# Decoding Customer Behaviour: Relevance of Web and Purchasing Behaviour in Predictive Response Modeling

Sunčica Rogić, Ljiljana Kašćelan

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Sunčica Rogić, Ljiljana Kašćelan. Decoding Customer Behaviour: Relevance of Web and Purchasing Behaviour in Predictive Response Modeling. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.369-380, 10.1007/978-3-031-34111-3_31. hal-05449774

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# Decoding Customer Behaviour: Relevance of Web and Purchasing Behaviour in Predictive Response Modeling

Sunčica Rogić(✉) and Ljiljana Kašćelan

Faculty of Economics, University of Montenegro, Jovana Tomaševića 37, 81000 Podgorica, Montenegro
{suncica,ljiljak}@ucg.ac.me

**Abstract.** In this paper, an approach is presented to improve the online direct marketing process regarding predictive customer response modeling. Namely, customer response models are usually faced with a class imbalance problem, due to a low conversion rate, in relation to the entire number of targeted offers. To avoid the bias towards the negative class in machine learning process, this paper proposes a combination of random undersampling and Support Vector Machine method for data pre-processing and increasing the predictive performances of subsequently used classifiers – Decision Tree and Random Forest. In addition, different attribute groups are tested in terms of their influence to the model performance, which enables marketing decisions makers to better understand which attributes have the highest impact in determining the customer's response to a direct marketing campaign. The results showed that the proposed method successfully solved the class imbalance and significantly increased the accuracy of the response prediction, as well as that web behavior is the most significant when predicting the customer's response.

**Keywords:** Customer Response Model · Class Imbalance · Web Metrics · Customer Behaviour Metrics · Predictive analytics · Support Vector Machine

## 1 Introduction

Technology and the digital domain are expanding quickly, and customer lifestyles are changing in practically every aspect, which makes marketing in the 21st century unique. In particular, consumer behavior has evolved and been redefined, as have the ways in which individuals find information and spend their leisure time [1]. Therefore, it is evident that businesses cannot remain unaffected and that they must reevaluate their everyday operations and marketing strategies. As of October 2022, the average online time for users globally was six hours and 37 min, with an increasing tendency [2]. Therefore, there is an evident desire and need for businesses to move some of their activities (if not all of them) online and use online media for customer communication, product promotion, and brand building.
According to previous research, using big data opportunities and technologies accelerates changes in marketing through the implementation of precise strategies, the efficient use of limited marketing resources, and the targeting of valuable customers. This has a significant impact on the market's development in the era of new retail [3]. Direct marketing became more significant as Internet technologies advanced and communication costs dropped quickly. The ability for businesses to establish a direct connection with thousands of customers in a way that was previously unthinkable with the use of middlemen has emerged as a result of the low cost of Internet access [4]. In the context of direct marketing, some of the primary objectives include identifying and selecting customers for targeting in future campaigns as well as assessing their needs and behavior patterns in order to tailor future marketing efforts based on this information.

When it comes to prediction of customer response, the number of positive instances is typically much smaller than the negative ones, therefore, class imbalance is one of the major issues. The number of customers that make a purchase in response to a campaign, i.e. the response rate or conversion rate, is, as a consequence very low when compared to the entire group of potential customers who were targeted and notified of the offer. With a response rate often less than 5%, we can say that a minor class problem exists in this context. In the literature that addresses this issue, it is noted that bias and misclassification of the minor class exist in the most of predictive classification algorithms [5, 6].

In relation to the aforementioned, earlier research has demonstrated the benefits of the Support Vector Machine (SVM) method for resolving the issue of class imbalance, by complementing the minor class with new, relevant instances from the bigger class [7–10]. Additionally, it is demonstrated that the SVM approach performs substantially better than undersampling and oversampling at class balancing, and that SVM may be used to improve the performance of other classifiers by refining the data they utilize.

Thus, SVM was used as a pre-processor to balance the data and improve overall classification accuracy. The SVM is biased in favor of the major class when there is a severe class imbalance, as there is in our data (response rate of 0.41%). In order to avoid bias, SVM in combination with random undersampling (balanced SVM) was employed as a pre-processor during the training of the SVM pre-processor. Following the SVM data balancing procedure, two classification algorithms will be applied on different subsets of attributes – Decision Tree (DT) and Random Forest (RF), with the aim to uncover groups of attributes that have the most significant impact in predictive customer response modeling.

