Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements
TL;DR: In this article, a new method for optimizing shelf arrangements is proposed and operationalized, where the authors show that there are important dependencies between the layout of the shelf and stock-keeping unit (SKU) sales and marketing effectiveness.
read more
Abstract: In this paper, we propose and operationalize a new method for optimizing shelf arrangements. We show that there are important dependencies between the layout of the shelf and stock-keeping unit (SKU) sales and marketing effectiveness. The importance of these dependencies is further shown by the substantive profit gains we obtain with our proposed shelf optimization approach. The basis of our model is a standard sales equation that explains sales using item-specific marketing effect parameters and intercepts. In a Hierarchical Bayes (HB) fashion, we augment this model with a second layer that relates the effect parameters to shelf and SKU descriptors. We also take into account potential endogeneity of facings. After estimating the parameters of the two-level model using Bayesian methodology, we carefully investigate the dependencies of SKU sales and SKU marketing effectiveness on the shelf layout. Next, we search for the shelf arrangement that maximizes the expected total profit using simulated annealing (SA). We appear to be able to increase profits for all the stores analyzed, and our approach appears to outperform well-known rules of thumb.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Figures
Figure 5: Impact of distance to shelf end on marketing-effectiveness parameters (dashed lines show 95% HPD). 
Figure 3: Impact of different number of facings on marketing-effectiveness parameters (dashed lines show 95% HPD). 
Figure 4: Impact of shelf number (varying from 1 to 5) on marketing-effectiveness parameters (dashed lines show 95% HPD). 
Table 6: Profit results for current layout, various rules of thumb and optimization algorithm. 
Figure 6: Number of facings before and after optimization for store 5. Black bubbles reflect items that have reduced profit after optimization, white bubbles are used for items that have increased profit after optimization. 
Figure 2: Histogram per marketing instrument across all βi,t.
Citations
Do private labels increase retailer bargaining power
Sergio Meza,K. Sudhir +1 more
TL;DR: In this article, the authors investigated the effect of private label entry on the retailer's bargaining power with respect to manufacturers and found that the gain is greater in niche categories than in mass categories, suggesting that niche national brands with limited pull power lose greater bargaining power.
164
The role of physical distribution services as determinants of product returns in Internet retailing
TL;DR: In this article, the authors use a large data set of customer purchases and returns to identify how process attributes in physical distribution service (PDS) influence product returns and find that the likelihood of orders being returned depends on the consistency between retailer promises of timeliness in the delivery of orders and the actual delivery performance of the orders.
164
Retail store operations: Literature review and research directions
TL;DR: A number of research gaps are identified and several opportunities for advancing retail expertise in the operations management community are proposed by discussing the limitations present in these papers.
164
Do Private Labels Increase Retailer Bargaining Power
Sergio Meza,K. Sudhir,K. Sudhir +2 more
TL;DR: In this paper, the authors investigate the effect of private label entry on retailer's bargaining power with respect to manufacturers and find support for the "bargaining power" hypothesis, but qualified support for "strategic retailer pricing" hypothesis.
111
The relationship between in-store marketing and observed sales for organic versus fair trade products
TL;DR: In this paper, the authors used sales data and a multilevel modeling approach to explain the market shares of sustainable products according to shelf layout factors, price level, price promotions, and consumer demographics.
References
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
46.9K
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
The calculation of posterior distributions by data augmentation
Martin A. Tanner,Wing Hung Wong +1 more
TL;DR: If data augmentation can be used in the calculation of the maximum likelihood estimate, then in the same cases one ought to be able to use it in the computation of the posterior distribution of parameters of interest.
Markov Chains for Exploring Posterior Distributions
TL;DR: Several Markov chain methods are available for sampling from a posterior distribution as discussed by the authors, including Gibbs sampler and Metropolis algorithm, and several strategies for constructing hybrid algorithms, which can be used to guide the construction of more efficient algorithms.
Explaining the Gibbs Sampler
George Casella,Edward I. George +1 more
TL;DR: A simple explanation of how and why the Gibbs sampler works is given and analytically establish its properties in a simple case and insight is provided for more complicated cases.