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Answer Upon - Recency, Frequency, RFM techniques for Customer Retention & Value Building
Distribution packaging provides the first and most important line of defense against the hazards of the distribution environment. A well-designed distribution package can make an immediate and significant contribution to a company’s bottom line by reducing or eliminating product damage and decreasing transportation costs. A properly designed package will also enhance company image.The packaging design mission is to achieve optimum cost by balancing the sensitivity of the product with the protection provided by the packaging to match the hazards existing in the distribution environment.The science of distribution packaging is more sophisticated and complex than most people expect. There are dozens of methods, techniques and systems for improving distribution packaging and reducing total cost.Let’s take a look at the distribution environment and examine the hazards cargo will encounter.Consider the AirplaneShipping product via airfreight presents a variety of challenges. There is no faster way to get your shipment from point “a” to point “b”, but the additional cost and the potential for damage creates a problem.Let’s first consider the handling. A shipment that travels via airfreight will be handled many times by numerous people with a variety of material handling equipment and a diver
• Monetary value of the interactions
The sort of interaction (or Customer contact event) can vary according to the market and the analysis goals. Usually it involves Customer orders or service usage (e.g. usage of a credit card, usage of telecom services), but it can also involve faults, complaints, web site visits, registration to services, or any other event of importance to the business.
The RFM concept is the following:
• Customers who ordered recently are more likely to order again than those who ordered in a less recent period
• Customers who ordered frequently are more likely to order again than those who ordered less frequently
• Customers who ordered a higher monetary value (spent more) are more likely to order again than those who ordered a lower monetary value,
and it has been tested heavily in the catalog businesses.
Recency, frequency and monetary value, form the basis of database marketing.
Frequency is often a powerful predictor of response, but it is seldom as powerful as Recency. Recency is the most powerful predictor and the easiest to define. There may be alternative ways to measure frequency. One should test alternative measures to figure out which is best suited. This can be done by testing the response on these alternatives.
Recency enables the prediction of future value, while frequency and monetary value enable the estimation of the current value. The combination of the 3 dimensions (RFM) allows the combined analysis of current and future Customer value.
The sequence R->F->M reflects a decreasing series of predictive power (however, this is not always true for the ‘M’ predictor: if one tries to promote an expensive product, M has propably increased predictive power).
In case there is no monetary value attached to an interaction (e.g. a web visit o
Your marketing weak link could be undermining the rest of your marketing. It is vital that each link in your marketing system supports and builds upon the previous link. Each link must bring your customer closer to the next sale. Any link that takes the customer further away from the sale, makes your marketing program inefficient and causes you to lose money.There are many areas where the system can break down and cause expensive waste of potential. For example, I see many retailers who invest enormous amounts on advertising to bring customers into their store, only to lose potential sales through poorly trained sales people who don’t know how to help people buy what they want. There are many examples of businesses advertising discount prices to attract customers because they haven’t worked out how to be different from the competition. As a consequence, they only attract price sensitive shoppers who squeeze down prices and margins as low as possible. I frequently experience poor service in restaurants and cafes which puts me, and others, off returning to those establishments. All these weak links undermine the marketing effort and reduce sales and profits.These examples of weak links in marketing are not only evident in small businesses. As an example, I recently experienced the results of some wea
When is a party (an individual or a business) considered a prospect ? (define the sales pipeline stages for each Customer segment, e.g. lead, prospect, stage at which a proposal is submitted, an order is placed etc). When is a party a new customer ? (1 order, after 2 orders ?) Which is the Customer lifecycle ? Which events mark stages of the lifecycle (1st order, 2nd order, service call, billing inquiry, complaint, etc) ? When is a party no longer a Customer – when is the Customer lifecycle ended ? What is a Customer LifeCycle? Customers begin interacting with a business, and over time, either decide to continue this interaction, or end it. At any point in this LifeCycle, the Customer is either becoming more or less likely to continue interacting. If data from these interactions are captured (purchases, visits, complaints etc.) this data can be used to predict where the Customer is in their LifeCycle. By predicting that, one can focus on Customers most likely to buy, and try to "save" valuable (or profitable) Customers who have declining interest (an info-driven focused approach), instead of wasting money on Customers unlikely to continue interacting (‘blind’ unfocused approach).
