1. Introduction
Companies need to devise a strategy for attracting new customers and generating revenue in order to achieve long-term success [
1,
2]. In today’s competitive environment, the Internet enables businesses to keep track of their customers’ points of search and behaviour on a real-time basis [
3,
4]. In this manner, the information that is collected may be used to provide input for the customisation of products, services, prices, and improvements in the method of communication [
5,
6]. Data mining (DM), optimisation techniques, and a hybrid of methodologies are often used in the study of online consumer contact as the primary problem in electronic customer relationship management (e-CRM) [
7,
8,
9]. The launch and the selling of goods on the Internet and online contact with consumers require a thorough understanding of the needs and desires of the target market [
10,
11]. The progressively accumulated knowledge of consumer wants aids a business in improving the electronic presentation of products and services in order to ensure that they meet or exceed the customer’s expectations [
12]. Given the ease with which customers may obtain the same information supplied by many manufacturers, effective online communication for sales promotion is critical for a successful advertisement effort [
13]. Effective online communication of information for sales promotion in a virtual environment and the quality of the information that is provided and conveyed to customers is key in their decision making.
Following the collection of the information gathered from previous experiences in online customer contact, the information may be evaluated and used to enhance the company’s marketing strategy and to create a long-term connection with its consumers [
14,
15]. Companies keep track of their customers’ previous behaviour and changes in order to respond discreetly to their expectations in the future [
16]. The information recorded in a company or other databases is afterwards utilised to forecast future customer behaviour based on the information contained in the database [
17].
These days, a wide range of tools and techniques from different fields are being employed in e-commerce [
2,
18,
19,
20]. Data mining involves employing machine learning and statistical techniques to large amounts of raw data in order to uncover exciting patterns [
21,
22,
23]. The current research indicates that this technique is the most widely used technique for detecting trends, patterns (or habits), and associations in consumer purchasing behaviour, as well as for clustering and categorising customers’ preferences, and finally for forecasting future purchases through regression analysis, sequence discovery, or visualisation [
24,
25]. The customer interests and preferences are clustered and classified using data mining methods, which are also used to assist sales marketing and market segmentation [
26]. Among the elements in transaction databases or other data, repositories are intriguing correlations, common patterns, connections, and casual structures that may be extracted. When it comes to clustering and categorising consumer interests, the information on the goods wanted by each customer is gathered and analysed. It may pertain to all of the client’s previous purchases or to the particular characteristics that the consumer prefers in relation to a specific group of goods.
Given the importance of having a reliable approach to assisting decision makers in e-commerce environments in absorbing more potential customers through appropriate advertising strategies, “The primary aim of this research is to conduct a comparative study of metaheuristic algorithms in order to assess their potential to assist decision makers in dealing with the challenges that businesses are currently facing in selecting more appropriate options for attracting more customers while operating with a limited budget.” The specific research objectives of this research are as follows:
- (1)
Investigating the existing studies in e-commerce environments, particularly those that addressed the website design for attracting customers in e-commerce environments using decision-making techniques.
- (2)
Evaluating the accuracy and robustness of the metaheuristic algorithms as an optimisation tool in the website design process.
The following are the study’s research questions:
- (1)
What are the limitations of the existing approaches for an effective website design for attracting customers in e-commerce environments?
- (2)
How are the robustness and accuracy of metaheuristic algorithms in finding appropriate decisions for website design to attract potential customers in e-commerce environments?
This research uses information about anticipated consumer behaviour to create web pages that classify clients into similar groups based on their past behaviour. Because of the high level of complexity of this issue, it seems that there is no way to avoid using metaheuristic algorithms to find optimum or near-optimal solutions to the problem under consideration. Advanced computational techniques, especially metaheuristic methods, are becoming more popular among academics at present [
27,
28,
29,
30,
31,
32]. Metaheuristics have been proven to successfully solve a broad range of complex problems in an acceptable amount of time [
33,
34,
35]. They can provide the desired solutions in a fair amount of time [
36]. Although these techniques have been extensively used in a broad range of study fields, no one algorithm can obtain the optimum solution for all problems. As a result, the search for innovative and efficient optimisation methods continues to be an open problem [
37]. In recent years, the area of metaheuristics has seen rapid growth, with many metaheuristic algorithms being created to date [
38,
39]. For comprehensive reviews about metaheuristic algorithms, readers are referred to [
40,
41,
42,
43]. In the present research, a comprehensive comparative study of metaheuristic algorithms among ten new metaheuristics, including the following: The ant lion optimiser (ALO) [
44], Dragonfly algorithm (DA) [
45], Grasshopper optimisation algorithm (GOA) [
46], Harris hawks optimisation (HHO) [
47], Moth-flame optimisation algorithm (MFO) [
48], Multi-verse optimiser (MVO) [
49], sine cosine algorithm (SCA) [
50], Salp Swarm Algorithm (SSA) [
51], The whale optimisation algorithm (WOA) [
52], and Grey wolf optimiser (GWO) [
53], are conducted.
