Automated end-to-end sales process of storage appliances of storage systems using predictive modeling

Patent metadata

Field	Value
Publication number	US-9646256-B2
Application number	US-201514793583-A
Country	US
Kind code	B2
Filing date	Jul 7, 2015
Priority date	Jun 29, 2012
Publication date	May 9, 2017
Grant date	May 9, 2017

Abstract

Techniques for generating end-to-end sales leads based on storage capacity forecast using predictive modeling are described herein. According to one embodiment, diagnostic data is received from a data collector that periodically collects the diagnostic data from a storage system having one or more storage units to store data objects. A capacity forecaster coupled to the data collector forecasts a full capacity date using predictive modeling based on the diagnostic data, where the full capacity date estimates a date in which the one or more storage units reach a full storage capacity. A context generator coupled to the capacity forecaster generates a context having information identifying the one or more storage units of the storage system and an operator operating the storage system, wherein the context is used to communicate with the operator for acquiring an additional storage unit to increase storage capacity prior to the full capacity date.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
receiving diagnostic data associated with a storage system having one or more storage units to store data objects;
forecasting, by a capacity forecaster, a full capacity date using predictive modeling based on the diagnostic data, wherein the full capacity date corresponds to a date on which the one or more storage units are predicted to reach a full storage capacity;
generating, by a generator, an indication identifying a type of the one or more storage units of the storage system and an estimated size of additional storage units needed to accommodate additional data objects after the full capacity date, wherein the indication is used to communicate information regarding an increase in a storage capacity of the storage system before the full capacity date;
updating the generator and the capacity forecaster based at least in part on an event that occurs in response to the indication, wherein the event corresponds to an increase in a total storage capacity or a decrease in utilized storage; and
using the updated generator to generate a subsequent indication based at least in part on a subsequent full capacity date that is determined using predictive modeling, wherein the subsequent indication identifies a type of the one or more storage units of the storage system and an estimated size of additional storage units needed to accommodate additional data objects after the subsequent full capacity date.

2. The method of claim 1 , further comprising:
evaluating the event that occurs in response to the indication.

3. The method of claim 1 , further comprising:
transmitting the indication to a user, wherein the indication is transmitted to the user at a predetermined time before the forecasted full capacity date.

4. The method of claim 3 , wherein the transmitting of the indication comprises:
transmitting the indication to the user to enable the user to contact an operator of the storage system to acquire more storage capacity before the forecast full capacity date.

5. The method of claim 3 , further comprising:
identifying the user based on a set of one or more rules, wherein the user is associated with an operator of the storage system.

6. The method of claim 5 , wherein the identified user has a prior relationship with the operator of the storage system.

7. The method of claim 5 , wherein the identified user is located within a same geographic region as the operator of the storage system.

8. The method of claim 5 , wherein the subsequent indication includes information indicating whether an operator of the storage system has acquired more storage capacity based on communications between the user and the operator that occurred based at least in part on a previous indication.

9. The method of claim 1 , wherein forecasting the full capacity date using predictive modeling comprises performing a linear regression on the diagnostic data to predict the full capacity date.

10. The method of claim 9 , wherein the diagnostic data includes the total storage capacity and a current capacity usage of the one or more storage units of the storage system at specified points in time.

11. The method of claim 1 , wherein the using of the updated generator to generate a subsequent indication includes updating a predetermined time before the forecasted full capacity date using predictive modeling.

12. The method of claim 1 , wherein the indication relates to a sales lead, and the event that occurs in response to the indication is evaluated therefor determining a lead conversion rate.

13. The method of claim 1 , wherein the one or more storage units includes a plurality of storage units, and two or more of the plurality of storage units use different compression methods.

14. The method of claim 1 , wherein a context generator is included in a Customer Relationship Management (CRM) system and the capacity forecaster is configured to interface with the CRM system such that the capacity forecaster communicates information relating to the full capacity date to the CRM system and receives information relating to the evaluation from the CRM system.

15. A non-transitory computer-readable medium having computer instructions stored therein, which when executed by a processor, cause the processor to perform a method, the method comprising: receiving diagnostic data associated with a storage system having one or more storage units to store data objects;
forecasting, by a capacity forecaster, a full capacity date using predictive modeling based on the diagnostic data, wherein the full capacity date corresponds to a date on which the one or more storage units are predicted to reach a full storage capacity;
generating, by a generator, an indication identifying a type of the one or more storage units of the storage system and an estimated size of additional storage units needed to accommodate additional data objects after the full capacity date, wherein the indication is used to communicate information regarding an increase in a storage capacity of the storage system before to the full capacity date;
updating the generator and the capacity forecaster based at least in part on an event that occurs in response to the indication, wherein the event corresponds to an increase in a total storage capacity or a decrease in utilized storage; and
using the updated generator to generate a subsequent indication based at least in part on a subsequent full capacity date that is determined using predictive modeling, wherein the subsequent indication identifies a type of the one or more storage units of the storage system and an estimated size of additional storage units needed to accommodate additional data objects after the subsequent full capacity date.

16. The non-transitory computer-readable medium of claim 15 , wherein the method that the processor is caused to further perform further comprises:
evaluating the event that occurs in response to the indication.

17. The non-transitory computer-readable medium of claim 15 , wherein the evaluating the event that occurs in response to the indication method that the processor is caused to further perform further comprises:
transmitting the indication to a user, wherein the indication is transmitted to the user at a predetermined time before the forecasted full capacity date.

18. The non-transitory computer-readable medium of claim 17 , wherein the transmitting of the indication comprises: transmitting the indication to the user to enable the user to contact an operator of the storage system to acquire more storage capacity before the forecast full capacity date.

19. The non-transitory computer-readable medium of claim 17 , wherein the transmitting of the indication comprises:
identifying the user based on a set of one or more rules, wherein the user is associated with an operator of the storage system.

20. The non-transitory computer-readable medium of claim 19 , wherein the identified user has a prior relationship with the operator of the storage system.

21. The non-transitory computer-readable medium of claim 19 , wherein the identified user is located within a same geographic region as the operator of the storage system.

22. The non-transitory computer-readable medium of claim 19 , wherein the subsequent indication includes information indicating whether the operator of the storage system has acquired more storage capacity based on the communications between the user and the operator that occurred based a

Assignees

• Emc Corp
• Emc Ip Holding Co Llc

Inventors

• Chamness Mark
• Singer David

Classifications

• G06Q30/02

  Marketing; Price estimation or determination; Fundraising · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N7/005

  Physics · mapped topic

• G06N5/048 Primary

  Fuzzy inferencing · CPC title

• G06Q30/00

  Commerce · CPC title