CRM and Data Warehousing for NDIS and Aged Care
Data mining is a powerful, new and emerging technology with great potential in information system. It can be best defined as the automated process of extracting useful knowledge and information including, patterns, associations, trees, changes, trends, anomalies and significant structures from large or complex data sets that are not classified like the My Aged Care Portal. The knowledge discovered by data mining techniques would enable Australian NDIS and Aged Care Service providers in making better decisions, having more advanced planning in services, predicting individual behaviors with higher accuracy and enabling to allocate resources and staff more effectively. It results in improving the quality, effectiveness and efficiency of the processes.
To add-on CRM-data mining framework establishes close customer relationships and manages relationship between organisations and customers in today’s advanced world of businesses. Data mining has gained popularity in various CRM applications in recent years and classification model is an important data mining technique useful in the field. The model is used to predict the behaviour of customers to enhance the decision-making processes for retaining valued customers. Data mining is defined as a process that uses mathematical, statistical, artificial intelligence and machine learning techniques to extract and identify useful information and subsequently gain knowledge from databases. Information technology tools, advanced internet technologies and explosion in customer data has improved the opportunities for marketing and has changed the way relationships between organisations and their customers are managed.
Keywords: CRM; data warehouses, ETL, quality data, customer relationship management, NDIS, Aged Care.

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CRM help in developing a business strategy for NDIS and Aged Care

Customer Relationship Management helps in building long term and profitable relationships with valuable customers. The set of processes and other useful systems in CRM help in developing a business strategy and this enterprise approach understands and influences the customer behaviour through meaningful communications so that customer acquisition, customer loyalty, customer retention and customer profitability are improved. The key factor in the development of a competitive CRM strategy is the understanding and analysing of customer behaviour and this helps in acquiring and retaining potential customers so as to maximise customer value. CRM-data mining framework helps organisations to identify valuable customers and predict their future. Each CRM element can be supported by various data mining models based on the tasks performed.

Data mining can be used in organisations for decision making and forecasting and one of the most common learning models in data mining that predicts the future customer behaviours is classification. The prediction is done by the classification of database records into a number of predefined classes based on certain criteria.

Data Mining Process Map

Detailed classification of Taxonomy of Web mining

CRM help to enable Australian NDIS and Aged Care Service providers in making better decisions

Data mining is a powerful, new and emerging technology with great potential in information system. It can be best defined as the automated process of extracting useful knowledge and information including, patterns, associations, trees, changes, trends, anomalies and significant structures from large or complex data sets that are not classified like the My Aged Care Portal. The knowledge discovered by data mining techniques would enable Australian NDIS and Aged Care Service providers in making better decisions, having more advanced planning in services, predicting individual behaviors with higher accuracy and enabling to allocate resources and staff more effectively. It results in improving the quality, effectiveness and efficiency of the processes.

data web mining process map ndis

Data web mining process map

In general, Web mining tasks can be classified into three categories:

  • Web content mining,
  • Web structure mining and
  • Web usage mining.

However, there are two other different approaches to categorise Web mining. In both, the categories are reduced from three to two: Web content mining and Web usage mining. In one, Web structure is treated as part of Web Content while in the other Web usage is treated as part of Web Structure. All of the three categories focus on the process of knowledge discovery of implicit, previously unknown and potentially useful information from the Web. Each of them focuses on different mining objects of the Web.

Proposed System: Agent-Based Approach for Data Mining, Web Content Mining and Data Warehousing

To propose an efficient CRM-data mining framework for the prediction of customer behaviour in the domain of banking applications. Within the framework proposed, two classification models are studied and evaluated.

The system is divided into three types of intelligent agents: a “master”, a “crawler” and a specialised “extractor” that is constructed according to the site that is subject to data scraping. The implemented intelligent agents framework allows Australian NDIS and Aged Care Service providers to onsale the cloud based system to other organisations in the Aged Care industry.

The Master/Crawler cooperate to the scheduling job of fetching of data from a presented URL. They were made separate so has to be able to have several fetching agents (crawlers) and to be able to make them anonimate if necessary. This is sometimes a requirement that depends on the amount of data to be extracted and how paranoid the external sites are to automatic non-human browsing. Based on this they can block access. The master agent (we are assuming that only one is necessary) acts as a maestro that is responsible for distributing URL’s to be fetched by the crawler community.

Building a simple a user friendly web-based data mining tool reducing the amount of time need for decision making phase and increase the productivity of decision makers since it requires only a web browser. Using Intelligent Agents for the My Aged Care Portal to collect useful data: These agents use information retrieval techniques and characteristics of open hypertext Web documents to automatically retrieve, filter, and categorise them. Intelligent Agents developed as a Firefox plugin and based on the Mozilla WebExtensions API to collect the right data of the Aged Care portal and later on other Internet resources. The My Aged Care portal, the primary platform mined by the agents includes individual pages, PDF data, Mining Data Records and can be used to group, categorise, analyse, and retrieve documents of your clients in the portal.

Following are the various fields of market where data mining is used:

  • Customer Profiling – Data Mining helps to determine what kind of people buy what kind of products.
  • Identifying Customer Requirements – Data Mining helps in identifying the best products for different customers. It uses prediction to find the factors that may attract new customers.
  • Cross Market Analysis – Data Mining performs Association/correlations between product sales.
  • Target Marketing – Data Mining helps to find clusters of model customers who share the same characteristics such as interest, spending habits, income etc.
  • Determining Customer purchasing pattern – Data mining helps in determining customer purchasing pattern.
  • Providing Summary Information – Data Mining provide us various multidimensional summary reports

The proposed CRM-data mining framework is understanding the business goals and requirements. An efficient CRM-data mining framework for the prediction of customer behaviour and to generate leads and effectively sales or resales. The system is a multi purpose discovery data tool for Australian NDIS and Aged Care Service providers, one part is to collect data and information of the Aged Care Government portal and collect that data in the data warehouse to action. Another part would be the collecting data to generate leads and up sales.


References:
Christian Krauter
Founder at Datanova
The Founder of Datanova, a visionary and digital business solution architect with 24 years experience in the rapidly expanding fields of information management systems, data governance and customer focused-strategy. As Director of Datanova, he leads a great team focused on cloud based services and solutions improving a clients business result through enabling a competitive advantage from all their information assets to drive top business imperatives – Christian Krauter, is a recognised expert on analytical applications, CMS, CRM, focused on improving client’s business results through cloud development, information management and data governance.
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