SMU Data Science Review
Abstract
In this paper, we present a quantitative approach to model the manufacturer’s suggested retail price (MSRP) for children’s doll- houses and establish relationships among key features that contribute most to establishing MSRP. Determination of the MSRP is a critical step in how consumers respond with their wallets when purchasing an item. KidKraft, a global leader in toys and juvenile products, sets MSRP subjectively using product experts. The process is arduous and time consuming requiring the focus of specialized resources and knowledge of the interaction between key attributes and their impact on consumer value. An accurate prediction of MSRP during the early stages of the design process is critical to aligning the cost of design features with the expected revenue. Finding out that the MSRP is set incorrectly too late in the design process can result in costly redesign. Four models are constructed for a simple objective approach to calculating a dollhouses MSRP. Each model is evaluated for accuracy and simplicity, and a model using linear regression with forward selection is chosen based on ease of interpretation, limited sample size and to prevent over-fitting. The top five features with the greatest impact on MSRP are also highlighted. The chosen model allows for a quick and easy determination of MSRP and can validate that proposed features align with the predicted MSRP while still early in the design process.
Recommended Citation
Byrd, Peter; Knowles, Jonathan; Andreev, Dmitry; Turner, Jacob; Mente, Brian; and Wallace, LaRoux
(2019)
"Quantitative Model for Setting Manufacturer's Suggested Retail Price,"
SMU Data Science Review: Vol. 2:
No.
3, Article 12.
Available at:
https://scholar.smu.edu/datasciencereview/vol2/iss3/12
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Included in
Applied Statistics Commons, Multivariate Analysis Commons, Sales and Merchandising Commons, Statistical Models Commons