In many cases the answer to the above questions (business rule definitions) is not so simple. How can one be sure that a Customer is no longer a Customer, in the retail market. In subscription based service markets the answer is probably easier to give: a party is a Customer, as long as a subscription to a service is active.
Concepts like latency, recency, RFM (recency - frequency - monetary value) are applied many decades, in order to identify the active Customers and achieve higher response rates to the Customer retention & loyalty efforts. In order to apply these techniques, one has to develop a database which stores all Customer contact history on all channels (or CTPs – Customer touch points).
Latency
Latency refers to the average time between Customer activity events (e.g. orders, use of services, visit on web sites). There are alternative ways to estimate the average time between events in order to determine latency. For example if one has captured the dates of a Customer’s orders, she/he can derive the intervals between orders: Time between 1st & 2nd order : 70 days, Time between 2nd & 3rd order : 50 days, latency can be determined to be 60 days (the average time between orders in this sequence of three orders). The trend in ‘time between events’, can also be analysed in order to evaluate the dynamics of a Customer’s behavior. An increasing ‘time between orders or web visits’ is not a good sign (this type of analysis is equivalent to frequency analysis which shall be described below). On the other hand if the event is related to customer dissatisfaction (e.g complaints), increasing ‘time between complaints ’ is a good sign. How can latency be used to develop a simple customer retention program. One has to estimate latency for a Customer group and then measure the days since last event for each customer in that group. When this measurement reaches the latency estimate or exceeds it for a specific Customer, one has to act in order to influence that Customer’s behavior. By offering a discount, this Customer is encouraged to continue interacting with the business.
Recency concept
It has been proven in practise that there is a recency effect in Human behaviour (relevant theoretical studies have been performed in the past but the empirical evidence is also sufficient). This effect viewed in the Customer behaviour analysis field, leads to the following concept: The more recently a Customer has ordered a product or used a service from a certain Business, the more likely it is she/he will purchase a product or use a service again from that Business. In other words, Customers who have transacted with a Business recently are more likely to transact with it again, than Customers who have transacted with that Business less recently.
The recency metric could be defined as the number of days/weeks/months (the scale is relevant to the business/product), since the last relevant transaction occurred (the definition of recency is key to its successful use - testing of alternatives may be needed). Central in this analysis is the dimension of time. ‘How long since a Customer event happened’, is key to understand past and predict future customer behaviour. However the exact scale of time which is relevant in each business/product has to be identified (e.g. in retail sales a second purchase may take place a few months after the first purchase, in home loans a second purchase may never take place). If the Customer Lifecycle is understood (for a specific Business / product), the recency effect can be used to produce actionable ideas. Customer contact history can be used to identify standard Customer lifecycle stages (identify types of milestone events, stages and average stage duration).
Given that a relatively more recent Customer is more likely to buy again, than a less recent one, the former has relatively higher Customer value. This is strictly true if we compare Customers of similar purchase value. We notice here the fact that we reach a comparative conclusion. Recency is widely acknowledged as the most powerful predictor of future behaviour. The future behavior under analysis may be any of the following: Subscriptions to services, purchases, usage of services, visits, complaints In order to perform a simple recency analysis, you have to divide the recent past in few (2 or 3) time periods. Each period should be relevant to the estimated lifecycle stage duration ( e.g. estimated average time between 1st and 2nd purchase).
