New Product Development Under Channel Acceptance

Luo, Lan; Kannan, P.K.; Ratchford, Brian T.

doi:10.1287/mksc.1060.0240

Cited by 72 publications

(68 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assortment decisions are beyond the scope of our research. Item deletions and new product acceptance are driven by other factors such as inventory costs, retailer power, and retail chain positioning (see Sloot et al 2006 andLuo et al 2007). …”

Section: Our Approachmentioning

confidence: 99%

Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements

Nierop¹,

Fok

Franses

2008

Marketing Science

View full text Add to dashboard Cite

I n 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.

show abstract

Section: Our Approachmentioning

confidence: 99%

Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements

Nierop¹,

Fok

Franses

2008

Marketing Science

View full text Add to dashboard Cite

show abstract

“…Previous research has investigated how firms' new product strategies interact with, for example, buyer self-selection (Moorthy 1984), product obsolescence (Levinthal and Purohit 1989), R&D and Engineering metrics (Hauser 1998), distribution channel relationship (Villas-Boas 1998), demand uncertainty (Desai 2000), dynamic competition (Ofek and Sarvary 2003), network effects (Sun, Xie, and Cao 2004), firm alliances (Amaldoss and Rapoport 2005), idea generation (Toubia 2006), channel acceptance (Luo, Kannan, and Ratchford 2007), subjective characteristics (Luo, Kannan, and Ratchford 2008), sequential entry (Ofek and Turut 2008), reference group effects (Amaldoss and Jain 2010), innovation incentives (Manso 2011), and consumer deliberation (Guo and Zhang 2012). The incentive design aspect of this paper is also related to the literature on sales force compensation (e.g., Basu et al 1985;Lal and Staelin 1986;Rao 1990;Coughlan and Narasimhan 1992;Raju and Srinivasan 1996;Kalra, Shi, and Srinivasan 2003;Lim, Ahearne, and Ham 2009;Simester and Zhang 2014).…”

Section: Model Setupmentioning

confidence: 99%

Deadlines in Product Development

Zhang

2012

SSRN Journal

View full text Add to dashboard Cite

Deadlines are common in product development and are often felt to be too harsh -many development efforts are still worth continuing at the time of mandated termination. We examine the value of deadlines from the agency-theoretic perspective. We consider a firm that pays an agent to lead product development activities. The chance of success depends on the viability of the project and the effort of the agent. As the project proceeds without success, doubts grow as to whether the project is viable. To motivate continued effort, the firm must promise the agent a generous reward if success is achieved during the late stage of development. However, rewarding late success undermines effort incentives in the early stage. The firm may find it more profitable to impose a hard, early deadline to eliminate the agent's dynamic incentive to procrastinate. We derive conditions under which the firm should impose such deadlines.

show abstract

“…We apply the proposed methodology to the MNL model for several reasons: (1) it is the most widely used discrete choice model, especially due to its closed form choice probabilities and interpretability [44]; (2) several discrete choice models evolved from MNL, such as MIXL and G-MNL, and a better understanding of how uncertainty affects NPD under MNL models can lead to general conclusions and intuition about the effects of uncertainty under different models; and (3) even though MNL was introduced more than thirty years ago by McFadden [40], it is still widely used in the NPD literature [14,[45][46]. For the purposes of this paper, we assume that the model is correct and that the uncertainty arises from the parameter estimation and not model misspecification.…”

Section: Application To Multinomial Logit Demand Modelmentioning

confidence: 99%

“…Simple models to determine the most profitable characteristics of a single new product [1][2] have progressed to account for issues such as product-line design and preference heterogeneity [3][4][5][6][7], competitor reactions [8][9][10], cost structure [11][12], distribution channels [9,[13][14][15][16], choice-set-dependent preferences [17], and coordination with constrained engineering design decisions [18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

Resende

Heckmann

Michalek

2011

Volume 5: 37th Design Automation Conference, Parts a and B

View full text Add to dashboard Cite

In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1-α) chance of exceeding, given the distribution of possible outcomes. The parameter α [0,1] INTRODUCTIONOver the last three decades, a significant portion of the new product development (NPD) literature has been dedicated to the integration of engineering design and marketing processes for differentiated markets. Simple models to determine the most profitable characteristics of a single new product [1-2] have progressed to account for issues such as product-line design and preference heterogeneity [3][4][5][6][7], competitor reactions [8-10], cost structure [11][12], distribution channels [9,13-16], choice-set-dependent preferences [17], and coordination with constrained engineering design decisions [18][19][20][21][22][23][24][25][26].As Hsu and Wilcox [27] argue, the trend towards estimating marketing models at lower levels of aggregation that are more structural 1 in consumer behavior representation has led to models with many parameters and consequently greater uncertainty of those parameters. However, despite the advances in NPD methods, the research has not given much consideration to the intrinsic parameter uncertainty of the demand models. Demand uncertainty directly affects the risk of introducing a new product into the market, and firms evaluate potential projects not only in terms of expected return, but also in terms of risk.The purpose of this work is threefold. First, we define a robust a-profit metric and propose a general framework to incorporate demand uncertainty arising from choice model parameter estimation into the design decision process such that it accounts for varying levels of loss tolerance. Second, we apply the delta method to approximate the a-profit function in closed form for multinomial logit (MNL) demand models to be used efficiently in numerical optimization routines. Finally, we show how ignoring demand uncertainty can lead to suboptimal decisions for risk averse firms.We do not intend to consider all the various sources of demand model uncertainty [28], and several questions will remain open. In particular, we assume the discrete choice model is correctly specified and ignore uncertainty due to model misspecification, and we assume that the model parameters do not change over time or from the context in which the data were collected to the context in which predictions will be made. Nevertheless, the proposed methodology can be useful, and it serves as a first step in addressing design for profit maximization...

show abstract

New Product Development Under Channel Acceptance

Cited by 72 publications

References 47 publications

Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements

Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements

Deadlines in Product Development

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

Contact Info

Product

Resources

About