José Castel-branco is an independent business consultant and Professor at Catholic University of Mozambique.

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2 José Castel-branco is an independent business consultant and Professor at Catholic University of Mozambique. Please address all correspondence concerning this paper to: 2

3 ABSTRACT In order to allocate resources within the marketing budget, firms must value their customers and treat them as assets. This paper intends to demonstrate that in the absence of any demographic and socio-economic variables, behavioural information that can be automatically collected in the digital context is enough to group customers according to their value to the business. In order to segment the customer base we started by using the RFM model (Recency, Frequency, Money value). By inserting another behavioural variable extracted from the collected data, the RFM model was extended. A segment composed by super customers emerged from the model. By using the Monte Carlo method we simulated a coupon discount campaign. We concluded that the use of the RFM model as a criterion to select a campaign target produces better results than blind criteria in the non-contractual context of digital products. In addition, the extended model clearly outperforms the results obtained with the classical RFM model. Keywords: CRM, RFM, customer behaviour, digital products. 3

4 1 INTRODUCTION Nowadays, CRM (Customer relationship management) is seen like a panacea to management problems. Firms spend thousands and even millions of dollars implementing such systems. Unfortunately only a few succeed (Newell, 2003). The problem is that CRM is perceived as a technological solution. There s no doubt that technology is definitely very important to CRM however, technology must be a means and not an end. CRM is a knowledge circle that must be feed with data and information in order to produce management wisdom. Without a data model and a set of tools the CRM is an empty process. Most firms report that they are unable to find criteria to segment their customer base and regret the absence of units of measure in this area. Nevertheless, they decline the value of information and insist on reward programs towards every customer regardless of their profitability. In this paper we claim that a heuristic behavioural model is as much as necessary to make the first steps towards the implementation of a CRM program. We will show that the economic feasibility of a marketing campaign can be measured in the non-contractual context for digital products. Our results show that profitability can be greatly improved by using behavioural segmentation variables against blind criteria when selecting a campaign target. The remainder of the paper is organized as follows: section 2 introduces a resumed theoretical framework and the research propositions. In section 3 we introduce the methodological process used to test the propositions. In section 4 we lay out the information and discuss the results. In section 5 we draw conclusions, show the limitations and suggest applications. 4

5 2 THEORETICAL FRAMEWORK CRM is a critical success factor for business performance (Sin et al. 2005). Nowadays it is known that it is more expensive to acquire a new customer than to retain an existing one (Reichheld 1996). Therefore, many firms concentrate their efforts to retain their customers. The problem is that as Hallberg (1995) says, all customers are not created equal, and firms must separate the profitable ones from the non-profitable (Gupta et al 2005). To pursue such purpose, firms need to assemble data and feed the CRM process. However this is not enough. Without analysis CRM simply will not work (Hahnke 2001). The IT (Information technology) that allows firms to collect, store and integrate data is an innocuous process if data is not transformed into information that can be measured and used to support decision (Casais 2002). Digital products such as home banking, digital books, software or information services, can be bought from virtually any place on Earth and delivered without shipping costs as the product is delivered through a digital channel (Merisavo 2003). The geographical barriers between seller and buyer can be overcome with a mouse click or a phone call. To do their business these firms do not need to know, and they usually do not, the customer location, sex, age, income or any other demographic or socio-economic features. To be effective, and improve marketing efforts, the segmentation process should group the firm customers into homogeneous groups. Firms usually use data with demographic, geographic and lifestyle characteristics during this process (Aaker et al. 2003). This method assumes that people with identical features have the same needs and therefore, the same behaviour. This approach to the segmentation process has opponents. In the early seventies O Toole (1973) tried to demystify this generalization with a simple question: are Grace Slick and Tricia Cox the same person? He defined the following outline: A woman. 25 to 35 age group. Family income over $ Urban. Working. College graduate. Let s say a college like Finch in New York. 2 to 3 members of household. White. (O Toole, 1973:32). Tricia Cox, daughter of Richard Nixon married in the White House with a Dwight Eisenhower grandson and exemplifies the strait life. Grace Slick, singer and leader of the Jefferson Airplane rock band is an unmarried mother whose daughter was originally named God but to whom Grace Slick refers to as China. They both fit in the same a priori defined outline and belong to the same marketing target and yet we do not know if they have the same buying behaviours. This practice does not allow the understanding of customer behaviour patterns and does not permit any kind of predictions (Bisgaard-Bohr 2002). Nevertheless, this a priori method of segmentation is useful in some conditions, like for example the start of a new business when there is no behavioural data available. For the time being many firms have great difficulty in determining segmentation criteria that allow them to identify and target the most profitable customers (Newell 2003). Kumar et al. (2004) report that in a non-contractual context, retailers do know how many products are sold but they do not usually know who bought them. They also do not know which kind of products each customer bought. The main problem is: how can we, in non contractual context, segment the customer base and identify the most valuable customers? We believe that the solution to this problem lays in the knowledge of consumer behaviour rather than on demographic or socio economic data. Because they knew their customer identity, catalogue industry was a pioneer in tracking consumer behaviour. They use the RFM (Recency, Frequency, Money value) model to select the customers to whom they send their catalogues and as a result they reduce the cost of non response (Libey et al. 2005). As in the non-contractual digital context each transaction leaves a record, it is therefore possible to associate a customer identity to each transaction (Hui et al. 2002). Thus the seller can always know when, what and how many products a certain customer bought. This information is enough to build a behavioural model that can be used to segment the customer base according to behavioural patterns. The RFM model simplicity has been widely criticized and the stochastic models such as the Pareto/NBD (NBD stands for negative binomial distribution) or the BG/NBD (BG stands for Beta Geometric) are understood as the state-of-the art to identify the most profitable customers. However, the practical application of 5

