Returns management

from Wikipedia, the free encyclopedia

Returns management is a sub-area of reverse logistics and customer management and deals with the planning , management and control of return deliveries ("returns"). The aim is an efficient, cost-effective and customer-oriented organization of information, financial and goods flows and avoidance of unnecessary returns. This is to ensure customer satisfaction , the likelihood of repurchase and positive recommendation. Returns management attracts particular attention in e-commerce , where there are a large number of returns.

Return rates

The return rate is one of the most common key figures in returns logistics and describes the relationship between the order item and the return item in percent. The return rate is split into three different rates, which can be viewed separately. These three are: Alpha, Beta and Gamma return rates.

  • The alpha return rate corresponds to the number of parcels sent back and is calculated from the ratio of parcels returned to parcels sent. The number of goods returned in a package is irrelevant. The formula for the calculation is:
  • The beta return rate, on the other hand, corresponds to the number of returned articles and is consequently formed by the ratio of returned articles to dispatched articles. By determining this quota, those articles can be identified that have a noticeably high return share compared to others in order to take countermeasures. It is calculated by:
  • The gamma return rate corresponds to the value of the items that have been returned and thus describes the ratio of the value of the returned items to the value of the items sent. If this rate is combined with the beta rate, conclusions can be drawn as to whether higher-priced items are more likely to be returned than lower-priced items. The following formula is used for the calculation:

Strategies

In returns management, the strategies can be divided into the two categories of preventive returns management and reactive returns management .

  • The Preventive returns management encompasses all measures to avoid returns before and after the order by the customer. This can be for example:
    • Advice, meaningful descriptions and information offers
    • Avoid false expectations through advertising
    • (virtual) samples
    • Reviews and customer ratings
    • algorithmic predictions
  • The reactive returns management includes all measures for an efficient handling of returns and how these can be reintroduced into the goods cycle. This can be for example:

Reasons for returns

There are many different reasons for returns. These can be, for example:

  • Material and quality defects ;
  • unintended purchases ( purchase remorse );
  • People do not like the product or do not meet their expectations;
  • Price was too high and the money is needed for another product;
  • lack of information and knowledge about the functionality and design of the product;
  • Product requires other products that are not available or compatible;
  • prior conscious decision to just try the product or to order several products to try out;
  • The product poses a danger to oneself or others or is forbidden or should or can no longer be kept secret;
  • legal ties are to be dissolved.

criticism

Poor returns management often leads to unnecessary spending on money (personnel, materials, transport, warehouse, etc.), pollution and time. This often means that the employees have to work more or under tougher conditions and wages are cut or deliberately kept low and the image of the company falls as a result. The return rate is very high with many providers. For example, every second parcel is returned at Zalando . The average return rate in all industries is 13 percent.

Large providers are therefore already working on the use of drones and other technologies for the delivery and return of goods or are introducing sanctions , such as account blocks for too many return deliveries. Better management should be done through the optimization of enterprise resource planning systems and better monitoring and scanning through augmented reality . In addition, artificial intelligence should provide better predictions and suggestions.

literature

Individual evidence

  1. ↑ Definition of »returns management« | Gabler Economic Lexicon . ( gabler.de [accessed on January 2, 2018]).
  2. ^ Frank Deges: Returns management in online retail . Springer Gabler, ISBN 978-3-658-18068-3 , pp. 5 ff .
  3. ↑ Steffen Könau: This is how parcel deliverers suffer: “Asshole below left” . In: Mitteldeutsche Zeitung . ( mz-web.de [accessed on January 2, 2018]).
  4. ↑ Returns management in e-commerce: this is how retailers keep the rate low . In: t3n magazine . ( t3n.de [accessed on January 2, 2018]).
  5. ↑ Delivery from the air: Amazon delivers packages with drones - Golem.de . ( golem.de [accessed on January 2, 2018]).
  6. ↑ Online trade: Amazon threatens to block accounts after four returns - Golem.de . ( golem.de [accessed on January 2, 2018]).
  7. ↑ How you can optimize the return of returns with Google Glass . In: t3n News . ( t3n.de [accessed on January 2, 2018]).