On Machine Learning towards Predictive Sales Pipeline Analytics


  • Junchi Yan East China Normal Univesity; Shanghai Jiao Tong University
  • Chao Zhang IBM Research - China
  • Hongyuan Zha East China Normal University
  • Min Gong IBM Research - China
  • Changhua Sun IBM Research - China
  • Jin Huang IBM Research - China
  • Stephen Chu IBM Research - China
  • Xiaokang Yang Shanghai Jiao Tong University




Sales pipeline win-propensity prediction is fundamental to effective sales management. In contrast to using subjective human rating, we propose a modern machine learning paradigm to estimate the win-propensity of sales leads over time. A profile-specific two-dimensional Hawkes processes model is developed to capture the influence from seller's activities on their leads to the win outcome, coupled with lead's personalized profiles. It is motivated by two observations: i) sellers tend to frequently focus their selling activities and efforts on a few leads during a relatively short time. This is evidenced and reflected by their concentrated interactions with the pipeline, including login, browsing and updating the sales leads which are logged by the system; ii) the pending opportunity is prone to reach its win outcome shortly after such temporally concentrated interactions. Our model is deployed and in continual use to a large, global, B2B multinational technology enterprize (Fortune 500) with a case study. Due to the generality and flexibility of the model, it also enjoys the potential applicability to other real-world problems.




How to Cite

Yan, J., Zhang, C., Zha, H., Gong, M., Sun, C., Huang, J., Chu, S., & Yang, X. (2015). On Machine Learning towards Predictive Sales Pipeline Analytics. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9455



Main Track: Machine Learning Applications