6 such models is complex and expensive. Wübben et al. (2006 and 2006a) tested those models against a simple recency analysis and clearly recommend that practitioners should not use the Pareto/NBD or the BG/NBD models. On the other hand, Stone et al. (2004) argue that the RFM model is a cost effective alternative to the CHAID (Chi square automatic interaction director) model. Our purpose is to test, in non-contractual context of digital products, the following propositions: Proposition 1: The RFM model can be used to segment the customer base. Day et al. (1999) argue that to evaluate their capabilities there is a need to test different methodologies in different contexts. To our knowledge there are no empirical studies that use the proposed model to segment the customer base in digital products and therefore the need to research is justified. Proposition 2: The use of a purchase regularity factor improves the capabilities of the RFM model to identify the best customers. Buckinx et al. (2003) and Poel (2003) argue that the model needs to be improved by the use of other variables that allow identifying a segment with super customers. As far as we know the purchase regularity factor has never been used to test this assumption and we expect, as we introduce a time factor in the model, to be able to identify a segment with the most loyal and valuable customers. Proposition 3: The RFM segments can be used as criteria to select the most profitable target to a coupon discount marketing campaign. There is a profitability gap between the use of a blind criteria and the use of behavioural data to select a marketing campaign target (Storbacka et al and Rossi et al 1996). We expect that the use of the best segment generated by the RFM model after the introduction of a new variable will contribute to increase the campaign profitability. 6

7 3 RESEARCH METHODOLOGY To conduct this study, data was collected from a nationwide telephone information service provided by a telecom company. Data was gathered on a monthly basis during twelve consecutive months. A database was built for each dataset from month 1 to month 12. Each transaction is recorded in the company billing system having the following parameters: customer identity (customer phone number), date and duration of each transaction. As the price works in a pay per time basis, the duration of each transaction was converted to money through a simple algorithm (money value variable). Customer s identity were grouped and counted. This operation gave us the number of transactions made by each customer (frequency). The most recent transaction date associated to each customer identity allowed us to measure the recency of the last purchase for each customer in the databases. From this point forward, all the inputs to build the RFM model were found. We ran the model on each one of the 12 databases using the quintil method, therefore we have created 5x5x5=125 segments with equal number of customers for each database as illustrated in the next figure: Table 3.1. The RFM model algorithm: DATA R RF RFM To be effective the segmentation process should increase the scale of homogeneity in each segment when compared to the scale of homogeneity of the raw data. Being the coefficient of variation a measure of homogeneity (Manchanda et al. 2006) we used it to measure the behaviour homogeneity regarding the frequency of purchase and the money value variables. This measure was performed inside each one of the 125 segments created by the RFM model for each one of the 12 databases. The monthly raw data coefficient of variation was then used as a control group to compare the scale of homogeneity in each segment. 7

