## 3 Method
### Data Description and Preprocessing  
In this study, we use the insurance policy loan outbound telemarketing data from a domestic life insurance company that performs outbound telemarketing for insurance customers. The data covered an eight-month period from March to October 2020. The raw data before preprocessing are data collected from 171,\(A\)24 people allocated for outbound telemarketing of insurance policy loans, as shown in Table 2. Among the marketing targets, 64,359 customers attempted a call, 49,727 customers completed the call, and 45,155 customers completed the insurance policy loan information. Finally, the target variable is whether to execute a loan within one month after the completion of the insurance policy loan guide, and 8530 customers have executed the loan. Among the customers who received loan information, the proportion of customers who executed loans was 18.9%. This refers to the success rate of outbound telemarketing.  
The dataset consisted of numerical and nominal attributes. The number of numerical variables was 128, and the data range of numerical variables was transformed to a value between [0,1] through min-max scaling. The number of nominal attributes before data preprocessing was 25, including gender, whether e-mail or mobile phones were accepted, whether \"Do not call\" was registered, whether complaints were received over the past two years, whether the customer had insurance policy loans, and whether they had personal pension tax benefits. The 25 categorical variables were converted to 82 numerical dummy variables using one-hot encoding techniques, and each data representation value was set to either 0 or 1. Therefore, the input of the CNN was 210 data representations, all of which were numerical types.  
As shown in Table 3, six types of analysis data were used, namely customer characteristic information, insurance transaction information, insurance policy loan transaction information, general loan transaction information, campaign execution information, and call list information. There are a total of 210 representation dimensions for each variable, and 210 data representations are used in the analysis. The customer characteristic information consisted of 72 variables, such as the customer's age and occupation, and the insurance transaction information includes 55 variables, such as insurance type, payment amount, and withdrawal amount. There are 66 variables, such as loan experience, execution frequency, and limit exhaustion rate, as variables related to insurance policy loans.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & **Marketing** & **Attempted** & **Completed** & \multicolumn{2}{c}{**Loan**} & \multirow{2}{*}{**Loan**} \\  & **Targets** & **Call** & **Call** & **Completed** & **Completed** \\ \hline Number of & & & & & \\ customers & 171,\(A\)24 & 64,379 & 49,727 & 45,155 & 8530 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Loan execution: Number of customers who executed a loan within one month of completion of the loan information.  
\end{table}
Table 2: Number of customers by outbound telemarketing stage.  
Figure 3: Overview of the analysis procedure.  
Through data cleansing for missing values and outliers, 44,412 data sets were confirmed, 70% (31,089 cases) of the total data were used as training data, and the remaining 30% (13,323) were used as validation and testing data. In addition, the SMOTE was applied to the training data to solve the imbalance problem between classes of target variables. As shown in Table 4, while maintaining the scale of the number of \"Loans not executed\" in the major group, the proportion of \"Loans executed\" in the minor group increased from 19.2% to 50.0%.