Shipping strategies for online retailers: threshold free
shipping strategy or shipping fee strategy?
Ke Zhou - k.zhou.2@student.rug.nl
Faculty of Economics and Business
University of Groningen
Abstract 2
1. Introduction 3
2. Literature review 5
2.1 Shipping policy 6
2.2 Customer acquisition and retention 7
2.3 Basket size, basket value, sales, revenue 8
2.4 Product returns 10
2.5 promotion 11
3. Methodology 12
3.1 Setting 12
3.2 Measurement 13
3.3 sample construction and summary statistics 15
3.4 Model development 16
4. Result 17
4.1 customer acquisition and customer retention 17 4.2 Basket size, basket value, sales and revenues 18
4.3 percentage of returns 20
5. Discussion and implication 22
6. Limitation 24
Abstract
This paper studies the influence of shipping strategies on the customer behaviors,
while accounting for the effect of promotion. Based on the data collected from an
online retailer that engages in two different shipment policies, namely, the shipping
fee strategy and the threshold free shipping strategy, we investigate customer
purchase behaviors (i.e. basket size and value), return behaviors (i.e. the percentage of returns) and company performance (i.e. customer acquisition and retention, sales and revenue). The results suggest that online retailers who apply the threshold free shipping strategy would have a higher customer retention rate than the shipping fee strategy when there is no promotion. Whereas, the differences regarding customer
acquisition rate is not significant. In addition, without a promotion, although the
threshold free shipping strategy would result in smaller basket size and value per order compared to the shipping fee strategy, the total revenue earned per day is larger because of higher customer retention. Finally, the threshold free shipping strategy results in a higher percentage of returns per day than the shipping fee strategy when there is no promotion. However, with a promotion, the difference between the
threshold free shipping strategy and the shipping fee strategy regarding percentage of returns per day becomes smaller.
1. Introduction
empirically exploring the impact of shipping strategies on returns behaviors while accounting for the influence of promotion. This is surprising as the promotion also plays a role on product returns (Lee & Yi,2017; Lee & Yi,2019). Our research thus aims to fill the gap, that is, to explore the effect of shipment strategies on customers’ purchase behavior and return behavior while also considering the impact of
promotion. To this end, a conceptual framework is developed, which we then investigate the dataset from an online retailer that engages in two different shipment policies, namely, the shipping fee strategy and the threshold free shipping strategy. The shipping fee strategy refers to the policy that online retailers charge additional delivery fee of initial orders and the threshold free shipping strategy refers to the policy that online retailers provide free delivery service for initial orders that exceed the threshold. The primary goal of our study is to investigate the effectiveness of the shipping fee strategy and the threshold free shipping strategy. Specifically, the effect of shipping strategies on customer purchase behaviors (i.e. basket size and value), product returns and company performance (i.e. customer acquisition and retention, sales and revenue).
The result shows that although the threshold free shipping strategy has lower basket size and basket value per order, it has a higher customer retention rate compared to the shipping fee strategy. Overall, the threshold free shipping strategy results in higher sales and revenue per day. Further, with the impact of promotion, the difference between the threshold free shipping strategy and the shipping strategy regarding percentage of returns becomes smaller. These imply that online retailers would prefer the threshold free shipping policy rather than the shipping fee policy to some extent.
2. Literature review
In this section, the most relevant literature was reviewed to build a conceptual model for further analysis. Specifically, different types of shipping policies, as well as their potential connections, were firstly studied in section 2.1. Further, in order to investigate the impact of shipping policies, we followed the process of customer journey when purchasing online, it mainly contains three stages, engagement, purchase and after-sale. In terms of engagement step, section 2.2 discussed the relationship between customers acquisition, retention and shipping fee policy. Then, section 2.3 analyzed the effect on purchase behaviors in terms of basket size and value. In section 2.4, product returns were reviewed since it plays a very important role in the after-sale stage. Lastly, the effect of promotion, as well as the interaction between shipping strategy and promotion, on these customers behaviors were discussed in section 2.5.
Based on the above, a conceptual model was developed to demonstrate how different shipping strategies may influence customer behaviors and the performance of online retailers. Figure 1 shows an overview of the conceptual model which will be discussed individually in the following sections.