The remainder of this article is organised as follows:
Section 2 contains a review of the literature.
Section 3 explains the problem under investigation, and
Section 4 provides optimisation algorithms to address the research problem.
Section 5 assesses the performance of the algorithms.
Section 6 of the article summarises the most important findings and makes recommendations for future research.
2. Literature Review
In this section, available studies in this research area are reviewed. In one of the first studies in this research area, Kleinberg, et al. [
54] propose a sampling-based algorithm by simply itemising and assessing all of the possible divisions of a selected group of customers. They demonstrated that catalogue segmentation could, at times, be an NP-complete problem, though with only two catalogues. Steinbach, et al. [
55] argue that in the generic hierarchical clustering, in many cases, the nearest neighbours of a document belong to different classes. As a solution to the inefficiency of the collective sampling-based algorithm, they introduce three algorithms based on the K-means clustering approach [
55]. Xu, et al. [
56] developed an approximation algorithm based on semidefinite programming with a performance guarantee of 1/2 for any catalogue size of r and a value greater than 1/2 for a catalogue size of at least m/3, where m is the number of available products.
Kleinberg, et al. [
57] show that a sampling-based enumeration algorithm is an inefficient approach to actual problem sizes. Alternatively, they developed two heuristic algorithms (ICC and DCC) and one hybrid algorithm (HCC). In the Indirect Catalogue Creation (ICC), similar customers are grouped together, and then the best catalogue is derived for each subgroup (segment). The second algorithm, called direct catalogue creation, simultaneously tries to identify both a catalogue and its associated customer segment. Finally, the third algorithm, called hybrid catalogue creation, solves the problem by combining elements of the earlier two algorithms [
55].
Ester, et al. [
58] investigated an alternative problem formulation that they call customer-oriented catalogue segmentation, where the overall utility was measured by the number of customers that had at least a specified minimum interest in the t items in the catalogues and found that the use of the new algorithms significantly enhanced the utility of the catalogues compared to the classical catalogue segmentation algorithms. However, the underlying concept in this study was, in fact, a reproduction of the minimum support in association rule mining, which was first proposed by Agrawal and Srikant [
59].
Amiri [
60] proposed a two-algorithm model to capture the customer-oriented catalogue segmentation problem. The first one, the Greedy Out algorithm (GO), constructs the catalogues one at a time. Each catalogue is constructed by initially including all of the products and then removing the undesirable products one by one from the catalogue in a greedy fashion so that the number of uncovered customers is minimised. The construction of the second algorithm, Association-Based (AB) catalogue, which likewise builds the catalogues one at a time, is inspired by association rule mining. In the grouping of products in one catalogue, it tries to maximise the association between the products, which is defined as the customer interest relationship. He demonstrated the superior performance of the Greedy Out algorithm relative to both the AB algorithm and Randomised Best Product Fit (RBPF) proposed by Ester, Ge, Jin, and Hu [
58]. In another study, a self-adaptive genetic algorithm was proposed by Mahdavi, et al. [
61] for designing customer-oriented catalogues in an e-CRM environment. Namvar, et al. [
62] proposed a customer segmentation method based on using a customer lifetime value (LTV) model and a recency, frequency, and monetary (RFM) model, as well as demographic parameters with the aid of data mining tools. First, various combinations of RFM and demographic variables were utilised for clustering in this approach. Second, the optimal clustering was selected using LTV. Finally, in order to create consumer profiles, each section was compared to the other segments in terms of various characteristics. The technique was applied to a dataset from a food chain retailer.
Yousefpoor and Olfat [
63] examined the possibilities of current markets using an analytical hierarchy approach. They assumed that items are then represented in the markets via the use of an online catalogue. The customers browse the online catalogues and choose items. They proposed a mathematical approach to optimise the anticipated profit while taking into account the length of viewing. Hsu, et al. [
64] proposed a model for a mobile-oriented catalogue (MOC) segmentation problem to improve consumer attractiveness for mobile applications in m-commerce. They utilise query-based learning (QBL) to create MOCs with the goal of attracting the highest number of consumers with the fewest amount of MOCs. Makinde, et al. [
65] developed an integrated model for B2B CRM that improves decision making by combining data mining techniques and a genetic algorithm (GA). The approach divides the consumers into the following two groups: repeat customers and shop-and-go customers. For customer classification, a modified data mining—C5.0 was employed, and a GA was utilised to optimise the rules produced by the decision tree algorithm.
