Successful companies fuel business with predictive analytics
Cover Illustration by Oliver Burston
Most commercial organizations share similar goals: attract the best, most profitable customers; grow the business through up- and cross-sell; retain high-quality customers; minimize risk to the organization; and detect and prevent fraudulent activity. Supply-chain considerations, such as effective assortment planning in retail stores and efficient production lines in manufacturing, are also key considerations that can significantly affect the customer experience and ultimately customer value. In short, the goal is to grow revenues and minimize costs, thereby producing profitable revenue growth. Typical applications include:
- Delivering well-targeted campaigns
- Classifying high-quality customers and, conversely, identifying those who are costly
- Focusing investigators and minimizing the effect of fraudulent activity
- Leveraging effective and reliable inventory management
- Predicting maintenance needs
Public agencies manage a unique set of challenges and often face additional scrutiny because of the personal nature of public security, healthcare and education. Because they’re also often funded or subsidized by government-run agencies, these organizations face additional pressures and bureaucracy around operational budgets. Typical public-sector applications include:
- Government agencies that manage functions as diverse as tax audit selections, military force recruitment, and proactive policing and public safety.
- Healthcare organizations that seek to proactively manage their resources and fine-tune their practices to provide better patient care.
- Colleges and universities that manage the entire student lifecycle: recruiting the right mix of students, offering a selection of programs and assistance to keep students enrolled, and managing alumni development programs.
At the highest level, predictive analytics enables better business decisions. Analysis might reveal new insights that help senior management drive far-reaching strategic decisions and deliver step changes in business value. However, these insights are more often applied at the individual-case level, enhancing key business decisions that are made frequently and repeatedly, where improvement leads to a higher proportion of good outcomes and clearly measurable, incremental ROI. A helpful way to think of these decisions is: If we could make better decisions about “X,” we could deliver greater value by doing “Y.” For example:
- If we could reliably predict which of our high-net-worth customers were likely to defect to a competitor, we could ensure their continued business by offering incentives.
- If we knew how likely each of our customers would be to respond to a particular cross-sell offer, we could reduce the size and cost of campaigns (and increase response rates and revenues) by not targeting people unlikely to respond.
- If we could accurately assess the risk of each insurance claim as we receive it, we could reduce costs and increase customer satisfaction and loyalty by fast-tracking safe claims and increase our fraud-detection rate by ensuring our investigative resources focus on genuinely high-risk cases.
Integrating the results of analytics with business processes and operational systems, or deployment, can be relatively simple: one point of integration in one process and system. For example, at a single point in processing tax returns, a predictive model scores every return on the likelihood of noncompliance, and adds those with high scores to an investigations-team audit list. In other solutions, the approach might be more complex.
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