Applications of recency analysis are:
• Customer retention programs (identify groups with decreased purchasing recency or identify groups with very recent complaints)
• In a large product portfolio, identify products which achieve high customer loyalty (high recency according to the defined cycle) and products that do not
• Management & optimization of campaign plans (adapt offers according to recency groups e.g. with a discount ladder)
• Identify campaign response rates per used recency group, in a test campaign
• Adjust estimation of Customer value ranking, according to recency
• Identification of high value Customers
Recency concepts are applied in TV shopping or on-line shopping (e.g. on Amazon.com when you buy one book, a message appears saying ‘If you make a second order within 90 minutes order shipment will be combined’) The concept of recency and its uses have only meaning and business value when adopted to the specific context of a business and its products. Recency metrics should be applied only to the same product, since different products have different characteristics (customer lifecycles, comparative product price (monetary value)). In order to analyse Customer value for all products, more complex metrics are needed (see RFM).
RFM (recency - frequency - monetary value) customer scoring techniques
RFM is a technique analysing the three dimensions of Customer activity:
• Recency: when was the last customer interaction
• Frequency: how frequent was the Customer in its interactions with the business
• Monetary value of the interactions
The sort of interaction (or Customer contact event) can vary according to the market and the analysis goals. Usually it involves Customer orders or service usage (e.g. usage of a credit card, usage of telecom services), but it can also involve faults, complaints, web site visits, registration to services, or any other event of importance to the business.
The RFM concept is the following:
• Customers who ordered recently are more likely to order again than those who ordered in a less recent period
• Customers who ordered frequently are more likely to order again than those who ordered less frequently
• Customers who ordered a higher monetary value (spent more) are more likely to order again than those who ordered a lower monetary value,
and it has been tested heavily in the catalog businesses.
Recency, frequency and monetary value, form the basis of database marketing.
Frequency is often a powerful predictor of response, but it is seldom as powerful as Recency. Recency is the most powerful predictor and the easiest to define. There may be alternative ways to measure frequency. One should test alternative measures to figure out which is best suited. This can be done by testing the response on these alternatives.
Recency enables the prediction of future value, while frequency and monetary value enable the estimation of the current value. The combination of the 3 dimensions (RFM) allows the combined analysis of current and future Customer value.
The sequence R->F->M reflects a decreasing series of predictive power (however, this is not always true for the ‘M’ predictor: if one tries to promote an expensive product, M has propably increased predictive power).
In case there is no monetary value attached to an interaction (e.g. a web visit or
Being a student, a person needs to look for summer jobs, to keep up with the expenses for school and fun activities. This task is not always easy, especially when you are studying at an American branch university and you have to pay tuition as well. So getting a summer job obviously rules out getting a job in your home Eastern European country as that would pay for only a couple of beers the most.So here we go, approching the winter and thinking about the summer. There are few agencies in England, most of them under the Seasonal Agricultural Workers Scheme /SAWS/ where we want get in, as this is happening just before we enter the EU. We send off the applications to Concordia and wait. The waiting continues and so we call the office. The lady is a little bit hesitant about what we are studying and where, but we just keep talking that we need the summer farm job just like nothing in life at the moment. In two weeks we get the workcards, happily jumping all around and celebrating the victory, so far.From papers it seems just fine, a small farm in Kent, all in all we are suppose to be twelve people there, and so we should get on. Happing previous experience and knowing that the less people there is, the better you have a relationship with the farmer, we are quite content with what we got and start preparing f
Concepts like latency, recency, RFM (recency - frequency - monetary value) are applied many decades, in order to identify the active Customers and achieve higher response rates to the Customer retention & loyalty efforts. In order to apply these techniques, one has to develop a database which stores all Customer contact history on all channels (or CTPs – Customer touch points).
Latency
Latency refers to the average time between Customer activity events (e.g. orders, use of services, visit on web sites). There are alternative ways to estimate the average time between events in order to determine latency. For example if one has captured the dates of a Customer’s orders, she/he can derive the intervals between orders: Time between 1st & 2nd order : 70 days, Time between 2nd & 3rd order : 50 days, latency can be determined to be 60 days (the average time between orders in this sequence of three orders). The trend in ‘time between events’, can also be analysed in order to evaluate the dynamics of a Customer’s behavior. An increasing ‘time between orders or web visits’ is not a good sign (this type of analysis is equivalent to frequency analysis which shall be described below). On the other hand if the event is related to customer dissatisfaction (e.g complaints), increasing ‘time between complaints ’ is a good sign. How can latency be used to develop a simple customer retention program. One has to estimate latency for a Customer group and then measure the days since last event for each customer in that group. When this measurement reaches the latency estimate or exceeds it for a specific Customer, one has to act in order to influence that Customer’s behavior. By offering a discount, this Customer is encouraged to continue interacting with the business.
