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Many organizations are investing heavily in predictive analytics. Predictive analytics involves using a variety of statistical, data mining, and game theory to analyze current and historical facts to make predictions about future events. Marketers are exploring how to use predictive analytics to build models that will determine what customers will do based on analysis of their past behavior.

Invest in building predictive models.

Use predictive modeling to forecast outcomes.

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 in which 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 the experienced person.

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 in Marketing.

1. Scope and define the predictive analytics model you want to build for marketing. In this step you want to determine what marketing processes will be analyzed and what the desired business outcomes areñ 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 data that is needed, where itís stored and whether itís 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 test the model using historical data to see if the results produced by the model to prove the hypothesis.

5. Incorporate analytics into business processes. To make the model of value, 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 a marketing analytics center of excellence.

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