Adobe, Inc.

Jin Xu

Zhenyu Yan

Abhishek Pani

Tianyu Wang

Wenqing Yang

Maoqi Xu

Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

Quantitative rating system for prioritizing customers by propensity and buy size

Methods, systems, and non-transitory computer readable storage media are disclosed for using reject inference to generate synthetic data for modifying lead scoring models. For example, the disclosed system identifies an original dataset corresponding to an output of a lead scoring model that generates scores for a plurality of prospects to indicate a likelihood of success of prospects of the plurality of prospects. In one or more embodiments, the disclosed system selects a reject inference model by performing simulations on historical prospect data associated with the original dataset. Additionally, the disclosed system uses the selected reject inference model to generate an imputed dataset by generating synthetic outcome data representing simulated outcomes of rejected prospects in the original dataset. The disclosed system then uses the imputed dataset to modify the lead scoring model by modifying at least one parameter of the lead scoring model using the synthetic outcome data.

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San Jose, CA, United States of America

Jin Xu