Method, apparatus, and computer-readable medium for determining customer adoption based on monitored data

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.

2. The method of claim 1 , wherein the product comprises a cloud product that is hosted on the computer network.

3. The method of claim 1 , wherein the one or more product usage parameters comprise one or more of: a frequency of logins, a recency of logins, a trend of logins over a period of time, a frequency of job executions, a recency of job executions, a trend of job executions over a period of time, a volume of data processed, or a trend in volume of data processed over a period of time.

4. The method of claim 1 , wherein the one or more customer parameters comprise one or more of: an age of an account associated with the customer, a duration of usage of the product by the customer, a level of investment in the product by the customer, a segment of the customer, customer renewal patterns, customer financial strength, or a situational factor.

5. The method of claim 1 , wherein the one or more service parameters comprise one or more of: a quantity of incidents reported; a quantity of bugs reported, a quantity of negative customer satisfaction records, or a quantity of escalations reported.

6. The method of claim 1 , wherein generating a usage-based adoption score by applying a machine learning model to the one or more product usage parameters and the customer profile comprises:

applying the machine learning model to the one or more product usage parameters and the customer profile to generate a usage-based product adoption probability; and

generating the usage-based adoption score by scaling the usage-based product adoption probability to a value between 0 and 100.

7. The method of claim 1 , wherein the services index weighting is determined based at least in part on 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.

8. The method of claim 1 , wherein the one or more products comprise a plurality of products and further comprising:

generating, by at least one of the one or more computing devices, a customer adoption score for the customer based at least in part on a plurality of product adoption scores corresponding to the plurality of products and a plurality of product weights corresponding to the plurality of products, wherein the customer adoption score corresponds to overall adoption of the plurality of products by the customer.

9. An apparatus on a computer network for determining customer adoption based on monitored data, the apparatus comprising:

one or more processors; and

one or more memories operatively coupled to at least one of the one or more processors and having instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to:

receive 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;

store 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;

receive 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;

generate 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;

generate 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; and

generate 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.

10. The apparatus of claim 9 , wherein the instructions that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to generate a usage-based adoption score by applying a machine learning model to the one or more product usage parameters and the customer profile further cause at least one of the one or more processors to:

apply the machine learning model to the one or more product usage parameters and the customer profile to generate a usage-based product adoption probability; and

generate the usage-based adoption score by scaling the usage-based product adoption probability to a value between 0 and 100.

11. The apparatus of claim 9 , wherein the one or more products comprise a plurality of products and wherein at least one of the one or more memories has further instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to:

generate a customer adoption score for the customer based at least in part on a plurality of product adoption scores corresponding to the plurality of products and a plurality of product weights corresponding to the plurality of products, wherein the customer adoption score corresponds to overall adoption of the plurality of products by the customer.

12. At least one non-transitory computer-readable medium storing computer-readable instructions that, when executed by one or more computing devices, cause at least one of the one or more computing devices to:

receive 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;

store 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;

receive 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;

generate 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;

generate 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; and

generate 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.

13. The at least one non-transitory computer-readable medium of claim 12 , wherein the instructions that, when executed by at least one of the one or more computing devices, cause at least one of the one or more computing devices to generate a usage-based adoption score by applying a machine learning model to the one or more product usage parameters and the customer profile further cause at least one of the one or more computing devices to:

apply the machine learning model to the one or more product usage parameters and the customer profile to generate a usage-based product adoption probability; and

generate the usage-based adoption score by scaling the usage-based product adoption probability to a value between 0 and 100.

14. The at least one non-transitory computer-readable medium of claim 12 , wherein the one or more products comprise a plurality of products and further storing computer-readable instructions that, when executed by at least one of the one or more computing devices, cause at least one of the one or more computing devices to:

generate a customer adoption score for the customer based at least in part on a plurality of product adoption scores corresponding to the plurality of products and a plurality of product weights corresponding to the plurality of products, wherein the customer adoption score corresponds to overall adoption of the plurality of products by the customer.

15. The apparatus of claim 9 ,

wherein the one or more product usage parameters comprise one or more of: a frequency of logins, a recency of logins, a trend of logins over a period of time, a frequency of job executions, a recency of job executions, a trend of job executions over a period of time, a volume of data processed, or a trend in volume of data processed over a period of time; and

wherein the one or more customer parameters comprise one or more of: an age of an account associated with the customer, a duration of usage of the product by the customer, a level of investment in the product by the customer, a segment of the customer, customer renewal patterns, customer financial strength, or a situational factor.

16. The at least one non-transitory computer-readable medium of claim 12 ,

wherein the one or more product usage parameters comprise one or more of: a frequency of logins, a recency of logins, a trend of logins over a period of time, a frequency of job executions, a recency of job executions, a trend of job executions over a period of time, a volume of data processed, or a trend in volume of data processed over a period of time; and

wherein the one or more customer parameters comprise one or more of: an age of an account associated with the customer, a duration of usage of the product by the customer, a level of investment in the product by the customer, a segment of the customer, customer renewal patterns, customer financial strength, or a situational factor.