Row 20679

Row ID: 20679 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 20679 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I manage Sales Analytics and Sales DS teams. Our Sales model is largely inbound call center.

Analytics wise we focus on production metrics. How many calls came in, how many did we answer, how many did we convert, how much time was spent on the phone, how much capacity was left on the table, etc. We track against forecast and dissect drivers of over/under performance.

On the Data Scientist side we deploy models that optimize the Call Center. Lead Scoring for inbound calls and Rep Scoring to match best calls to best reps. We also have a recommendation engine to provide best product offering for the customer.

There’s a lot of other stuff that goes on but that’s the core.

FieldValue
text I manage Sales Analytics and Sales DS teams. Our Sales model is largely inbound call center. Analytics wise we focus on production metrics. How many calls came in, how many did we answer, how many did we convert, how much time was spent on the phone, how much capacity was left on the table, etc. We track against forecast and dissect drivers of over/under performance. On the Data Scientist side we deploy models that optimize the Call Center. Lead Scoring for inbound calls and Rep Scoring to m…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-21
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Raw Record

{
  "text": "I manage Sales Analytics and Sales DS teams. Our Sales model is largely inbound call center. \n\nAnalytics wise we focus on production metrics. How many calls came in, how many did we answer, how many did we convert, how much time was spent on the phone, how much capacity was left on the table, etc. We track against forecast and dissect drivers of over/under performance. \n\nOn the Data Scientist side we deploy models that optimize the Call Center. Lead Scoring for inbound calls and Rep Scoring to match best calls to best reps. We also have a recommendation engine to provide best product offering for the customer. \n\nThere’s a lot of other stuff that goes on but that’s the core.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5LakxfcFV0Nm04djdRWm9Zd3g2NGJSZHdmWkFfR3YtQ2lZazdRbFBVVzQ0aXBvdG56M2Q5NGxTeVZYLVM5VFZqbEhiTW1rSGRRZF9MbURlQ3ZmZGJjRjhURnc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9QOXZlTzVldW5kU3NvUjc5dWszTk9fUHZaYTF3ZGlQQ2hZLWFSS2dCWFpvUksxN0lhRlNIRGZhVXdSTnBXQmFQUFdYTldTRXpOQTdtMnRrcnBpTzVVOUk2d1hmZnFWYVdwUHEwZkdKS0lraVJ3eFhmdTQweTZLX1NudEFod0dTTjRpT3RSY01aWWRfMUJWaEd3bXU0UHREdzJ0THJBWjhkZHQxaU90a2tuYjJkTUxrWDdzOVhFMzVhZ3AwdnN6d05x"
}
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