Six Keys to Recognizing a Staggering ROI with Predictive Analysis

According to a recent IDC study, the use of predictive analysis yields a median ROI (Return on Investment) of 145%, which equates to almost 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 to take the steps, as Predictive Analysis will become a necessity to compete by the end of this decade. In fact, the Gartner Group has recently published an article entitled “Top-10 Marketing Processes for the 21st Century”; all of the processes identified would be well served by Predictive Analysis, either directly or indirectly. The recurring theme in these 10 marketing processes is building stronger relationships between organizations and their customers. Predictive Analysis goes a long way in addressing this challenge.

What is Predictive Analysis?

Simply stated, predictive analysis is the implementation of statistical modeling to generate ranked lists based upon propensity to exhibit a certain behavior. What does that mean to business?

How is Predictive Analysis Utilized?

There are an unimaginable number of ways in which Predictive Analysis can be used in any number of commercial arenas. Following are a few examples to help clarify how predictive analysis is used to help organizations:

• Customer Retention – Predictive Analysis can help organizations identify which customers are likely to churn (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 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 aid organizations in identifying which products and services individual customers are likely to buy. Furthermore, 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 – we will touch on the two most relevant:

• Self-Improving - As the business 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 fine-tuned with each pass. Hence, marketers can easily recognize the positive impacts, such as improvements in response rate or reductions in customer attrition.
• Measurable - Predictive Analysis allows for organizations to effectively measure key metrics that feed into ROI analyses. This is mainly due to the 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.

This is accomplished through the analysis of large volumes of behavioral data within the modeling process (versus a limited amount of demographic data), which increases the power of the explanatory information and allows for a large boost in accuracy over traditional analysis methods.

Predictive Analysis is 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 the example below, 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 her customer universe into demographic segment, and sends the offer to 50% of the customer universe. Over the coming weeks, the marketer calculates that her response rate was 5% (this is represented by the left side of the graphic below). During a subsequent campaign for a similar product the marketer employs Predictive Analysis. The results of the Predictive Model show that the marketer should target 30% of the customer base. However the target audience is not a demographic segment of the customer base, but rather is characterized by a complex combination of behavioral, environmental, and demographic data. In tracking the offer, the marketer realizes a 14% response rate, which translates into a life of 2.8.

From the graphic above, one can derive that Predictive Analysis yielded two major benefits to the marketer:

It reduces the number of customers an organization must target, which reduces the total marketing expenditures (or allows funds to be shifted to other marketing campaigns).

• In the example above, the marketer sent out 5M offer letters at a cost of $1M using Conventional Analysis.
• Through the use of Predictive Analysis, the marketer was able to send out 3M offer letters at a cost of $600K, netting a savings of $400K.
• The savings equate to a 40% expense reduction through the use of Predictive Analysis.
• It increases the number of respondents, allowing for a greater return on the investment.
• In the example above, the marketer realized a 5% response rate yielding a total of $1.2M in additional revenue.
• Through the use of Predictive Analysis, the marketer was able to increase the response rate to 14% generating $3.36M in revenue, which equates to a $2.16M increase in net revenues.
• This equates to a 180% increase in revenues and the response rate through the use of Predictive Analysis.

So you may be asking yourself, “With such significant, quantifiable gains, why aren’t more companies using this technology?” Our research shows that most companies aren’t using Predictive Analysis for the following reasons:

• There is a perception that the technology is cost prohibitive.
• There is a lack of understanding regarding the potential uses and depth of the technology.
• Some feel that they are already utilizing Predictive Analysis (via traditional reporting and segmentation).
• There is an underlying fear of:
  • the unknown;
  • reducing the potential value of the marketing professional;
  • being accurately measured.
• Traditional incentive programs reward based upon gross sales, rather than rewarding based upon an increased efficiency rating (e.g. – better ROI).

