Business Problem with Customer Segmentation(RFM Model)
There are many models that can be used to segment customer data. If your organization is not currently using a framework to drive segmentation, i recommend starting with the RFM model because it’s simple and effective. This article series consists of 2 parts and this is the first part of the series.
First of all, I will explain what the RFM model is and what it consists of, and then try to explain in order what I do on the notebook in my Kaggle account.
In fact, scoring and labeling in RFM is very important, but I don’t want to go into detail. I think it would be pretty easy to see the rest on the notebook.
Let’s begin to explain what RFM is and what it consists of.
What Is Recency, Frequency, Monetary Value (RFM)?
Recency, frequency, monetary value is a marketing analysis tool used to identify a company’s or an organization’s best customers by using certain measures. The RFM model is based on three quantitative factors:
- Recency: How recently a customer has made a purchase
- Frequency: How often a customer makes a purchase
- Monetary Value: How much money a customer spends on purchases
RFM analysis numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (this is the higher the number, the better the result). The “Best” customer would receive a top score in every category.
Important Note:
Nonprofit organizations, in particular, have relied on RFM analysis to target donors, as people who have been the source of contributions in the past are likely to make additional gifts.
Understanding Recency, Frequency, Monetary Value (RFM)
The concept of recency, frequency, monetary value (RFM) is thought to date from an article by Jan Roelf Bult and Tom Wansbeek, “Optimal Selection for Direct Mail”, published in a 1995 issue of Marketing Science. RFM analysis often supports the marketing adage that “80% of business comes from 20% of the customers”.
Let’s look more closely at how each RFM factor works, and how companies might strategize on the basis of it.
Recency
The more recently a customer has made a purchase with a company, the more likely he or she 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 remind recent customers to revisit the business soon to continue meeting their purchase needs. In an effort not to overlook lapsed customers, marketing efforts could be made to remind them that it has been a while since their last transaction while offering them an incentive to rekindle their patronage.
Frequency
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 new groceries, marketing efforts could be directed towards reminding them to visit the business when items such as eggs or milk have been depleted.
Monetary Value
Monetary value stems from the lucrativeness of expenditures the customer makes with the business during their transactions. 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 have not spent as much with each transaction.
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.
Let me put a stop to our topic here and say we will see you in our next topic, Stages of RFM Model Implementation.
References
1. https://www.cloudkettle.com/blog/how-to-build-an-rfm-model-for-customer-segmentation/
2. https://www.kaggle.com/mathchi/business-problem-with-customer-segmentation
3. https://github.com/Mathchi/Customer-Segmentation-with-RFM-Analysis
4. https://www.investopedia.com/terms/r/rfm-recency-frequency-monetary-value.asp
5. https://www.jstor.org/stable/184136?seq=1
6. https://www.ordorite.com/how-customer-segmentation-can-improve-your-profits/
7. https://simpli.fi/timing-everything-recency-impacts-performance/
8. http://www.plusxp.com/2011/02/back-to-the-future-the-game-episode-1-review/