8 To perform this operation we used the mean convergence. The following equation was used: MC= CVt1- CVt2 CVt1 100 t2 - t1 Where: MC = mean convergence CVt1 = coefficient of variation of each segment on each database CVt2 = coefficient of variation of each month dataset By putting together all the data in the 12 databases we built another database that included all the transactions made during the 12 month period. This database has transactions made by customers. The scale of homogeneity of each segment included in this database was also measured by the same method. The dataset allowed to establish the number of monthly periods in which each customer was active and followed his path across time. The number of active monthly periods was used as an activity index (A). As the collected data covers a 12-month period, the activity index ranges from A1 to A12. As stated in proposition 2, we intended to find a segment with the best customers. Consequently we decided to use the new behavioural variable only in the best segment created by the RFM model: the 555 segment. Through a query created in the database we found all the customers that where always classified in the 555 segment and used them to create 12 new segments as shown below: Table 3.2. New segments created after the insertion of the activity index RFM Segment Activity Index 555A A1 8

9 Each of these segments only included customers whose path along the 12-month period was always through the 555 segments. The 555A12 segment comprised only the customers who were classified in the 555 segment every month/through the 12 month period. Customers who bought throughout eleven months and during that period were always classified in the 555 segment compose the 555A11 segment and so on for the other segments. In order to test proposition 3 and select the best target to our marketing campaign we have conducted a completely randomised design of experiment. The targets subjected to the experiment were: the raw collected data, the 555 segment and the new 555A12 segment. The campaign to be tested was a 5% coupon discount and the experiment was conducted through Monte Carlo simulations with trials. The statistical sales distribution for each target was determined by the nonparametric Kolmogrov Smirnov goodness-of-fit test. Samples with equal error parameter and 95% of confidence level were randomly selected from each target dataset to conduct the test. We did not know the value of the PED (price elasticity of demand). To overcome this difficulty we assumed that a randomly statistical uniform distribution with parameters minimum=0 and maximum=0,1 would be a reasonable response to the 5% coupon discount campaign. The option for the uniform distribution was justified by the fact that any value within the range had equal probability of being chosen. The parameter values were justified as follows: the range minimum=0 and maximum=0,1 represented a 0_ < PED <2. This means that in the worst case the campaign will not work and the best response to a 5% price reduction is a 10% increase in demand. The marketing return ratio (MR) and the campaign net present value (NPV) were used to measure the results and select the best target as follows: (2) To calibrate the equation the following parameters were considered: The campaign profits were measured by the difference between the campaign sales and the campaign costs. The campaign costs were split in fixed and variable costs. Fixed costs included the campaign diffusion. The 5% price reduction on each sale represents the variable cost. The campaign sales were obtained by multiplying the average sales per customer by the number of customers in the each target. The average sales per customer were obtained by a simulated statistical lognormal distribution with parameters mean and variance collected from each target dataset. However, the mean distribution was affected by the PED generated by the statistical uniform distribution as described. Variable costs are sales dependent. Thus they were calculated as 5% of the campaign sales. From here we will have: Campaign sales = [(1+Δ) x AMSC x TS] (3) Where: (1+Δ) is the possible sales ranging increment resulting from the simulated PED. AMSC are the lognormal distributed average monthly sales per customer. TS is the target size. 9

