Row 20768

Row ID: 20768 | Dataset Entry | Axioma AXP Content Repository

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Data science in Sales and marketing is an interesting area with lot of opportunities. The KPIs typically boils down to the net profit / revenue maximization in some form. However some if the ideas you could consider to implement are the following:

1. Demand forecasting (focus on accuracy enhancement) 2. Marketing spend optimization (which channel to spend more, which product to spend more on ads, which ones are effective in what time). Also called market mix modeling (mmm) 3. Pricing optimization (typically product team decides, but sales also determines this), 4. Discount modeling and optimization 5. Leads prioritization, 6. Campaign effectiveness and modeling 7. Uplift modeling (estimate incremental lift in purchase probability due to a campaign) 8. Attrition risk modeling (probability of given customer unsubscribing) 9. Conjoint analysis (feature utility score mapping), 10. Dynamic pricing 11. Attribution modeling (which channel to attribute a given sale to?) 12. Customer lifetime value (CLTV) estimation.

FieldValue
text Data science in Sales and marketing is an interesting area with lot of opportunities. The KPIs typically boils down to the net profit / revenue maximization in some form. However some if the ideas you could consider to implement are the following: 1. Demand forecasting (focus on accuracy enhancement) 2. Marketing spend optimization (which channel to spend more, which product to spend more on ads, which ones are effective in what time). Also called market mix modeling (mmm) 3. Pricing optimizati…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-21
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Raw Record

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  "text": "Data science in Sales and marketing is an interesting area with lot of opportunities. The KPIs typically boils down to the net profit / revenue maximization in some form. However some if the ideas you could consider to implement are the following:\n\n1. Demand forecasting (focus on accuracy enhancement)\n2. Marketing spend optimization (which channel to spend more, which product to spend more on ads, which ones are effective in what time). Also called market mix modeling (mmm)\n3. Pricing optimization (typically product team decides, but sales also determines this),\n4. Discount modeling and optimization\n5. Leads prioritization,\n6. Campaign effectiveness and modeling\n7. Uplift modeling (estimate incremental lift in purchase probability due to a campaign)\n8. Attrition risk modeling (probability of given customer unsubscribing)\n9. Conjoint analysis (feature utility score mapping),\n10. Dynamic pricing\n11. Attribution modeling (which channel to attribute a given sale to?)\n12. Customer lifetime value (CLTV) estimation.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5LakxfVUoyQTEteDY3U2t6WEtlTEJJZ2lkdWVMbW1EanFjSlBmRF9Ja09rN08td1k2N2lwQWlsaDdfRlBudFVqbllJd3MwRGFOeEg4SmkwWl9MeFQxOWZZZGc9PQ==",
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}
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