2.1 Shipping policy
As noted above, researches have revealed that consumers take shipping fees into consideration when purchasing online. Therefore, different shipping policies are developed to satisfied consumers’ needs. According to Internet Retailer (2015), there are 16.8% of online retailers, out of 116 e-retailers, provide free delivery service on all orders. Most e-retailers, representing 39.6%, provide free shipping service when a minimum purchase amount (value) is met. Additionally, 28.7% of online retailers promote it periodically. Only 14.9% of them never provide free delivery service. One main stream of studies focuses on exploring the impact of shipping fees on customer behaviors. Lewis (2006) illustrated that there is a significant negative relationship between the level of shipping fees and store traffic, order incidence. However, the higher levels of shipping fee may result in larger order size and value as consumers are inclined to make larger orders when they need to pay for shipping fees (Lepthien & Clement, 2019). This is also supported by Lewis’s study (2006).
All studies mentioned above investigated shipping policies separately. however, they are actually not mutually exclusive. There is a competitive dynamic between different formats of shipping policies in the market. Gümüs et al (2013) develops a game-theoretic model to capture this dynamic. The model demonstrates how online retailers’ strategic decision regarding which policy should be used is influenced by firm and product level characteristics. One initial result indicates that the tendency of an online retailer to adopt the free shipping strategy increases as they become more and more popular.
Table 1 : The summary of literature reviews
2.2 Customer acquisition and retention
In order to evaluate different shipping fee strategies, it is necessary and important to investigate how different shipping strategies influence customer acquisition in the short term as well as customer retention in the long term. In most cases, this is hard to realize since pure-aggregated level data do not have sufficient detail about individual activities while pure individual level data normally lack essential aggregation. The data which presents in the next section includes sufficient both aggregate and individual data to explore customer acquisition and retention.
them. From prospective customers perspective, the initial purchase always involves substantial uncertainties (Cohen and Axelrod, 1984). They lack experience with the online retailer, and therefore, will typically pay more attention to e-retailers' shipping policies since the majority of the uncertainty related to online purchase decisions depends on the service provided by the retailer. While, for an existing customer, free shipping policy is a guarantee to avoid risk.
The effect of shipping strategy on customers acquisition and retention can also be explained by a special zero price argument (Shampanier et al, 2007). Free provision of shipping service serves as a special bond that can attract consumers and alter their behavior. Consumers perceive the benefits associated with free products higher. Therefore, to gain the benefit, more customers are intended to purchase.
The market shows that consumers concern shipping charges a lot when they shop with online retailers. For example, the survey of eMC (2014) presents that people are willing to purchase more products online if shipping is free, and in another report, PayPal (2009) discovered that people will abandon the shopping cart when the shipping cost is higher than their expectation. Consistent with prior researches, we arrive at the following hypothesis:
H1: online retailers will have a higher customer acquisition rate when applying threshold free shipping strategy than shipping fee strategy.
H2: online retailers will have a higher customer retention rate when applying threshold free shipping strategy than shipping fee strategy.
2.3 Basket size, basket value, sales, revenue
supported by Wilson’s research (1993). The finding showed that shipping strategies which provide benefits for larger orders will drive consumers to shift to larger order sizes.
In addition, Lewis et al. (2006) illustrated that the threshold free shipping strategy motivates customers to purchase more in order to fulfill the threshold for free
shipping. It gives consumers a direct economic motivation to alter purchase volume, and the overall revenue of online retailers are increased compared to shipping fee strategy.
In contrast, a fixed shipping fee strategy may affect order size through their relative impact on consumer expenditures. Fixed shipping fees can act as quantity discounts (Dolan & Simon 1996) since customers who prefer larger orders receive better value. For example, if shipping fees are a fixed €4, a customer buying €40 of products is paying a 10 percent surcharge while a customer buying €20 of product is paying a 20 percent surcharge. This is also supported by a prior research (Lepthien & Clement, 2019), demonstrating that the higher levels of shipping fee may result in larger order size and value as consumers are inclined to make larger orders when they need to pay for shipping fees. We thus have two side hypothesis that
H3.a: online retailers will have larger basket size when applying threshold free shipping strategy than shipping fee strategy.
