RFM Analysis - Revision history

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'''RFM Analysis''' aka '''Recency, frequency, monetary value''' is a marketing analysis tool used to identify an organization's best customers by measuring and analyzing spending habits. The RFM model is based on three quantitative factors:
*Recency: How recently a customer has made a purchase: The more recently a customer has made a purchase with a company, the more likely they will continue to keep the business and brand in mind for subsequent purchases. Compared with customers who have not bought from the business in months or even longer periods, the likelihood of engaging in future transactions with recent customers is arguably higher. Such information can be used to get recent customers to revisit the business and spend more. In an effort not to overlook lapsed customers, marketing efforts might be made to remind them that it's been a while since their last transaction, while offering them an incentive to resume buying.
*Frequency: How often a customer makes a purchase: The frequency of a customer’s transactions may be affected by factors such as the type of product, the price point for the purchase, and the need for replenishment or replacement. If the purchase cycle can be predicted — for example when a customer needs to buy more groceries — marketing efforts may be directed towards reminding them to visit the business when staple items run low.
*Monetary Value: How much money a customer spends on purchases: Monetary value stems from how much the customer spends. A natural inclination is to put more emphasis on encouraging customers who spend the most money to continue to do so. While this can produce a better return on investment in marketing and customer service, it also runs the risk of alienating customers who have been consistent but may not spend as much with each transaction.
RFM analysis numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (the higher the number, the better the result). The "best" customer would receive a top score in every category. These three RFM factors can be used to reasonably predict how likely (or unlikely) it is that a customer will do business again with a firm or, in the case of a charitable organization, make another donation.<ref>Definition - What Does RFM Analysis Mean? [https://www.investopedia.com/terms/r/rfm-recency-frequency-monetary-value.asp Investopedia]</ref>

== Steps to Performing RFM Analysis<ref>Performing RFM Segmentation and RFM Analysis, Step by Step [https://www.optimove.com/resources/learning-center/rfm-segmentation Optimove]<?ref>
The following is a step-by-step, do-it-yourself approach to RFM segmentation. Note that with the aid of software, RFM segmentation – as well as other, more sophisticated types of segmentation – can be done automatically, with more accurate results.
*Step 1: The first step in building an RFM model is to assign Recency, Frequency and Monetary values to each customer. The raw data for doing this, which should be readily available in the company’s CRM or transactional databases, can be compiled in an Excel spreadsheet or database:
**Recency is simply the amount of time since the customer’s most recent transaction (most businesses use days, though for others it might make sense to use months, weeks or even hours instead).
**Frequency is the total number of transactions made by the customer (during a defined period).
Monetary is the total amount that the customer has spent across all transactions (during a defined period).
*Step 2: The second step is to divide the customer list into tiered groups for each of the three dimensions (R, F and M), using Excel or another tool. Unless using specialized software, it’s recommended to divide the customers into four tiers for each dimension, such that each customer will be assigned to one tier in each dimension:

{| class="wikitable"
|'''Recency'''
|'''Frequency'''
|'''Monetary'''
|-
|R-Tier-1 (most recent)
|F-Tier-1 (most frequent)
|M-Tier-1 (highest spend)
|-
|R-Tier-2
|F-Tier-2
|M-Tier-2
|-
|R-Tier-3
|F-Tier-3
|M-Tier-3
|-
|R-Tier-4 (least recent)
|F-Tier-4 (only one transaction)
|M-Tier-4 (lowest spend)
|}