The suggested models are developed with the aim to improve the effectiveness of the decision-making process in direct marketing, which should lead to a more profitable campaign, better communication with the most crucial customers, smoother development of a particular offer, and a higher level of customer retention.

The paper is structured as follows: the second section provides a literature review, focusing on the class imbalance problem in direct marketing, as well as customer response modeling. Data and methods are presented in the third section, followed by the Results and Discussion in the fourth part of the paper. Finally, the Conclusion is given in the last, fifth section.
## 2 Literature Review

When allocating marketing resources to consumers that are active and have a high potential value to the company information from the campaign response model is extremely vital. After the era of direct mail, the emergence of the Internet and social networks, along with the possibilities for online marketing campaigns, has created a space for investigation into the efficiency of this medium. The Internet has made it easy to gather a lot of data and has emerged as one of the best systems for storing it. Since it incorporates information technology capable of producing and storing a significant amount of complicated and complex data, the idea of direct marketing becomes particularly important in that virtual world [11].

Many types and forms of data are used as inputs when modeling the response to a social media campaign, but the goal of this process is always the same - correctly identifying respondents [12]. The lowering of the overall costs of marketing operations is influenced by the proper selection of the target group of respondents. This suggests that, in comparison to conventional, descriptive models, improved predictive models and methodologies can help marketers make decisions that are more cost-effective [13]. It's important to note that the problem of low response rate, or conversion rate, is evident in online direct marketing, as it was in traditional direct marketing which utilized direct mail. In the digital sphere, it is even more emphasized, having in mind that it's relatively less expensive for companies to target a larger group of customers than ever before. Hence, class imbalance occurs as the count of online sessions that resulted in completed purchases being far lower than the overall session count [14]. When the predictive model is trained using a small number of positive instances, the class imbalance problem results in biased results of the predictive model. Such biased models typically perform poorly in classification tasks since they frequently assign all test instances to the dominating class [15], which is, in this case, the segment of customers that did not respond to the campaign, or that did not complete the purchase.

According to Sun et al. [16], to tackle the problem of class imbalance in classification models, solutions were developed at the level of data, at the level of algorithms, as well as cost-sensitive solutions. At the data level, the goal is to balance the classes with resampling, which can be random or targeted under-sampling and over-sampling. At the algorithm level, the solutions tend to adapt the algorithm, in order to improve training for small classes. Cost-sensitive solutions, both at the data level and at the algorithm level, will attach higher misclassification costs to examples from the minor class. During the last few years, this problem has been addressed in several studies [17–20].

This issue was tackled in the literature using different approaches. Kurniawan et al. [21] proposed a method in their paper that combines feature selection from Particle Swarm Optimization (PSO) with data-level techniques like Random Under-Sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE), implemented through the AdaBoost algorithm, and assessed using 10-fold cross-validation. In Noviantoro and Huang's study [22], they used clickstream data from online businesses to explore customer behavior and purchase prediction and employed DT, Random Forest, Neural Networks, Deep Learning, Naive Bayes, k-NN, Logistic Regression, and Rule Induction. The neural network model outperformed the Random Forest model, which had slightly lower accuracy. Rogić and Kašćelan [23] evaluated the degree to which the
problem of the minor class (the most valuable consumers) may be solved by a hybrid model integrating SVM and DT. According to the findings, this method can predict the segment of the most valued consumers with an accuracy of 77%, which is 44% better than the DT model used alone. As a result, SVM worked successfully as a preprocessor to improve the minor class's prediction accuracy. Furthermore, SVM was put to the test by Farquad and Bose [7] as a preprocessor for class balancing of insurance customer data. They emphasized how applying classifiers to the refined set results in considerably higher sensitivity, or the number of real instances of the minor class that the model properly categorizes.