H3.b: online retailers will have smaller basket size when applying threshold free shipping strategy than shipping fee strategy.
H4.a: online retailers will have larger basket value when applying threshold free shipping strategy than shipping fee strategy.
H4.b: online retailers will have smaller basket value when applying threshold free shipping strategy than shipping fee strategy.
H5.a: online retailers will have larger sales when applying threshold free shipping strategy than shipping fee strategy.
H5.b: online retailers will have smaller sales when applying threshold free shipping strategy than shipping fee strategy.
H6.a: online retailers will have larger revenues when applying threshold free shipping strategy than shipping fee strategy.
H6.b: online retailers will have smaller revenues when applying threshold free shipping strategy than shipping fee strategy.
2.4 Product returns
In general, product returns have been studied both from an online retailer and a consumer’s perspectives. From the point of view of online retailers, product returns have been always associated with company performance. The profitability of an online retailer decreases as product returns increases (Stock et al. 2006). Therefore, it is necessary to manage product returns. A number of prior researches have studied how to decrease the inevitable cost of product returns (e.g., Min et al. 2006). Whereas, some online retailers are trying to control and influence return decisions by restocking fee policy (Shulman et al. 2009). It is worth noting that customers have their own independent perceptions of responsibility, psychological reactions to return fees, and, most importantly, capability to determine if they will repurchase from the retailer. Therefore, rather than focusing on short-term profit, online retailers are encouraged to provide good product return service because returning is an important factor for consumers who seek to minimize the sense of risk when purchasing online (Petersen & Kumar 2009).
From a consumer’s perspective, a return decision is made because products cannot satisfy their needs in online selling practice. Sparks et al. (2014) undertook a
This could be caused by product features and personal circumstances, post-purchase concerns regarding product value, financial capability reassessments, reflections on sales presentations, and cautionary reference group effects.
To better evaluate the optimal shipping policy, return behaviors are therefore needed to be investigated. Although the threshold free shipping strategy encourages larger orders as argued in previous parts, it may also increase the number of returns. Customers add more products than their needs to the shopping basket to qualify for free shipping. Whereas, since some products are not really needed for customers at the moment, and they would just return afterwards. Therefore, more returns will then be induced since some products are added for fulfilling the requirement. Indeed, this is supported by Lepthien & Clement (2019). They found that the number of returns increased when online retailers adopted threshold free shipping strategy. Hence, we hypothesized that
H7: the percentage of product returns will be higher when applying threshold free shipping strategy than shipping fee strategy.
2.5 promotion
By reviewing literature, we found that promotion is a crucial factor that influences both customer purchase behaviors and product returns, which ultimately impact the profitability of online retailers.
Khouja et al. (2019) discussed different types of promotions, these include price reduction, coupons, cash mail-in rebates, free gift cards, and buy-one-get-one
together with a free gift to return the products. Further, Lee and Yi (2019) report that purchase-with-gift promotion results in a lower product return rate than an
economically equivalent bundle promotion. Therefore, promotion is assumed to have an influence on the customer purchase behavior and product returns, and included as another independent variable in each hypothesis.
3. Methodology
In this section, we first discuss our data setting. Next, we investigate how related variables are measured for further analysis. Lastly, we explain how samples are constructed and summaries of them.
3.1 Setting
We examine data from a leading online retailer in the Netherlands. This online retailer provides a wide variety of products in the field of fashion and living. Due to restrictions by the retailer, we are not able to investigate all shipping strategies as mentioned in the literature. This research paper mainly focuses on the threshold free shipping strategy and the shipping fee strategy.
Table 2: Shipping fee strategy
3.2 Measurement
The measurement of each variable can be found in this section. We discuss them in terms of shipping policy; customer acquisition and retention; basket size, basket value, sales and revenues; product returns; and promotion.
1. shipping policy
In order to evaluate the effect on customer behaviors and company performance, threshold_condition is manipulated regarding shipping policies. Customers in
experimental condition are exposed to threshold free shipping policy whereas those in comparison condition receive shipping fee policy. This variable is used as one of independent variables for all the analysis in the following.