3. Problem Statement
Increasing profit margins is a major and essential challenge for commercial organisations to address in today’s competitive markets. The approach of selling more products necessitates presenting the products to a greater number of potential customers. When it comes to drawing in more customers, introducing products via Internet websites is an effective technique that results in a larger profit than was anticipated. Because of technological advancements, we can collect a great deal of information on our clients. Companies are utilising a variety of presentation techniques to introduce their goods to consumers.
One of these approaches is to advertise through web pages, which are already being utilised in EC. Every client examines digital catalogues in order to learn about the many features of a company’s current product offerings. The various categories of products are presented in a hierarchical structure on the first page of every catalogue, starting with the most important category of the goods. Following that, more information is given under the relevant headings. At the same time, any customers who are interested in a particular product are encouraged to learn more about it by clicking on the table.
By collecting customers’ transaction data, their preferences may be obtained. Consider that each catalogue has products. We assume that a catalogue covers a customer if he or she is interested in at least items within it. The objective of the problem is to maximise the profit by increasing the number of customers covered by all of these catalogues. Let represent the set of all of the customers, and represent the set of products in the database. is the layer set, and there are numerous screens. In the -th layer, . The -th screen of the -th layer is made up of catalogues and is denoted by . Each catalogue has items.
Customers will not be bothered to browse through several screens for a product in the MOC segmentation problem, therefore, the first catalogue is given greater importance. As a result, we assign the top weight to the catalogue of the first layer and the first screen. The weights are given to the following catalogues in decreasing order. In order to equalise the screen weight in each layer, we utilise the biggest screen size from all of the layers. The weight of the final screen of the first layer in this design may be less than the weight of the first screen of the second layer. This is a fair technique since swiping the screen five times is more time consuming than touching to reach the second tier. Our object function was created on the basis of a commission. The compensation given to the m-commerce platform provider is referred to as commissions. As a result, the supplier of the m-commerce platform may optimise the MOCs to maximise the income. For the e-commerce platform, we define a commission as screen commission and layer commission. In order to formulate the problem, sets, indices, parameters, and decision variables described below are employed.
| Set of customers, |
| Set of websites, |
| Set of websites, |
| Set of screens in website , |
| Size of screen |
| Set of potential size for advertising, |
| Interest of customer to product if it is advertised in screen and at size |
| Minimum customer interest threshold |
| A big number |
| Available budget |
| Cost of advertising product in website in screen and at size |
| Binary variable that equals 1 if product is advertised in website in screen and at size , else 0, |
| Binary variable that equals 1 if customer is covered by website , else 0, |
| Binary variable that equals 1 if customer is covered, else 0, |
Based on the above-mentioned assumptions and descriptions, we developed the following mathematical model:
Equation (1) maximises the number of potential customers absorbed by the advertising strategy. Equation (2) controls the available budget for advertising. Equation (3) guarantees that each product cannot be advertised on each website more than one time. Equation (4) ensures that the sum of advertisements on each page cannot exceed the maximum space that is available on that page. Equations (5) and (6) control covering, or not covering, a customer by products advertised on different websites. Equation (7) satisfies that decision variables are integer.
6. Conclusions and Future Work
Because of advancements in Internet technology, businesses can now monitor the behaviour and performance of their customers. In order to regulate their electronic customer relationship management, online shopping websites use optimisation techniques to analyse and interact with their customers (e-CRM). A comprehensive metaheuristic analysis is proposed in this study to decide the items displayed on each page of a website. Based on the available information about consumer behaviour, the results showed that MFO performance is acceptable.
Furthermore, based on these findings, it is possible to conclude that metaheuristic algorithms can provide appealing opportunities for decision makers to obtain good answers to challenging e-CRM problems. Marketing managers will benefit from this useful tool as well, as it allows them to quickly search for important information based on consumer transaction data and modify their advertising strategies. As a result, they can develop marketing programmes that boost sales and profits in a short period of time. Based on this, the findings revealed that, in order to achieve the aforementioned objectives, many e-customer relationship management systems in businesses must create their own customer profiles that include a set of their most important information.
Furthermore, because individuals and businesses without access to the Internet and related technologies are unable to benefit from the electronic services provided, they may gradually lose competitiveness in global markets, which e-CRM in the context of information and communication technology aids in. Moreover, its emergence as the most effective tool for gaining a competitive advantage through customer attraction can be advantageous.
Companies that use e-commerce to brand themselves in a competitive environment while creating a new distribution channel in a virtual space, on the other hand, can relate to the customer more than they could before. As a result, it appears that many changes in the current problems are required for all businesses in order to successfully use modern e-commerce settings, and some additional realistic assumptions, such as random parameters for page availability, may exist. To make the problem more realistic, using fuzzy parameters in the model can accurately depict real-world conditions. Finally, using other metaheuristic algorithms, or hybridising metaheuristic algorithms with deep learning methods, can be viewed as an exciting future research topic that should be pursued further.