Let's jump right into the stuff we only have two minutes :-). In this post I would like to write about the RFM algorithm.
RFM analysis assigns value-scores to each customer on the basis of her past behavior. Using the quintile system explained above, at the most, 125 different scores (5x5x5) can be assigned. These cells differ in size from one another. A customer’s score can range from 555 being the highest, to 111 being the lowest. The best customers are in quintile 5 for each factor (555) that have purchased most recently, most frequently and have spent the most money.
RFM provides a simple framework for quantifying customer behavior. For example, it is possible to infer that customer which has RFM score 155, has made a high number of purchases with high monetary values but not for a long time.
Something might have gone wrong with this customer, for example, he/she has most likely defected to a competitor's products and services or has found an alternate source and that is why his/her recency score is low. At this situation, marketers can contact this customer and get feedbacks about how to do it better because he/she is one of the valuable customers according to his frequency and monetary values.
Moreover, it is possible to plan a customer reactivation program and send him/her an extreme promotion in an effort to get his/her attention. While customers with score 155 need a reminder, 551's need to be upsold, and 515's need a sticky recurring relationship. For example, if the RFM score of a customer is identified as 515, marketers can prepare a special customer packet that includes a thank-you letter, a list of company benefits, and an incentive to make another purchase from the online store within the next 30 days.
RFM analysis assigns value-scores to each customer on the basis of her past behavior. Using the quintile system explained above, at the most, 125 different scores (5x5x5) can be assigned. These cells differ in size from one another. A customer’s score can range from 555 being the highest, to 111 being the lowest. The best customers are in quintile 5 for each factor (555) that have purchased most recently, most frequently and have spent the most money.
RFM provides a simple framework for quantifying customer behavior. For example, it is possible to infer that customer which has RFM score 155, has made a high number of purchases with high monetary values but not for a long time.
Something might have gone wrong with this customer, for example, he/she has most likely defected to a competitor's products and services or has found an alternate source and that is why his/her recency score is low. At this situation, marketers can contact this customer and get feedbacks about how to do it better because he/she is one of the valuable customers according to his frequency and monetary values.
Moreover, it is possible to plan a customer reactivation program and send him/her an extreme promotion in an effort to get his/her attention. While customers with score 155 need a reminder, 551's need to be upsold, and 515's need a sticky recurring relationship. For example, if the RFM score of a customer is identified as 515, marketers can prepare a special customer packet that includes a thank-you letter, a list of company benefits, and an incentive to make another purchase from the online store within the next 30 days.
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