Data Mining and Direct Marketing
Why Would My Organization Need Data Mining?
The explosion in data accumulated by organizations is well known. Increased competition has intensified the need to operate more cost effectively in today’s global marketplace. Organization can monetize their data through the use of new and powerful predictive tools using a technique called Data Mining. Data Mining is widely used in business, science and military applications.
So how can Data Mining help?
Data Mining can allow an organization to market to customers on an individual or household basis – selecting those are most like to be responsive and suggesting targeted creative messages.
What Is Data Mining?
Data Mining is the process of using analytic methods to explore data to discover meaningful patterns that enable organizations to operate in a more cost effective manner. It does this by exploiting the adage that “experience is the best teacher”.
Historical data (i.e. experience) is “mined” to cull useful patterns from data by creating predictive models so that the “good” patterns can be identified and the “bad” ones avoided.
Data Mining Technologies Inc. offers a proprietary technology that is embodied in it two flagship products Nuggets and eNuggets to address this need. It also provides Data Mining as a service.
What Data Mining Methods Are Available?
The most common methods used today are Statistics, Neural Networks and Rule Induction. Please contact us for a White Paper, which discusses these methods in more detail.
Data Mining Technologies Inc. uses the most advanced, proprietary and easy to use Artificial Intelligence Rule Induction method that finds patterns in the form of understandable English statements and is capable of modeling very complex and powerful patterns not likely to be found by other methods.
What If I need Results In Real Time?
eNuggets a product offered by Data Mining Technologies provides real time Data Mining for applications that require instant response such as customer relationship management, personalized web response pages, medical management/patient care, telemarketing and many others.
Examples of Model Types
. The discussion on types of models below is a sampling of some of the types of models built Data Mining Technologies Inc. and their benefits. These model types can be used in a wide variety of industries.
Response Models – Get more bang for your marketing dollar
The best method for identifying the customers or prospects to target for a specific product offering is through the use of a model developed specifically to predict a desired behavior. We will use “response” as an example of a desired behavior for this discussion. Some other examples of desired behavior, besides responding to an offering, that can be modeled are: purchasing from a mailing or emailing, soliciting for loyalty club membership and finding customers who are likely to defect to the competition.
Response models are used to identify the most likely members of a list of prospects to exhibit the behavior being targeted. The modeling software uses data from past campaigns to identify patterns associated with responders and patterns associated with non-responders. These patterns can be thought of as profiles, which best characterize the prospect’s likelihood of responding. Target prospects are then processed by the modeling software to “score” each one as to the likelihood they will respond based on their profile. The organization can then present the offering to the best prospects
Customer Clone Models – Find prospects that “look like” your customers
The process for selectively targeting prospects for your acquisition efforts often utilizes a sophisticated analytical technique called "best customer cloning." These models estimate which prospects are most likely to respond based on characteristics of the company’s current "best customers". To this end, we build the models or demographic profiles that allow you to select only the best prospects or "clones" for your acquisition programs.
In a retail environment, we can even identify the best prospects that are close in proximity to your stores or distribution channels. Customer clone models are appropriate when insufficient response data from previous campaigns is available, providing an effective prospect ranking mechanism when response models cannot be built
Customer Segmentation Modeling – Understand Your Best Segments
The objective of Customer Segmentation is to find patterns in customer transactions, and demographic, financial and lifestyle character traits that help identify highly responsive customers. The process is similar to the Prospect Segmentation Modeling, except that we also make use of the customer transaction data in addition to the demographic, financial and lifestyle overlays. After finding the patterns and traits that help distinguish the responsive and profitable customers from the rest, we then analyze the entire customer file and then develop segments. We can then select the responsive groups for promotions or the non-responsive groups for suppression.
The segments developed by this type of modeling use both transaction history, and appended data. For example, we may find that those customers who bought an average of $50 worth of clothing along with decorative products, returned products less than 5% of the time, contacted the company fewer than once a year, live in New York, have a college degree and reside in a predominantly a white-collar area will be twice as likely to buy from catalog promotions as the average customer.