2. customer acquisition and retention
3. basket size, basket value, sales and revenues
The basket size is easily calculated by summing up the number of products within an order. The same process goes for basket value, and they serve as dependent
variables for hypothesis 3 and 4. Further, we aggregated the order level data to daily level to get sales and revenues, such that sales is calculated by summing up basket size per day and revenue is calculated by summing up basket value per day. They are used as dependent variables for hypothesis 5 and 6.
4. product returns
In this case, the percentage of product returns is used as a proxy for return behaviors. Thus, the percentage of returns is calculated by the number of products returned divided by the total number of products ordered, and it is used as a dependent variable for hypothesis 7.
5. promotion
The literature review shows that promotion could also have significant impacts on customer behaviors and company performance.We therefore include the
3.3 sample construction and summary statistics
Using the insight obtained above, we actually build two samples deriving from the original session-level data. One for testing H3, H4 and H7 and the other for H 1, H2, H5, H6 and H7.
First, we create a new dataset for hypothesis 3, 4, and 7 by aggregating the session-level data to a higher level, namely, order-level data. We identify a total of 36537 orders over a year. The age of customers who have purchased products over the period presents 45 years at average (standard deviation = 12.67 years) and it ranges from 0 to 106. It is worth noting that 65 customers may not want to share their privacy information and fill in 0 years old as their age. These observations are not excluded from the sample because firstly age, which serves as covariate, is not the focal point of this research and secondly 65 is not a lot compared to the total sample size (36537). Most orders are placed by females (27358), representing 74.9% of the total observations. Among these orders, 35656 (97.6%) of them are exposed to one of the promotion campaigns. The mean basket size is 3.28 per order and the mean basket value is 152.74 euros. Customers return 1.43 units of products on average and the average value of the return products are 60.37 euros. In addition, the percentage of product returns per order is 31.38% on average (SD = 39.68). More detailed information can be found in table 4.
Table 4: Descriptive result of panel A
observations in total. Promotion campaigns are implemented by the retailer for 294 days over a year. The retailer obtains 64 existing customers per day on average (SD = 30), and with an average of 36 (SD = 22) new customers can be developed per day. The mean of sales per day is 328 units with standard deviation of 126. The online retailer generates 15289 euros per day on average. Customers return 143 products on average per day and the average value of the return products are 6044 euros. On average, 43.25% of products are returned per day with standard deviation of 6.34%. In detail, summary statistics are presented in table 5.
Table 5: Descriptive result of panel B
3.4 Model development
demonstrated above, namely, new customers, current customers, basket value, sales, revenues and percentage of returns are defined by the following equation:
β X W + X W β Z ε
Ynij = αni + 0ni nij+ β1ni nij β2ni nij nij + 3ni nij + nij
The “n” is identified as different dependent variables; the “i” refers to 2 different samples; and the “j” is identified as the jth observation in the sample. Such that, Xnij is the independent variable of shipping strategies and equals 1 if threshold free shipping policy is applied for jth observation in i sample; 0 otherwise. Wnij
represents the moderator variable, promotion. It equals 1 if jth observation is exposed to a promotion campaign in sample i. Znij refers to covariates. Furthermore, αnij is the intercept
regarding different dependent variables, and ɛnij is the random error term.
Second, to test the effect of shipping strategies and promotion on basket size (H3), the negative binomial model was used because it is not realistic to assume a normal distribution for basket size. It is more like a count variable, most observations are distributed to “1”.
4. Result
In this section, we firstly examine the effect of shipping strategy on customer acquisition (H1) and retention (H2). Then, we further discuss the effect of shipping strategy on basket size (H3), basket value (H4), sales (H5) and revenues (H6). Lastly, we explain the relationship between shipping strategy and percentage of returns (H7).
