On Machine Learning towards Predictive Sales Pipeline Analytics
Authors: Junchi Yan, Chao Zhang, Hongyuan Zha, Min Gong, Changhua Sun, Jin Huang, Stephen Chu, Xiaokang Yang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform our study on a Fortune 500 multinational technology company in the B2B market environment. Throughout this section, due to the sensitivity of the proprietary company-owned selling data, we de-identified the brand name and other profile information, only leave relative metrics such as AUC score. |
| Researcher Affiliation | Collaboration | 1Software Engineering Institute, East China Normal University, Shanghai, 200062, China 2IBM Research China, Shanghai, 201203, China 3Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China 4College of Computing, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA |
| Pseudocode | Yes | Algorithm 1 Learning profile-specific decoupled two-dimensional Hawkes processes for lead win-propensity estimation |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | No | Throughout this section, due to the sensitivity of the proprietary company-owned selling data, we de-identified the brand name and other profile information, only leave relative metrics such as AUC score. |
| Dataset Splits | No | The paper states 'we use 2013Q2 data as the training set, and 2013Q3 as the testing set.' but does not provide explicit details about validation splits, percentages, or absolute sample counts for each split. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as GPU/CPU models or other system specifications. |
| Software Dependencies | No | The paper describes the proposed model and algorithm but does not list any specific software dependencies or their version numbers required for replication. |
| Experiment Setup | No | The paper describes the model and learning algorithm in detail but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or other training configurations. |