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DESCRIPTION:Management Science Research Centre & Bensadoun School of Retail
  Management Present\n\nGeorgia Perakis\n\nMIT Sloan School of Management\n
 \n \n\nFriday\, November 30\, 2018\n\n1:30PM - 3:30PM\n\nBRONF 340\n\nHigh
 -Low Promotion Policies for Peak-End Demand Models\n\n \n\nAbstract:\n	Prom
 otions are a highly effective marketing tool that can have a significant i
 mpact on a retailer’s profit. A strong understanding of how changes in the
  price affect consumers' purchasing behavior can lead to more effective pr
 omotions policies and as a result\, to a substantial increase in profit fo
 r retailers. Incorporating important consumer behavioral effects in the de
 mand model is crucial in order to better predict demand. In this talk\, we
  will present a new demand model that relies not only on current and past 
 period prices but more importantly\, on the minimum price set within a set
  of past periods (bounded memory peak-end). Furthermore\, using these as f
 eatures and employing machine learning tools\, we show that this new deman
 d model predicts actual sales more accurately than current methods. We tes
 t our prediction approach on sales data from a large retailer and demonstr
 ate that there is a 9% relative improvement in the precision of the demand
  prediction.\n\nThis new demand model also allows us to determine the opti
 mal promotion strategy more efficiently. That is\, subsequently\, we sugge
 st a compact Dynamic Programming (DP) approach that uses the proposed dema
 nd model. We examine when this DP solves the problem optimally. That is\, 
 we establish when\, for some commonly used demand models (including the on
 e proposed in this talk)\, the proposed DP solves the promotion planning o
 ptimization problem exactly. In fact\, we confirm a common practice by ret
 ailers\, that is when and for what demand models\, the optimal promotion s
 trategy is to either promote (always at the same level of promotion) or no
 t promote an item at all. For demand models where these conditions do not 
 hold\, we provide an analytical guarantee for our proposed DP and illustra
 te that still the proposed DP yields near optimal solutions fast. Furtherm
 ore\, on the same sales data we tested our demand prediction approach on\,
  we demonstrate that the proposed DP yields on average a 9.1% increase in 
 profit relative to the retailer's current practices.\n\n \n\nAll are cordi
 ally invited to attend.\n
DTSTART:20181130T183000Z
DTEND:20181130T203000Z
SUMMARY:High-Low Promotion Policies for Peak-End Demand Models - Georgia Pe
 rakis\, MIT Sloan School of Management 
URL:https://www.mcgill.ca/bensadoun-school/perakis-nov30
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