Create Customer Group - Smart RFIM Grouping

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SHOPLINE's "Customer Group" feature will allow you to use a variety of filters to segment your audience accurately. You can also export relevant reports of specific customer groups so that you can formulate the best marketing strategies and promotional activities for this customer group.

In this article, you will learn the Smart RFIM filter conditions for the customer groups.

 

1. Customer group - Smart RFIM introduction 

There are 8 Smart RFIM customer grouping filter conditions:

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Name

Definition

Update frequency

Engaged Customer

Customer who has visited* within three repurchase cycles** but hasn't placed an order

*Visit means interactions on social media platforms (messages and comments) and online store visits. But if the customer has only interacted with the social media platform, and has not left information in the online store to bound as an online customer, it will not be included. 

Daily

New Shopper

Shoppers who just recently completed  their first order

Daily

Active Shopper

Customer who recently placed an order and has at least 2 orders

Daily

Ready-to-Repurchase

Customer who has at least 2 orders and is ready to repurchase recently

Daily

Sleeping Shopper

Customer who hasn't placed any order recently but has at least 2 orders

Daily

Lost Shopper

Customer who only purchased once and hasn't placed any order recently

Daily

Loyal Shopper

Customer who has at least 3 orders and recently placed an order

Daily

Top Shopper

Customer who recently placed an order and has at least 3 orders; and the total spent (LTV) is higher than the average

Daily

*Repurchase cycle: The repurchase cycle is calculated by taking the median of the number of days between each purchase of all re-purchased customers within 2 years to predict customer repurchase status. 

For new merchants who haven't accumulated orders for more than 6 months, the Smart RFIM model will use the average repurchase cycle as the new merchant's repurchase cycle until this new merchant has orders for more than 6 months.

*Note: The system will multiply the store's "predicted repurchase cycle" by 0.8 to automatically calculate the actual number of "recent" days

 

Summary and example of Smart RFIM Grouping

A quick view of definitions for Smart RFIM segments based on repurchase status, lifetime order count, and spending amount.

*Note: You can right-click the image and select Open Image in New Tab to enlarge and browse the tables below.Screen_Shot_2022-07-05_at_11.47.52_AM.png

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*Note: The Smart RFIM grouping might occur overlapping eligibility. If you do not wish to send repeated marketing campaigns such as store credits, member points, and broadcasts, please avoid selecting the filter conditions that cause this situation. 

Eligibility Overlap Scenarios:

  1. A customer could be an Active Shopper & Loyal Shopper simultaneously 
  2. A customer could be a Ready-to-Repurchase & Loyal Shopper simultaneously 
  3. A customer could be a Loyal Shopper & Top Shopper simultaneously
  4. A Customer could be an Active Shopper & Loyal Shopper & Top Shopper simultaneously 
  5. A customer could be a Ready-to-Repurchase & Loyal Shopper & Top Shopper simultaneously 

 

2. "High/ Mid/ Low" options for Purchase Behavior filter condition

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Name

Definition

Update frequency


"High/ Mid/ Low" smart options for Money Spent

Divide the spent amount of store customers into quartiles

  • High: Cumulative spent amount is higher than the first quartile (top 25%)
  • Mid: Cumulative spent amount is between the third and first quartile
  • Low: Cumulative spent amount is lower than the third quartile (lower 25%)

Daily

"High/ Mid/ Low" smart options for AOV

Divide the AOV amount of store customers into quartiles

  • High: AOV is higher than the first quartile (top 25%)
  • Mid: AOV is between the third and first quartile
  • Low: AOV is lower than the third quartile (lower 25%)

Daily

 

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