# Online Platform Customer Shopping Repurchase Behavior Analysis

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Summary of Relevant Principles

#### 2.1. Power-Law Distribution

#### 2.2. Ordinary Least Squares

#### 2.3. Statistical Test of Linear Regression Model

^{2}is a comprehensive measure of the goodness of the model fit. The larger the coefficient of determination, the larger the proportion of the total variation of Y explained by the model, and the higher the goodness of the fit of the model. The specific expression is:

_{0}: variable X is not significant, β

_{1}= 0. Given the significance level α, a critical value ${\mathrm{t}}_{\frac{\mathsf{\alpha}}{2}}(\mathrm{n}-2)$ will be obtained, and then |t|>${\mathrm{t}}_{\frac{\mathsf{\alpha}}{2}}(\mathrm{n}-2)$ is a small probability event of the original Hypothesis H

_{0}. Then we obtain the value of t, if |t|>${\mathrm{t}}_{\frac{\mathsf{\alpha}}{2}}(\mathrm{n}-2)$ occurs, then the original Hypothesis H

_{0}is rejected at the level of α-significance, that is, the variable is significant and passed the significance test [33,34].

## 3. Data Source and Description

## 4. Data Research and Analysis

#### 4.1. The Relationship between the Number of Customer Repurchases and the Corresponding Number of People

^{2}= 0.9885, the residual sum of squares is 3.919448, and the deviation of the sample observation value from the estimated value is small. It can be seen that the above-mentioned power-law distribution logarithmic regression model fits very well. In the t-test, assuming the significance level α = 0.05, assuming that there is no relationship between the two variable sets, looking up the table can get t

_{0.025}(25) = 2.060 (The number of samples in this paper is 27, and the degree of freedom is n − 27 = 25), calculated by the Eviews software as shown in Figure 3, the t value is −46.39324, and its absolute value is much greater than t

_{0.025}(25). It shows that the explanatory variable lnx is at the 5% significance level, rejecting the null hypothesis and passing the significance test. It can be seen that this shows that lny and lnx are in a linear relationship, that is, it can be concluded that the distribution of the number of purchases of the seller’s customers and the corresponding number of people is similar to a power-law distribution, which is expressed as a straight line with a power exponent −5.0641 as the slope. It conforms to the characteristics of power-law distribution.

#### 4.2. Repurchase Situation of Free Shipping Products

#### 4.3. Repurchase Situation of Different Ordering Methods

#### 4.4. Repurchase Situation in Each Province

^{−7}x + 0.2343, and the linear fit is 0.7998, the residual sum of squares is 0.001112, the degree of fitting is relatively high, and the deviation between the sample observation value and the estimated value is small, indicating that there is a correlation between the regional economy and the repurchase rate, that is, the repurchase rate is higher in economically developed regions than in economically backward regions. The t-test is performed on the regression equation between the total GDP of each province and the repurchase rate. Under the condition of the significance level α = 0.05, assuming that there is no difference between the two variable sets, look up the table to get t

_{0.025}(25) = 2.060 (the number of samples in this example is 27. That is, the degree of freedom is n − 2 = 25), calculated by the Eviews software as shown in Figure 8, the t value is 9.976444, and its absolute value is much greater than t

_{0.025}(25). It shows that the GDP of the explanatory variable area is at a significance level of 5%, rejecting the null hypothesis and passing the significance test. It can be seen that this shows that y and x are in a linear relationship, that is, the regional GDP has a strong correlation with the repurchase rate. The line chart in Figure 6 also shows the sales of each province from 16 to 20 years. It can be seen that all developed coastal cities occupy the top position in the sales volume ranking and have a large gap in sales volume from the subsequent provinces. It is inferred that the sales volume in economically developed regions also far exceeds the sales volume in other regions. (The verification here is the same as the verification of the GDP and repurchase rate mentioned above, no detailed explanation will be given here).

#### 4.5. Analysis of Sales per Hour in a Single Day

#### 4.6. Time Sequence Analysis of Repurchase

## 5. Conclusions

- (1)
- Compared with the products that are not delivered, the repurchase rate of free shipping products is obviously much higher. Most sellers of the same product try to provide customers with free shipping discounts, so as to enhance the goodwill of customers and keep customers making repeated purchases. Through the analysis of the ordering method, it can be seen that the customer repurchase rate on mobile phones is much higher than that on PCs. Therefore, some preferential activities can be added to the orders on mobile phones, and the layout of the homepage interface of the mobile phone store should be optimized to make the user’s operation convenient and fast, attracting more mobile phone users to join the store and make purchases to increase the repurchase rate.
- (2)
- The repurchase rate and sales volume vary from province to province. Through the analysis of 4.4, it can be seen that the repurchase rate and sales volume are higher in economically developed areas. Therefore, stores can build warehouses and deliver goods in some economically developed coastal areas, speed up the receiving speed of more customers as much as possible, and bring them a good experience. At the same time, they can reduce their own operating costs and unnecessary operating waste, so as to achieve the sustainable operation of the store economically.
- (3)
- Within 30 days of the customer’s repurchase, the customer’s message push must be targeted and timely, and the store’s after-sales service after the customer purchases the item must be attentive, so that the customer has a more comfortable experience in the process of purchasing the product.
- (4)
- According to the analysis of the average hourly sales number of stores every day, it can be seen that the order quantity in the early morning is the highest because of the existence of the store’s preferential activities. However, noon and evening are the most potential time for the order quantity to rise. First of all, there are few preferential activities in these two time periods, but there are peaks and valleys in the hourly sales number, indicating that users in this time period have a lot of free time and can concentrate on deciding their shopping goals. Therefore, it is recommended that the store can appropriately add some preferential activities in these two time periods.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Orders Attribute | Attribute Description |
---|---|

Main order number | Main keys for associating commodity tables and evaluation information |

Customer member name | Used for the linked order table |

Unique identification id | Consumer’s unique identification |

Customer payable postage | The amount of postage paid by the consumer |

Sub-order number | Primary key used to associate with the order table |

Total amount of sub-orders | Order payment amount before discount |

Actual payment amount of sub-order | Order payment amount before discount |

Sub-order order status | Successful transaction/closed transaction |

Customer Message | Customer’s message when placing an order |

Consignee name | Consumer’s recipient name |

recipient address | Customer’s address |

contact number | Consumer contact information |

Order creation time | The time when the consumer’s purchase behavior occurred |

Order payment time | Consumer payment completion time |

Baby title | product name |

Logistics company | Zhongtong/Postal/Shunfeng etc. |

Total number of babies | Total number of products purchased by consumers |

Whether mobile phone order | Yes/No is a mobile phone order |

Confirm receipt time | Time when the consumer receives the item |

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**MDPI and ACS Style**

Ji, C.; Zhao, W.; Wang, H.; Yuan, P.
Online Platform Customer Shopping Repurchase Behavior Analysis. *Sustainability* **2022**, *14*, 8714.
https://doi.org/10.3390/su14148714

**AMA Style**

Ji C, Zhao W, Wang H, Yuan P.
Online Platform Customer Shopping Repurchase Behavior Analysis. *Sustainability*. 2022; 14(14):8714.
https://doi.org/10.3390/su14148714

**Chicago/Turabian Style**

Ji, Chong, Wenhui Zhao, Hui Wang, and Puyu Yuan.
2022. "Online Platform Customer Shopping Repurchase Behavior Analysis" *Sustainability* 14, no. 14: 8714.
https://doi.org/10.3390/su14148714