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7 Steps to Execute a Successful Predictive Analytics Plan
A successful plan of design, creation, verification and execution is required for predictive analytics to work efficiently for variety of applications in the industry. Predictive analytics should be aligned with business goals which help each step to be related to company specific business needs and objective.
Here are the 7 steps:
1) Defining Business Goals
Mapping out specific goals of a project is critical before executing predictive analytics modeling. It is essential to align the model objective function with the business goals as well as the overall strategy of the firm. Later, the data sources and the expected format of analysis comes into play. It is also important to include the risk factor with the respected type of modeling with the expected outcomes and lucid deliverables.
2) Data Collection
Data must be accurate, actionable and accessible. Incorrect data leads to incorrect output. Data collection can be done both internally and externally and valid samples for model development can be generated by using numerous ways. After all the process, industries use this data for customer acquisition, retention, cross-selling and up-selling.
3) Data Preparation
Data scientists spend majority of time prepping data. The data preparation process starts with the classification of data and how it can be involved in predictive modeling. Techniques like missing values and outliers are used for handling common data problems.
4) Transforming Variables
It is essential to understand the relationship between independent and dependent variables which further helps understanding the power and the longevity of the model. Binning and transforming independent variables help deliver the best fit with dependent variable. With the help of coded programs, powerful variable can be segmented and transformed to ensure the best fit. Final stage of modeling becomes easy with the combination of selection process and the variables.
5) Processing and Evaluating the Model
The preparatory groundwork work laid so far helps things work smoothly on this level. The modeling stage requires usage of tools to find the best fit model. Open source programming languages like python and R can also be used for capturing the information and display the data.
6) Validation of the Model
Models should do justice to the development of data and the model performance should do justice to validation data. There are three ways to find the model fit:
• Scoring alternate data is the best way to check whether the model will perform or not.
• Bootstrapping can also be used because it uses simple resampling techniques to find out the confidence intervals around the estimates.
• Important market factors are calculated by key variable analysis which might affect the model, reinforcing the model further.
7) Implementation of the Model
Efficient execution is a blend of business intelligence and well-crafted practices. After the model has been implemented, the outcome needs to be interpreted, re-implemented into the business practices after fixing errors. The model should be incorporated into all facets of business, daily rules and governing processes in the organization.