Recency concept
It has been proven in practise that there is a recency effect in Human behaviour (relevant theoretical studies have been performed in the past but the empirical evidence is also sufficient). This effect viewed in the Customer behaviour analysis field, leads to the following concept: The more recently a Customer has ordered a product or used a service from a certain Business, the more likely it is she/he will purchase a product or use a service again from that Business. In other words, Customers who have transacted with a Business recently are more likely to transact with it again, than Customers who have transacted with that Business less recently.
The recency metric could be defined as the number of days/weeks/months (the scale is relevant to the business/product), since the last relevant transaction occurred (the definition of recency is key to its successful use - testing of alternatives may be needed). Central in this analysis is the dimension of time. ‘How long since a Customer event happened’, is key to understand past and predict future customer behaviour. However the exact scale of time which is relevant in each business/product has to be identified (e.g. in retail sales a second purchase may take place a few months after the first purchase, in home loans a second purchase may never take place). If the Customer Lifecycle is understood (for a specific Business / product), the recency effect can be used to produce actionable ideas. Customer contact history can be used to identify standard Customer lifecycle stages (identify types of milestone events, stages and average stage duration).
Given that a relatively more recent Customer is more likely to buy again, than a less recent one, the former has relatively higher Customer value. This is strictly true if we compare Customers of similar purchase value. We notice here the fact that we reach a comparative conclusion. Recency is widely acknowledged as the most powerful predictor of future behaviour. The future behavior under analysis may be any of the following: Subscriptions to services, purchases, usage of services, visits, complaints In order to perform a simple recency analysis, you have to divide the recent past in few (2 or 3) time periods. Each period should be relevant to the estimated lifecycle stage duration ( e.g. estimated average time between 1st and 2nd purchase).
Applications of recency analysis are:
• Customer retention programs (identify groups with decreased purchasing recency or identify groups with very recent complaints)
• In a large product portfolio, identify products which achieve high customer loyalty (high recency according to the defined cycle) and products that do not
• Management & optimization of campaign plans (adapt offers according to recency groups e.g. with a discount ladder)
• Identify campaign response rates per used recency group, in a test campaign
• Adjust estimation of Customer value ranking, according to recency
• Identification of high value Customers
Recency concepts are applied in TV shopping or on-line shopping (e.g. on Amazon.com when you buy one book, a message appears saying ‘If you make a second order within 90 minutes order shipment will be combined’) The concept of recency and its uses have only meaning and business value when adopted to the specific context of a business and its products. Recency metrics should be applied only to the same product, since different products have different characteristics (customer lifecycles, comparative product price (monetary value)). In order to analyse Customer value for all products, more complex metrics are needed (see RFM).
RFM (recency - frequency - monetary value) customer scoring techniques
RFM is a technique analysing the three dimensions of Customer activity:
• Recency: when was the last customer interaction
• Frequency: how frequent was the Customer in its interactions with the business
• Monetary value of the interactions
The sort of interaction (or Customer contact event) can vary according to the market and the analysis goals. Usually it involves Customer orders or service usage (e.g. usage of a credit card, usage of telecom services), but it can also involve faults, complaints, web site visits, registration to services, or any other event of importance to the business.
The RFM concept is the following:
• Customers who ordered recently are more likely to order again than those who ordered in a less recent period
• Customers who ordered frequently are more likely to order again than those who ordered less frequently
• Customers who ordered a higher monetary value (spent more) are more likely to order again than those who ordered a lower monetary value,
and it has been tested heavily in the catalog businesses.
Recency, frequency and monetary value, form the basis of database marketing.