This results in 64 distinct customer segments (4x4x4), into which customers will be segmented. Three tiers can also be used (resulting in 27 segments); using more than four, however, is not recommended (because the difficulty in use outweighs the small benefit gain from the extra granularity). As mentioned above, more sophisticated and less manual approaches – such as k-means cluster analysis – can be performed by software, resulting in groups of customers with more homogeneous characteristics.
*Step 3: The third step is to select groups of customers to whom specific types of communications will be sent, based on the RFM segments in which they appear. It is helpful to assign names to segments of interest. Here are just a few examples to illustrate:
**Best Customers – This group consists of those customers who are found in R-Tier-1, F-Tier-1 and M-Tier-1, meaning that they transacted recently, do so often and spend more than other customers. A shortened notation for this segment is 1-1-1; we’ll use this notation going forward.
**High-spending New Customers – This group consists of those customers in 1-4-1 and 1-4-2. These are customers who transacted only once, but very recently and they spent a lot.
**Lowest-Spending Active Loyal Customers – This group consists of those customers in segments 1-1-3 and 1-1-4 (they transacted recently and do so often, but spend the least).
**Churned Best Customers – This segment consists of those customers in groups 4-1-1, 4-1-2, 4-2-1 and 4-2-2 (they transacted frequently and spent a lot, but it’s been a long time since they’ve transacted).<br />Marketers should assemble groups of customers most relevant for their particular business objectives and retention goals.
*Step 4: The fourth step actually goes beyond the RFM segmentation itself: crafting specific messaging that is tailored for each customer group. By focusing on the behavioral patterns of particular groups, RFM marketing allows marketers to communicate with customers in a much more effective manner. Again, here are just some examples for illustration, using the groups we named above:
**Best Customers – Communications with this group should make them feel valued and appreciated. These customers likely generate a disproportionately high percentage of overall revenues and thus focusing on keeping them happy should be a top priority. Further analyzing their individual preferences and affinities will provide additional opportunities for even more personalized messaging.
**High-spending New Customers – It is always a good idea to carefully “incubate” all new customers, but because these new customers spent a lot on their first purchase, it’s even more important. Like with the Best Customers group, it’s important to make them feel valued and appreciated – and to give them terrific incentives to continue interacting with the brand.
**Lowest-Spending Active Loyal Customers – These repeat customers are active and loyal, but they are low spenders. Marketers should create campaigns for this group that make them feel valued, and incentivize them to increase their spend levels. As loyal customers, it often also pays to reward them with special offers if they spread the word about the brand to their friends, e.g., via social networks.
**Churned Best Customers – These are valuable customers who stopped transacting a long time ago. While it’s often challenging to re-engage churned customers, the high value of these customers makes it worthwhile trying. Like with the Best Customers group, it’s important to communicate with them on the basis of their specific preferences, as known from earlier transaction data.<br />Of course, deciding which groups of customers to target and how to best communicate with them is where the art of marketing comes in!

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	<u>''Avoiding the Bottom 20%''</u><br />		<u>''Avoiding the Bottom 20%''</u><br />
	Improving profitability and optimizing your marketing budget with RFM Analysis isn’t only about building value among your RFM Score customers but saying goodbye to your lowest RFM Score customers—not every customer you have will love you equally, and that’s okay. The Bottom 20% (RFM Score 9 and 10) of the brand’s customers only make up 3.1% of its total LTV, and the Bottom 10% make up a tiny 0.9% of LTV. For your customers with these low RFM Scores, you may be best served lowering/discontinuing your marketing spend dedicated to them and refocusing it on your High Value Customers to generate even greater value from them.		Improving profitability and optimizing your marketing budget with RFM Analysis isn’t only about building value among your RFM Score customers but saying goodbye to your lowest RFM Score customers—not every customer you have will love you equally, and that’s okay. The Bottom 20% (RFM Score 9 and 10) of the brand’s customers only make up 3.1% of its total LTV, and the Bottom 10% make up a tiny 0.9% of LTV. For your customers with these low RFM Scores, you may be best served lowering/discontinuing your marketing spend dedicated to them and refocusing it on your High Value Customers to generate even greater value from them.
		+
		+
		+	== Analyzing RFM Segmentation<ref>Analyzing RFM Segmentation [https://clevertap.com/blog/rfm-analysis/ CleverTap]</ref> ==
		+	*Champions are your best customers, who bought most recently, most often, and are heavy spenders. Reward these customers. They can become early adopters for new products and will help promote your brand.
		+	*Potential Loyalists are your recent customers with average frequency and who spent a good amount. Offer membership or loyalty programs or recommend related products to upsell them and help them become your Loyalists or Champions.
		+	*New Customers are your customers who have a high overall RFM score but are not frequent shoppers. Start building relationships with these customers by providing onboarding support and special offers to increase their visits.
		+	*At Risk Customers are your customers who purchased often and spent big amounts, but haven’t purchased recently. Send them personalized reactivation campaigns to reconnect, and offer renewals and helpful products to encourage another purchase.
		+	*Can’t Lose Them are customers who used to visit and purchase quite often, but haven’t been visiting recently. Bring them back with relevant promotions, and run surveys to find out what went wrong and avoid losing them to a competitor.

