Evaluation of customer satisfaction level of different projects

Area of Science:

• Business analytics
• Marketing science
• Statistical modeling

Background:

• Customer satisfaction is crucial for business success across all industries.
• Identifying potential customers is a key marketing challenge.
• Existing methods may not fully leverage customer data for predictive marketing.

Purpose of the Study:

• To develop a novel customer satisfaction index for a software solutions company.
• To utilize principal component analysis (PCA) for index creation.
• To provide a data-driven tool for marketing divisions to identify potential customers.

Main Methods:

• Principal Component Analysis (PCA) was employed to derive key satisfaction factors.
• A composite customer satisfaction index was constructed based on PCA results.
• The index was applied to historical customer data for validation.

Main Results:

• A statistically robust customer satisfaction index was successfully developed.
• The index effectively identified key drivers of customer satisfaction.
• The index served as a valuable input for marketing to pinpoint potential customers.

Conclusions:

• The proposed customer satisfaction index offers a quantifiable measure of customer sentiment.
• PCA is an effective technique for building such indices in a business context.
• This methodology is adaptable and can be applied to diverse industries facing similar customer identification challenges.