Frequency is often a powerful predictor of response, but it is seldom as powerful as Recency. Recency is the most powerful predictor and the easiest to define. There may be alternative ways to measure frequency. One should test alternative measures to figure out which is best suited. This can be done by testing the response on these alternatives.
Recency enables the prediction of future value, while frequency and monetary value enable the estimation of the current value. The combination of the 3 dimensions (RFM) allows the combined analysis of current and future Customer value.
The sequence R->F->M reflects a decreasing series of predictive power (however, this is not always true for the ‘M’ predictor: if one tries to promote an expensive product, M has propably increased predictive power).
In case there is no monetary value attached to an interaction (e.g. a web visit o
One of my business goals is to increase subscribers to the mezhermnt Handy Hints ezine, so I can get lots of useful information out to lots of people, and also help people get to know me and the PuMP approach to performance measurement.Obviously I can't control whether someone joins the ezine list - it is unethical to simply add people to the list without their permission (do you recall the confirmation you had to give in order to be added to the mezhermnt Handy Hints list?). But I can influence a few things that increase the number of people that find out about it, and even the proportion of those people that go to the next step and sign up.So whether your improvement initiatives are small like mine, or much larger and more complex, there are a few good tips to consider when you measure the impact your improvement initiatives have on the intended results.tip #1: start with some baseline dataThe performance measure for building my list is the number of new subscribers. Before starting any list building initiatives, subscriptions were averaging about 10 per week. That's my measure's baseline.What's your performance measure's baseline? Did you measure it before you began your improvement initiatives? Can you establish the baseline from historic data, or estimate where it was at that tim
The recency metric could be defined as the number of days/weeks/months (the scale is relevant to the business/product), since the last relevant transaction occurred (the definition of recency is key to its successful use - testing of alternatives may be needed). Central in this analysis is the dimension of time. ‘How long since a Customer event happened’, is key to understand past and predict future customer behaviour. However the exact scale of time which is relevant in each business/product has to be identified (e.g. in retail sales a second purchase may take place a few months after the first purchase, in home loans a second purchase may never take place). If the Customer Lifecycle is understood (for a specific Business / product), the recency effect can be used to produce actionable ideas. Customer contact history can be used to identify standard Customer lifecycle stages (identify types of milestone events, stages and average stage duration).
Given that a relatively more recent Customer is more likely to buy again, than a less recent one, the former has relatively higher Customer value. This is strictly true if we compare Customers of similar purchase value. We notice here the fact that we reach a comparative conclusion. Recency is widely acknowledged as the most powerful predictor of future behaviour. The future behavior under analysis may be any of the following: Subscriptions to services, purchases, usage of services, visits, complaints In order to perform a simple recency analysis, you have to divide the recent past in few (2 or 3) time periods. Each period should be relevant to the estimated lifecycle stage duration ( e.g. estimated average time between 1st and 2nd purchase).
Applications of recency analysis are:
• Customer retention programs (identify groups with decreased purchasing recency or identify groups with very recent complaints)
• In a large product portfolio, identify products which achieve high customer loyalty (high recency according to the defined cycle) and products that do not
• Management & optimization of campaign plans (adapt offers according to recency groups e.g. with a discount ladder)
• Identify campaign response rates per used recency group, in a test campaign
• Adjust estimation of Customer value ranking, according to recency
• Identification of high value Customers
Recency concepts are applied in TV shopping or on-line shopping (e.g. on Amazon.com when you buy one book, a message appears saying ‘If you make a second order within 90 minutes order shipment will be combined’) The concept of recency and its uses have only meaning and business value when adopted to the specific context of a business and its products. Recency metrics should be applied only to the same product, since different products have different characteristics (customer lifecycles, comparative product price (monetary value)). In order to analyse Customer value for all products, more complex metrics are needed (see RFM).
