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This systematic review focused on understanding the impact of big data on marketing productivity, following the guidelines of systematic literature reviews and using the PICOC (problem/population, intervention, comparison, results, context) method. 50 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science and business, and published between 2020 and 2023. These articles, selected for their relevance and contribution to the study objectives, showed that the big data offers notable benefits in the marketing industry. The ability to customize marketing strategies to individual customer needs, improved optimization, and a better understanding of customer behaviors and preferences were key aspects. These findings highlight how big data can boost productivity in marketing, strengthening customer relationships and increasing loyalty by improving understanding and adaptation to the specific demands and preferences of each customer. This deeper, more personalized approach to consumers represents a significant shift in the effectiveness and efficiency of marketing strategies in the current era. [ABSTRACT FROM AUTHOR]

The omnichannel business model has undergone a revolutionary transformation, propelled by technological advancements. This evolution has effectively blurred the boundaries between physical and digital commerce, enabling seamless integration across various platforms and touchpoints. Utilizing advanced technologies like AI, big data analytics, and Internet of Things (IoT) devices, businesses now possess the capability to tailor customer experiences, streamline supply chain operations, and optimize overall efficiency to unprecedented levels. This transformative shift has empowered companies to effectively respond to the dynamic needs of contemporary consumers, who demand a cohesive, convenient, and personalized journey across all channels, whether online, in-store, or via mobile devices. Harnessing the wealth of data generated from these channels offers invaluable insights for strategic decision-making and further innovation. In this paper, we introduce a response system designed to utilize advanced feature engineering techniques, including the RFM model. Our aim is to provide valuable managerial insights. The dataset used in this study was gathered from a Moroccan omnichannel business based in Rabat, employing sophisticated data collection and labeling techniques. Subsequently, the data underwent preprocessing, cleaning, and balancing, and significant features were identified through advanced methods. To assess the effectiveness of our approach, we conducted experiments comparing the performance of our proposed deep learning model with decision trees and Naive Bayes, both with and without genetic algorithms. The results of these experiments consistently demonstrated that our proposed model outperformed the other methods. [ABSTRACT FROM AUTHOR]

Previous research indicates that personalized tourism recommendation (PTR) is becoming increasingly important in tourism marketing. However, many areas of PTR remain unexplored. This study is based on Stimulus-Organism-Response theory; integrated constructs from PTR, big data, and artificial intelligence; and the technology acceptance model. The quantitative approach was conducted through an online survey from 496 users of Ctrip. PLS-SEM was used to test the collected data. Three factors were found to stimulate consumers' perceptions of PTR: perceived personalization, visual appearance, and information quality. Consumers' reactions to PTR can be divided into an internal processing organism, which includes the perception of the technology as "technology trust" and the perception of the recommended content as "PTR attitude." This study contributes to the literature on smart tourism and marketing by developing and empirically testing an integrated model and providing a guide to determine users' trust and attitudes toward PTR or other personalized e-services. [ABSTRACT FROM AUTHOR]

Classifying customers according to their characteristics can effectively meet the genuine needs of different customer groups. It also helps enterprises formulate reasonable marketing strategies and obtain considerable profits. Currently, there are many ways to classify customers. However, the procedures involved are complicated and cannot comprehensively and objectively reflect customer characteristics. Therefore, a customer group classification model is designed based on the deep cross network (DCN). The DCN algorithm can automatically learn simple data features, achieving data clustering. For the defects in this model, the deep weighted k-means clustering network (DWKCN) customer group classification method is constructed, improving the DCN algorithm. From the results, the algorithm has a high accuracy of 99.5%. Therefore, the proposed DWKCN algorithm can realize the customer group's precise division and the marketing plan design, providing the references for different types of customers to formulate personalized needs. [ABSTRACT FROM AUTHOR]

Omni-channel marketing is an enhanced cross-channel business model involving shared data that allows enterprises to enhance and facilitate customer experience. Omni-channel opportunities shape retail business and shopper behaviours by coordinating data across all channel platforms while enabling their simultaneous use. Artificial intelligence (AI) has played an increasingly critical role in marketing analysis. With the proper training, AI can predict consumer preferences and provide recommendations based on historical data to achieve precision marketing in e-commerce. At present, however, the existent chatbots on many product-ordering platforms lack AI refinement, resulting in the need to ask customers multiple questions before generating a reliable suggestion, yet an effective way to incorporate AI in an omni-channel platform has remained vague. Hence, the aim of this study was to develop an omni-channel chatbot that incorporates iOS, Android, and web components. The chatbot was designed to achieve personalised service and precision marketing using convolutional neural networks (CNNs). A shared kitchen case study demonstrates the advantages of the proposed method, which is transferable to other consumer applications such as clothing selection or personalised services. The number of food offerings and the quality of image classifiers set the research limitations, pointing toward the direction of future research. [ABSTRACT FROM AUTHOR]

