“Know your customers and give them what they want” is the fundamental principle of marketing.

This principle is simple in theory, but increasingly challenging to put into practice. Short of being a mind reader or having a crystal ball, it's difficult for marketers to know what's on a customer's mind today, or anticipate what the customer may need or want tomorrow.

The challenge doesn't stem from lack of customer data. The fact is, customers and prospects are giving us information about themselves all the time. Through every response, customer contact, event, transaction and Web site hit, they reveal something about themselves.

Databases are chock full of these useful tidbits, and call centers and other customer management systems are overflowing with details about customers and contacts. The challenge is that raw data does not have value per se; it needs to be turned into useful information.

That 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 that you can discover valuable information and gain insight.

An array of analytical products is available for desktop and enterprise systems and for pros and novices alike.

Generally, analytics fall into four categories:

  1. Statistical analysis 
  2. On-line analytical processing (OLAP) 
  3. Data mining 
  4. Text mining

Statistical analysis refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends.

Numerous statistical analysis procedures can be applied, but the two most commonly used by direct marketers are classification and segmentation. Classification uses predictor fields to predict a categorical target field, such as which groups of people will respond to a mailing. Segmentation divides subjects, objects or variables into various relatively homogeneous groups (e.g., segmenting customers into usage-pattern groups).

Popular statistical software can handle the entire analytical process—planning, data collection, data access, data management and preparation, data analysis, reporting and deployment.

For example, Rural Cellular Corporation (RCC), which provides wireless service to subscribers in 14 states covering a population of 5.9 million, 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 money 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.

Online Analytical Processing enables users to easily and selectively extract data and then view it from different perspectives. For example, a user can request that data be analyzed and presented in a format that shows all of a company's widgets sold in Wyoming in the month of August, compares revenue figures with those for the same products in October, and then compares other product sales in Wyoming for the same time period.

To facilitate this kind of analysis, OLAP data is stored in a multidimensional database, which considers each data attribute (such as product, geographic sales region and time period) as a separate “dimension.” This management tool allows marketers to quickly review history and trends to take advantage of emerging opportunities, and take corrective action on developing problems.

For example, Johnsonville Sausage Inc., a manufacturer and marketer of fresh, smoked and cooked sausage products, uses OLAP to access operational and financial data. Johnsonville can compare sales by customer, region and brand. With this information, it develops more accurate sales forecasts for production and manufacturing scheduling.

Data mining discovers the meaningful patterns and relationships in data—separating signals from noise—and provides decision-making information about the future. Data mining procedures include the following:

  • Association: looking for patterns where one event is connected to another event 

  • Sequence or path analysis: looking for patterns where one event leads to a later event

  • Classification: looking for new patterns 

  • Clustering: finding and visually documenting groups of facts not previously known 

  • Forecasting: discovering patterns in data that can lead to reasonable predictions about the future

Data mining provides a clear picture of what is going to happen—in time to change it—such as which customers might be most valuable, which customers are likely to defect, or, if the right data is gathered, which carry the risk of adverse reactions to marketing offers.

For example, Standard Life, a global mutual financial services company, needed to expand its share of the increasingly competitive mortgage market. A major part of its efforts was to develop models that could identify customer characteristics relevant to any mortgage product. Data mining enabled Standard Life to better understand the characteristics of its mortgage customers so that it could more accurately search for potential new clients. As a result, the company achieved a nine-times greater response to offers and has secured approximately $50 million worth of mortgage application revenue.

Text mining analyzes unstructured textual data by finding and discovering the patterns and relationships within thousands of documents, such as emails, call reports, Web sites and other information sources.

Text mining extracts terms and phrases and then classifies the terms into related groups, such as products, organizations or people, using the meaning and context of the text. This distilled information can be combined with other data sources and used with traditional data mining techniques such as clustering, classification and predictive modeling.

Questions to explore include… Which concepts occur together? What else are they linked to? What do they predict? With answers to such questions, the marketer is better able to identify potential customer defection, head it off and then maximize consumer satisfaction.

For example, a major online retailer combines data mining with text mining to analyze customer calls, emails, Web surveys and other customer communications to better understand what offers and recommendations are appropriate for each customer. As a result, the retailer has tripled its profits from the previous year.

With the massive amounts of customer data being generated every moment of every day, and the absolute necessity of carefully managing the customer relationship, analytics are no longer a nice thing to have; they are essential. The backlash against spam marketing, and new privacy legislation put into place as a result of this backlash, is forcing a more scientific approach to the art of marketing.

It will no longer be a matter of just throwing out a hook and seeing who bites; it will be about taking the time and using the right tools to truly understand customers, satisfy their needs and wants, and anticipate what they may want tomorrow.

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Colin Shearer is vice-president of customer analytics at SPSS Inc. (www.spss.com), a global provider of predictive analytics.