RFM (recency - frequency - monetary value) customer scoring techniques
RFM is a technique analysing the three dimensions of Customer activity:
• Recency: when was the last customer interaction
• Frequency: how frequent was the Customer in its interactions with the business
• Monetary value of the interactions
The sort of interaction (or Customer contact event) can vary according to the market and the analysis goals. Usually it involves Customer orders or service usage (e.g. usage of a credit card, usage of telecom services), but it can also involve faults, complaints, web site visits, registration to services, or any other event of importance to the business.
The RFM concept is the following:
• Customers who ordered recently are more likely to order again than those who ordered in a less recent period
• Customers who ordered frequently are more likely to order again than those who ordered less frequently
• Customers who ordered a higher monetary value (spent more) are more likely to order again than those who ordered a lower monetary value,
and it has been tested heavily in the catalog businesses.
Recency, frequency and monetary value, form the basis of database marketing.
Frequency is often a powerful predictor of response, but it is seldom as powerful as Recency. Recency is the most powerful predictor and the easiest to define. There may be alternative ways to measure frequency. One should test alternative measures to figure out which is best suited. This can be done by testing the response on these alternatives.
Recency enables the prediction of future value, while frequency and monetary value enable the estimation of the current value. The combination of the 3 dimensions (RFM) allows the combined analysis of current and future Customer value.
The sequence R->F->M reflects a decreasing series of predictive power (however, this is not always true for the ‘M’ predictor: if one tries to promote an expensive product, M has propably increased predictive power).
In case there is no monetary value attached to an interaction (e.g. a web visit o
Many small business owners, remember small is defined as companies with under 500 employees, fail to market themselves and consequently continue to lose market share. The inability to increase sales revenue goes beyond the expertise of your sales staff and is directly tied into your marketing plan.First, do you have a marketing plan to deliver your marketing message? How are you marketing yourself and your business? Even if you are a Single Office Home Office Business (SOHO) or home based business, you need a marketing plan. Obviously if you are a larger organization, you understand the value of having such a plan. HINT: If you don’t have a plan or have been procrastinating about writing a plan, STOP right now. Find; hire someone to help you write that plan. These plans should be directly tied to your strategic plan of Who Does What By When within your business plan. Read about where to begin at www.processspecialist.com/articles/DoYouKnowandPlanforthe3RsforBusiness.pdfSecond, does your marketing message interrupt your prospect or suspect? What makes your message stop your prospects’ behaviors to want to further listen to what you have to say? Look to those now infamous Super Bowl commercials or even the ever-changing ones on television. Why does one commercial literally STOP YOU? Is it the graphics
Applications of recency analysis are:
• Customer retention programs (identify groups with decreased purchasing recency or identify groups with very recent complaints)
• In a large product portfolio, identify products which achieve high customer loyalty (high recency according to the defined cycle) and products that do not
• Management & optimization of campaign plans (adapt offers according to recency groups e.g. with a discount ladder)
• Identify campaign response rates per used recency group, in a test campaign
• Adjust estimation of Customer value ranking, according to recency
• Identification of high value Customers
Recency concepts are applied in TV shopping or on-line shopping (e.g. on Amazon.com when you buy one book, a message appears saying ‘If you make a second order within 90 minutes order shipment will be combined’) The concept of recency and its uses have only meaning and business value when adopted to the specific context of a business and its products. Recency metrics should be applied only to the same product, since different products have different characteristics (customer lifecycles, comparative product price (monetary value)). In order to analyse Customer value for all products, more complex metrics are needed (see RFM).
RFM (recency - frequency - monetary value) customer scoring techniques
RFM is a technique analysing the three dimensions of Customer activity:
• Recency: when was the last customer interaction
• Frequency: how frequent was the Customer in its interactions with the business
• Monetary value of the interactions
The sort of interaction (or Customer contact event) can vary according to the market and the analysis goals. Usually it involves Customer orders or service usage (e.g. usage of a credit card, usage of telecom services), but it can also involve faults, complaints, web site visits, registration to services, or any other event of importance to the business.
