Revisiting the RFM Marketing Model: tips on how and why customer segments improve performance

If you're a marketer, then you know that understanding your customers is key to creating successful marketing campaigns. And one of the best ways to understand your customers is through the RFM marketing model. RFM stands for recency, frequency, and monetary value, and it's a way to segment your customers based on how recently they've interacted with your business, how often they interact with your business, and how much money they've spent with you. RFM models have been used for years because it works. In this blog post, we'll explain the RFM marketing model and why it's so important, and we'll give you tips on how to use it to improve your marketing campaigns. Stay tuned!

What is the RFM Model doing?

RFM analysis ranks customers on the following factors related to a transaction:

1. Recency: How recent was their last purchase? Businesses often measure this with regards to days but depending upon what you sell — there can sometimes even exist other time frames such as years instead-of weeks/months, etc. Customers who recently bought a product will still have it in mind and be more likely to buy themselves again or recommend that others do so too!
2. Frequency: How often did they consume goods altogether during the said period? First-timers might need some follow-up advertising before becoming regular clients.
3. Monetary Value: Total Sales are often used but can be Total profit. It depends on what makes sense for the business. Sometimes "new" customers can be high value, and "old" customers can be low value.

The RFM marketing model is a powerful tool that can help you segment your customers and create more personalized and successful marketing campaigns because it gives marketers better context to create better messaging. When used correctly, the RFM marketing model can help you increase conversions.

Computing Segment Scores and Marketers' Discretion

Marketers have relied on RFM modeling to determine the most valuable customers and group them more specifically. At VIEWN, RFM scores for each customer in the CDP are automatically grouped into quintiles or five groups. They should average together before sorting the top position within your business' ranking criteria. Some companies consider how heavily they weigh certain factors when determining this number, so you'll see them come up with different ways depending on what the company does best!

For example, a home-goods business may recognize that an average customer is unlikely to buy several new sofas in just a few years. But a customer who does buy several sofas — a high-frequency customer — should be highly sought after. So, the business owner may choose to weigh the value of the frequency score accordingly.

Here's another example, businesses that do not rely on direct customer payments may use different factors in their analysis. For example, websites and apps that prioritize readership can measure an engagement value instead of monetary when performing an RFM (recency frequency) test using the same techniques as those used for standard RFE calculations.

When considering the unit,s divide the data into as many discrete little segments as possible and see where that leads. Consider these segmentation characteristics:

Purchasing behavior

• How many times has the person purchased in the past 24, 12, 6, and 3 months?
• How many different versions of the product? (e.g., for example, Woxers, all colors? Some colors? Some prints?)
• When was the last time the person purchased it?
• What were the average purchase in the past 24, 12, 6, 3 months, and last time?

Personal behavior

• Belong to the loyalty club?
• Have you ever filled out a survey?
• Have you ever posted on social media?
• Ever engage with your social campaigns?
• Ever return something to the store?

Personal Characteristics

• What is the avg? What income of the person's zip code?
• What is their state

Once you lay out the columns (which is the information you have), populate it with all the individuals going down in rows to see if there are some natural breaks.

Segmentation of customers in RFM analysis

Ultimately, the scoring will result in eleven distinct segments where Champions are your most valued customer segments and Lost the least. Regardless of where an individual profile is, your marketers have way more context to speak, market, and promote even business for that individual. With this map, marketers can engage and nudge customers up the ladder.

1. Champions
2. Loyal customers
3. Potential Loyal
4. Recent Customers
5. Promising
6. Customers Needing Attention
7. About to Sleep
8. At Risk
9. Can't Lose Them
10. Hibernating
11. Lost

Limitations of RFM analysis

Using RFM modeling can provide valuable insights about customers. But it does not take into account many other factors about the customer.

In-depth targeted marketing may also use the type of item purchased or customer campaign responses as factors. Customer demographics such as age, sex, and ethnicity are not covered in RFM analysis either. If there are factors that you believe help you define who the customer is, then ask them in a survey.

Additionally, RFM only uses historical data about customers and may not predict future customer activity. Predictive methods may identify future customer behavior that RFM analysis cannot. These limitations drive marketers to deploy many marketing models and run their data through software like customer data platforms (CDPs).

RFM analysis is a powerful tool for marketing, but it has limitations. It is best used with other marketing strategies to provide the most comprehensive customer understanding possible.

RFM analysis can be valuable to any marketer's toolkit when used correctly. By understanding how and why customers buy, businesses can create targeted campaigns.

The RFM marketing model is a tried and true way to understand your customers. By segmenting your customers based on their recency, frequency, and monetary value, you can create more effective marketing campaigns that speak to the needs of each group. And by using RFM with other customer data (like age, gender, location, etc.), you can get even more accurate insights into who your customers are and what they want. If you're looking for help putting together an effective RFM marketing strategy, our team of experts is here to help. Contact us today to learn more about how we can help you improve your marketing campaigns!

#CX #CDP #CDPsaas #digitaltransformation #startupmarketing #ecommercemarketing #ecommercesoftware #ecommercebusiness #customerdataplatform #customerexperience #customerengagement #digitalbusinesstransformation #marketingtransformation #retailermarketing #CXmarketing