Online Web Research Services & Data Research, Data mining

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.

For any queries related to Data mining CRM applications, please feel free to contact us. We would be pleased to answer each of your queries in detail. Get more information at http://www.outsourcingwebresearch.com

Source: EzineArticles.com

Every day your company or business unit is generating large volume of information and Data. As per an estimate, 80% of this information lies in the text form. However, most of this text data/information is hardly even used to obtain better insight into the customer behavior or analyzed to aid in strategic decision making. Most companies do not have right toolset to organize this form of unstructured textual data for future use.

Modern Text mining techniques help you transform this unstructured data or information into handy tools which in turn help you to better understand your customer and take informed business decisions.

Text mining use correct set of tools to uncover hidden patterns, connections, relationships and trends in text which otherwise would be totally untouched. It’s quite useful for businesses that have lot of Reports, Articles, Survey data, Call center notes, Business mails, Online chats and other relevant unstructured text documents.

Major benefits that you can realize using text mining include:
• Enhance the performance of predictive models by applying right information
• Effectively summarize the large volume of text
• Organize your content by easily categorizing articles or body of text
• Pull out specific type of articles from a huge pile of article base
• Extract concepts, correlations and profiles from unstructured data/information
• Take customer service to next level by detailing call center notes
• Efficiently detect fraudulent claims
• Analyze customer feedback for future action
• Better control over your company’s information sources.

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Application of Web Data Mining:
• Gain Marketing intelligence
• Predict/Analyze user’s behavior
• Effectively Target potential customers
• Customize/Personalize your services & products
• Improve Advertisement effectiveness
• Fraud/Intrusion detection

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Source: Newdesignworld.com

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Source: Pressbox.co.uk

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Benefits of Web-based Market Research:

• Allows users to complete surveys whenever and wherever its possible for them
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• Best suited for any consumer research work
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• Survey Programming
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• Web Data mining
• Secondary research
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Customer relationship management (CRM) is critical activity of improvising customer interactions while at the same time making the interactions more amicable through individualization. Data mining utilizes various data analysis and modeling methods to detect specific patterns and relationships in data. This helps in understanding what a customer wants and forecasting what they will do.

Using Data mining you can find out right prospects and offer them right products. This results in improved revenue because you can respond to each customer in best way using fewer resources.

Basic process of CRM data mining includes:
1. Define business objective
2. Construct marketing database
3. Analyze data
4. Visualize a model
5. Explore model
6. Set up model & start monitoring

Let me explain above steps in detail.

Define the business objective:
Every CRM process has one or more business objective for which you need to construct the suitable model. This model varies depending on your specific goal. The more precise your statement for defining the problem is the more successful is your CRM project.

Construct a marketing database:
This step involves creation of constructive marketing database since your operational data often don’t contain the information in the form you want it. The first step in building your database is to clean it up so that you can construct clean models with accurate data.

The data you need may be scattered across different databases such as the client database, operational database and sales databases. This means you have to integrate the data into a single marketing database. Inaccurately reconciled data is a major source of quality issues.

Analyze the data:
Prior to building a correct predictive model, you must analyze your data. Collect a variety of numerical summaries (such as averages, standard deviations and so forth). You may want to generate a cross-section of multi-dimensional data such as pivot tables.

Graphing and visualization tools are a vital aid in data analysis. Data visualization most often provides better insight that leads to innovative ideas and success.

Should you have any queries regarding Data Mining or Data mining CRM applications, please feel free to contact us. We would be pleased to answer each of your queries in detail. Find more information at http://www.outsourcingwebresearch.com

Source: EzinArticles.com

For almost every business, the cost of acquiring a new customer exceeds the cost of retaining existing loyal customers. The first thing a business need to do is gather data to predict which customers will leave and which will stay. The business need to carefully choose the variables from its customer database and incorporate them in a single model for intuitive analysis.

Next, the business needs to figure out who are “loyal” customers. This is not a data mining issue but a business intelligence followed by a calculation. It has to construct a model to profile its loyal or profitable customers and unprofitable customers. This model can be used for customer retention and identify customers who are not yet profitable but might become so in near future.

You can also construct a model to foretell which of its profitable customers will leave. As in most data mining issues, identifying what data to use and how to collaborate existing data is much of the challenge in model development. Based on the results of this modeling, a business can identify some potential offers that can encourage customers to stay.

A business can provide their customers a higher-fee service with lots of value addition. It can then build models that would predict which would be the most effective offer for a specific user. The net result is a drastic improvement in business’s customer retention and allows it to save half of its money per month in customer acquisition costs.

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