Paper Type
full
Description
Today’s information tracking technology and Big Data open up new opportunities for e-commerce. Online stores can collect personal information to estimate customers’ willingness-to-pay. This enables the application of price differentiation where different customers are charged different prices for the same product. Lower prices offered to customers who share the word have an advertisement effect, while higher prices have adverse effects. In this paper we develop a decision model for individualised prices in online stores that considers the sharing of prices by word of mouth which is mostly neglected by current literature. Complex decision models in e-commerce are caught between the need of adequately representing the reality and the demand of being solvable within reasonable time limits. We use various artificial intelligence solution methods to solve the decision model for numerical examples. Our results indicate that despite word of mouth differential pricing can be financially worthwhile.
Recommended Citation
Be?er, Alper; Lackes, Richard; and Siepermann, Markus, "Different Prices for Different Customers – Optimising Individualised Prices in Online Stores by Artificial Intelligence" (2019). ICIS 2019 Proceedings. 25.
https://aisel.aisnet.org/icis2019/data_science/data_science/25
Different Prices for Different Customers – Optimising Individualised Prices in Online Stores by Artificial Intelligence
Today’s information tracking technology and Big Data open up new opportunities for e-commerce. Online stores can collect personal information to estimate customers’ willingness-to-pay. This enables the application of price differentiation where different customers are charged different prices for the same product. Lower prices offered to customers who share the word have an advertisement effect, while higher prices have adverse effects. In this paper we develop a decision model for individualised prices in online stores that considers the sharing of prices by word of mouth which is mostly neglected by current literature. Complex decision models in e-commerce are caught between the need of adequately representing the reality and the demand of being solvable within reasonable time limits. We use various artificial intelligence solution methods to solve the decision model for numerical examples. Our results indicate that despite word of mouth differential pricing can be financially worthwhile.