Besides solving the class imbalance issue, the aim of this paper is to determine which attribute groups have the highest effect on the model performance in terms of predicting the customer's response. The researchers have previously employed a variety of datasets and databases when choosing data for analysis and predicting campaign responses. Hauser et al. [24] examined the effectiveness of combining “soft” data (demographic and psychographic information) from secondary sources with “hard” data (information on previous purchases) from the company's database. According to the study's findings, “hard” data were selected as predictors to a substantially greater extent than the other kind. These results suggest that accurate campaign response models can be developed without the use of demographic and psychographic data using only historical customer-level transaction data [24]. This eliminates the extra expense of gathering and processing additional data from secondary sources. Kaatz et al. [25] combined clickstream and customer survey data to analyze the effect of using various devices, such as desktop computers, tablets, and smartphones, on conversion rate. According to their survey findings, purchase decisions made by computer users are mostly influenced by cognitive aspects of the shopping experience, but those made by smartphone users are primarily influenced by emotive and behavioral factors. In addition, Rho et al. [26] merged demographic data with web log data to segment clients and determine their value using clustering and the DT approach.

This group of attributes was also employed in earlier research by Lee et al. [27], which aimed to establish associative rules to create marketing strategies based on patterns in consumer behavior. Web attributes have been used in prior studies to model online search, assess consumer behavior, estimate and anticipate campaign reactions, as well as identify purchasing trends and customer segmentation. Based on metrics like visits to specific pages, time spent, and frequency of visits, Hofgesang and Kowalczyk [28] divided users into three groups: browsers, searchers and purchasers. Such categorization of potential customers is crucial from a marketing perspective because it allows for the development of targeted advertising campaigns that will maximize the value of each individual customer or group of customers.

Therefore, based on the review of the previous literature, the assessment of the influence of previous purchasing and web behavior on prediction using the response model, based on SVM class balancing, has not been considered so far.
# 3 Data and Methods

## 3.1 Data

For the purpose of testing the proposed customer response model empirically, and to assess the influence of different attributes, a dataset was acquired from a renowned Montenegrin sports distributor. For a period of four months, from October 2018 to January 2019, the dataset included visits to e-commerce websites from paid social media posts (sponsored posts from Facebook and Instagram). 9660 different website visitors who exhibited interest in the offered deal during the monitored period clicked on a link from a targeted Instagram or Facebook post, making them potential consumers. 33,662 sessions were recorded across six online direct marketing programs on social media, with a response rate of 0.41. The dataset included the following attribute groups: Recency, Frequency, Monetary (RFM) attributes - previous purchase history data, where Recency represents the length of the time period since the last purchase, Frequency indicates the number of purchases made in the observed period, while Monetary determines the total monetary value of the customer's transactions in that period (R1 - recency obtained by splitting the dataset into five equal parts from least to most recent transactions, R2 - recency obtained by assigning numbers from 2 to 5 based on the last campaign the customer ordered from, F1 - number of campaigns with orders, F2 - total number of orders in all campaigns, F3 - number of orders in the last campaign, M1 - the average transaction amount in all campaigns, M2 - average amount of transactions in the last campaign and M3 - total sum of realized transactions); Web metrics (the average number of sessions in all campaigns, the average session duration in all campaign, the average bounce rate for all selected campaign visits, number of sessions from the Central, Southern and Norther, number of sessions using desktop, mobile device and tablet, number of sessions using Android OS, iOS and Windows); and product description data (number of purchased products in apparel sector, footwear and equipment, number of purchased products for boys, girls, men, women and unisex products, number of purchased products for performance, lifestyle or outdoor, number of purchased products from A brands (high end) and License brands (low end), number of purchased products for adults, kids, teens and for all ages, number of purchased products on discount less than 30% and between 30% and 50%).

As the main aim of the paper was predicting the customer response to the direct marketing campaign on social media, only a completed transactions were taken as positive instances, which were marked as respondents. In line with that, initial customer action – following the link to find out more information regarding the offer, was not recorded as a response.

