Prediction of quote acceptance in a B2B environment using Random Forests and Gradient Boosting Machines
(2021) In Master's Theses in Mathematical Sciences FMSM01 20211Mathematical Statistics
 Abstract
 For a business to be as successful as possible it needs a sound pricing strategy. A B2B environment allows the business more freedom to tailor each quote to maximize the performance.In order to do this, proper understanding of how probable a quote is to succeed is crucial. This work employs a statistical approach to predict the probability of acceptance based on historical data. Two different architectures for models were mainly used to compute the probability of acceptance, Gradient Boosting Machines and Random Forests. To improve the models, feature engineering, feature selection, hyperparameter optimization and probability calibration were used. Each step was evaluated in order to determine its success. Feature engineering, using domain... (More)
 For a business to be as successful as possible it needs a sound pricing strategy. A B2B environment allows the business more freedom to tailor each quote to maximize the performance.In order to do this, proper understanding of how probable a quote is to succeed is crucial. This work employs a statistical approach to predict the probability of acceptance based on historical data. Two different architectures for models were mainly used to compute the probability of acceptance, Gradient Boosting Machines and Random Forests. To improve the models, feature engineering, feature selection, hyperparameter optimization and probability calibration were used. Each step was evaluated in order to determine its success. Feature engineering, using domain knowledge from sales, significantly improved the results, by 10 percentage points in the models’ F1score. The final binary classification results for the two models are similar, both producing ca 90% F1score. Where the two models differ is in the behaviour when a single explanatory variable, the price of the quote, is altered. GBM produces probabilities that are more aligned with expectations from experts. The results show that direct price optimization is difficult to use, regardless of the model, as the probabilities are not entirely trustworthy. The thesis proves the possibility of working with quote prediction using quantitative methods, but also highlights the many challenges it poses for a company. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/9042046
 author
 Gunnarsson, Jesper ^{LU} and Tyrberg, Jacob ^{LU}
 supervisor

 Magnus Wiktorsson ^{LU}
 organization
 alternative title
 Prediktion av offertacceptans i en B2B miljö med Random Forest och Gradient Boosting Machines
 course
 FMSM01 20211
 year
 2021
 type
 H2  Master's Degree (Two Years)
 subject
 keywords
 B2B, pricing, Random Forest, Gradient Boosting, calibration, feature engineering
 publication/series
 Master's Theses in Mathematical Sciences
 report number
 LUTFMS34062021
 ISSN
 14046342
 other publication id
 2021:E4
 language
 English
 id
 9042046
 date added to LUP
 20210409 14:15:55
 date last changed
 20210603 15:27:09
@misc{9042046, abstract = {{For a business to be as successful as possible it needs a sound pricing strategy. A B2B environment allows the business more freedom to tailor each quote to maximize the performance.In order to do this, proper understanding of how probable a quote is to succeed is crucial. This work employs a statistical approach to predict the probability of acceptance based on historical data. Two different architectures for models were mainly used to compute the probability of acceptance, Gradient Boosting Machines and Random Forests. To improve the models, feature engineering, feature selection, hyperparameter optimization and probability calibration were used. Each step was evaluated in order to determine its success. Feature engineering, using domain knowledge from sales, significantly improved the results, by 10 percentage points in the models’ F1score. The final binary classification results for the two models are similar, both producing ca 90% F1score. Where the two models differ is in the behaviour when a single explanatory variable, the price of the quote, is altered. GBM produces probabilities that are more aligned with expectations from experts. The results show that direct price optimization is difficult to use, regardless of the model, as the probabilities are not entirely trustworthy. The thesis proves the possibility of working with quote prediction using quantitative methods, but also highlights the many challenges it poses for a company.}}, author = {{Gunnarsson, Jesper and Tyrberg, Jacob}}, issn = {{14046342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Prediction of quote acceptance in a B2B environment using Random Forests and Gradient Boosting Machines}}, year = {{2021}}, }