Revenue and Profit Prediction Models – Get more profitable customers
Revenue and Profit Prediction models combine response/non-response likelihood with a revenue estimate, especially if order sizes, monthly billings, or margins differ widely. Not all responses to ads or offers have equal value, and a model that maximizes responses doesn’t necessarily maximize revenue or profit. Revenue and profit predictive models indicate those respondents who are most likely to add a higher revenue or profit margin with their response than other responders.
These models use a scoring algorithm specifically calibrated to select revenue-producing customers and help identify the key characteristics that best identify better customers. They can be used to fine-tune standard response models or used in acquisition strategies.
Cross Sell and Upsell – Sell more to your existing customer base
Models Cross-sell/up-sell models identify customers who are the best prospects for the purchase of additional products and services and for upgrading their existing products and services. The goal is to increase share of wallet. Revenue can increase immediately, but loyalty is enhanced as well due to increased customer involvement.
Attrition Models – Prevent good customers from leaving
Efficient, effective retention programs are critical in today’s competitive environment. While it is true that it is less costly to retain an existing customer than to acquire a new one, the fact is that all customers are not created equal. Attrition models enable you to identify customers who are likely to churn or switch to other providers thus allowing you to take appropriate preemptive action.
When planning retention programs, it is essential to be able to identify best customers, how to optimize existing customers and how to build loyalty through "entanglement". Attrition models are best employed when there are specific actions that the client can take to retard cancellation or cause the customer to become substantially more committed. The modeling technique provides an effective method for companies to identify characteristics of chumers for acquisition efforts and also to prevent or forestall cancellation of customers.
Creative Content Models – Tailor the most effective message to each customer through conventional mail or email
Often the message that is passed on to the customer is the one of the most important factors in the success of a campaign. Models can be developed to target each customer or prospect with the most effective message.
In direct mail campaigns, this approach can be combined with response modeling to score each prospect with the likelihood they will respond given that they are given the most effective creative message (i.e. the one that is recommended by the model).
In email campaigns this approach can be used to specify a customized creative message for each recipient.
Real Time Web Personalization Modeling – Maximize effectiveness by creating tailored web pages on the fly
Using our eNuggets real time Data Mining system, websites can interact with site visitors in an “intelligent” manner to achieve desired business goals. This type of application is useful for eCommerce and CRM sites. eNuggets is able to transform Web sites from static pages to customized landing pages, built on the fly, that match a customer profile so that the promise of true one-to-one marketing can be realized. Each site that interacts with the your site gets its own web page structured to generate the goal you choose.
eNuggets is a revolutionary new business intelligence tool that can be used for web personalization or other real time business intelligence purposes. It can be easily integrated with existing systems such as CRM, outbound telemarketing (i.e. intelligent scripting), insurance underwriting, stock forecasting, fraud detection, genetic research and many others.
e-Nuggets(tm) uses historical data (either from company transaction data or from outside data) to extract information in the form of English rules understandable by humans. The rules collectively form a model of the patterns in the data that would not be evident to human analysis. When new data comes in, such as a request for information about a product you offer, e-Nuggets(tm) interrogates the model and finds the most appropriate course of action will provide the best result (i.e. buy or continue on the site).
Data Mining Technologies, Inc. (DMT) provides complete predictive modeling services to the direct response industry using state-of-the-art, proprietary data mining technology to help marketers target highly responsive prospects and customers. If needed DMT will extract customer data, append it with extensive demographic, financial and lifestyle information, then identify hidden, profitable market segments that are highly responsive to promotions. Based on its findings, DMT will score and deliver to the client the best prospects identified from a U.S. consumer database of 115 million households or from a business database of 11 million. For customer promotions, DMT will score the client’s list to select the most responsive customers for in-house campaigns.
Please contact us to discuss ways you can use these techniques in your enterprise.