From density to destiny: Using spatial dimension of sales data for early prediction of new product success
From density to destiny: Using spatial dimension of sales data for early prediction of new product success
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DOI：
10.1287/mksc.1040.0051
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发表时间：
2004-06-01
影响因子：
5
通讯作者：
Muller, E
中科院分区：
文献类型：
--
作者：
Garber, T;Goldenberg, J;Muller, E
与新产品成功的早期评估相关的主要问题之一是缺乏足够的销售数据来进行可靠的预测。我们发现，管理者可以使用销售数据的空间维度，以获得一个新产品的成功发布时间后不久的预测评估。基于扩散理论，我们预计，对于许多创新产品，口碑和模仿发挥了重要作用，在创新的成功。由于口碑传播通常与相关各方之间的地理接近程度有关，因此可以预期采用者的“集群”将开始形成。或者，如果市场的反应是普遍不愿意采用新产品，那么口碑效应预计会小得多，导致更统一的销售模式（假设没有外部原因导致集群）。因此，产品的分布越不均匀，产生“传染过程”的可能性就越大，因此也就越有可能取得成功。如果基础基线分布是不均匀的，这也是正确的，只要它是一个经验分布已知的firm.We使用的交叉熵散度措施的基础上的空间发散方法来确定两个分布函数之间的“距离”。使用模拟和现实生活中的数据，我们发现，这种方法已经能够预测成功的采用过程的开始，正确地预测16个实际的产品介绍在两个产品类别中的14。我们还讨论了我们的方法的局限性，其中包括地理人口集群的自然形成和口碑为基础的集群之间可能的混淆。
One of the main problems associated with early-period assessment of new product success is the lack of sufficient sales data to enable reliable predictions. We show that managers can use spatial dimension of sales data to obtain a predictive assessment of the success of a new product shortly after launch time.Based on diffusion theory, we expect that for many innovative products, word of mouth and imitation play a significant role in the success of an innovation. Because word-of-mouth spread is often associated with some level of geographical proximity between the parties involved, one can expect "clusters" of adopters to begin to form. Alternatively, if the market reaction is widespread reluctance to adopt the new product, then the word-of-mouth effect is expected to be significantly smaller, leading to a more uniform pattern of sales (assuming that there are no external reasons for clustering). Hence, the less uniform a product's distribution, the higher its likelihood of generating a "contagion process" and therefore of being a success. This is also true if the underlying baseline distribution is nonuniform, as long as it is an empirical distribution known to the firm.We use a spatial divergence approach based on cross-entropy divergence measures to determine the "distance" between two distribution functions. Using both simulated and real-life data, we find that this approach has been capable of predicting success in the beginning of the adoption process, correctly predicting 14 of 16 actual product introductions in two product categories. We also discuss the limitations of our approach, among them the possible confusion between natural formation of geodemographic clusters and word-of-mouth-based clusters.