E-commerce return rates have grown by 95% over the past 5 years, incurring excessive costs for shops, causing inconvenience for shoppers and devastating greenhouse emissions.
We are on a mission to reduce avoidable returns and convert unavertable returns into new revenues.
Reduce avertable returns by letting shoppers keep items at an additional discount.
Increase revenue by offering
higher value vouchers instead of refund.
73% of refunds take more than 3 days to be paid out and therefore block an immediate re-purchase
10% of customers demand a refund because they found a better price somewhere else in the meantime
56% of customers would refrain from refund if a compensation was granted
Based on the consumer's past behavior, our algorithm
determines which alternative return options
are most profitable for the e-commerce shop.
The consumer always gets the choice of
taking the deal or sticking to the regular
return policy of the shop.
Based on these choices the deep learning AI adjusts
its behavioral model to enable an optimization of
future offers and prevent fraud.
Purchase
Delivery
Customer requests return
Our custom machine learning algorithm first selects customers that have a high probability of benefiting from alternatives to the classical product return option on the basis of e-commerce return and purchase histories.
Each customer is vetted individually for past fraudulent behavior and price sensitivity by detecting conspicuous behavioral patterns. Detection is performed with a neural network architecture specifically developed for this purpose.
Finally, offers are selected that will both maximize the benefit for the Ecommerce shop and the individual customer. Offers are continuously customized for the specific customer and the specific return request based on return processing costs and customer profile.
Keep at discount
Store credit
Other benefits
Purchase
Delivery
Customer requests return
Classical product return
Transport back
Verifications or discarting
Our custom machine learning algorithm first selects customers that have a high probability of benefiting from alternatives to the classical product return option on the basis of e-commerce return and purchase histories.
Each customer is vetted individually for past fraudulent behavior and price sensitivity by detecting conspicuous behavioral patterns. Detection is performed with a neural network architecture specifically developed for this purpose.
Keep at discount
Store credit
Other benefits
Finally, offers are selected that will both maximize the benefit for the Ecommerce shop and the individual customer. Offers are continuously customized for the specific customer and the specific return request based on return processing costs and customer profile.
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