4.1 customer acquisition and customer retention
To test the effect of shipping strategy on customer acquisition (H1), a general linear regression was modeled based on panel B(daily-level) as it was explained in the methodology section.
shipping strategy vs shipping fee strategy) regarding customer acquisition, which is inconsistent with H1. Further, we also examined the effect of promotion, there was not a significant difference between promotion condition (promotion vs no
promotion) regarding customer acquisition (p=0.645) and the coefficient for interaction of shipping strategy by promotion was also not significant (p=0.603). Regarding H2, we estimated another general linear regression with the same independent variables, but a different dependent variable (customer retention). There is a significant difference between shipping strategies regarding customer retention (p < 0.05). Specifically, without a promotion, compared to shipping fee strategy
(threshold_condition = 0), the threshold free shipping strategy (threshold_condition = 1) resulted in 27.1 more current customers repurchasing, indicating support for H2. In addition, the difference between promotion conditions regarding customer retention was still not significant (p=0.329), neither for the interaction of shipping strategy by promotion (p=0.374). The more detailed result is presented in table 6.
Table 6: customer acquisition and customer retention
4.2 Basket size, basket value, sales and revenues
customers (i.e., new or current) as demonstrated above, we also retained it in the following models as covariate. The result revealed that, without a promotion, if the threshold_condition equals 1 (the threshold free shipping strategy) rather than 0 (shipping fee strategy), the log of basket size decreased by 0.11 (p < 0.05) and the basket value decreased by 16.946 euros (p < 0.05), keeping all other variables in the model constant. Therefore, the H3.b and H4.b was supported, indicating that online retailers will have smaller basket size and value when applying threshold free
shipping strategy than shipping fee strategy. To further explore the model for H3 and H4, when the threshold condition equals to 0 and keeping all other variables in the model constant, the log of basket size increased by 1.03 units and the basket value increased by 171.172 euros for customers who exposed to a promotion campaign compared to the condition when customers are not exposed to any promotion
campaigns. In addition, accounting for the interaction effect of promotion by shipping strategy on basket size and basket value, with a promotion, the log of basket size decreased by 0.33 (0.11+0.22), and the basket value decreased by 75.29
(16.95+58.34) euros when the threshold free shipping strategy was applied compared to shipping fee strategy. Regarding the effect the covariates, the status of customers (new vs current) had a negative significant impact on basket size and value; gender (female vs male) had a positive significant impact on basket size, whereas, a negative significant impact on basket value; Age had consistent negative effects on basket size and value. The more detailed information is shown in table 7.
Table 7: Basket size and basket value
To test the effect of shipping strategy on sales (H5) and revenues (H6), we estimated two general linear regression models. Threshold condition, promotion and the interaction between shipping strategies and promotion were included as
shipping fee strategy (threshold_condition = 0), the threshold free shipping strategy (threshold_condition = 1) resulted in 49.08 units more of total daily sales (p=0.1061 slightly bigger than 0.01) and the total daily revenue is 2256.7 euros more (p=0.0868 < 0.1). Therefore, the H5.a and H6.a were supported, meaning that online retailers will have bigger daily sales and revenues when applying threshold free shipping strategy than shipping fee strategy. Further, when the threshold condition equals 0, customers who are exposed to a promotion campaign (promotion =1) would purchase 52.34 units more products (p=0.0587 slightly bigger than 0.05) and pay 2716.3 euros more (p=0.0239 < 0.05) than customers who are not exposed to any promotion campaigns (promotion =0). Regarding the interaction of promotion by shipping strategy on sales and revenues, the coefficients were neither significant (p=0.5269 and p=0.6281 respectively). The more detailed information is shown in table 8.
Table 8: Sale and revenues
4.3 percentage of returns
neither significantly different from 0 (p=0.73 and p=0.22 respectively). With respect to the effect of covariates, the status of customers (new vs current) had a negative significant impact on percentage of returns (b= -9.98, p<0.05); gender (female vs male) had a positive significant impact on percentage of returns (b=12.71, p<0.05); Age had positive effects on percentage of returns (b= 0.115, p <0.05).
Second, regarding panel B(daily-level) data, the result showed, without a
promotion, the threshold free shipping strategy (threshold_condition = 1) resulted in a 3.52% higher percentage of returns compared to shipping fee strategy
(threshold_condition = 0), p=0.029 < 0.05. Therefore, the H5 was supported at a more aggregated level (daily-level). Further, when the threshold condition equals 0, customers who are exposed to a promotion campaign (promotion = 1) would return 2.67% more products than customers are not exposed to any promotion campaigns (promotion = 0), and this difference is significant (p=0.068). Accounting for the marginally significant interaction (p=0.069), we could conclude that, with a promotion, customers returned 0.27% (3.52%-3.25%) more products when the threshold free shipping strategy was applied compared to shipping fee strategy. The more detailed information is shown in table 9.