The final dataset was divided into training and test datasets. The training dataset included data from Campaign 1 to 5, with Campaign 5 being the indicator of their response. The data for the test set included 7929 visitors from Campaigns 1 through 5 and the response indicating whether a consumer responded to Campaign 6. However, new website visitors who first appeared in Campaigns 5 or 6 were not included in the database. Additionally, all targeted customers which had a website session shorter than 30 s were excluded from the database in the initial preparation for the analysis.
## 3.2 Methods

In this paper, a combination of Support Vector Machine and random undersampling was applied, with the aim to overcome the problem of class imbalance and perform data pre-processing. In the data set provided for this research, there is an extremely high level of class imbalance, with only 0.41% of respondents. With this in mind, balanced SVM was applied, i.e. a combination of 1:1 random undersampling and SVM method. After data pre-processing, two classifiers, DT and RF, were trained on the training data, and then applied for the prediction of the respondents on unknown, test data. In order to determine the impact of different attribute groups on the model performance, each group was, during several iterations, individually excluded from the model training and testing. Such results can indicate which customer features are the most relevant in predicting the response to the direct marketing campaign. The proposed procedure is presented in Fig. 1 [20].

<figure><img src="image_4.png" /><figcaption>Fig. 1. Proposed predictive procedure</figcaption></figure>

The SVM approach, proposed by Vapnik [29] in the 90s for the tasks of pattern recognition, is a supervised machine learning method that successfully seeks to solve overlapping and imbalanced classes, by establishing a hyperplane between examples from various classes, which can differentiate the class to the maximum distance, despite the quantity of available examples for training. By moving the margin to the examples of the major class that are closest and most similar examples to the minor class, and placing them into the smaller class, SVM completes the minor class with the most relevant examples. This led to the adoption of SVM as a data pre-processor to balance the data and provide higher accuracy for classifiers that are subsequently applied. In situations of significant class imbalance, such as the one present in this paper, the SVM is predisposed to favor the dominant class. Therefore, in this paper, balanced SVM was used as pre-processor, by applying it in combination with 1:1 random undersampling, as stated above.

Following this procedure, DT and RF classifiers were tested on balanced data. The DT method divides the data based on attribute values, selecting the attribute that produces
the purest subsets in respect to the target class. For the purity of data subsets obtained by division, many measurements such as information gain, gain ratio, and Gini index are utilized [30, 31]. The division technique is recursively repeated on the produced data subsets until totally pure subsets are acquired or some user-defined requirement, such as maximum tree depth, is met. Concurrently with the divisions, a model in the form of a tree-shaped structure is generated, with the nodes representing the attributes by which the division is made and the branches representing the values of those attributes. The root-to-leaf path defines if-then classification rules in terms of predictive attributes. On the other hand, Random Forest is based on a combination of bootstrap training dataset, random selection of predictors and DT method [32]. For each randomly chosen subset of data, Random Forest creates a “forest” of DT models, and optimization entails determining the ideal number of DT models to use. The following phases make up the RF model development process [33]: selecting a bootstrap sample and initiating tree construction; selecting a random selection of predictors for division at each node (without pruning the tree); Repeating this process several times, until a forest of trees is created; Completing a prediction for each tree and selecting the class that received the most votes, in order to categorize the new example.

## 4 Results and Discussion

In this section, the results of the proposed procedure will be presented, focusing on the data preprocessing and balancing procedure, followed by the influence of different attribute groups on the model performance. Each classifier (SVM, DT and RF) was applied to test data after undergoing cross-validation on an initial, pre-processed training set. Table 1 shows the predictive performance of both models before and after SVM pre-processing on the test data.

<table><caption>**Table 1.** Predictive performance of classification algorithms without and with SVM pre-processing.</caption><thead><tr><td>Model</td><td>Accuracy</td><td>Sensitivity</td><td>AUC</td><td>Fallout</td></tr></thead><tbody><tr><td>DT</td><td>99.43%</td><td>12.50%</td><td>0.608</td><td>0.13%</td></tr><tr><td>SVM + DT</td><td>90.36%</td><td>80.54%</td><td>0.898</td><td>8.16%</td></tr><tr><td>RF</td><td>99.48%</td><td>0.00%</td><td>0.827</td><td>0.01%</td></tr><tr><td>SVM + RF</td><td>89.27%</td><td>75.99%</td><td>0.921</td><td>8.74%</td></tr></tbody></table>

It is apparent from the results that approach with SVM pre-processing outperforms standalone classifier approaches in class balancing, i.e. finding solutions for minor class problem. Table 1 demonstrates that after data pre-processing, sensitivity and AUC were enhanced across both models. For instance, SVM + RF obtained 75.99% while standalone RF obtained 0% sensitivity prior to data balancing. Also, standalone DT models' AUC metric was quite low, coming in at 0.608, which is too close to a model being
unable to effectively distinguish between the positive and negative classes, while the improvement of the metric can be observed after applying SVM pre-processing.

