CRM

(Customer Relationship Management)簡稱CRM，指的是顧客關係管理。內容包顧客開發、顧客銷售、顧客服務、提高顧客滿意度等。在CRM機制中，將銷售、行銷、顧客服務等資料集中在一個資料庫，業務人員將所有資料輸入此共享資料庫中，如此業務人員可以掌握客戶所需資訊，透過顧客客群分析, 找出客戶的可能的消費行為，針對不同客群區隔規劃相關行銷活動, 以有效提昇銷售率。現今部分企業在運用CRM機制上，主要運用範疇為客服中心，而顧客開發方面，則著重在開發顧客需要的產品，並分析顧客需要的時間點為何，應用的軟體包括資料倉儲 (Data Warehouse)等，在顧客取得方面，則是協助企業尋求並開發新顧客來源。

Information Drives Successful Customer Relationships

Information Drives Successful Customer Relationships
Oracle's integrated Customer Relationship Management (CRM) solution is a set of applications that give you information-driven sales, service, and marketing. Oracle CRM is built on an open, standards-based architecture that streamlines business processes, improves data quality, and allows all your key divisions to draw from the same source of data. With Oracle CRM, your company owns the single best tool for customer success—accurate information.

Over 50 CRM-Specific Applications
From precisely measuring marketing campaigns to automatically dispatching field technicians to remote locations, Oracle has more than 50 CRM-specific applications that help you address every phase of your CRM cycle. Find out more: Oracle Sales, Oracle Services, Oracle Marketing.

Most Companies Like to Analyze Data

Posted By: Jim Davies, Research Director
Most companies like to analyze data. It gives them a sense of security: "We understand our business and our customers because the metrics on the reports says so." The reality is somewhat different, however. The analysis of operational customer data alone explores only one of several dimensions and can lead to incorrect conclusions. Other data sources need to be included, such as informal interaction data (that is, what was said and how it was said during customer-agent conversations) and more-formal survey data, both of which collate data directly from the customer (with or without their knowledge) for analysis.

I am currently writing a case study on an organization that embraced this broader concept of data analytics with startling results. It identified several areas for process improvement and highlighted discrepancies between what its traditional operational analysis was telling it compared with the reality of the situation. One example is highlighted below:

Survey analysis revealed that customer dissatisfaction in specific locations fluctuated at different times of the year. The question the company asked was: "Was this due to those customers genuinely receiving a poorer level of field service and, if so, which aspects of the service need to be delivered differently, or were they just more difficult to please?" The company mined the associated operational service data for insight and found an interesting correlation: customers became more tolerant of a lower level of service during extreme weather conditions. For example, if there was flooding or heavy snow, customers were much more tolerant of engineers being late and service levels slipping.

From an operational insight perspective, the company could see a reduction in service efficiency in those areas at the same time and would normally have diverted extra resources to those regions to fund field engineer overtime to bring the service levels back up. Instead, with this insight, it increased the number of call center agents and got them to proactively confirm adjusted engineer arrival times (via call, SMS and so forth). This reduced inbound calls and the need for engineer overtime to meet slipping operational service levels. It also improved customer satisfaction. The proverbial win-win. Nice.

So, is this company alone or have you found some interesting insights since taking a more holistic approach to data? I'd love to know.

from http://blog.gartner.com/blog/crm.php