Thu. Nov 26th, 2020
Understanding RFM

One of the most effective ways of using and analysing customer data is RFM (Recency, Frequency, Monetary Value). Conducting an analysis of this data helps to get a clearer idea of the client base.

RFM is used to analyse and calculate the customer’s value that is established by three data areas.

Recency – When was the most recent purchase made?

Frequency –How frequently does a customer make purchases?

Monetary Value –How much does the customer spend?

Analysing these three factors can predict to a reasonable extent how unlikely or likely is the possibility that the customer will make a repurchase.

For those companies or businesses that do not have a monetary aspect to determine RFM, e.g. (readership, viewership, browsing based items) other parameters of engagement can be used in places of monetary values. This is RFE that is a variant of RFM. The engagement parameters for RFE could have a composite value that depends on other metrics like duration of visit, time spent on each page, number of page visits, bounce rate etc.

RFM helps to clarify these three facts –

Recent purchases by a customer increase the likelihood of the customer responding better to promotions

The greater the frequency by which the customer makes purchases the more satisfied and engaged are they

The monetary value helps to separate customers that spend heavily from those customers that conduct purchases of low value

RFM Analysis

Depending on the frequency, monetary value and recencyof purchase RFM analysis help to determine customers of the lowest and highest value to predict which of those are more likely to make purchases later. For any business’sservice or product purchase cyclecustomers can be evaluated on recency with a scale of 1-10. The 10 shows the customer purchased within the last month and 1 showing the last purchase was made 10-12 months ago.

After a business has determined using a 1-10 scale for every one of the three categories, a review of the CRM can be done, and scores allotted to every customer in each category. When the combination of three scores is totalled an RFM analysis can be carried out to find the customer most likely to make a purchase in the future. This information can be used to reach out to such customers and offer value for these customers.

A point to consider is that although RFM offers a clear picture of recent customers that can be targeted for priority selling, it does not essentially mean the customer would have an interest in all offers made all the time. For this,a mechanism must be in place to avoid harassing customers with calls and emails. Over targeting such customers might turn them off to buy from elsewhere. RFM analysis scores are meant to be well informed about customers, and not just trying to sell more to them immediately.

The bottom line is to think of RFM analysis as a tool that can help to determine the profits coming in from new vs. repeat customers, and what steps to take to increase customer satisfaction. An RFM analysis of a business could show customer dissatisfaction with the business’ service or productafter they make the first purchase. It could also show those customers that are cross or upsold are more probable to purchase than others. In simple terms, RFM analysis helps to determine differences and common aspects of customers both non-repeat and repeat to understand the levels of customer satisfaction.

Subject to any businessone can decrease or increase the significance of RFM values to get the final result. E.g.

For content sites like Facebook or Netflix, those that are binge-watchers will have a longer length session than regular watchers at intervals. Therefore,frequency and engagement can be focussed on over recency for bingers. For regular watchers, frequency and recency could be given more importance over engagement to get the RFE scores.

For retail businesses e.g. fashion, customers that search and buy products on a monthly basis get higher frequency and recency scores over monetary. Hence when calculating the RFM scores F and R scores should be given greater importance over M.

For consumer items, generally, the monetary profit for every transaction is higher with lower recency and frequency. E.g. Customers cannot be expected to buy cars or bikes every month, therefore greater significance is allotted to recency and monetary value over frequency.

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