This performance increase operationally means that the proposed approach will recognize about 70% more respondents compared to stand-alone classifiers. Bearing in mind that most of the classifiers applied in the existing solutions largely misclassify the minor class (especially when it is extremely small), and therefore the most likely respondent, it is obvious that the proposed approach can significantly increase the chances of generating income from campaigns.

Table 2 contains the prediction performance metrics for DT and RF models, tested on all attributes, as well as by excluding specific attribute groups.

<table><caption>**Table 2.** Prediction Models of DT and RF Classification Algorithms</caption><thead><tr><th>Model</th><th>Performance metric</th><th>All attributes</th><th>Web attributes excluded</th><th>Product attributes excluded</th><th>RFM attributes excluded</th><th>Recency excluded</th><th>Frequency excluded</th><th>Monetary excluded</th></tr></thead><tbody><tr><td rowspan="6">DT</td><td>Accuracy</td><td>90.36%</td><td>88.47%</td><td>90.36%</td><td>90.20%</td><td>90.36%</td><td>90.36%</td><td>90.36%</td></tr><tr><td>AUC</td><td>0.898</td><td>0.567</td><td>0.898</td><td>0.852</td><td>0.898</td><td>0.898</td><td>0.898</td></tr><tr><td>Fallout</td><td>8.16%</td><td>0.09%</td><td>8.16%</td><td>8.29%</td><td>8.16%</td><td>8.16%</td><td>8.16%</td></tr><tr><td>TP</td><td>832</td><td>125</td><td>832</td><td>828</td><td>832</td><td>832</td><td>832</td></tr><tr><td>Sensitivity</td><td>80.54%</td><td>12.10%</td><td>80.54%</td><td>80.15%</td><td>80.54%</td><td>80.54%</td><td>80.54%</td></tr><tr><td>FP</td><td>563</td><td>6</td><td>563</td><td>572</td><td>563</td><td>563</td><td>563</td></tr><tr><td rowspan="6">RF</td><td>Accuracy</td><td>89.27%</td><td>88.54%</td><td>88.38%</td><td>88.88%</td><td>89.38%</td><td>89.10%</td><td>88.42%</td></tr><tr><td>AUC</td><td>0.921</td><td>0.567</td><td>0.931</td><td>0.929</td><td>0.924</td><td>0.921</td><td>0.918</td></tr><tr><td>Fallout</td><td>8.74%</td><td>0.23%</td><td>10.37%</td><td>9.74%</td><td>8.60%</td><td>8.98%</td><td>9.74%</td></tr><tr><td>TP</td><td>785</td><td>140</td><td>827</td><td>823</td><td>784</td><td>788</td><td>787</td></tr><tr><td>Sensitivity</td><td>75.99%</td><td>13.55%</td><td>80.06%</td><td>79.67%</td><td>75.90%</td><td>76.28%</td><td>76.19%</td></tr><tr><td>FP</td><td>603</td><td>16</td><td>715</td><td>672</td><td>593</td><td>619</td><td>672</td></tr></tbody></table>

All classification algorithms were applied on the entire test set (all attributes), and also on the subset of attributes, by excluding each of the attribute groups separately (Web attributes, Product attributes and RFM attributes). In addition, to determine the potential influence of individual Recency, Frequency and Monetary attributes on the model performance, they were excluded both as a group and individually.

From Table 2, it can be observed that both DT and RF models achieved overall good predictive performance. The first, DT model, achieved Accuracy of 90.36%, Sensitivity of 80.54% and AUC of 0.898. On the other hand, RF model achieved slightly lower Accuracy and Sensitivity, of 89.27% and 75.99%, respectively, but a higher AUC of 0.921. Taking into account the AUC result, we can conclude that the DT model can distinguish between the positive and negative class in 89.8% chances, while the RF model improves this metric to 92.1%. The higher the AUC, the better differentiation capacity between classes in the observed model.