The insurance sector plays a crucial role in fostering sustainable economic development within a country. As the customer base grows, insurance companies must prioritize transitioning to data-driven strategies to cut costs and make more informed marketing choices in today’s digital era. This study proposes a new decision-making framework for precision marketing, based on a real case study from a Moroccan insurance company that aims to solve a practical problem. The proposed decision-making system consists of four components, with each component involving important steps. Firstly, data preparation was performed, consisting of four critical stages: data acquisition, data cleaning and filtering, feature selection, and oversampling. Secondly, top 20% and top 50% consumers are taken as examples to present their customer persona in detail. Based on the processed data, we analysed consumer consumption behaviors using four ML algorithms and made a performance comparison of the four algorithms. Additionally, we conducted feature selection methods to identify the most relevant features and evaluate the system’s performance. The aim of the proposed precision decision-making system is to assist managers in discerning the distinctive characteristics of potential customers and proposing tailored precision marketing strategies. This approach is expected to substantially reduce advertising expenses and enhance overall marketing efficiency. A case study using real-world data from a Moroccan insurance company was conducted to demonstrate the practical implementation of the proposed framework. The results of the study indicate that the proposed system yielded favourable outcomes.

The development of mobile Internet promotes the updating of cross-border e-commerce models, and the precision marketing realized by relying on big data technology better meets the all-around demand of users for content, socialization, and transactions. The article establishes a cross-border e-commerce marketing process on the basis of STP marketing management and builds a cross-border e-commerce precision marketing model by combining the STP marketing model. The user behavior characteristics of cross-border e-commerce users are extracted based on the RFM model, and the user behavior model is established by combining the user’s interest in purchasing goods. Then, the K-Means clustering algorithm is used to process the subgroups of cross-border e-commerce customer samples so as to construct a precise portrait of users. The cross-border e-commerce enterprise Z is selected as the research object, and the impact of precision marketing strategy on its user growth, merchandise sales, click-to-purchase conversion rate, and marketing optimization effect is analyzed. The number of effective users grew from 10,516 in 2020 to 16,804 in 2022, and the click-to-purchase conversion rate of products improved by 20%~46%, and different types of customers have various degrees of improvement under the precision marketing strategy. Based on big data technology, cross-border e-commerce users can be accurately portrayed, marketing products can be provided to users with more accuracy, and cross-border e-commerce enterprises can effectively enhance their marketing capabilities.

Precision marketing emerges as a pivotal mechanism to foster market development, enhancing the market economy and propelling internal economic growth. This study collates data pertinent to marketing activities, standardizes the raw datasets to compute descriptive statistics such as the mean and variance, and constructs a correlation coefficient matrix under specified conditions. Through factor analysis, the structure of this correlation matrix is meticulously examined. Factor loadings are employed to elucidate the relationships between factors and variables, thereby establishing a link between the precision of marketing endeavors and consumer attribute variables for further analytical probing. To assess the impact of implemented marketing strategies on consumer response behaviors, this research develops a precision marketing model using the Uplift algorithm. This model innovatively addresses the challenge of individual causal effects—which are realistically unresolvable—by transforming it into estimating the conditional average causal utility derivable from observed data. The factor analysis feasibility test yields a Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy at 0.792, and Bartlett's test of sphericity attains a significance level of 0.000, indicating robust factorability. Subsequent tests on variance and principal factor extraction reveal that five variables—such as income, gender, and age—are common factors across the datasets analyzed. Application of the Uplift model to the MineThatData and MegaFon datasets further substantiates the efficacy of the proposed marketing model. Particularly, the results from the MegaFon dataset validate the comprehensive applicability of this model, demonstrating its effectiveness in real-world scenarios.

