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Sales Lift Project

5/9/23

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  • Tiara Adhikari
  • Ashley Annanpersaud
  • Pooja Choudhary
  • Xiaofeng Liang
  • Anuar Mukhambetzhanov
  • Kavindra Sahabir
  • Jenny Samaroo
  • Anthony Vallejo
  • Matthew Van Praagh

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  • Professor Jaramillo
  • Professor Venkatesh

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Agenda:

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  • Review our project subject
  • Summary of methods
  • Results
  • Usages guideline

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Review our project subjects

  • Price elasticity of demand
  • Cluster Skus by similar elasticity
  • Sales lift projection helps decision making

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What we accomplished

Sales Lift Output

Preprocessing

Outlier Detection

SKU Clustering

Within-Cluster Processing

Cluster-Level Regression

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Outliers handling

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‘Good’ SKUs Per Lower Limit

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SKUs w/ at least 70% of the monthly-stock present after Cook’s Distance outlier filtering

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Clustering SKUs

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Group 1

Group 2

Group 3

Group 4

Group 5

Group 6

Group 7

Group 8

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Before vertical shift

After vertical shift

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SKUs in a group before and after shifting

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*Log-Log model (percent change on price)

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sales lift = ((1-markdown)a - 1)*100%

*a = slope

Sales Lift Calculation

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Sales Lift Table Output

  • File Name: Sales Lift by Grouping.csv

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Usages guideline

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Recap of Topics:

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  • Review our project subject
  • Summary of methods
  • Results
  • Usages guideline