US 11,816,684 B2
Method, apparatus, and computer-readable medium for determining customer adoption based on monitored data
Ansa Sekharan, Saratoga, CA (US); Ashok Gunasekaran, Saratoga, CA (US); Kali Prasad Vittala, Bangalore (IN); Arjun Krishnamoorthy, Bangalore (IN); Vivekanand Kompella, Sunnyvale, CA (US); and Rengarajan Margasahayam, Sunnyvale, CA (US)
Assigned to Informatica LLC, Redwood City, CA (US)
Filed by Informatica LLC, Redwood City, CA (US)
Filed on Sep. 23, 2019, as Appl. No. 16/578,719.
Prior Publication US 2021/0090095 A1, Mar. 25, 2021
Int. Cl. G06Q 30/0201 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 30/0201 (2013.01) [G06N 20/00 (2019.01)] 16 Claims
OG exemplary drawing
 
1. A method executed by one or more computing devices on a computer network for determining customer adoption based on monitored data, the method comprising:
receiving, by at least one of the one or more computing devices, one or more product usage parameters from a product data store on the computer network, each product usage parameter corresponding to usage of a product in one or more products by a customer and being determined based at least in part on tracking, by one or more first monitoring agents executing on the computer network and communicatively coupled to the product data store, usage of the product by the customer over a predetermined time period;
storing, by at least one of the one or more computing devices, a customer profile for the customer comprising one or more customer parameters, the one or more customer parameters being determined based at least in part on customer information stored in a customer database on the computer network;
receiving, by at least one of the one or more computing devices, one or more service parameters from a customer support data store on the computer network, each service parameter corresponding to a support service provided to the customer for the product and being determined based at least in part on tracking, by one or more second monitoring agents executing on the computer network and communicatively coupled to the customer support data store, support services provided to the customer for the product over the predetermined time period;
generating, by at least one of the one or more computing devices, a usage-based adoption score by applying a machine learning model to the one or more product usage parameters and the customer profile, wherein the machine learning model is trained by applying the machine learning model to a training data set comprising a plurality of previous product usage parameters, a plurality of previous customer profiles, a plurality of previous service parameters, and a plurality of previous product adoption scores;
generating, by at least one of the one or more computing devices, a services index corresponding to the one or more service parameters based at least in part on one or more linear-weighted moving average scores corresponding to the one or more service parameters, the one or more linear weighted moving average scores comprising a weighted average of recency and frequency of the one or more service parameters;
generating, by at least one of the one or more computing devices, a product adoption score by adjusting the usage-based adoption score based at least in part on the services index and a services index weighting assigned to the services index.