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According to an IDC study, the use of predictive analysis yields a median ROI (Return on Investment) of 145%, which equates to nearly double the ROI when non-predictive analysis is used. Predictive analysis is now available to the masses and has become a distinct competitive advantage for those organizations savvy enough to implement it.
For those who have not yet taken the plunge, the time has come: Predictive analysis will become a necessity to compete by the end of this decade. This is, due, in large part, to its ability to help organizations turn customer intelligence data into actionable information for improving the accuracy, efficiency and success with which they can target specific customers and prospects, effectively lowering the cost of marketing campaigns. In fact, all ten processes identified by the Gartner Group in its "Top-10 Marketing Processes for the 21st Century" article would be well served by the application of predictive analysis, either directly or indirectly. The recurring theme in these 10 marketing processes is building stronger relationships between companies and their customers. Predictive analysis provides critical insights that help companies address this challenge.
What Is Predictive Analysis and How Is it Utilized?
Simply stated, predictive analysis is the implementation of statistical modeling to generate ranked lists based upon customers' propensity to exhibit a certain behavior. What does that mean to business?
Predictive analysis can be used in a variety of commercial arenas in a number of important ways. The following are a few key examples:
- Customer Retention: Predictive analysis can help companies identify which of their customers are likely to churn (i.e., cancel service, stop using a product, etc.). Additionally, it can identify likely causes for the attrition at the individual customer level.
- Customer Acquisition: Predictive analysis can help identify which prospective customers (i.e., "prospects") should be targeted. Furthermore, it can identify which specific offers are likely to be effective, as well as estimate future customer value.
- Cross-Selling and Up-Selling Opportunities: Predictive analysis can help companies identify which products and services individual customers are likely to buy. Further, it can help identify future profitability of individual customers who add specific products or services.
Key Benefits of Predictive Modeling
So, why is predictive analysis so effective? There are several reasons and here are the two most relevant:
- Self-Improving: As an organization learns about the key factors that affect their business through the use of predictive analysis, they become more in tune with their customers. This, in turn, allows them to gather more accurate data to use in the predictive modeling process, making the results more focused with each pass. Hence, marketers can easily recognize the positive impacts, such as improvements in response rate or reductions in customer attrition.
- Measurability: Predictive analysis allows organizations to measure effectively key metrics that feed into ROI analyses. This is mainly due to the efficient and organized manner in which predictive analysis embeds itself into both business and IT processes.
Unlike traditional data segmentation, which relies heavily on demographic data, predictive analysis focuses on individual customers by taking into account the behavioral patterns of individuals. The differences between the two are displayed in Figure 1.
This is accomplished through the analysis of large volumes of behavioral data within the modeling process (versus a limited amount of demographic data). The data is thoroughly investigated until hidden patterns are revealed.
These patterns transform raw "data" into useful and actionable business information. The results of predictive analysis are often measured using a concept called "lift," which represents the increases in the response (or take) rate over and above the current hit rate. In Figure 2, a marketer is striving to address a customer need through the use of a product-based campaign. The marketer conducts a conventional analysis by dividing the customer universe into demographic segment and sends the offer to 50% of the customer universe. Over the coming weeks, the marketer calculates that the response rate was 5% as represented on the left side of the Figure 2. This rate will serve as a baseline against which future campaigns that utilize predictive analysis will be evaluated.
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