ISI 2009: August 16-22, Durban, South Africa

1. ISI2009: August 16-22, Durban, South Africa Consumer-based market segmantation based on Association Rule and RFM JongHoo Choi, ChunKyung Cha* (Korea University)

2. 1. Background and Purpose 2. Analyzed Dataset 3. RFM 4. Association Rule 5. Association Rule based on the RFM 6. Conclusion and discussion

3. 1. Background and Purpose • Recently, statistical approaches are required to set up marketing strategies founded • on the database of buying history • Customer segmentation is necessary for CRM(Consumer Relationship Management) • Association rules based on RFM method can give good strategies of differentiated • marketing along with segment’s characteristics • The purpose of this study is to give a new method based on RFM(Recency- • Frequency-Monetary) and association rules for customer segmentation marketing.

4. 2. Analyzed dataset • Dataset used for this study is the buying history of solution products which are • manufactured by company ‘I’ from 2003 to 2005 • Dataset is composed of ‘information of buying company’ and ‘product list of purchase’ • Information of buying company consists of ‘recent buying quarter’, ‘sales’ and • ‘frequency of buying item’ • Total size of dataset is 3,886 • The 6 variables, buying items list of each company, and 12 buying items list used for • analysis is represented in the table 2.1, 2.2 and 2.3, respectively

5. 2. Analyzed dataset • Table 2.1: 6Variables used for analysis

6. 2. Analyzed dataset • Table 2.2: Buying items list of each company (part of a list)

7. 2. Analyzed dataset • Table 2.3: 12 Buying items

8. 3. RFM 3.1 Scoring • RFM is the most generalized method for customer segmentation • Scoring for customer segmentation in the RFM is proceeded by linear combination • of three indicators, which are ‘Recency’, ‘Frequency’ and ‘Monetary’ • We quantify the ‘Recency’, ‘Frequency’, ‘Monetary’, respectively and then add up the • three indicators weighting R, F and M values • where A,B,C are weighted value • It is a critical for assigning weighted values in the RFM RFM = A×Recency + B×Frequency + C×Monetary

9. 3. RFM 3.1 Scoring • We use the Pareto’s rule 1), to solve the assigning problem of weighted values to RFM • We obtain the weighted values from the R,F and M ratios of the upper 20% customers • in the sense of total sales amount 1) Pareto rule : Customers belonging to the upper 20% are theoretical that we gained 80% of total sales

10. 3. RFM 3.1 Scoring • Customer’s score= 0.3×Recency + 0.2×Frequency + 0.5×Monetary The weights according to ratios of R, F and M values using the upper 20% customers based on the buying amount of money The buying period of upper 20% customers Total buying period Upper 20% Remainder 80% The buying frequency of upper 20% customers Total buying frequency The buying amount of upper 20% customers Total buying amount 53.6 53.6+37.2+90.2 37.2 53.6+37.2+90.2 90.2 53.6+37.2+90.2 = 0.3 = 0.2 = 0.5 • Weight of R value = • Weight of F value = • Weight of R value =

11. 3. RFM 3.1 Scoring • The RFM model is not to induce new customers but to efficiently operate by • segmenting existed customers • The RFM supports that we can execute a concentrative marketing action to the loyal • customer • Basically, the RFM method is more useful for creating profits than increasing sales • amounts

12. 3. RFM 3.2 Set up R,F and M values • Table 4.2: Set up R-value and distribution based on the recent buying period(Q:Quarter)

13. 3. RFM 3.2 Set up R,F and M values • Table 4.3: Set up F-value and distribution based on the recent buying frequency

14. 3. RFM 3.2 Set up R,F and M values • Table 4.4: Set up M-value and distribution based on the recent buying amount

