Saturday, November 9, 2019

Data Science Study Notes: RFM model to identify the best customers

80% of your sales come from 20% of your customers. As a small business owner, even if you’ve never heard of the Pareto Principle, you know this rule of thumb intuitively. You’re in business largely because of the support of a fraction of your customer base: your best customers.

RFM is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries.

RFM stands for the three dimensions:

Recency – How recently did the customer purchase?
Frequency – How often do they purchase?
Monetary Value – How much do they spend?

Customer purchases may be represented by a table with columns for the customer name, date of purchase and purchase value. One approach to RFM is to assign a score for each dimension on a scale from 1 to 10. The maximum score represents the preferred behavior and a formula could be used to calculate the three scores for each customer. For example, a service-based business could use these calculations:

Recency = the maximum of "10 – the number of months that have passed since the customer last purchased" and 1
Frequency = the maximum of "the number of purchases by the customer in the last 12 months (with a limit of 10)" and 1
Monetary = the highest value of all purchases by the customer expressed as a multiple of some benchmark value

Alternatively, categories can be defined for each attribute. For instance, Recency might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. If each dimension has 3 categories, then there will be 3*3*3=27 cells.

Usually we can use the equal percentile to break those categories, so each category has the same number of customer, even though those 27 cells might not have the same number of customers.

There are very few absolutes in marketing, but one of them is this: the people most likely to respond to a new offer are those people who have made a purchase from you most recently. There is something about people’s psychology that makes them more likely to open your envelope and act on what is inside if they have recently had a satisfactory transaction with you.

No comments:

Post a Comment

Data Science Study Notes: recommendation engine notes 1: Deep matrix factorization using Apache MXNet

Deep matrix factorization using Apache MXNet ( notes from Oreilly , github notebook ) Recommendation engines are widely used models th...