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In any competitive market, the ability to leverage data to understand a customer's next move is essential. This is particularly true in businesses driven by ongoing relationships and a steady stream of customer data. For that reason, companies in relationship-driven industries, from telecommunications to banking, must continuously evolve their use of customer data to stay competitive.
To achieve a competitive advantage in customer marketing, companies have invested millions of dollars in customer intelligence systems. Despite this investment, many companies still struggle to increase marketing ROI. This is due largely to the absence of an effective predictive analytics approach to customer data.
An Effective Predictive Analytics Practice Delivers Key Advantages A predictive analytics approach to customer marketing leverages statistical modeling to generate ranked lists based on each customer's propensity to exhibit a certain behavior. Predictive analytics can deliver several significant business benefits, including:
- Improved Targeting: An improved ability to focus appropriate marketing activity to each customer.
- Enhanced Revenue: Better targeting yields campaign uplift, translating directly into enhanced revenue.
- Better Cost Control: Enables companies to focus resources on high-value, high-potential customers to improve effectiveness and reduce overall marketing costs.
- Competitive Advantage: A well-executed predictive analytics strategy can keep a company in front of the pack in the race to improve relationships and build customer revenue.
The good news is that an effective predictive analytics approach can deliver significant results for companies that put it into practice. In a recent example, a global telecommunications company realized a 40% reduction in customer churn rates and achieved nearly $100 million from campaign uplift, thanks in large part to an effective predictive analytics practice.
So, what makes an effective approach? The answer lies in the fundamentals. Building a top tier predictive analytics practice entails more than simply buying a software package or implementing a statistical toolset. It requires a well-planned and well-executed approach that comprises careful development across five key "building block" disciplines, including: Strategy, Resources, Analytic Systems, Business Process, and Measurement.
The use of each of these disciplines varies extensively across industries and companies. Some companies have built highly sophisticated infrastructure while others are still managing via spreadsheets. For any company, the end goal is to realize a competitive advantage in the marketplace and implement a strategy that provides a marked increase in revenue or a reduction in costs.
Structure Married with Flexibility
One of the main characteristics of a successful predictive analytics program is the ability to balance the need for structure with the need for flexibility. An established structure keeps the analytics process focused, consistent and relevant. Tactical flexibility enables organizations to address opportunities as they arise and to support a company's unique needs and core competencies. Most importantly, the structure of a predictive analytics program must scale as the practice grows and change as business develops over time.
Building Block #1: Strategy
The first building block to a successful predictive analytics practice is a flexible strategy that creates the foundation for evolutionary improvement in analytics. This overarching strategy helps to ensure that implementation matches to key business goals, and that there is an effective measure for proving success. This is key to the further development and expansion of your predictive analytics practice moving forward.
An effective strategy requires three elements. First, planners must ensure that they approach strategy development with an understanding of the obstacles to success. They are not necessarily difficult to overcome or avoid if they are recognized at the outset. Second, there is the need to set objectives. These strategic and tactical goals are crucial for measuring success and gaining support for expanding your predictive analytics practice. Finally, there is the need to establish an evolutionary approach to your predictive analytics program. The fact is, predictive analytics can deliver results quickly, but the real value of the approach is in its ability to "learn" and expand over time. The strategy must allow for this growth if it is to maximize ROI.
Understanding the Obstacles to Success To build a successful analytics practice, your company must understand and address (or avoid) the potential obstacles to progress early in the planning stage. Knowing the pitfalls and learning how to remain focused will enable a more efficient system.
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