The technology can fully explore the user’s consumption behavior habits and help the e-commerce platform formulate more precise marketing strategies in a targeted manner. This paper firstly analyzes the optimization of marketing strategy based on the 3R marketing theory, gives the design process of the precise marketing strategy of an e-commerce platform, and analyzes the personalized service based on consumer classification. Secondly, for the shortcomings of the KNN algorithm in the process of accurate classification, the Gaussian function is introduced to weight the optimization of the algorithm, which further realizes the construction of the G-KNN algorithm. Finally, the testing and application analysis of the algorithm model was carried out using the actual user consumption data of the e-commerce platform. The results show that the classification accuracy of the G-KNN algorithm has been maintained at about 95% when the K value exceeds 800, and the F1 composite value of this paper’s algorithm fluctuates around 56% when the K value exceeds 1000. On the e-commerce platform, except for the electrical appliances category classification test, the fit and accuracy of other categories basically match. Using the KNN algorithm incorporating the Gaussian function can effectively realize the accurate classification of user characteristics on the e-commerce platform and provide data support for the e-commerce platform to formulate accurate marketing strategies based on consumer preferences.

This paper proposes and improves the traditional K-means algorithm, utilizes the HC-Kmeans algorithm to deeply analyze the marketing status quo of the M e-commerce platform, and constructs the overall framework of precision marketing based on big data technology. The RFM model is used to measure customer value and segment customer behavior. Formulate marketing strategies that correspond to the consumption habits and preferences of different categories of users. The data from 40 consecutive days of observation is used to verify the precision marketing effect of the A/B testing method, and the final results show a good improvement in the click rate, order rate, payment rate, and order amount. During the Double Twelve Shopping Festival, compared with the traditional mode, the payment amount of M Company’s product recommendation increased by 64,604 yuan, and the order increased by 301 units, so the implementation of the precision marketing strategy was effective.

The development of the industrial world towards industry 4.0 has resulted in changes in the lifestyle of the wider community in carrying out their activities through digital media, one of which is shopping. This has an impact on the emergence of many business actors in the e-Commerce field, which brings its own challenges to stay alive and face the competition. The demands for innovation in competitive competition are also increasingly diverse with various approaches ranging from technology, social science, management science, and even artificial intelligence. One form of innovation that is widely carried out by e-Commerce today is looking for an ideal and effective form of marketing, where the form of marketing itself is considered less able to accommodate e-Commerce needs. One form of real innovation in finding the ideal and effective marketing is precision marketing. Precision marketing itself is marketing that is carried out by utilizing data where consumers are the center of preference for data collection. In fact, many of the e-commerce companies that were launched were unable to keep up with the competition because they were unable to develop marketing strategies and eventually went bankrupt. Therefore, we need a special way to bridge these problems so that e-Commerce can stay alive, especially for e-Commerce classified as Small and Medium Enterprises (SMEs). This research will focus on developing a precision marketing model in e-Commerce for small businesses, namely orebae.com which can be used as a tool in the development of marketing strategies. This research was carried out using a machine learning approach by adopting a decision tree algorithm. The results of this study showed that the precision marketing model for orebae.com based on customer preferences can be used to increase the number of sales of orebae.com and to reduce marketing costs.

Intelligent finance is a new form of business with deep integration of artificial intelligence technology and financial industry. An important application of intelligent finance is the precise marketing of financial products. As a key link in precision marketing, click through rate(CTR) prediction has made great progress, but there is still room for improvement in multiple features fusion, feature interactions learning and other aspects. In view of these needs and challenges, we propose a CTR prediction model named MCGM, which is used to realize precision marketing of financial products. The main characteristics of the model are as follows: (i) in order to effectively fuse multiple features, we design a hierarchical gated mechanism to select salient feature information at different levels; (ii) in order to fully learn the nonlinear relationship between features, we design a multi-channel feature interactions learning module. Specifically, it adopts factorization machine(FM), improved CrossNet(ICN) and multilayer perceptron(MLP) components to model the feature interactions from high-order to low-order, in order to obtain the abstract features containing rich information. Comprehensive and sufficient experiments on real world datasets show that the proposed model achieves better prediction performance compared with baselines. The proposed model not only has specific application value in the field of financial products marketing, but also provides an idea reference for data-driven marketing modeling. [ABSTRACT FROM AUTHOR]