5. Discussion and implication
The present study makes both theoretical and managerial contributions to studies about online retailer research. we develop a conceptual framework to demonstrate the influence of different shipping strategies (i.e., threshold free shipping strategy and shipping fee strategy) on customer behaviors (i.e, purchase behaviors and returns behaviors), which ultimately affects online retailers’ performance and profitability. Table 10 summarizes our findings.
Table 10: Overview of findings
Specifically, we found that the difference between the threshold free shipping strategy and the shipping fee strategy is not significant regarding the number of new customers online retailers acquired in a day. However, there are indeed more
acquisition(De Vries et al., 2017), our data did not show such an effect, as well as the interaction effect of shipping strategy by promotion.
Second, from the perspective of order-level, the threshold free shipping strategy results in a smaller basket size and lower basket value compared to shipping fee strategy. However, when we aggregate order-level basket size and basket value to daily-level, namely sales and revenue, the effect becomes opposite. In other words, online retailers had higher daily revenue by implementing threshold free shipping strategy. This contradiction can be explained by customer retention as discussed above. Although customers would purchase less per order when confronting the threshold free shipping strategy, they tend to purchase more frequently over a specified period. In addition, we also found that new customers purchase less products, and basket value per order is lower than customer repurchased. It is interesting to note that although females tend to purchase more products in an order than male, the value of it is lower. Younger customers manage to purchase more. As for the effect of promotion, the results show that promotion contributes to larger basket size and higher basket value per order; larger sales and higher revenues per day.
Finally, the results demonstrate that, without a promotion, the threshold free shipping strategy results in a larger percentage of returns per day compared to shipping fee strategy. However, with a promotion, the difference between the
There are several implications obtained from these results. First of all, in general, the threshold free shipping policy is found to generate more sales and revenues than the shipping fee strategy for online retailers. Although the threshold free shipping strategy would result in smaller basket size and value per order, the total revenue earned per day is larger than the shipping fee strategy because customers are more willing to repurchase. Thus, in order to increase the firm’s profit in the long run, managers would consider implementing the threshold free shipping policy rather than the shipping fee strategy.
Second, promotion is found to positively increase basket size, basket value, sales and revenues, and with the effect of promotion, the difference between shipping strategies (the threshold free shipping strategy vs shipping fee strategy) regarding the percentage of returns becomes smaller in day-level. It implies that managers would consider conducting promotion campaigns periodically especially when the threshold free shipping strategy is applied.
Finally, age is found to negatively related to basket size and basket value, and is positively related to percentage of returns. Thus, managers may consider capturing the needs for younger customers through advertising design. For example, rather than showing products which are attractive for older customers on the landing page, online retailers would prefer to present products which are more related to younger
customers on the landing page, as such more younger customers are stimulated to purchase.
6. Limitation
In this section, we discuss the limitations of our study and future research
because if the threshold level is too high, customers would consider it as a way to generate profits. Therefore, additional research can further investigate how to settle a threshold for threshold free shipping strategy.
Second, our research focuses on the relationship between shipping strategies of initial orders and product returns, while we did not account for the effect of return policy. Different from shipping strategies of initial orders, return policy refers to the rules online retailers establish to manage the process by which customers return products that they have purchased before. Pei et al. (2014) demonstrated that the return depth of different return policies positively influence purchase intention. Therefore, we leave it to future research to examine the interrelation between these three dimensions (shipping strategy of initial orders, return policy and product returns).
Third, our research discusses the interaction effect of shipping strategy and
promotion on customer behaviors, but we did not specify the types of promotions. For example, Khouja et al. (2019) showed that there are different types of promotions, including price reduction, coupons, cash mail-in rebates, free gift cards, and buy-one-get-one discount. Different types of promotions may result in different outcomes. Therefore, future research could further investigate the effect of shipping strategies on customer behaviors when confronting different kinds of promotion campaigns.
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