Neo-demographics: Opening Developing World Markets by Using Personal Data and Collaboration

NEO-DEM项目将使用非标准数据和新方法来影响商业效率，鼓励社区合作，并提供对消费者行为的学术见解。尽管英国企业在国内有着出色的业绩记录，但它们在发展中国家可能会举步维艰。以下原因在很大程度上解释了零售、服务和消费者导向行业的失败：* 由于文化、基础设施和行为差异，不可能将国内商业模式直接转移到新兴经济体。公司需要建立新的分析和战略模型，根据对新的行为和消费模式的理解，确定客户的不同需求。* 在发达国家，以消费者为导向的企业越来越受数据驱动。它们依赖于交叉引用的地理人口统计学、社会地理学和心理学数据以及交易数据（例如英国的Tesco & Boots）;它们的使用与公司战略密切相关。在许多国家，这类数据不完整或根本不存在，缺乏这类数据会抑制增长，并意味着目标选择和资源利用不理想。复制在英国容易获得的那种数据通常是不可能的，或者是昂贵和不切实际的。即使交易数据即将到来（例如马来西亚的Tesco Clubcard），将其与英国视为理所当然的可靠地理人口数据集和模型（例如Experian的Mosaic）进行交叉引用的范围也有限。尽管在基础设施发展方面落后，但发展中国家经历了数字革命;提供了一个基本上尚未开发的机会来生成商业智能。2010年，全球50亿部移动的电话中有80%在发展中国家，这一比例还在继续增长。非洲国家已经接受了新的金融技术，如移动的支付：超过1700万肯尼亚人使用移动的货币;该国约25%的国民生产总值以这种方式流动。像Ushahidi这样的众包系统在社会因素的聚合方面处于领先地位。该项目将创建一个决策支持和市场细分平台，通过个人数据、协作汇总和众包反馈生成，从而生成新的消费者行为模型，以支持创新。（坦桑尼亚，马来西亚和中国）在那里，我们将通过新颖的计算和聚类方法，并与一系列数据提供商合作，国际知名公司包括：Alliance Boots、Dairy Farm International、Bakhresa Group、Boots、E-fulusi、Tesco、Marks & Spencer和Experian。通过开发技术，将大大推进对消费者行为模式的学术研究，其在这一领域的应用是新颖的。有空间利用先进形式的计算和集群，更容易解释市场的复杂性。该项目很有可能提供迄今为止仍不清楚的消费者行为的见解。因此，对这一领域研究的贡献可能是方法论、经验主义和背景性的（对发展中国家消费者的深刻见解更为罕见）。这种探索性的合作可能会为商业/消费者分析和计算分析领域之间的持续对话打开大门。

The NEO-DEM project will use non-standard data and novel methods to impact business efficiency, encourage community collaboration and provide scholarly insights into consumer behaviour. UK businesses can struggle in the developing world, despite excellent track records at home. The following reasons explain a good deal of this failure in retail, service and consumer oriented sectors:* It is not possible to directly transfer domestic business models into emerging economies due to cultural, infrastructural and behavioural differences. Companies need to generate new analytical and strategic models that identify the differing needs of customers based on an understanding of the novel behavioural and consumption patterns exhibited. * In the developed world consumer oriented businesses are increasingly data-driven. They rely on cross-referenced geo-demographic, socio-graphic, and psychographic data as well as transactional data (e.g. Tesco & Boots in the UK); their use is enmeshed within company strategy. In many countries this kind of data are incomplete or non-existent, their absence inhibits growth and means that targeting and resource use is sub-optimal. Replicating the kind of data that is readily available in the UK will often be impossible or expensive and impractical. Even when transactional data is forthcoming (e.g. Tesco Clubcard Malaysia) there is limited scope to cross-reference them with reliable geo-demographic data-sets and models that are taken for granted in the UK (e.g. Experian's Mosaic).Despite lagging behind in infrastructural developments, developing countries have experienced digital revolutions; providing a largely untapped opportunity to generate business intelligence. In 2010 of the 5 billion mobile phones in the world 80% were in developing countries and this proportion is continues to grow. African countries have embraced new financial technologies such as mobile payment: over 17m Kenyans use mobile money; around 25% of the country's GNP flows in this way. Crowd sourcing systems such as Ushahidi lead the way in the aggregation of social factors. The project will create a decision support and market segmentation platform generated via personal data, collaborative aggregation and crowd-sourced feedback, that will allow the generation new models of consumer behaviour to support innovation.Our work will hinge on three case studies in exemplar developing economies (Tanzania, Malaysia and China) where we will develop example behavioural segmentations via novel computational and clustering methods and in partnership with a range of data providers and internationally significant companies including: Alliance Boots, Dairy Farm International, Bakhresa Group, Boots, E-fulusi, Tesco, Marks & Spencer and Experian.Academic research into consumer behaviour patterns will be significantly advanced by the techniques developed, their application in this field is novel. There is scope to exploit advanced forms of computation and clustering that more readily account for market complexities. There is a very high chance that the project will provide insights into consumer behaviour that have hitherto remained obscure. So the contribution to research in this area could be both methodological and empirical and contextual (robust insights into developing world consumers are more rare). This expeditionary collaboration is likely to open the door to and on-going conversation between the fields of business/consumer analytics and computational analysis.