Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. Predictive analytics is used to understand what is going to happen in order to decide on the most effective course of action to maximize customer value

Most of us believe that knowledge is power and that the ability to predict the future is invaluable. Imagine the possibilities if as a marketer you could predict which customers would buy, which messages would have the greatest impact, and so on.This is the power of predictive analytics. Predictive analysis is a methodology companies deploy to help predict customer product preferences and purchasing habits. Marketers use this information so they can craft the most relevant marketing messages and communicate these to the correct target at the right time. 

Data is the Key to Predictive Analytics

Data is the essential ingredient for all analytics. When using predictive analytics, you must start by aggregating and cleansing the data. Cleansing entails scouring records to identify those with missing or incomplete data. Records with missing or incomplete data will most likely negatively impact the accuracy of the predictive model.

Once the data is aggregated and cleansed, you will want to divide the data into two groups: an in-sample group and out-of-sample group. The in-sample group will be used to develop the model and the out-sample group will be used to test the model. Data mining, the analysis of data to identify underlying trends, patterns or relationships, is the cornerstone for creating the model. Data mining catalogs all the relationships found among the data. The data that the mining process identifies as relevant is used to develop the model. The data mining enables you to gather the knowledge and the model enables you to apply this knowledge.

Keep in mind that predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The predictor is a variable that can be measured for an individual or other entity to predict future behavior. For example, credit card companies use predictive analytics to determine whether to give someone credit and how much. A life insurance company using predictive analytics might take into account potential predictors such as age, gender, and medical history when issuing a life insurance policy. These multiple predictors are then used to create a predictive model, which when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. Weather forecasting is an excellent example of using predictive analytics. Predictive analytics can also be extremely valuable for marketers.

Create and Validate Your Model

When the data mining is complete you can develop the model. Predictive models come in many shapes and sizes. Some of the most common approaches 

• Regression predictive models that describe a relationship between the dependent variable and the independent variable.
• Linear regressions, partial and stepwise regressions (used to measure how one independent variable and the dependent variable are related after determining the effect of all the other independent variable)
• Logit or probit regressions (are used to predict a discrete outcome) and regression splines (used to model data over different regions of the dependent variable)are all examples of regression predictive models.

There are more advanced models such as neural networks (which are nonlinear statistical modeling tools) if you need to handle more variables than the regression techniques allow.

Once you’ve created your predictive model you should validate through out-of-sampling testing. Out-of-sample testing dives data into in-sample data (the data used to develop the model) and out-of-sample data which includes only data not used.

The Power of Predictive Analytics is in the Patterns

The value of predictive analytics is to understand which variables are key. Predictive analytics uncovers patterns in order to improve your customer insight and drive improvement in organizational performance. Using predictive analytics will also help when it comes to monitoring results and connecting these results to key customer metrics, such as lifetime value and share of wallet.

We all know some customers are more profitable than others. With predictive analytics you can understand what drives profitable customer relationships.  Predictive analytics also help you understand which customers are generating value by identifying where those profitable customers come from and what drives them to buy more, more often, through less expensive channels, return less, make more referrals, cross purchase more, have longer tenure.  

Once you identify interrelationships among data you can determine the different value contributions from each customer group. As a result you can segment customers into groups that behave differently and that require specific differential treatments. For each of these identified subgroups, you then can establish independent investment strategies for each group determined by your estimated return you can read, monitor, and track those segment dynamics and watch their migration and growth. You can use analytics to cluster similar customers into discrete marketable units and then apply technologies, techniques, campaigns, and tactics against each of them to optimize each customer touch point to maximize that segment’s total unique value.

Purchase Your Assessment

Predictive analytics plays a key role in enabling you to optimize your message, channel, product, and promotion efforts. With predictive analytics you can maximize the value created by each customer by fitting the products, offerings and promotions to them rather than the other way around. The point of predictive analytics is that it should enable you to do something in the marketplace and create programs that will maximize customer value.

The are numerous potential uses for predictive analytics.  In summary, the four most common ways marketers gain value from predictive analytics  includes:

• To gain insight into how to drive higher growth in customer value and improve your segmentation, targeting, and messaging strategies.
• To better anticipate customer actions and therefore drive better decision-making.
• To improve programs design to increase cross-sell revenue generation or increase in promotional campaign response rates.
• To leverage the organizational data that you currently have at your disposal and the data that you might want to collect in the future. 

Predictive analytics takes data, skills, and tools.  For many organizations this means extensive investment. Is is worth it? 

In an IDC study on Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study, the median ROI for the projects that incorporated predictive technologies was 145 percent, compared with a median ROI of 89 percent for those projects that did not. Additionally, DM Review recently demonstrated that the approach and applications of predictive analytics technologies can positively impact the ROI for banner ads, direct mail and merchandising initiatives.

The Predictive Analytic Process and MarTech

The predictive analytic process discovers the meaningful patterns and relationships in data and provides decision-making information about the future. Many companies incorporate predictive analytics into their operational CRM systems so they can automatically scan and evaluate the data quickly enabling marketers to query the results for specific answers. Marketers then use this information to develop better targeted offers based on a deeper understanding of the customer and prospects. This approach enables you to create a model that assigns scores based on customer behaviors, so you can match the most relevant product offers to the demographic and purchasing behavior data available for each customer.

Because the scoring process evaluates past data to forecast the probability of future customer behavior, you will most likely need to tailor your CRM system to respond with specific offers for specific customers. Now that you can match a specific offer to a specific individual you can fine-tune specific messages to specific customers within each marketing channel.

Learn more about how to improve your analytics muscle. 

FAQ:

• Improve segmentation, targeting, and messaging for higher customer value growth
• Anticipate customer actions to inform better decisions
• Enhance cross-sell and campaign response rates
• Leverage organizational data for ongoing optimization and future data collection

• Regression models (linear, partial, stepwise, logit/probit, splines) for relationships between variables
• Neural networks for handling complex, nonlinear data with many variables

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