RFM Automation

Automatically segment customers based on the time since the last transaction, the frequency of purchases and the amount of money spent (Recency, Frequency, Monetary). Automate the communication to address changes in the buying behaviour, increase retention and CLV.

RFM Automation

RFM automation is used to:

increasing customer retention by detecting a decrease in their shopping activity and automatically launching engaging win-back campaigns,

increasing customer life value (CLV) and increasing profits by directing up- and cross‑selling campaigns to the most loyal users,

optimization of the base activation costs by adjusting the amount of discounts for the next purchase depending on the customer's value.

How does this work?

RFM Automation

The module allows you to configure the parameters for your store that differentiate customers in each of the three RFM dimensions. For example, you specify at what amount of expenses within 3 months the customer will be considered "saver" and at which as "spender".

The system in real time calculates for each user individually the time from purchase, the frequency of transactions and the amounts spent, and then groups the contacts in your database and sets together segments, e.g. "spenders", whose time since the last purchase is "long". For such generated segments, you can plan marketing campaigns that are launched when a customer is added to this segment.

Use Cases

Automatically respond to a decrease in shopping activity

Retention

Automated e-mail with product recommendations when the user has changed the segment from common / regular to casual customer.

Retargeting

Automatically adding to Custom Audience and launching Facebook campaigns when a Spender user has changed the segment to Medium.

Increase customer engagement

LTV increase

Reactivation of users whose time since the last purchase is long and the frequency of purchases low, through personalized campaigns containing recommendations and special offers as well as discount codes.

Adjust the content to the customer's shopping preferences

Personalization of messages

Informing “common spenders” about new products, while about promotions “casual savers”.

Take care of active users

Loyalty program

Special offers, discount coupons, rewards for users who spend the most and return most often. Use data generated by RFM analytics to build a loyalty program.

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