Buyer:
Customer who has exactly one purchase
-
Budget Buyer:
Customer who has attended at least 3 events
The spending average is below a subtenant specific threshold
Threshold average calculation: 0.5 * of item prices across the tenant (or subtenant if the setup is such)
-
Bargain Hunter:
Customer who has attended at least 3 events
The spending average is above a product-specific threshold
Threshold average calculation: 0.5 *of item prices.
Statistics calculated on the set of products that the customer has attended, i.e. if the customer has bought tickets to events A, B and C then we calculate the average and of item prices for transactions to those events. -
High-Value Customer:
If a customer's total event purchases are above 95% of customer spending
-
Early Buyer:
Customer who has attended at least 4 events
Customer who has never bought a ticket within 45 days of the show date -
Impulse Buyer:
Customer who has attended more than 3 events
Customer who has attended at least 3 events where tickets were bought within 10 days of the show date -
High-Frequency Buyer:
Customer who has made more than 4 purchases per year for the last three years
-
Single Item Buyer:
Customer who has bought a single ticket (non-zero price) to more than 2 events
-
Invitee:
Customer who has at least 4 purchases in the last year and zero spending for the whole period
-
Volume Buyer:
Customer who has more than X purchases with the required amount of tickets
We have three different volume categories, based on the number of tickets:
Volume buyer 6+: More than 6 tickets
Volume buyer 10+: More than 10 tickets
Volume buyer 20+: More than 20 tickets -
Loyal Customer:
If the number of non zero purchases is greater than 3
-
Very loyal customer:
If the number of non zero purchases is greater than 6
Donation Tags:
-
Lapsed High-Value Donor:
Customer who has donated more than 95% of customers and the donated amount is more than 100, but they have not made a donation in the last year
-
Top Donor:
Customer who has donated more than 95% of customers and the donated amount is more than 100, within the last year
-
Lapsed Repeat Donor:
Customer who has more than one donation but none made in the last year
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Repeat Donor:
Customer who has more than one donation and at least one made in the last year
-
Lapsed One Time Donor:
Customer who has have exactly one donation but it was made over a year ago
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First Time Donor:
Customer who has have exactly one donation and it is made in the last year
Mixed Tags:
-
Long Term Loyalty:
Customer who has at least one purchase or donation per year for the last 3/5/10 years
Because every client is different, using customer tags will allow you to filter your clients through a smaller lens, to identify their interest groups and how they buy tickets. If you'd like a few ideas on how to use them check out our articles on Buying Patterns or on Targeting Clients from 2+ Years Ago.