10 The campaign fixed cost will be designated by FC and the variable costs will be: Variable costs = [5% x (1+Δ) x AMSC x TS] (4) By putting the costs together we will have the campaign costs as follows: Campaign costs = [5% x (1+Δ) x AMSC x TS] + FC (5) Replacing equations (3) and (5) in equation (2) the MR ratio will be as follows: (6) The NPV ratio is: NPV = - Investment + (7) Where: (1+r) is the discount factor during n time periods As the campaign duration is one-month period a discount factor equals to 1 was considered. The campaign fixed costs represents the investment value which cannot be recovered by the end of the project. As this campaign had a short duration all the fixed costs (investment) were consumed during the campaign duration. Thus the campaign cash flow was: Cash flow = Campaign sales - Variable costs (8) And so the NPV equation was calibrated as follows: NPV = - Fixed Costs + [((1+Δ) x AMSC x TS) (5% x (1+Δ) x AMSC x TS)] (9) The campaign targets were randomly selected from each universe, i.e., raw data, 555 segment and 555A12 segment. The Monte Carlo simulation was calibrated to use equations (6) and (9) in order to give an output for each target marketing return ratio and the NPV statistical frequency distributions. The campaign costs and target size were designed according to directions given by the firm from which the data was collected. This attempt to approach the simulation to reality became a problem. The 555A12 segment size was smaller than the campaign target size we had decided to use in the first place. Therefo- 10

11 re, this target will present higher fixed costs per unit. To overcome this problem we decided to extend the 555A12 target to the 555 segment. To avoid selecting the same client twice, the 555A12 customers were withdrawn from the 555 segment. Then customers were randomly selected in order to fulfil the sample size. Following this procedure all targets had equal size and the same fixed cost per unit. 4 RESULTS AND DISCUSSION The results are disclosed in the same order as the propositions. Regarding proposition 1, we found that the RFM model can be used as a segmentation method in the non-contractual context of digital products. The mean convergence revealed that the degree of homogeneity concerning the frequency of purchasing is quite different from the raw data. At least 124 out of the 125 equal size segments converge in 85% and only one segment converges less then this value. However, this segment converges more than 70%. Similar results were found regarding the money value. Here, at least 123 out of the 125 segments converge in 85%. By putting the two variables together, the results show that it is possible through the segmentation process to obtain 123 segments that are at least 85% more homogeneous than the universe of data from which they were collected. The 555 segment customers behave quite differently from the other customers. This segment represents (as all the others) 0,8% of the total customers and concentrates the heavy buyers. It represents by itself, 32% of the sales amount found in the database. This means that the model can isolate a group of high value customers by itself. Even so we do not know if the identification process of high value customers can be improved. The answer to this question is held in test of proposition 2. By crossing the activity factor with the 555 segment we found 12 new different size segments. Customers that belong to these segments are heavy users. All of them were classified as 555 customers. Yet, each segment is quite different from the other 555A segments and from the other 124 RFM segments. Very significant differences were found by comparing the average sales per customer on each segment. The best customers were found in the 555A12 segment. These customers average sales per customer are 107% to 1 627% higher than the average sales per customer of the other eleven 555A segments. This particular segment stands for 0.07% of the customers in the database and it represents by itself 19.5% of the business value. These results show that the use of another behavioural parameter in the RFM model allows the model to became dynamic improves the model segmentation capabilities. The activity index allows the model to be microscopic and identify a small segment of high value customers. Knowing these customers and his behaviour has great impact on business performance. The question at the time is: what is effect on the business if 436 (0.07%) out of customers defect? The model gives the answer: If these clients belong to the 555A12 segment there will be a 19.5% sales breakdown. To test proposition 3, we used Monte Carlo simulations to check the campaign profitability and NPV. The Monte Carlo simulations revealed that the 555A12 segment is the best campaign target. In spite of the higher fixed costs per unit, those customers are the best target. However, in order to achieve economic efficiency, the fixed cost per unit must be reduced to their minimum. Therefore, the campaign was extended to the 555 segment. This solution increased the marketing return ratio and the campaign NPV. 11

