Online Web Research Services & Data Research, Data mining

1. Define project goals and success criteria. Before any data collection or model building, it is essential to start with the basics. First, define the goal of the predictive modeling project, whether it is to boost customer acquisition, increase retention or improve customer satisfaction.

2. Identify resources required for success. This phase involves the design and review for identifying the best predictive modeling approach to reach the project goals, including the required data sources and potential analytic approaches. Essentially, this phase is focused on mapping the business requirements to the data sources collected from purchases, subscriptions, customer behavior and any additional data necessary to support the goals.

3. Format and integrate data. Once the analytics plan is in place, the next phase is acquisition, analysis and cleansing of the data. The data gurus will need to work closely with the statistical modelers in this phase to audit the data and identify transformations required to support optimal predictive modeling.

Having clean data sources to support data mining is paramount to success. Customer data tends to be collected in disparate silos and frequently contains incomplete geo-demographic data, misspellings and out-of-range values.

4. Design and develop models. In this phase, the actual predictive modeling begins. It is strongly recommended to take an iterative approach to building models, regularly sharing results with key project stakeholders to ensure the analyses are meeting project goals. It is also important to choose the right algorithms for the application; knowing how to tune these algorithms for optimal performance will help maximize the accuracy and performance of your data mining models.

5. Validate and review models. In this phase, identify the best-scoring data mining model(s) and run tests to ensure it is accurate over a larger set of customer data. It's essential to pretest your models with a subset of your customer base to ensure the models are properly scoring and the system is running in a timely manner. As results and project goals are reviewed, the team may also identify organization-specific business logic to be added on top of the models.

6. Deploy and test analysis. The core value of most predictive analytics applications is automating the process of updating the models continuously with new customer data (e.g., to identify what customers are most likely to purchase). Along these lines is the reusability of the models, or the ability to leverage the process for future data mining goals.

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