Subscribe to DSC Newsletter

Pantry loading effect and modeling the variable

Hello Data Scientists,

I have a requirement of deriving causal factors for my target Product (SKU) sales. I have the weekly sales of the product for last 3 years along with its competitor sales along with promotional data. The business wants to know if there an effect of pantry loading (Add Stock?). Suppose below is my data set.

Week Volume(EA)  Price
201301 6369 10.87
201302 8606 10.99
201303 9258 10.94
201304 9461 10.59
201305 8879 10.76
201306 8910 10.75
201307 62925 7.96
201308 80494 7.87
201309 51733 7.75
201310 21507 9.03
201311 27747 8.57
201312 29384 8.37
201313 34538 8.41
201314 9213 10.02
201315 9267 10.71
201316 9432 10.76
201317 8288 10.76

I can regress independent variable price on dependent variable volume to find the co-effecients of price. We are using Log Linear Regression model to capture the impact of the price on the volume. 

Now, I would like to understand how we can model the Pantry loading effect variable. We can see that week of 14 in 2013, the price was made closer to your RRP, the sales dipped significantly. want to capture that effect in the variable so that I can then regress it against the volume to see the positive co-efficient. ( The more price drop on the previous week, the less sales week after)

Does anyone has done this before who can guide me?

Shrirama



Views: 109

Reply to This

Replies to This Discussion

Anyone who can help?

Reply to Discussion

RSS

Follow Us

Videos

  • Add Videos
  • View All

Resources

© 2017   Data Science Central   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service