The RFM concept is the following:
• Customers who ordered recently are more likely to order again than those who ordered in a less recent period
• Customers who ordered frequently are more likely to order again than those who ordered less frequently
• Customers who ordered a higher monetary value (spent more) are more likely to order again than those who ordered a lower monetary value,
and it has been tested heavily in the catalog businesses.
Recency, frequency and monetary value, form the basis of database marketing.
Frequency is often a powerful predictor of response, but it is seldom as powerful as Recency. Recency is the most powerful predictor and the easiest to define. There may be alternative ways to measure frequency. One should test alternative measures to figure out which is best suited. This can be done by testing the response on these alternatives.
Recency enables the prediction of future value, while frequency and monetary value enable the estimation of the current value. The combination of the 3 dimensions (RFM) allows the combined analysis of current and future Customer value.
The sequence R->F->M reflects a decreasing series of predictive power (however, this is not always true for the ‘M’ predictor: if one tries to promote an expensive product, M has propably increased predictive power).
In case there is no monetary value attached to an interaction (e.g. a web visit o
In economics, unemployment refers to the condition and extent of joblessness within an economy, and is measured in terms of the unemployment rate, which is the number of unemployed workers divided by the total civilian labor force. Hence, unemployment is the condition of not having a job, often referred to as being "out of work", or unemployedThe terms unemployment and unemployed are sometimes used to refer to other inputs to production that are not being fully used, for example, unemployed capital goods.The history of unemployment is the history of industrialization. It was not considered an issue in rural areas, despite the "disguised unemployment" of rural laborers having little to do, especially in conditions of overpopulation.The Office for National Statistics (ONS) produces official estimates of unemployment using the International Labour Organization definition. Under this definition people aged 16 and over are unemployed if they are out of work, want a job, have actively sought work in the last four weeks and are available to start work in the next two weeks; or are out of work, have found a job and are waiting to start it in the next two weeks.Unemployment levels are increasing dramatically in many parts of the world. There are several causes behind this increasing rate. But the mai
• Monetary value of the interactions
The sort of interaction (or Customer contact event) can vary according to the market and the analysis goals. Usually it involves Customer orders or service usage (e.g. usage of a credit card, usage of telecom services), but it can also involve faults, complaints, web site visits, registration to services, or any other event of importance to the business.
The RFM concept is the following:
• Customers who ordered recently are more likely to order again than those who ordered in a less recent period
• Customers who ordered frequently are more likely to order again than those who ordered less frequently
• Customers who ordered a higher monetary value (spent more) are more likely to order again than those who ordered a lower monetary value,
and it has been tested heavily in the catalog businesses.
Recency, frequency and monetary value, form the basis of database marketing.
Frequency is often a powerful predictor of response, but it is seldom as powerful as Recency. Recency is the most powerful predictor and the easiest to define. There may be alternative ways to measure frequency. One should test alternative measures to figure out which is best suited. This can be done by testing the response on these alternatives.
Recency enables the prediction of future value, while frequency and monetary value enable the estimation of the current value. The combination of the 3 dimensions (RFM) allows the combined analysis of current and future Customer value.
The sequence R->F->M reflects a decreasing series of predictive power (however, this is not always true for the ‘M’ predictor: if one tries to promote an expensive product, M has propably increased predictive power).
In case there is no monetary value attached to an interaction (e.g. a web visit or a complaint), the analysis may be limited to recency & frequency (RF analysis).
The higher the RFM score, the more probable it is for a Customer to respond to a marketing program. This fact has been clearly confirmed in practice.
Why is this fact actionable ? Because if one classifies Customers to groups according to the RFM score, she/he can expect each distinct RFM group to have substantially different response to an offer, from the rest (especially if the number of groups is limited). Therefore she/he can focus only on certain Customer groups which are expected to respond highly, or adjust the offering in a way to achieve high response from many targeted groups. This can be achieved by offering a more attractive deal (e.g. a higher discount) to the lower RFM (or RF) groups (which are less likely to respond), than to the higher RFM group(s), in order to achieve a satisfactory response from more than one group.
Copyright 2006, Kostis Panayotakis
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