Problem C: A Wealth of Data
In the online marketplace it created, Amazon provides customers with an opportunity to rate and review purchases. Individual ratings - called “star ratings” – allow purchasers to express their level of satisfaction with a product using a scale of 1 (low rated, low satisfaction) to 5 (highly rated, high satisfaction). Additionally, customers can submit text-based messages – called “reviews” – that express further opinions and information about the product. Other customers can submit ratings on these reviews as being helpful or not – called a “helpfulness rating” –towards assisting their own product purchasing decision. Companies use these data to gain insights into the markets in which they participate, the timing of that participation, and the potential success of product design feature choices.
Sunshine Company is planning to introduce and sell three new products in the online marketplace: a microwave oven, a baby pacifier, and a hair dryer. They have hired your team as consultants to identify key patterns, relationships, measures, and parameters in past customer-supplied ratings and reviews associated with other competing products to 1) inform their online sales strategy and 2) identify potentially important design features that would enhance product desirability. Sunshine Company has used data to inform sales strategies in the past, but they have not previously used this particular combination and type of data. Of particular interest to Sunshine Company are time-based patterns in these data, and whether they interact in ways that will help the company craft successful products.
To assist you, Sunshine’s data center has provided you with three data files for this project: hair_dryer.tsv, microwave.tsv, and pacifier.tsv. These data represent customer-supplied ratings and reviews for microwave ovens, baby pacifiers, and hair dryers sold in the Amazon marketplace over the time period(s) indicated in the data. A glossary of data label definitions is provided as well. THE DATA FILES PROVIDED CONTAIN THE ONLY DATA YOU
SHOULD USE FOR THIS PROBLEM.
Requirements
- Analyze the three product data sets provided to identify, describe, and support with mathematical evidence, meaningful quantitative and/or qualitative patterns, relationships, measures, and parameters within and between star ratings, reviews, and helpfulness ratings that will help Sunshine Company succeed in their three new online marketplace product offerings.
- Use your analysis to address the following specific questions and requests from the Sunshine Company Marketing Director:
a. Identify data measures based on ratings and reviews that are most informative for Sunshine Company to track, once their three products are placed on sale in the online marketplace.
b. Identify and discuss time-based measures and patterns within each data set that might suggest that a product’s reputation is increasing or decreasing in the online marketplace.
c. Determine combinations of text-based measure(s) and ratings-based measures that best indicate a potentially successful or failing product.
d. Do specific star ratings incite more reviews? For example, are customers more likely to write some type of review after seeing a series of low star ratings?
e. Are specific quality descriptors of text-based reviews such as ‘enthusiastic’,
‘disappointed’, and others, strongly associated with rating levels? - Write a one- to two-page letter to the Marketing Director of Sunshine Company summarizing your team’s analysis and results. Include specific justification(s) for the result that your team most confidently recommends to the Marketing Director.
问题C:数据的价值
在其创建的在线市场中，亚马逊为客户提供了对购买进行评分和评价的机会。个人评级称为“星级评级”——即允许购买者使用1 (低评级，低满意度)到5 (高评级，高满意度)的等级来表示他们对产品的满意度。此外，客户可以提交基于文本的消息(称为“评论”) ,以表达有关产品的更多意见和信息。其他客户可以根据这些评论提交有帮助或无帮助的评级(称为“帮助评级”) ，以协助他们自己的产品购买决策。公司使用这些数据来深入了解他们参与的市场、参与的时机以及产品功能设计选择的潜在成功性。
阳光公司计划在在线市场上推出和销售三种新产品:微波炉，婴儿奶嘴和吹风机。他们已聘请您的团队作为顾问，通过客户提供的历史评级和评论，以确定与其他竞争产品相关的关键模式、关系、度量和参数，以: 1)告知其在线销售策略; 2) 确定潜在的重要设计特征，以提高产品的吸引力。Sunshine Company过去曾使用数据为销售策略提供信息，但他们以前从未使用过这种特殊的组合和数据类型。阳光公司特别感兴趣的是这些数据中基于时间的模式，以及它们是如何帮助公司打造成功产品的（时间和产品的交互影响）。
为了帮助您，Sunshine的数据中心为您提供 了该项目的三个数据文件：hair_dryer.tsv、microwave.tsv和pacifier.tsv。这些数据代表了亚马逊市场上销售的微波炉、婴儿奶嘴和吹风机在数据所示时间段内的客户提供的评级和评价。还提供了数据标签定义的词汇表。提供的数据文件包含您应用于此问题的唯一数据。
要求：
1.分析提供的三个产品数据集，根据识别，描述和支持的数学证据，确定有意义的定量和/或定性模式，关系，量度和参数，这些数据将在星级，评论和帮助等级之内和之间进行，这将有助于Sunshine 公司在他们的三个新的在线销售的产品取得成功。
2.使用您的分析解决Sunshine Company市场总监的以下特定问题和要求:
a. 一旦三种产品在在线市场上出售后，就可以根据评级和评论确定最能为阳光公司跟踪的数据度量。
b.在每个数据集中识别并讨论基于时间的度量和模式，这些度量和模式可能表明产品的声誉在在线市场中正在增加或减少。
c.基于文本的度量值和基于评级的度量值的组合，确定最能指示潜在的成功或失败产品的度量值。
e.具体的星级是否会煽动更多的评论？例如，客户在看到一系列低星级后，是否更有可能撰写某种类型的评论？
f.基于文本的评论的描述特征，如“热情”、“失望”等，是否与评星等级密切相关？
3.写一封一到两页的信给阳光公司的营销总监，总结你的团队的分析和结果。包括你的团队最自信地向市场总监推荐的结果的具体理由。
