1 Defining project objectives and success criteria. Before any data collection or model, it is important to start with the basics. First, define the goal of predictive modeling project, whether it is customer acquisition, improve retention increase or improve customer satisfaction.
2 Identify resources needed for success. This phase includes the design and review to determine the best predictive models approach to achieve the objectives of the project, including the necessary data sources and possible analytical approaches. In essence, this phase is the mapping of business requirements to data sources from purchases, subscriptions, customer behavior, and additional data needed to focus to support the objectives collected.
3 Format and integration of data. Once the analysis plan is in place, the next phase, recording, analysis and purification of the data. The data gurus need to look closely to the statistical modelers in this phase to the data and identify needed transformations to support optimal predictive modeling.
Once clean data sources, data mining to support vital for success. Customer data tends to be in different silos collected and often contains incomplete geo-demographic data, typographical errors and out-of-range values.
4 Design and development of models. In this phase begins the actual predictive modeling. It is strongly recommended to take an iterative approach to building models on a regular basis to ensure sharing of results with key project stakeholders, the analysis is meeting project objectives. It is also important to choose the right algorithms for the application, to know how the tune algorithms for optimal performance to help maximize the accuracy and performance of your data mining models
5 . Validation and verification models. In this phase, identify the best-scoring data mining model (s) and run tests to ensure that it properly over a greater amount of customer data. It is important to ensure that your models are a subset of your customer base, the models performed correctly and the scoring system in time pre-test. be reviewed as results and project objectives, the team can also identify organization-specific business logic to be added to the model.
6 Deploy and test analysis. The core value of most predictive analytics applications to automate the process of updating the models continuously with new customers Data Entry India (for example, to determine which customers are most likely to be available). In this sense, the reusability of the models or the possibility to use the process for future data mining goals.
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