Designing provocative conversations with consumers and stakeholders to spark strategic imagination.
Designing provocative conversations with consumers and stakeholders to spark strategic imagination.
Using CRM data to harness clients' irritating experiences feedback thanks to machine learning enrich outcomes with a social data deep dive into customers' pain points and related emotions.
Using CRM data to harness clients' irritating experiences feedback thanks to machine learning enrich outcomes with a social data deep dive into customers' pain points and related emotions.
Don't stop in the middle of the road by analyzing data only! Don't be an android as your customers are not. Enhance your data analytics with your customers' emotions understanding. That helps to reveal surprising insights.
The boosting effect of international co-ordination on research.
The authors of this paper argue that the rise of new technologies such as databases and the Internet offer both new challenges and opportunities to the market research industry. At a time when CRM is reported to have grown more than 30% a year with projected total revenues of $12.1 billion by the end of 2001, it is noteworthy that few traditional market research firms have seized the opportunity. Despite embracing the Internet for data collection, few research companies have been able to translate research technology and talent to take advantage of this huge business opportunity. Although data collection is a key benefit of the Internet, a bigger potential benefit is the delivery of information and insights to decisions makers via the Internet. The emerging CRM industry makes this a central goal and is reaping the benefits. The paper presents both theoretical and case study findings that offer a new way to relate to market research that can potentially bring the research industry to the top of executives priority list.
This paper describes a new approach for analyzing customer behavior from data collected in customer relationship management systems. The very short response times of the new approach allows one to browse interactively through the variables describing a customer. This allows discovery and understanding of behavior patterns with little effort. The same approach can also provide customer segmentation into segments of similar behavior without the need to define criteria for similarity in advance. Thirdly the approach is able to make real time predictions about user behavior which can be used to personalize web pages, make caller specific offers in call centers, or to target campaigns. A case study from an online computer magazine is presented.