← Older revision		Revision as of 22:06, 1 December 2021
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	*Monetary Value: How much money a customer spends on purchases: Monetary value stems from how much the customer spends. A natural inclination is to put more emphasis on encouraging customers who spend the most money to continue to do so. While this can produce a better return on investment in marketing and customer service, it also runs the risk of alienating customers who have been consistent but may not spend as much with each transaction.		*Monetary Value: How much money a customer spends on purchases: Monetary value stems from how much the customer spends. A natural inclination is to put more emphasis on encouraging customers who spend the most money to continue to do so. While this can produce a better return on investment in marketing and customer service, it also runs the risk of alienating customers who have been consistent but may not spend as much with each transaction.
	RFM analysis numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (the higher the number, the better the result). The "best" customer would receive a top score in every category. These three RFM factors can be used to reasonably predict how likely (or unlikely) it is that a customer will do business again with a firm or, in the case of a charitable organization, make another donation.<ref>Definition - What Does RFM Analysis Mean? [https://www.investopedia.com/terms/r/rfm-recency-frequency-monetary-value.asp Investopedia]</ref>		RFM analysis numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (the higher the number, the better the result). The "best" customer would receive a top score in every category. These three RFM factors can be used to reasonably predict how likely (or unlikely) it is that a customer will do business again with a firm or, in the case of a charitable organization, make another donation.<ref>Definition - What Does RFM Analysis Mean? [https://www.investopedia.com/terms/r/rfm-recency-frequency-monetary-value.asp Investopedia]</ref>
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		+	__TOC__

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	**Lowest-Spending Active Loyal Customers – These repeat customers are active and loyal, but they are low spenders. Marketers should create campaigns for this group that make them feel valued, and incentivize them to increase their spend levels. As loyal customers, it often also pays to reward them with special offers if they spread the word about the brand to their friends, e.g., via social networks.		**Lowest-Spending Active Loyal Customers – These repeat customers are active and loyal, but they are low spenders. Marketers should create campaigns for this group that make them feel valued, and incentivize them to increase their spend levels. As loyal customers, it often also pays to reward them with special offers if they spread the word about the brand to their friends, e.g., via social networks.
	**Churned Best Customers – These are valuable customers who stopped transacting a long time ago. While it’s often challenging to re-engage churned customers, the high value of these customers makes it worthwhile trying. Like with the Best Customers group, it’s important to communicate with them on the basis of their specific preferences, as known from earlier transaction data.<br />Of course, deciding which groups of customers to target and how to best communicate with them is where the art of marketing comes in!		**Churned Best Customers – These are valuable customers who stopped transacting a long time ago. While it’s often challenging to re-engage churned customers, the high value of these customers makes it worthwhile trying. Like with the Best Customers group, it’s important to communicate with them on the basis of their specific preferences, as known from earlier transaction data.<br />Of course, deciding which groups of customers to target and how to best communicate with them is where the art of marketing comes in!
		+
		+
		+	== The Power of RFM Analysis<ref>What makes RFM Analysis so powerful? [https://www.daasity.com/post/rfm-analysis Daasity]</ref> ==
		+	'''Identifying High Value Customers (HVCs)'''<br />
		+	RFM Analysis is the clearest way an eCommerce brand can identify its High Value Customers: the customers who have an outsized impact on a brand’s profits (specifically, gross margin per customer, a.k.a. Customer Lifetime Value), and those who you may lose money on, all expenses considered.
		+
		+
		+	[[File:RFM Analysis.png\|400px\|RFM Analysis]]<br />
		+	source: Daasity
		+
		+
		+	In the top chart of this screenshot, it is clear that there’s a sizable difference between customers with an RFM score of 1 and an RFM score of 2. Specifically, customers with an RFM score of 1 have a 2.3x greater Customer Lifetime Value than customers with an RFM score of 2, which, in the case of this brand, translates to about 4 million dollars in gross margin for the brand. The differences between the value of RFM segments become dramatic as the RFM Scores decrease. Customers with an RFM Score of 1 have a 43x greater Customer Lifetime Value than customers with an RFM Score of 10.
		+
		+	'''Leveraging RFM Analysis for your brand'''<br />
		+	<u>''Retaining the Top 20% (and especially the Top 10%)''</u><br />
		+	As a general rule, the more valuable a customer (or, in this case, customer segment) is to your business, the more you should do to ensure that they remain regular customers for as long as possible. In the case of the brand above, RFM Analysis reveals that the Top 20% (RFM Score 1 and 2) of its customers are responsible for 60% of its profits, and the Top 10% of its customers are responsible for 42% of its profits. These are remarkable numbers, and they serve as a bold, size 1000 font sign that says, “SELL TO ME!” RFM Score 1 customers should be given the highest priority (your most compelling offers, plenty of loyalty points, early access to products, upsells and cross-sells, etc.), but RFM Score 2 customers have the highest potential to become RFM Score 1 customers. It is vital to nurture them in order to level them up into RFM Score 1. By increasing the number of High Value Customers with high RFM Scores, your brand will grow faster, be more profitable, and have a more efficient and effective marketing spend. The brands that focus efforts on maximizing value from their High Value Customers grow to be large, profitable brands.
		+
		+	<u>''Avoiding the Bottom 20%''</u><br />
		+	Improving profitability and optimizing your marketing budget with RFM Analysis isn’t only about building value among your RFM Score customers but saying goodbye to your lowest RFM Score customers—not every customer you have will love you equally, and that’s okay. The Bottom 20% (RFM Score 9 and 10) of the brand’s customers only make up 3.1% of its total LTV, and the Bottom 10% make up a tiny 0.9% of LTV. For your customers with these low RFM Scores, you may be best served lowering/discontinuing your marketing spend dedicated to them and refocusing it on your High Value Customers to generate even greater value from them.
		+
		+
		+	== See Also ==
		+	*[[Customer Value]]
		+	*[[Customer Value Analysis (CVA)]]
		+	*[[Customer Acquisition Cost (CAC)]]
		+	*[[Customer Lifecycle]]
		+	*[[Customer Lifetime Value]]
		+
		+
		+	== References ==
		+	<references/>

