Row 83443

Row ID: 83443 | Dataset Entry | Axioma AXP Content Repository

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Because you’re looking at buckets of promotions and they’re all related, you can’t use an independent modeled approach because the promotions run on top of each other and they’re usually highly correlated. You either get into it using a hierarchical linear regression where coefficients are modeled separately and you create a new dependent variable or you use the HLM which allows you to bucket things. Consider it a mixed effects regression model if it makes you understand it better.

Edit to add: when I ran price and promotion studies at Nielsen for manufacturers, we often used the HLM to understand either promotional campaigns as the buckets or we ran the types of promotions as their own buckets. The point is, both tease out the over lapping issues and autocorrelation that you run into when you’re looking at promotional effectiveness.

FieldValue
text Because you’re looking at buckets of promotions and they’re all related, you can’t use an independent modeled approach because the promotions run on top of each other and they’re usually highly correlated. You either get into it using a hierarchical linear regression where coefficients are modeled separately and you create a new dependent variable or you use the HLM which allows you to bucket things. Consider it a mixed effects regression model if it makes you understand it better. Edit to add:…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-24
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Raw Record

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  "text": "Because you’re looking at buckets of promotions and they’re all related, you can’t use an independent modeled approach because the promotions run on top of each other and they’re usually highly correlated. You either get into it using a hierarchical linear regression where coefficients are modeled separately and you create a new dependent variable or you use the HLM which allows you to bucket things. Consider it a mixed effects regression model if it makes you understand it better.\n\nEdit to add: when I ran price and promotion studies at Nielsen for manufacturers, we often used the HLM to understand either promotional campaigns as the buckets or we ran the types of promotions as their own buckets. The point is, both tease out the over lapping issues and autocorrelation that you run into when you’re looking at promotional effectiveness.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-24",
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