12 In the absence of criteria, that is, when the target is randomly selected among the raw data, the campaign results in a loss of money. This is confirmed by the negative NPV as shown in the table below: Table 4.1. Frequency distribution parameters of the units of measure for each campaign target Campaign Targets Parameters Raw Data 555 Segment 555L12 Segment Campaign roll out to the 555 segment MR Ratio NPV MR Ratio NPV MR Ratio NPV MR Ratio Average -91,13% -914,46 161,42% 2 487,04 207,54% 2 917,50 296,15% 4 209,01 NPV Median -95,32% -955,46 86,86% 958,10 168,03% 1 940,34 267,19% Break-even probability 0% 0% 69,69% 69,40% 81,96% 81,98% 94,37% 94,07% This table summarizes and compares the outcome of the Monte Carlo simulations for the selected campaign targets. As stated, when a blind criterion is used to select the target, the campaign is always unprofitable. In this case the probability of achieving the break-even-point is always zero. This result is outperformed when the target is selected among the RFM segments. The best RFM segment represents a target with an average profitability of 161%. This campaign has a 69% probability of achieving the break-even-point. However, by extending the RFM model with a new behavioural variable it was possible to find a way to improve the target quality and thus reducing the risk of failure. The campaign profitability improves when the 555A12 segment is selected as a target. Now the probability of achieving the campaign break-even is around 82% and the campaign NPV increases more than twice compared to the 555 segment NPV. In order to decide whether to carry on a marketing campaign a success probability hurdle rate should be drawn. Haden (2000) suggests that a 70% hurdle rate is a good limit. Following this criteria the 555 segment lays on the razors edge but the 555A12 segment goes beyond the rim. Besides the better performance of this target, this solution can be improved by minimizing the fixed cost per unit. In order to do so the campaign was extended to some 555 randomly chosen customers. As a result, the profit margin per customer increased as the fixed cost per unit decreased. This leverage effect increased the chance of success and the average return ratio as shown in the table above. 12

13 5 CONCLUSION To our knowledge the RFM model has never been tested in digital products. This study shows that the behavioural RFM model can be used as a segmentation tool in the non-contractual context of digital products. The measures unit showed that the segments produced by the model are much more homogeneous than the universe from where they were collected. As long as we can capture customer s identity and associate it with each transaction a behavioural segmentation method can be used. From here we can conclude that the use of demographic and socio economic data is not a sine qua non precondition to segment the customer base. It is clear in the literature that the RFM model is a static and non stochastic model without predictive capabilities. However the model is capable of perform the customer base segmentation and answer the fundamental question: which customers can not be lost? By using the activity index variable, the model becomes dynamic and a purchase pattern emerges from data. This outcome of the model can be used as an input to build Monte Carlo simulations and predict behaviours. Rossi et al (1996) state that there is a great potential to improve the profitability of a direct marketing campaign. They conclude that even small households purchase history can be used to improve revenues from target couponing compared to blanket target selection. When dealing with digital products and using the RFM model to segment the customer base and feed Monte Carlo simulations, our results go in the same direction. To count the number of time periods in which a customer has bought something is not a hard task. This activity index can be used to extend and improve the RFM model. Within the presence of this behavioural parameter collected from the raw data, the model identifies a small high value segment who behaves quite differently. Firms are anxious to know which customers they should retain. By identifying a segment that contains 0,07% of customers and is worth 19,5% of sales, it is possible to give an answer to the negative question which customers firms cannot afford to lose. The extended RFM model represents an inexpensive tool that can be used as a starting point in the development of a CRM business solution Limitations The first limitation arises from the data. The data was collected from a Portuguese nationwide telephone company and the results should not go beyond that context. As it does not allow measuring the long-term effects nor to identify possible seasonality in the data, the time span used is also a limitation. The price elasticity of demand is a post hoc variable. We do not know if this variable works identically in all segments neither its limits. The theoretical assumption concerning this variable must be seen as a limitation Applications Most telephone company customers have no contract. They use pre-paid cards and thus behavioural information is the only data available to the service provider. With this work we provide a simple and inexpensive way to segment a firm customer base and a way to select a marketing campaign target. The lack of information that allows firms to measure their marketing actions is a major concern. 13

