We conclude four main points for developing strategy to improve the return management in E-retailing.
1. Category
Since we have already known what kinds of merchandise were returned more frequently, coupled with the average sales volume, we can suggest which retailers should be emphasized to improve the high- frequent returns.
No matter from the viewpoint of gender or transaction frequency, customer return propensity is especially high for “Female Clothing” and
“Female Shoes”. Both of the merchandise categories are the main developing items in E-retailing thus they should be paid attention on even more.
2. Price
As we mentioned in Section 5.3, if “Price” is less than NT $400, then customers tend to not return. While “Price” becomes higher, the return propensity will increase simultaneously. This result could be inferred that return cost (such as the time and fee for return delivery) for customers bought low-price merchandise is relatively high. On the contrary, return cost is relatively low for customers bought high-price merchandise. Thus, low-price merchandise won’t be returned as often as higher ones.
Return Propensity
50 % 100%
0 %
Price Tend to
return
1000 2000 3000 14500
Figure 5.2 Return Propensity with Price Hence, we provide strategies as below:
(1) Some merchandise is used to attract new customers with less profit in E-retailing. Retailers who want to attract new customers by this kind of merchandise could try to keep the price under NT $400. Build up customer confidence for each retailer by means of promoting low-price merchandise.
(2) As the price of merchandise going up, return cost becomes relatively low for customers coupled with higher return propensity. For this situation, retailers should focus on the “display authenticity”, such as providing the details of color, size, function etc.. Or, retailers could try to reduce return by means of controlling delivery efficiency which will be described in next point.
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3. Delivery Days
Since we have found that especially for female and low-transaction frequency customers, “Average Delivery Days” could affect the decision of return. In particular, the return propensity will be increase rapidly while the delivery day is more than 3 days.
Return Propensity
100%
0 % Average
Delivery Days Tend to
return
Tend to not return
1 2 3 4 5
50 %
Figure 5.3 Return Propensity with Average Delivery Days To solve this problem, we develop strategies as below:
(1) For high return propensity categories, we should control the delivery days in 2 days. Namely, picking and packing must be finished and send the merchandise to the logistics provider in 2 days. The speed of delivery is especially important for female customers and low -transaction frequency customers. Here, we assume the time of delivery from logistic provider to customer is one day. Namely, customers could receive the merchandise no more than 3 days. In this way, we could control return by efficient logistics flow. Figure 5.4 illustrates this concept.
Mon. Tue. Wed. Thu. Fri.
Picking &
Packing Customer
Order Logistics
Provider
Figure 5.4 Delivery Scheduling on Work Days
(2) To accomplish the delivery on time, “safety stock” should be maintained to support the demand quantity. If the safety stock doesn’t work, there should be
a backup plan- “order lead time” must be controlled to assure supplier could replenish the short of items. Retailers must check the inventories frequently to assure the punctuality of inbound and outbound logistics.
(3) Orders on Friday and Saturday need to be handled exceptionally since there are day-off between ordering and receiving. If the retailers do not work on Weekend, then customer may receive the merchandise at least 5 days later from the order date (as shown in Figure 5.5). Retailers could reduce the waiting time of customers by handling these orders on Saturdays (as shown in Figure 5.6).
Furthermore, retailers could actively notify the delivery information to reduce the uncertainty of customers.
Mon.
Sat.
Fri. Sun. Tue. Wed. Thu.
Order
Fri. Sun. Tue. Wed. Thu.
Order
Figure 5.5 Delivery Scheduling on Weekend
Mon.
Sat.
Fri. Sun. Tue. Wed. Thu.
Order Logistics
Fri. Sun. Tue. Wed. Thu.
Order Logistics
Figure 5.6 Adjusted Delivery Scheduling on Weekend
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4. Remedy Service
To better understand the customer return behavior, we illustrate the frequency of transaction and average frequency of return in 2007.
Figure 5.7 Transaction Frequency and Average Return Frequency There are three reasons for us to contend that managers should put more attention on the low-transaction frequency customers.
First, Customers whose transaction frequency is equal to or less than 12 are approximately 70% of total customers in the data set. These customers might be new customers or defected customers. As we’ve known, the cost of attracting a new customer is 4 times than maintaining an old one. These potential customers are specifically important for expanding customer base and increasing profits. If the low-transaction frequency could not become higher one, the company simply retains a smaller group of (somewhat more satisfied) customers, but often with reduced sales and profits as a result.
Second, since new customers have not established their loyalty to this website yet, they might be easier to lose while they confronted a return (which is commonly an unsatisfying experience).
Finally, figure 5.7 shows that when the number of transaction increase, the number of return doesn’t increase with rapid growth. Therefore, the objective is obvious to encourage low-transaction frequency customers to higher one.
To sum up, it’s clear that managers should provide remedy service to avoid customer defection, especially for the low-transaction frequency customers.
0
Ave. Return Frequency Ave. Return Rate
Num. of Transaction
in 2007