RFM Automation, standing for Recency, Frequency, and Monetary, is a powerful marketing strategy for D2C brands, essential in the omnichannel era. RFM analysis segments customers based on their purchasing habits, thus aiding in personalised marketing. Businesses adopting RFM have seen a 10-15% average increase in revenue.

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Impact of RFM Automation on D2C Brands 📈

A Marketing Automation stratagem that helps increase OR by 29%, CTR by 41%, and Conversion Rates by 49%

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Marketing Automation is key in the present Omnichannel era of communication.

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Because with the rise in communication channels, it becomes more and more difficult to manually communicate with individual customers or potential customers.

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Practical & Customised Marketing Automation depends on 3 elements:

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Events
Conditions and
Actions

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And based on this theory, the most successful of all Marketing Automation is ‘RFM Automation’

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It stands for Recency, Frequency, and Monetary, which are three key metrics used to understand customer value and engagement.

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Recency: customers who have made a purchase within the last 30-90 days may be considered "short" while those who haven't made a purchase in over 6 months may be considered "long." The percentage of customers in each segment may vary depending on the business, but a common breakdown is as follows:

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Short: 20-30%

Medium: 30-40%

Long: 30-50%

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Frequency: customers who make multiple purchases per month may be considered "high frequency" or “casual buyer”, while those who only make one purchase every few months may be considered "low frequency" or “common buyer”.

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High frequency: 10-20%

Mid-range: 50-60%

Low frequency: 20-40%

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Monetary: customers who spend a high amount per purchase may be considered "high value" or “spender”, while those who spend very little per purchase may be considered "low value" or “saver”.

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High value: 10-20%

Mid-range: 50-60%

Low value: 20-40%

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FACTS!

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Businesses that use RFM analysis to segment their customers see an average increase in revenue of 10-15%.
Personalised campaigns based on RFM analysis can increase open rates by 29%, click-through rates by 41%, and conversion rates by 49%.

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RFM Automation is used to improve:

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Customer retention rate: Increase customer retention by detecting a decrease in their shopping activity and automatically launching engaging win-back campaigns

RFM analysis can help businesses identify customers who are at risk of churning. By targeting these customers with personalised marketing campaigns, businesses may be able to improve customer retention rates. The percentage of customers who are retained after a specific period of time (e.g., 30 days, 90 days, etc.) may vary depending on the business, but a common goal is to retain at least 50% of customers over the first 90 days.
Increase Customer Life Value (CLV) and increasing profits by directing up- and cross‑selling campaigns to the most loyal users
Average order value (AOV): Optimise the base activation costs by adjusting the amount of discounts for the next purchase depending on the customer's value.

RFM analysis can help businesses identify customers who have a high Monetary score and target them with promotions or offers to increase their AOV. The average order value may vary depending on the business, but a common goal is to increase the AOV by at least 10%.
Predictive analytics: Predictive analytics involves using data and statistical algorithms to make predictions about future customer behaviour. This can help businesses anticipate customer needs and preferences and tailor their marketing efforts accordingly.

Conclusion - Use Cases

1. Automatically respond to a decrease in shopping activity

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Retention

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Automated WhatsApp/Facebook/Instagram/SMS/Email notification with product recommendations when the user has changed the segment from common / regular to casual customer.

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Retargeting

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Automatically adding to Custom Audience and launching platform specific campaigns when a spender/user has changed the segment to Medium.

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2. Increase customer engagement

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LTV increase

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

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3. Adjust the content to the customer's shopping preferences

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Personalisation of messages

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

4. Take care of active users

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Loyalty program

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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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