Purpose

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

Methodology

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

Findings

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

Research implications

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

Practical implication

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

Originality/value

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

中文翻译：

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使用消费者偏好数据预测服装零售需求

目的

本研究旨在分析消费者偏好数据在预测服装零售需求中的重要性。

方法

为了收集偏好数据，我们使用全因子设计从六个属性和每个属性三个级别的组合中得出了 729 个假设库存单位 (SKU)。从假设的 SKU 中，选择了 63 个实际 SKU 进行进一步分析。通过商店拦截调查收集了 202 个答复。所有 63 个 SKU 的受访者效用得分均使用联合分析计算。在估计总需求时，为了允许消费者替代，并在消费者希望购买同一 SKU 中的多个商品时使 SKU 可用，选择了每个人最喜欢的前三个 SKU 效用分数，并使用决策树进行分类，被聚合。选择规则被建模为包括替换；通过应用这一选择规则，可以估计总需求。

发现

计算了受访者的效用分数。Kendall's tau 值为 0.88，Pearson's R值为0.98，使用 Kendall's tau 的内部预测有效性为 1.00，这表明获得的数据质量很高。所提出的模型用于估计 63 个 SKU 的需求。SKU 棉质、常规款式、半袖、中等价格、自有品牌的需求估计为 6.04%。与专家意见数据相比，所提出的使用消费者偏好数据估计需求的模型给出了更接近实际销售的估计。实际销售额和消费者偏好数据之间的 Spearman 等级相关性为 0.338，并且在 5% 的水平上显着。实际销售额与专家意见之间的Spearman等级相关性为-0.059，专家意见数据与实际销售额之间不存在显着相关性。因此，事实证明，消费者偏好模型比专家意见数据更能预测需求。

研究意义

在基于选择的模型中已经做了大量的工作。确定性模型的工作范围很大。

实际意义

所提出的基于消费者偏好的需求估计模型可以有利于服装零售商通过减少缺货和库存过剩情况来增加利润。尽管联合分析用于其他行业的需求估计，但它并未用于服装的需求估计，并且可以以其最简单的形式得到更多的使用。

原创性/价值

这项研究是第一个对基于消费者偏好的数据进行建模来估计服装需求的研究。这项研究在一家服装零售店进行了实际测试。它是原创的。