Since the aim of the paper is to determine and predict customer response in terms of purchase in a direct marketing campaign, this paper focuses on Sensitivity metric, rather than Accuracy. Sensitivity provides more relevant information regarding the potential
respondents with greater probability of purchase intention. Testing the model on all attributes in DT model, the obtained Sensitivity result was of 80.54%, and it keeps the same value when Product attributes are excluded, as well as all individual RFM attributes, while it slightly reduces by excluding RFM attributes as a group. Hence, we can state that these two sets of attributes do not have a significant influence in predicting the potential respondents in a direct marketing campaign. However, excluding the Web attributes influences this result greatly, as this metric falls to 12.10%. This shows the most significant influence on the DT model performance, where, in this case, only 12.10% of expected respondents would be targeted.

Impact of Web attributes on the DT model performance can also be seen by looking at the TP metric, as well as AUC. When these attributes are excluded from the analysis, only 125 potential respondents may be targeted, compared to 832 when the model is tested on all attributes. Also, the AUC is reduced to 0.567, which is very close to 0.5, i.e. the situation where the model cannot differentiate between the positive and negative class at all.

Testing on the second model (RF) shows almost identical scenario. Sensitivity obtained on all attributes is 75.99%, and is improved by three percentage points when Product attributes are excluded (80.06%) and when RFM attributes are excluded as a group (79.67%), but only shows a slight difference when they are excluded individually. Similar to testing on DT model, excluding Web attributes leads to significant decreasing of Sensitivity in RF model to only 13.55%. Same level of AUC, 0.567, is obtained in RF model when Web attributes are excluded.

In this case of excluding the Web attributes, the overall Accuracy remained high in both DT and RF models – 88.47 and 88.54, respectively and the Fallout is reduced to as much as 0.09% and 0.23%, which demonstrates a very high accuracy of the classification of non-respondents, i.e. algorithm bias towards a majority class. Based on the reported results, we can conclude that, when Web attributes are excluded, both classifiers under-perform, highlighting the relevance of web metrics in the development of the customer response model.

This strong influence of web variables is consistent with previous research, which has found that clickstream data and web metrics play an essential role in forecasting customer behavior [22, 34–36]. The results presented in this section point to the conclusion that Product attributes, as well as RFM attributes (as a group and individually) do not have a significant influence in the predictive model performance. With the increased use of online marketing, it appears that the prior relevance of purchase behavior for predicting responses is being replaced by web activity, which is demonstrated in previous research as well [36].

# 5 Conclusion

One of the most complex challenges of predictive segmentation is class imbalance. Although the problem of class imbalance is not new in research, it is becoming increasingly significant in direct marketing as a result of the increased use of very inexpensive and global channels for interaction with customers (such as social networks and online customer targeting). In reality, this means that the number of consumers who respond to
the campaign by making a purchase, or the conversion rate, is very small compared to the whole set of prospective customers to whom the offer was made. In this regard, bias and misclassification of the minor class occur with the greatest number of predictive classification algorithms in that situation.

One of the goals that affects the effectiveness of a direct marketing campaign is determining the number and structure of consumers who will respond to the campaign, which is exactly the problem that campaign response models address. These models classify potential consumers into responders and non-respondents, i.e., those who are more likely to respond to a direct marketing campaign by purchasing, in relation to those who are less likely to respond. Modeling customer reaction to a campaign is a key area of direct marketing in this regard, since identifying consumers who are more likely to respond may save marketing expenditures and improve campaign traffic. In line with this, as there are several data streams available to companies, both internal external, and this research aimed to determine the most relevant data types for customer response prediction. Results of such research can point to the use of selected attribute groups for customer response modeling, which have the greatest impact on the model performance. In practical terms, companies could avoid merging several databases when trying to address this issue, and focus on most important attributes in response prediction, which have, in this research, as well as several previous papers, shown to be web metrics, or clickstream data.

The accuracy of the prediction of possible respondents and the adequate interpreta-
tion of the results allow decision makers to segment and target potential consumers with
greater precision, as well as to identify attributes that can have a significant impact on
the response rate to the campaign.

In this paper, both aims were successfully achieved – the data was balanced using
undersampling with SVM method, thus solving the class imbalance issue, and also,
it was determined that web metrics have the most impact on the model performance,
making this attribute group the most significant in predicting the customer response.

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