A typical example of a predictive problem where data mining can be used for is targeting customer segments for marketing. Data mining uses data from past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
A recent Gartner Group Advanced Technology Research Note listed data mining and artificial intelligence at the top of the five key technology areas that "will clearly have a major impact across a wide range of industries within the next 3 to 5 years."2 Gartner also listed parallel architectures and data mining as two of the top 10 new technologies in which companies will invest during the next 5 years. According to a recent Gartner HPC Research Note, "With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast stores of detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage (0.9 probability)."
Techniques used in Data Mining:
The most commonly used techniques in data mining are:
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Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
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Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) .
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Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution.
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Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbor technique.
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Rule induction: The extraction of useful if-then rules from data based on statistical significance.
Data mining and Customer Relationship Management:
The way in which companies interact with their customers has changed dramatically over the past few years. Businesses have to be very adaptive in their approach to customer relationships; they have to quickly respond to the needs and wants of their customers.
Customer relationship management (CRM) is a process that manages the interactions between a company and its customers. The primary users of CRM software applications are database marketers who are looking to automate the process of interacting with customers.
To be successful, database marketers must first identify market segments containing customers or prospects with high-profit potential. They then build and execute campaigns that favorably impact the behavior of these individuals.
Identifying market segments requires significant data about prospective customers and their buying behaviors. Data mining applications automate the process of searching the humongous amount of data to find patterns/trends that are good predictors of purchasing behaviors.
How Data Mining Helps Database Marketing
Data mining helps marketing users to target marketing campaigns more accurately; and also to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. If the necessary information exists in a database, the data mining process can model virtually any customer activity. The key is to find patterns relevant to current business problems. Answers to the specific questions regarding customer behavior can help retain customers and increase campaign response rates, which, in turn, increase buying, cross-selling, and return on investment (ROI).
Scoring
Data mining builds models by using inputs from a database to predict customer behavior. The prediction provided by a model is usually called a score. A score (typically a numerical value) is assigned to each record in the database and indicates the likelihood that the customer whose record has been scored will exhibit a particular behavior. For example, if a model predicts customer attrition, a high score indicates that a customer is likely to leave, whereas a low score indicates the opposite. After scoring a set of customers, these numerical values are used to select the most appropriate prospects for a targeted marketing campaign.
The Role of Campaign Management Software
Database marketing software enables companies to deliver timely, pertinent, and coordinated messages and value propositions (offers or gifts perceived as valuable) to customers and prospects.
The present campaign management software goes one step futher - it manages and monitors customer communications across multiple touch-points, such as direct mail, telemarketing, customer service, point of sale, interactive web, branch office, and so on.
Campaign management automates and integrates the planning, execution, assessment, and refinement of possibly tens to hundreds of highly segmented campaigns that run monthly, weekly, daily, or intermittently. The software can also run campaigns with multiple "communication points," triggered by time or customer behavior such as the opening of a new account.
Increasing Customer Lifetime Value
Let us take an example in the banking sector. Some of the customers of a bank use the institution only for a checking account. Objective of the bank is to have customers to invest their money in the bank's other products. An analysis reveals that after depositing large annual income bonuses, some customers wait for their funds to clear before moving the money quickly into their stock-brokerage or mutual fund accounts outside the bank. This represents a loss of business for the bank.
To persuade these customers to keep their money in the bank, marketing managers can use campaign management software to immediately identify large deposits and trigger a response. The system might automatically schedule a direct mail or telemarketing promotion as soon as a customer's balance exceeds a predetermined amount. Based on the size of the deposit, the triggered promotion can then provide an appropriate incentive that encourages customers to invest their money in the bank's other products.
Finally, by tracking responses and following rules for attributing customer behavior, the campaign management software can help measure the profitability and ROI of all ongoing campaigns.
Combining Data Mining and Campaign Management
The closer data mining and campaign management work together, the better the business results. Today, campaign management software uses the scores generated by the data mining model to sharpen the focus of targeted customers or prospects, thereby increasing response rates and campaign effectiveness. Marketers who build campaigns should be able to apply any model logged in the campaign management system to a defined target segment.
Data Mining Challenges
Limited or inaccurate data- Sufficient information regarding the area that is being analyzed is required to accurately find trends and to make conclusions. Companies who have not been collecting data over a period may face this issue.
Changing database – the size of the database grows exponentially in most companies. An abundance of data is a benefit when trying to find trends over a few areas of interest, however, a huge amount of data can also make it more complicated to find trends.
Conclusion
Data warehousing and business intelligence provide a method for users to anticipate future trends from analyzing past patterns in organizational data. Data mining is more intuitive, allowing for increased insight beyond data warehousing. An implementation of data mining in an organization will serve as a guide to uncovering inherent trends and tendencies in historical information. It will also allow for statistical predictions, groupings and classifications of data. Data mining software allows users to analyze large databases to solve business decision-making problems. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions.
References:
- Mining E-Commerce data: The Good, the Bad and the Ugly, Ron Kohavi, Stanford University
- Practical Lessons of Data Mining at Yahoo!, Ye Chen, Dmitry Pavlov
- Gartner Group Advanced Technologies and Applications Research Note, 2/1/95.
- Gartner Group High Performance Computing Research Note, 1/31/95.
