Data Mining Can Bring Pinpoint Accuracy to Sales
By Kurt Thearling and Andrew Frawley
Mass High Tech, March 29, 1999
Data warehousing - the practice of creating huge, central stores of customer data that can be used throughout the enterprise - is becoming more and more commonplace. But data warehouses are useless if companies don't have the proper applications for accessing and using the data.
Two popular types of applications that leverage companies' investments in data warehousing are data mining and campaign management software. Data mining enables companies to identify trends within the data warehouse (such as "families with teenagers are likely to have two phone lines," in the case of a telephone company's data). Campaign management software enables them to leverage these trends via highly targeted and automated direct marketing campaigns (such as a telemarketing campaign intended to sell second phone lines to families with teenagers).
Data mining and campaign management have been successfully deployed by hundreds of Fortune 1000 companies around the world, with impressive results. But recent advances in technology have enabled companies to couple these technologies more tightly, with the following benefits: increased speed with which they can plan and execute marketing campaigns; increased accuracy and response rates of campaigns; and higher overall marketing return on investment.
Data mining automates the detection of patterns in a database and helps marketing professionals improve their understanding of customer behavior, and then predict behavior. For example, a pattern might indicate that married males with children are twice as likely to drive a particular sports car than married males with no children. A marketing manager for an auto manufacturer might find this somewhat surprising pattern quite valuable.
The data mining process can model virtually any customer activity. The key is to find patterns relevant to current business problems. Typical patterns that data mining uncovers include which customers are most likely to drop a service, which are likely to purchase merchandise or services, and which are most likely to respond to a particular offer.
The data mining process results in the creation of a model. A model embodies the discovered patterns and can be used to make predictions for records for which the true behavior is unknown. These predictions, usually called scores, are numerical values that are assigned to each record in the database and indicate the likelihood that the customer will exhibit a particular behavior. These numerical values are used to select the most appropriate prospects for a targeted marketing campaign.
Campaign management and data mining, when closely integrated, are potent tools. Campaign management software enables companies to deliver to customers and prospects timely, pertinent, and coordinated offers, and also manages and monitors customer communications across all channels. In addition, it automates and integrates the planning, execution, assessment and refinement of possibly tens to hundreds of highly segmented campaigns running monthly, weekly, daily or intermittently.
Unfortunately, for most companies today, the use of data mining models within campaign management is a manual, time-intensive process. When a marketer wants to run a campaign based on model scores, he or she has to call a modeler (usually a statistician) to have a model run against a database so that a score file can be created. The marketer then has to solicit the help of an IT staffer to merge the scores with the marketing database. This disjointed process is fraught with problems and errors and can take weeks. Often, by the times the models are integrated with the database, either the models are outdated or the campaign opportunity has passed.
The solution is the tight integration of data mining and campaign management technologies. Under this scenario, marketers can invoke statistical models from within the campaign management application, score customer segments on the fly, and quickly create campaigns targeted to customer segments offering the greatest potential. Here is how it works:
Step 1: Creating the Model
A modeler creates a predictive model using the data mining application. He or she then exports the model to a campaign management application, possibly by simply by dragging and dropping the data from one application to the other. This process of exporting a model tells the campaign management software that the model exists and is available for later use.
Step 2: Dynamically scoring the data
Once a model has been put into the campaign management system, marketers can then reference the model's score just as they would reference any other piece of data. Records can be selected based on the score, in conjunction with other characteristics in the data. When the campaign is run, the records in the database are scored dynamically using the model.
Dynamic scoring avoids manual integration of scores with the database, and eliminates the need to score an entire database. Instead, dynamic scoring marks only relevant customer subsets and only when needed. This shrinks marketing cycle times and assures fresh, up-to-date results. Once a model is in the campaign management system, the user can start to build marketing campaigns based upon it simply by choosing it from a menu of options.
Any company that is creating or has created a data warehouse should be considering the use of integrated data mining and campaign management applications, which unlock the data and put it to use. By discovering customer behavior patterns and then acting upon them quickly, companies can stave off competition; and increase customer retention, cross-selling and up-selling, all of which ultimately contribute to higher overall revenues.
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