Revenue prediction by mining frequent itemsets with customer analysis
Introduction
In the knowledge discovery in data domain, association rule mining (ARM) is an important data mining approach that can enable the discovery of consumer purchasing behaviors from transaction databases. Agrawal et al. (1993) first introduced the problem of ARM defining it as identifying all rules from the transaction data that satisfy the minimum support and confidence constraints. The discovery of interesting associations or correlations is helpful in many business decision-making processes (Han and Kamber, 2006).
However, general ARM does not take into consideration the relative benefit or significance of transactions belonging to different customers, and instead assumes that the importance of each customer is identical. In other words, every customer is of equal weight during the mining process. However, numerous studies in customer relationship management (CRM) have revealed that the contributions of customers to businesses and profit maximization differ. Therefore, the evaluation of customer value is necessary before designing effective marketing strategies.
Businesses have started applying data mining technologies to marketing planning. Their objective is to gain customer loyalty and discover the contribution of customer value. Recency–frequency–monetary (RFM) analysis depends on recency (R), frequency (F), and monetary (M) measures and is one of the most popular database marketing metrics for quantifying customer transaction histories. RFM scoring is a method for determining the score of current customers on the basis of their R, F, and M values, and has been proven to be highly effective in marketing database applications (Blattberg et al., 2008). Moreover, RFM analysis is a well-known, behavior-based data mining method, which extracts customer profiles by using specific criteria. Recently, the RFM model has been used for CRM applications such as customer segmentation (Shim et al., 2012, Dursun and Caber, 2016).
Because RFM analysis and market basket analysis (i.e., frequent pattern mining) are the two most important tasks in database marketing, this study extended the conventional association rule problem by associating a customer value (i.e., frequency–monetary (FM) weight, which is determined by applying the FM scoring method) with a transaction to reflect the interest or intensity of customer values. This facilitates the association of an FM weight parameter with each transaction, enabling the discovery of valuable patterns. In addition, we propose a new frequent itemsets frequency–monetary (FIFM)-weighted algorithm for identifying frequent itemsets from FM-weighted transactions for the prediction of customer revenue.
We addressed the following questions related to discovering frequent itemsets from FM-weighted transactions: (1) Do the top k frequent itemsets discovered using the proposed FIFM algorithm outperform those discovered using the conventional Apriori algorithm in terms of predicting customers’ purchasing itemsets? (2) Do the top k frequent itemsets discovered using the proposed FIFM algorithm outperform those discovered using the conventional Apriori algorithm in predicting customer revenue?
The remainder of this paper is organized as follows. A review of related work is presented in Section 2. The problem definitions are provided in Section 3. The proposed algorithm and an example are illustrated in Section 4. Section 5 uses survey data to demonstrate the usefulness of the proposed algorithm. Conclusions and future work are discussed in Section 6.
Section snippets
Related work
The main purpose of this study was to discover frequent itemsets from transaction data with customer values (FM weights). In this section, we mainly explore the problems and some techniques related to association rules and customer value (RFM value). Finally, we discuss the differences between the applications of the present study and a 2014 study by Hu and Yeh.
Problem definitions
In this section, we define the problem of the method for discovering frequent itemsets from FM-weighted transactions. Let I = {it1, it2, …, itm} be a set of itemsets. Let D be a set of database transactions in which each transaction T is a set of items such that T⊆I. A transaction T is considered to contain X if and only if X⊆T.
It is important to determine weight values of the transactions of customers before discovering frequent itemsets from FM-weighted transactions. For preventing attributes
Algorithm for mining frequent itemsets from FM-weighted transactions
We now explain the proposed approach (FIFM) and provide an example to illustrate the method for discovering the frequent (high-FMsup) itemsets from FM-weighted transactions.
Experimental results
We applied three data sets (Foodmart, Grocery-POS, and Supermarket datasets) to evaluate the performance of the proposed FIFM algorithm. The three datasets possess dissimilar sale properties (such as products, customers, and purchasing transactions).
Conclusion
Because of their practicality, ARM algorithms have been used in various applications and data sets. This study is the first to introduce the most efficient method for discovering frequent itemsets from transactions by considering the customer's FM value. Furthermore, we proposed a new algorithm, FIFM, to discover frequent itemsets from FM-weighted transactions. Experimental results from the survey data reveal that the proposed approach can enable the discovery of interesting and valuable
Acknowledgements
This research was supported by the Ministry of Science and Technology of the Republic of China under contract MOST 105-2410-H-166-002.
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