@@ Line 1: / Line 1: @@
-'''RFM Analysis''' aka '''Recency, frequency, monetary value''' is a marketing analysis tool used to identify an organization's best customers by measuring and analyzing spending habits. The RFM model is based on three quantitative factors:
+'''RFM Analysis''' aka '''Recency, frequency, monetary value''' is a technique used to identify existing customers who are most likely to respond to a new offer. This technique is commonly used in direct marketing.
 *Recency: How recently a customer has made a purchase: The more recently a customer has made a purchase with a company, the more likely they will continue to keep the business and brand in mind for subsequent purchases. Compared with customers who have not bought from the business in months or even longer periods, the likelihood of engaging in future transactions with recent customers is arguably higher. Such information can be used to get recent customers to revisit the business and spend more. In an effort not to overlook lapsed customers, marketing efforts might be made to remind them that it's been a while since their last transaction, while offering them an incentive to resume buying.
 *Frequency: How often a customer makes a purchase: The frequency of a customer’s transactions may be affected by factors such as the type of product, the price point for the purchase, and the need for replenishment or replacement. If the purchase cycle can be predicted — for example when a customer needs to buy more groceries — marketing efforts may be directed towards reminding them to visit the business when staple items run low.
@@ Line 6: / Line 8: @@
-== Steps to Performing RFM Analysis<ref>Performing RFM Segmentation and RFM Analysis, Step by Step [https://www.optimove.com/resources/learning-center/rfm-segmentation Optimove]</ref>
+== How RFM Analysis Works<ref>How RFM Analysis Works [https://www.ibm.com/docs/en/spss-statistics/27.0.0?topic=marketing-rfm-analysis IBM]</ref> ==
 The following is a step-by-step, do-it-yourself approach to RFM segmentation. Note that with the aid of software, RFM segmentation – as well as other, more sophisticated types of segmentation – can be done automatically, with more accurate results.
 *Step 1: The first step in building an RFM model is to assign Recency, Frequency and Monetary values to each customer. The raw data for doing this, which should be readily available in the company’s CRM or transactional databases, can be compiled in an Excel spreadsheet or database:

@@ Line 6: / Line 6: @@
-== Steps to Performing RFM Analysis<ref>Performing RFM Segmentation and RFM Analysis, Step by Step [https://www.optimove.com/resources/learning-center/rfm-segmentation Optimove]<?ref>
+== Steps to Performing RFM Analysis<ref>Performing RFM Segmentation and RFM Analysis, Step by Step [https://www.optimove.com/resources/learning-center/rfm-segmentation Optimove]</ref>
 The following is a step-by-step, do-it-yourself approach to RFM segmentation. Note that with the aid of software, RFM segmentation – as well as other, more sophisticated types of segmentation – can be done automatically, with more accurate results.
 *Step 1: The first step in building an RFM model is to assign Recency, Frequency and Monetary values to each customer. The raw data for doing this, which should be readily available in the company’s CRM or transactional databases, can be compiled in an Excel spreadsheet or database:
 **Recency is simply the amount of time since the customer’s most recent transaction (most businesses use days, though for others it might make sense to use months, weeks or even hours instead).
 **Frequency is the total number of transactions made by the customer (during a defined period).
-Monetary is the total amount that the customer has spent across all transactions (during a defined period).
+**Monetary is the total amount that the customer has spent across all transactions (during a defined period).
 *Step 2: The second step is to divide the customer list into tiered groups for each of the three dimensions (R, F and M), using Excel or another tool. Unless using specialized software, it’s recommended to divide the customers into four tiers for each dimension, such that each customer will be assigned to one tier in each dimension: