1. Planning is the key to a successful data mining project
As with any worthwhile endeavor, planning is half the battle. It is critical that any organization considering a data mining project first define project objectives from a business perspective, and then convert this knowledge into a coherent data mining strategy and a well-defined project plan.
Plan for data mining success by following these three steps:
- Start with the end in mind. Avoid the “ad hoc trap” of mining data without defined business objectives. Prior to modeling, define a project that supports your organization’s strategic objectives. For example, your business objective might be to attract additional customers who are similar to your most valuable current customers. Or it might be to keep your most profitable customers longer.
- Get buy-in from business stakeholders. Be sure to involve all those who have a stake in the project. Typically, Finance, Sales, and Marketing are concerned with devising cost-effective CRM strategies. But Database and Information Technology managers are also “interested parties” since their teams are often called upon to support the execution of those strategies.
- Define an executable data mining strategy. Plan how to achieve your objective by capitalizing on your resources. Both technical and staff resources must be taken into account.
2. Set specific goals for your data mining project
Before you begin a data mining project, clarify just how data mining can help you achieve your goal. For instance, if reducing customer defection or “churn” is a strategic objective, what level of improvement do you want to see? Next, commit to a standard data mining process, such as CRISP-DM (CRoss-Industry Standard Process for Data Mining). Then create a project plan for achieving your goals, including a clear definition of what will constitute “success.” Finally, complete a cost-benefit analysis, taking care to include the cost of any resources that will be required.
3. Recruit a broad-based project team
One of the most common mistakes made by those new to data mining is to simply pass responsibility for a data mining initiative to a data miner. Because successful data mining requires a clear understanding of the business problem being addressed, and because in most organizations elements of that business understanding are dispersed among different disciplines or departments, it’s important to recruit a broad-based team for your project. For instance, to evaluate the factors involved in customer churn, you may need staff members from Customer Service, Market Research, or even Billing, as well as those with specialized knowledge of your data resources and data mining. Depending upon your objective, you may want to have representatives from some or all of the following roles: executive sponsor, project leader, business expert, data miner, data expert, and IT sponsor. Some projects may require two or three people, other projects may require more.
4. Line up the right data
To help ensure success, it is critical to understand what kinds of data are available and what condition that data is in. Begin with data that is readily accessible. It doesn’t need to be a large amount or organized in a data warehouse. Many useful data mining projects are performed on small or medium-sized datasets—some, containing only a few hundreds or thousands of records. For example, you may be able to determine, from a sample of customer records, which of your
company’s products are typically purchased by customers fitting a certain demographic profile. This enables you to predict what other customers might purchase or what offers they might find most appealing.
5. Secure IT buy-in
IT is an important component of any successful data mining initiative. Keep in mind that the data mining tool you select will play an important role in securing buy-in from your IT department. The data mining tool should integrate with your existing data infrastructure—relevant databases, data warehouses, and data marts—and should provide open access to data and the capability to enhance existing databases with scores and predictions generated by data mining.
6. Select the right data mining solution
Successful, efficient data mining requires data mining solutions that are open and well integrated. Organizations save time and improve the flow of analysis by selecting solutions that support every step of the process. An integrated solution is particularly important when incorporating additional types of data, such as text, Web, or survey data. That’s because each type of data is likely to originate in a different system and exist in a variety of formats. Using an integrated solution enables your analysts to follow a train of thought efficiently, regardless of the type of
data involved in the analysis.
7. Consider mining other types of data to increase the return on your data mining investment
When you combine text, Web, or survey data with structured data used in building models, you enrich the information available for prediction. Even if you add only one type of additional data, you’ll see an improvement in the results that you generate. Incorporating multiple types of data will provide even greater improvements. To determine if your company might benefit from incorporating additional types of data, begin by asking the following questions: What kinds of business problems are we trying to solve? What kinds of data do we have that might address
these problems? The answers to these questions will help you determine what kinds of data to include, and why. If you are trying to learn why long-time customers are leaving, for example, you may want to analyze text from call center notes combined with results of customer focus groups or customer satisfaction surveys.
8. Expand the scope of data mining to achieve even greater results
One way that you can increase the ROI generated by data mining is by expanding the number of projects you undertake. With the right data mining solution—one that helps automate routine tasks—you can do this without increasing staff. Gain more from your investment in data mining either by addressing additional related business challenges or by applying data mining in different departments or geographic regions. If your company has already made progress on
your top-priority challenges—increasing the conversion rate for cross-selling campaigns, for example—consider whether there are secondary challenges that you might now address—such as trimming the cost of customer acquisition programs.
9. Consider all available deployment options
When mining data, organizations that efficiently deploy results consistently achieve a higher ROI. In early implementations of data mining, deployment consisted of providing analysts with models and managers with reports. Models and reports had to be interpreted by managers or staff before strategic or tactical plans could be developed. Later, many companies used batch scoring—often conducted at off-peak hours—to more efficiently incorporate updated predictions
in their databases. It even became possible to automate the scheduling of updates and to embed scoring engines within existing applications. Today, using the latest data mining technologies, you can update even massive datasets containing billions of scores in just a few hours. You can also update models in real time and deploy results to customer-contact staff as they interact with customers. In addition, you can deploy models or scores in real time to systems that generate sales offers automatically or make product suggestions to Web site visitors, to name just two possibilities.
10. Increase collaboration and efficiency through model management
Look into data mining solutions that enable you to centralize the management of data mining models and support the automation of processes such as the updating of customer scores. These solutions foster greater collaboration and enterprise efficiency. Central model management also helps your organization avoid wasted or duplicated effort while ensuring that your most effective predictive models are applied to your business challenges. Model management also provides a way to document model creation, usage, and application.