In recent years, global digitalization has developed rapidly. Driven by the gradual maturity of various technologies, the popularization of Internet and mobile devices, the Internet of Things and cloud computing services, the growth of various data has greatly increased and diversified data. The value of these data can be used to predict consumer behavior, differentiate user groups, develop effective marketing strategies, and create differentiated competitiveness. To predict consumer behavior in purchasing insurance products, this study collected 4,474 insurance transactions from a bank in Tainan, Taiwan. After data preprocessing, the number of available transactions is 3,430. In these organized transactions, we use the classification of the insurance product as the dependent variable and the features of the customer as the independent variable. Then, correlation analysis was performed by chi-square test, and uncorrelated factors were analyzed. Analyze influencing factors through a decision tree machine learning model. According to the analysis results of the decision tree model, the accuracy rate is almost 70%, and the most important influencing factors are the actual insurance fee and currency. These two influencing factors can be used as a reference for the precise marketing of Tainan Bank in Taiwan. [ABSTRACT FROM AUTHOR]

In precision marketing for online communities, the existing text-based methods of user positioning cannot position new users rapidly, and they have low positioning efficiency when there is a large number of users. This research proposes a systematic method for the positioning of online community users. In this method, text mining and clustering algorithms are combined to cluster users, and then the user clusters are effectively matched with users' basic attributes through a multinomial logistic regression model. By this means, efficient positioning under the circumstances of a rapid increase in new users and a large number of users can be achieved. Calculation results from a real world example show that this method can effectively solve the problems found in traditional user positioning methods and provides a productive new approach to community user positioning. The study also offers suggestions for user classification management from the perspective of precision marketing. [ABSTRACT FROM AUTHOR]

Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusterswhich is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set. Firstly, Analytic Hierarchy Process is used to calculate the subjective weight, then the entropy method is applied to calculate the objective weight. Finally, we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference. The experiment shows that the dynamic weight we used in RFM model is vital, affects the customer segmentation performance positively. Also, this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior. At last, we discuss the limitations of RFM analysis. [ABSTRACT FROM AUTHOR]

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing. [ABSTRACT FROM AUTHOR]

The current marketing data management method cannot analyze and mine the correlation between marketing data, which leads to the confusion of the attributes of marketing data mining results and the inability to realize efficient data scheduling. This paper introduces improved association rules into precision marketing data mining of e-commerce platform. Optimize the background hardware of the e-commerce platform, collect and analyze the multi-source data. Information fusion method is used to optimize association rules, and combined with distributed similarity, accurate mining of marketing data of e-commerce platform is completed. Experimental results show that the proposed method can realize precision marketing data mining and has high robustness. [ABSTRACT FROM AUTHOR]

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With the advent of the big data era, user needs have become more and more diversified and personalized. These new user characteristics are important for the sustainable marketing of enterprises. With intensified competition, how to attract clients at minimal cost has become the primary concern for community e-commerce platforms or other e-commerce platforms. One of the best solutions is precision marketing, which is an important part of sustainable marketing. In this paper, a modified AISAS model is proposed for evaluating precision marketing effectiveness using data from a real-world community e-commerce platform. Based on face-to-face expert interviews and questionnaire surveys, an analytic hierarchy process (AHP) method is used to determine weights of different indexes adopted in the evaluation model and then quantify the precision marketing effectiveness of the e-commerce platform. The marketing results are then verified. We found that community e-commerce platforms’ marketing ought to be gentle and sustainable due to service area constraints or capacity limitations. The community e-commerce platform is more successful in attracting customers’ attention and interest from the marketing effectiveness. However, it is inadequate to rely on customers’ purchase and repeat purchase behaviors. Therefore, the precise selection of platforms is critical to the cultivation of customers’ loyalty and the increases in their products’ repurchase rate.

With the advent of the digital economy era, business systems such as web advertising and recommendation system have put forward the demand for predicting the click through rate (CTR) of items. However, the current CTR prediction research is not enough to mine user behavior, resulting in the lack of accuracy of user interest representation. In this paper, we propose a CTR prediction model, called MLIM, which can deep mine the evolution law of user interest. Specifically, we first use BiGRU to obtain the low-level user interest representation in the interest extraction layer, and then continue to use attention mechanism, BiGRU and sliding time window multi-components collaborative modeling in the interest evolution layer to obtain multi-level user interest representation with richer information, which can improve the accuracy of CTR prediction to a certain extent. Comprehensive experiments on two real datasets show that the proposed model achieves better performance than the mainstream baselines integrating user behavior analysis. [ABSTRACT FROM AUTHOR]