14 However, if the customer identity is captured, financial instruments can be used to support decisions and to measure campaign performance. By using the RFM model and improving its performance with the activity index, firms can build a model that capture behavioural patterns across time. This is a step forward in creating a behavioural customer portfolio. Such portfolios can be helpful to allocate marketing resources and to differentiate the offer. It is still unknown how different segments react overtime to market flotations. Using the RFM model output as a starting point to estimate segments β (beta) and understand segments reaction would be a useful tool that firms could use to manage their customer base portfolio. 14

15 REFERENCES Aaker, D. V. Kumar e G. S. Day (2003) Marketing Research, 8th Edition, New York, John Wiley & Sons. Bisgaard-Bohr, Mikael (2002) Purchase Behaviour Profiling, Teradata White Paper, December. Buckinx, Wouter e D. Poel, (2003) Customer Base Analyses: Partial Defection on Behaviorally-Loyal Clients in a Non Contractual FMCG Retail Setting, Universiteit Gent, Working Paper 03/178. Casais, Eduardo (2002) Sociedade do betão ou sociedade da informação, Cadernos de Economia, Ano XV, No 58, Jan/Mar, Lisboa. Gupta, Sunil and D. Lehmann (2005) Managing Customers As Investments The Strategic Value of Customers in the Long Run, Wharton School Publishing. Hahnke, Julie (2001) The Critical Phase of the CRM Lifecycle, IDTech, available in: Hallberg, Garth (1995) All Customers Are Not Created Equal, New York, John Wiley and Sons. Handen, Lawrence (2000) Putting CRM to Work, in Brown, Stanley (Ed.) Customer Relationship Management, Toronto, John Wiley & Sons, Hui, K. e P. Chau (2002) Classifying Digital Products, Communications Of The ACM, Vol. 45, N. 6, Kumar, V., D. Shah e R. Venkatesan (2004) Managing retailer profitability one customer at a time, Journal of Retailing, 82, (4), Libey, Donal e C. Pickering (2005) Libey and Pickering on RFM And Beyond, New York, MeritDirect Press. Manchanda, Puneet, J. Dubé, K Goh and P. Chintagunta (2006) The Effect Of Banner Advertising On Intended Purchasing, Journal of Marketing Research, Vol. XLIII, Merisavo, Marko (2003) The Effects Of Digital Marketing On Customer Relationships, Working Paper W 343, Helsinki School Of Economics. Newell, Frederick, (2003) Why CRM Doesn t Work, First edition, Princeton, Bloomberg Press. O Toole, John E. (1973) Are Grace Slick and Tricia Nixon Cox the same person? Journal of Advertising 2, (2), Petrison, L., R. Blattberg e P. Wang (1997) Database Marketing. Past, Present and Future, Journal of Direct Marketing, 11 (4), Poel, Dirk Van den, (2003) Predicting Mail-Order Repeat Buying: Which Variables Matter? Universiteit Gent, Working Paper 03/191 Reichheld, Frederick F. (1996) The Loyalty Effect, Bain & Company, Boston, Harvard Business School Press. Rossi, Peter, R. McCulloch, e G. M. Allenby (1996) The Value of Purchase History Data in Target Marketing, Marketing Science, 15, (4), Sin, Y., A.Tse e F. Yim (2005) CRM: conceptualization and scale development, European Journal of Marketing, Vol. 39, N. 11/12, Stone, M., A. Bond, B. Foss e M. Patron (2004) Consumer Insights How to use data and market research to get closer to your customers, London, Kogan Page. Storbacka, Kaj, e J. R. Lehtinen (2001) Customer Relationship Management Creating Competitive Advan- 15

16 tage Through Win-Win Relationship Strategies, Singapore, McGraw-Hill. Wübben, Markus and F. Wangenheim (2006) Predicting Customer Lifetime Duration And Future Purchase Levels: Heuristics VS. Complex Models, Dortmund Scholl of Management, Working Paper. Wübben, Markus and F. Wangenheim (2006a) Predicting Customer Lifetime Duration And Future Purchase Levels: Heuristics VS. Complex Models, AMA, Marketing Theory and Applications, 17,

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