Hello folks,
Has anyone experimented with R for analysis? If you have, I've love to chat about your experience and use cases.
Have a super day,
John
I’m also very interested in this as well as other applications of machine learning. I may not have much to add to the discussion, but I am very interested in learning about it and figuring out how to apply it to the our systems.
Brian Ramos
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Opera Philadelphia
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From: Self-service Business Intelligence [mailto:groups-selfservicebi@tessituranetwork.com] On Behalf Of Arthur Curtis Sent: Friday, August 12, 2016 7:58 AM To: Ramos, Brian <ramos@operaphila.org> Subject: Re: [Self-service Business Intelligence] R Programming
Hi Tom and Jamie,
I am most interested as well and will be further investigating. Maybe we can set up a WebEx to further discuss? I am out on vacation all next week but will be back the following week.
Arthur
Sent from my iPhone
On Aug 11, 2016, at 5:49 PM, Jamie Shover <bounce-jamieshover9674@tessituranetwork.com> wrote:
Tom, We have kicked off a project to tackle some of the same issues. We have found anecdotally that many of our customers have 2, 3, sometime 5 or 6 customer records. The cost of this can be an issue as well as just accuracy in reporting and data analysis. It's' causing us to look at some of the processes by which these multiple accounts are created (Box Office rushing, customer forgets password and creates another with new email, etc). I had not considered using ML for this, but my curiosity is peaked. Let me know what you are thinking. Maybe we can try a few things in tandem. Missed you this year at TLCC! Jamie From: Tom Brown <bounce-tombrown3568@tessituranetwork.com> Sent: 7/10/2016 1:08:08 AM I'm becoming interested in Machine Learning for Record Linkage / De Duplication. There are a number of tools in R and Python that seemed to be focused on this subject. In Tessitura we seem to have some interesting data for this process when it comes to historical Merge Data from which these tools might "learn" our business rules. The reason this is important from my point of view is if 10% - 20% of the records in our system are un-discovered or un-merged duplicates this can really skew analytical counts, and of course could cause un-needed expenditure on mailings and the like, with related customer service concerns.
Tom,
We have kicked off a project to tackle some of the same issues. We have found anecdotally that many of our customers have 2, 3, sometime 5 or 6 customer records. The cost of this can be an issue as well as just accuracy in reporting and data analysis. It's' causing us to look at some of the processes by which these multiple accounts are created (Box Office rushing, customer forgets password and creates another with new email, etc).
I had not considered using ML for this, but my curiosity is peaked. Let me know what you are thinking. Maybe we can try a few things in tandem.
Missed you this year at TLCC!
Jamie
From: Tom Brown <bounce-tombrown3568@tessituranetwork.com> Sent: 7/10/2016 1:08:08 AM
I'm becoming interested in Machine Learning for Record Linkage / De Duplication. There are a number of tools in R and Python that seemed to be focused on this subject.
In Tessitura we seem to have some interesting data for this process when it comes to historical Merge Data from which these tools might "learn" our business rules.
The reason this is important from my point of view is if 10% - 20% of the records in our system are un-discovered or un-merged duplicates this can really skew analytical counts, and of course could cause un-needed expenditure on mailings and the like, with related customer service concerns.