15. 3. RFM 3.3 Customer segmentation using RFM • As we see from table 4.2 to table 4.4, R,F and M-values are classified with 6 egments • based on the ‘Recency’ of customers’ buying data, ‘Frequency’ of customers’ buying • frequency and ‘Monetary’ of customers’ buying amount, respectively • Consequently, R,F and M values can be quantified numeric values between 1 to 6 • It can be converted into standardizedvalue and finally figured out as RFM score. • More general equation is presented as follows. Customer’s score= 0.3×Recency + 0.2×Frequency + 0.5×Monetary RFM score= (Customer’s score×100)/6

16. 3. RFM 3.3 Customer segmentation using RFM • ex> If R=6, F=6, M=6 (R,F,M)=(6,6,6) • then Customer’s score= 0.3×Recency + 0.2×Frequency + 0.5×Monetary • = 0.3×6 +0.2×6 +0.5×6 = 6 • RFM score= (Customer’s score×100)/6 • = (6×100)/6 = 100 M (6,6,6) (1,1,1) R F (6,6,1) , (R,F,M) = (1,1,1) ~ (6,6,6)

17. The cutoff point of a best group The cutoff point of a better group of the upper 20% customer 3. RFM 3.3` Customer segmentation using RFM

18. 4. Association Rule • Association rule is a data mining technique for finding interesting association, • pattern and/or relationships from sequential and replicative events • Therefore, it is useful to discover relationships such as arrangement of product • and promotions • Table 4.1 shows the results of the first 4 association rules • They are selected by the ‘support’ value which is an evaluation criteria of • association rule(Jiawei Han and Micheline Kamber, 2006) • Table 4.1: Output of the association rule

19. Support :Pr(A∩B) Support of 'A→B’= (The number of transaction including A and B) The number of total transaction • Confidence :Pr(B|A) Confidence of 'A→B’= (The number of transaction including A and B) The number of total transaction including A 4. Association Rule

20. 5. Association rule based on the RFM method • Table 5.1: Association rule for best group • Table 5.1 shows the result of best group. • Table 5.1 shows the first 3 association rule that are selected by ‘Support’ • From the table 5.1, we can find that the company buy an ‘ITS’ also purchase an ‘ITS’ • and then buy a ‘pSeries’ • The company buy an ‘AIM’ also purchase an ‘ITS’ and then buy an ‘ITS’ and so on…

21. 5. Association rule based on the RFM method • Table 5.2: Association rule for better group • Table 5.2 represents the result about better group’s products • From the table 5.2, we can find that the company buy an ‘ITS’ also purchase an ‘ITS’ • and then buy a ‘xSeries’ • The company buy a ‘Storage’ also purchase an ‘ITS’ and then buy an ‘ITS’

22. 6. Conclusion and discussion • Customer segmentation using the RFM method and association rule is helpful to • construct the differentiated marketing strategies for segmented customers • As we see in the previous chapter, buying patterns are represented by segmented • customers • It becomes an useful information to establish marketing strategies and consumer • relationship management • In the further research, we can proceed the customer segment specified marketing • strategies and identify new customers with similar buying patterns, selectively and • concentratively

23. References [1] Don Peppers and Martha Rogers (1999). Enterprise One to One, First Edition, New York : Currency Doubleday. [2] Jiaewi Han and Micheline Kamber (2006). Data Mining: Concepts and Techniques, Second Edition, San Francisco : Morgan Kaufmann.

24. Thank you!

25. upper 20% customers Total buying amount Freq. % Cum.% 26,516,190 1 0.0 0.3 26,516,190 1 0.0 0.3 ： ： ： ： 422,000 1 0.0 20.0 3 0.1 418,881 1 0.0 20.1 Cutoff point of the upper 20% ： ： ： ： Total 3886 100.0 The number of the upper 20% customers is 799 companies among 3886 companies Total buying amounts is all the 90.2% occupancy 420,000 20.0

26. 2003.01 ~ 2005.12 36 Months 12 Quarters 2003 2005 period (Quarter) 1 2 3 4 5 6 7 8 9 10 11 12 Sum of the period of upper 20% customers Total buying period • Weight of R value =