Many organizations are investing heavily in predictive analytics. Predictive analytics uses a variety of statistical, data mining, and game theory to analyze current and historical facts, to make predictions about future events. Let’s explore how building predictive analytics helps determine what customers will do based on analyzing of their past behavior.

Predictive analytics enables organizations to understand who their customers and prospects are, how to up-sell and cross-sell products and services, and how to anticipate customer behavior.

Building predictive models is an iterative process. A model is created from an initial hypothesis and then refined until it produces a valuable business outcome. Statistical analysis, data mining or data visualization tools may be needed to run a predictive model. There is predictive analytics software and advanced data analytics tools you may want to invest in if you have predictive analytics-experienced team members.

Six Steps to Use and Develop Predictive Models

In addition to data and statistic expertise, predictive model builders and users need strong knowledge of an organization’s business operations and the industry in which the organization competes. As you can imagine these are usually highly skilled analytics personnel. However, if the quality of your data sets is superb and your analytical skills are finely honed, you may be ready to perform predictive analytics and build predictive models. These six steps will help you develop and use predictive models.

1. Scope and define the predictive analytics model you want to build.In this step you want to determine what business processes will be analyzed and what the desired business outcomes are, such as the adoption of a product by a certain segment of customers.

2. Explore and profile your data. Predictive analytics is data-intensive. In this step you need to determine the needed data, where it’s stored, whether it’s readily accessible, and its current state.

3. Gather, cleanse and integrate the data. Once you know where the necessary data is located, you may need to clean the data. You will want to build your model from a consistent and comprehensive set of information that is ready to be analyzed.

4. Build the predictive model. Establish the hypothesis and then build the test model. Your goal is to include, and rule out, different variables and factors and then test the model using historical data to see if the results produced by the model prove the hypothesis.

5. Incorporate analytics into business processes. To make the model valuable, you need to integrate it into the business process so it can be used to help achieve the outcome.

6. Monitor the model and measure the business results. We live and market in a dynamic environment, where buying, competitive and other factors change. You will need to monitor the model and measure how effective it is at continuing to produce the desired outcome. It may be necessary to make adjustments and fine tune the model as conditions evolve.

Serious about predictive analytics and models, learn more about how to create an analytics center of excellence.

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FAQ:

1. Scope and define the model: Identify the business process to analyze and the desired outcome (e.g., product adoption by a target segment).
2. Explore and profile the data: Determine what data is needed, where it resides, how accessible it is, and its current condition.
3. Gather, cleanse, and integrate data: Create a consistent, comprehensive dataset suitable for analysis.
4. Build the model: Establish a hypothesis, build a test model, evaluate variables, and validate using historical data.
5. Integrate analytics into business processes: Embed the model into workflows so it can influence decisions and outcomes.
6. Monitor and measure results: Track performance over time, measure business impact, and fine-tune as buying behavior, competition, and market conditions evolve.

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