In today's globalized marketplace Customer relationship management (CRM) is deemed as crucial business activity to compete efficiently and outdone the competition. CRM strategies heavily depend on how effectively you can use the customer information in meeting their needs and expectations which in turn leads to more profit.
Some basic questions include - what are their specific needs, how satisfied they are with your product or services, is there a scope of improvement in existing product/service and so on. For better CRM strategy you need a predictive data mining models fueled by right data and analysis. Let me give you a basic idea on how you can use Data mining for your CRM objective.
Basic process of CRM data mining includes:
1. Define business goal
2. Construct marketing database
3. Analyze data
4. Visualize a model
5. Explore model
6. Set up model & start monitoring
Let me explain last three steps in detail.
Visualize a Model:
Building a predictive data model is an iterative process. You may require 2-3 models in order to discover the one that best suit your business problem. In searching a right data model you may need to go back, do some changes or even change your problem statement.
In building a model you start with customer data for which the result is already known. For example, you may have to do a test mailing to discover how many people will reply to your mail. You then divide this information into two groups. On the first group, you predict your desired model and apply this on remaining data. Once you finish the estimation and testing process you are left with a model that best suits your business idea.
Explore Model:
Accuracy is the key in evaluating your outcomes. For example, predictive models acquired through data mining may be clubbed with the insights of domain experts and can be used in a large project that can serve to various kinds of people. The way data mining is used in an application is decided by the nature of customer interaction. In most cases either customer contacts you or you contact them.
Set up Model & Start Monitoring:
To analyze customer interactions you need to consider factors like who originated the contact, whether it was direct or social media campaign, brand awareness of your company, etc. Then you select a sample of users to be contacted by applying the model to your existing customer database. In case of advertising campaigns you match the profiles of potential users discovered by your model to the profile of the users your campaign will reach.
In either case, if the input data involves income, age and gender demography, but the model demands gender-to-income or age-to-income ratio then you need to transform your existing database accordingly.
Source: http://ezinearticles.com/?Customer-Relationship-Management-%28CRM%29-Using-Data-Mining-Services&id=4641198
Some basic questions include - what are their specific needs, how satisfied they are with your product or services, is there a scope of improvement in existing product/service and so on. For better CRM strategy you need a predictive data mining models fueled by right data and analysis. Let me give you a basic idea on how you can use Data mining for your CRM objective.
Basic process of CRM data mining includes:
1. Define business goal
2. Construct marketing database
3. Analyze data
4. Visualize a model
5. Explore model
6. Set up model & start monitoring
Let me explain last three steps in detail.
Visualize a Model:
Building a predictive data model is an iterative process. You may require 2-3 models in order to discover the one that best suit your business problem. In searching a right data model you may need to go back, do some changes or even change your problem statement.
In building a model you start with customer data for which the result is already known. For example, you may have to do a test mailing to discover how many people will reply to your mail. You then divide this information into two groups. On the first group, you predict your desired model and apply this on remaining data. Once you finish the estimation and testing process you are left with a model that best suits your business idea.
Explore Model:
Accuracy is the key in evaluating your outcomes. For example, predictive models acquired through data mining may be clubbed with the insights of domain experts and can be used in a large project that can serve to various kinds of people. The way data mining is used in an application is decided by the nature of customer interaction. In most cases either customer contacts you or you contact them.
Set up Model & Start Monitoring:
To analyze customer interactions you need to consider factors like who originated the contact, whether it was direct or social media campaign, brand awareness of your company, etc. Then you select a sample of users to be contacted by applying the model to your existing customer database. In case of advertising campaigns you match the profiles of potential users discovered by your model to the profile of the users your campaign will reach.
In either case, if the input data involves income, age and gender demography, but the model demands gender-to-income or age-to-income ratio then you need to transform your existing database accordingly.
Source: http://ezinearticles.com/?Customer-Relationship-Management-%28CRM%29-Using-Data-Mining-Services&id=4641198
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