Critical Factors for Success

Predictive Analysis is an extremely powerful tool, however, organizations must take a structured approach to the planning and implementation of the infrastructure:

1. Need for Business Insight – Predictive Analysis, like any other tool, must be implemented with a thorough understanding of the business which it is serving. This is especially true when validating the results of the model, as, many times, data attributes which are identified as key indicators are nothing more than noise. Utilizing a subject matter expert (SME) will allow for a proper mix of intuition and analytics when reviewing model results.
2. Have a Tactical Plan to Utilize the Results – A common output of a statistical model is a ranked list of customers which have a propensity towards some specific behavior (churn, cross-sell, etc). These lists can be used to drive marketing campaigns, however, in many cases, the users (e.g. – marketing and sales) are unsure how to utilize the lists. In a recent conversation, one CEO indicated that they were receiving ranked lists from a vendor; as the conversation progressed, he confessed that they had no methodology for utilizing the lists in any constructive manner. Organizations must have a predefined process to take the results of the modeling process and use it in an effective and efficient manner to support business objectives.
3. Review the Model Results Regularly – Many organizations that use Predictive Analysis fail to implement two simple and necessary steps:
   
   
   • Model Validation – This is the process of ensuring that the behaviors one believes have been predicted are, in fact, valid. This is done simply by testing the results of the model on a sample set of data and reviewing the results. This is a quick and painless process that can save money and provide confidence that the process will provide value.
   • Model Degradation – The process of Predictive Modeling requires that the results of a model be monitored over time, to review the effectiveness of the model as change occurs within the business. In fact, it is very common that the effectiveness of a particular model degrades over time. Why? Well, if a model accurately characterizes the consumers at a point in time and a marketing campaign affects consumer behavior (based upon that point in time), it stands to reason that the model will become less effective as the characteristics of the target consumers change over time. One way to ensure a model remains useful is to test the model periodically against known record sets and map the results over time. A simple model refresh will calibrate the model to the ever changing business environment.
4. Integration of Key MetricsDuring Implementation – Predictive Analysis can become a powerful marketing tool, especially when it is used with key business metrics. Some examples include the use of metrics such as Customer Value, Lifetime Value, or Credit Risk along with their ranked lists to make informed, fiscally responsible decisions regarding offers. For instance, if a mobile phone customer that generates $2 per month in profit is at risk of churn, it does not make sense to offer that customer a $200 phone upgrade, even with a 2 year contract extension. However, given a customer that generates $30 per month in revenue, that same offer would make fiscal sense.
5. Take a Phased Approach – It is important to remember that Predictive Analysis is a complex, iterative process that requires periodic measuring and tuning. Because of these factors, it is a wise choice to take small steps when implementing this toolset into an organization. This allows for minor changes to be implemented on an incremental basis with minimal impact, and the learnings from each project can be rolled into subsequent operations. In line with this thinking, organizations should start this process by picking a small goal, and stay focused on that goal throughout the implementation cycle.
6. Balance the Art and the Science – There is an age-old conundrum between statisticians and marketers; in general, statisticians tend to be quantitative and marketers tend to be qualitative. Each group has their strengths, but the sum of the two parts is certainly greater than the individual pieces. Organizations must strike the balance between the Art of Marketing and the Science of Predictive Analysis to be truly successful.

Conclusion

Predictive Analysis has been brought to the masses, and is no longer a tool just used by mega-corporations and government entities. Predictive Analysis is a powerful tool for many organizations, and can be used across industries to help drive efficiencies into the business. If organizations use care in planning and executing, success will be imminent and the financial returns that once seemed so unreal will become a reality. As strong customer relationships become increasingly important to companies and their customers, Predictive Analysis is there to help, as it:

• Allows companies to gain an in-depth understanding of their customers needs, bringing the customer-centricity of the “Mom and Pop shop” to companies of all sizes.
• Provides the knowledge and insight for organizations to build those ever-so-important relationships with their customers by anticipating their needs.
• Addresses customers’ needs at the individual level based upon the customer’s behavior (something that simply can’t be accomplished through traditional segmentation).
• Increases the efficiency of the marketing ROI, allowing organizations to focus resources on other critical areas.

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