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How to Anticipate Customer Behavior with Analytics
Know your customers and give them what they want—this is the fundamental principle of marketing. While simple in theory, it is challenging to put into practice. Short of mind reading, it’s difficult to know what’s on a customer’s mind today, or anticipate what he or she may need or want tomorrow.
The challenge doesn’t stem from lack of customer data. Through every response, customer contact, event, transaction and Web site hit, customers and prospects give information about themselves. Databases are chock-full of these useful tidbits, and call centers and other customer-management systems also are overflowing with details about customers and contacts. The challenge lies in the fact that raw data does not have value until it’s turned into information.
This is where analytical technology comes into play. A philosopher once wrote that finding the patterns in the randomness of life is the way we create beauty and make art. A similar statement could be made about analytics, which find patterns in the randomness of data so marketers can discover valuable information and gain insight.
There is an array of analytical products available for desktop and enterprise systems, and for pros and novices alike. Generally, analytics fall into four categories.
Statistical Analysis
Statistical analysis refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends. Although there are a number of statistical analysis procedures, the two most commonly used by direct marketers are classification and segmentation.
Classification uses predictor fields to forecast a categorical target field, such as which groups of people will respond to a mailing.
Segmentation divides subjects, objects or variables into a number of relatively homogeneous groups (e.g., segmenting customers into usage-pattern groups).
Statistical software available today can handle the entire analytical process—planning, data collection, data access, data management and preparation, data analysis, reporting and deployment.
Example: Rural Cellular Corporation (RCC), which provides wireless service to 5.9 million subscribers in 14 states, uses statistical analysis for market research. This research includes customer satisfaction and branding studies to determine positioning for its products and service features. Before investing in any new feature, RCC surveys its customers to determine exactly what features they want, what they want each of the features to do and how much they are willing to pay for them.
OLAP
Know your customers and give them what they want—this is the fundamental principle of marketing. While simple in theory, it is challenging to put into practice. Short of mind reading, it’s difficult to know what’s on a customer’s mind today, or anticipate what he or she may need or want tomorrow.
The challenge doesn’t stem from lack of customer data. Through every response, customer contact, event, transaction and Web site hit, customers and prospects give information about themselves. Databases are chock-full of these useful tidbits, and call centers and other customer-management systems also are overflowing with details about customers and contacts. The challenge lies in the fact that raw data does not have value until it’s turned into information.
This is where analytical technology comes into play. A philosopher once wrote that finding the patterns in the randomness of life is the way we create beauty and make art. A similar statement could be made about analytics, which find patterns in the randomness of data so marketers can discover valuable information and gain insight.
There is an array of analytical products available for desktop and enterprise systems, and for pros and novices alike. Generally, analytics fall into four categories.
Statistical Analysis
Statistical analysis refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends. Although there are a number of statistical analysis procedures, the two most commonly used by direct marketers are classification and segmentation.
Classification uses predictor fields to forecast a categorical target field, such as which groups of people will respond to a mailing.
Segmentation divides subjects, objects or variables into a number of relatively homogeneous groups (e.g., segmenting customers into usage-pattern groups).
Statistical software available today can handle the entire analytical process—planning, data collection, data access, data management and preparation, data analysis, reporting and deployment.
Example: Rural Cellular Corporation (RCC), which provides wireless service to 5.9 million subscribers in 14 states, uses statistical analysis for market research. This research includes customer satisfaction and branding studies to determine positioning for its products and service features. Before investing in any new feature, RCC surveys its customers to determine exactly what features they want, what they want each of the features to do and how much they are willing to pay for them.
OLAP
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