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Könyv Algorithmic Pricing based on Big Data. A Critical Reflection Lukas Kern

Algorithmic Pricing based on Big Data. A Critical Reflection

Szerző: Lukas Kern
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Grin Verlag
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
15 998 Ft
Master's Thesis from the year 2020 in the subject Business economics - Customer Relationship Managem...

Információk a könyvről

Szerző
Lukas Kern
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2020
oldal
84
EAN
9783346229397
ISBN
3346229394
Enbook ID
33095061
Kiadó
Grin Verlag
Súly
135
Méretek
148 x 210 x 7

Teljes leírás

Master's Thesis from the year 2020 in the subject Business economics - Customer Relationship Management, CRM, grade: 1,0, , language: English, abstract: Setting the right product prices is crucial for companies and is part of their marketing mix and image. For instance, deviations from "optimal" sales prices can lead to considerable losses in revenue and margin. However, a huge amount of data affect the "optimal" price and the pricing process requires extensive manual resources.Advanced algorithms like machine learning might have the potential to overcome the aforementioned challenges with almost no manual interactions. Pricing algorithms constantly automate and optimize pricing decisions based on the available data. Besides positive one-time effects of price optimizations, algorithmic pricing enables companies to implement new pricing strategies like dynamic pricing, price personalization, and markdown pricing.This master thesis combines the results of a literature review and expert interviews to solve three questions: What is the research gap between the current state of the literature and business practice regarding the use of advanced algorithms based on big data for algorithmic pricing? What progress and insights have companies made in using algorithmic pricing? And how can algorithmic pricing be enhanced for future application?The master thesis starts by explaining the basic concepts of algorithmic pricing and relevant technologies. Therefore, the results and takeaways are useful for business managers without prior experience in this area. This master thesis then provides corporate decision makers with recommendations on what to consider for new pricing algorithms and on opportunities for future development of existing pricing algorithms.

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