The ideal predictive analytics application will take your company to its endgame through crucial insights and data analyses that enable value-based decisions honed to customers with the greatest current, and forward-looking, profit potential. The solution you select should be able to define organizational goals and monitor them via key performance indicators (KPIs) and scorecards, visualize and aggregate metrics into an overall score and make it possible for any user in any department to review the data by using the application’s dashboards. 

Ease of access across the organization is a must. Predictive analytics empowers managers and line organization users, whatever their specialty or interest, with near-real-time actionable intelligence that provides a laser focus on a company’s most valuable customers—by name, by the products they use and where they use them. Moreover, the application goes one step further by applying capabilities that predict what these customers will want to buy “down the road,” what that will cost in terms of personalized product development to meet their needs and what the impact will be on the network.

With this pinpoint-accurate data at hand, everyone sees the full picture and is in a better position to contribute to value-based decisions on which actions to pursue—at the appropriate level of investment, of course—in order to fuel savings and accelerate profits.

Predictive analytics at work

Early movers have been quick to embrace predictive analytics, including a large tier 1 provider with a solution now in production that’s already realized significant gains in operations and its bottom line.

Before adopting predictive analytics, this CSP had faced issues common to all in the industry: it needed to leverage Big Data to drive profits but was swamped by growing volumes of data, plus it was held back by stovepiped systems and multiple tactical-analytics capabilities limited to a single perspective. Data gathering proved to be time-consuming and resource intensive, and the company faced numerous internal obstacles in its analyses of the results, including high-level allocations that didn’t represent actual activities or metrics.

The output of old-school analytics was too fractured to indicate a clear path of action. So, after years of relying on a platform that failed to provide the most essential data—profit value by customer, product and location—the CSP opted for a new approach to meet its objectives: Razorsight Predictive Analytics.

Razorsight’s team of data scientists deployed predictive analytics as an overlay hosted on a secure, private cloud, and today its application is helping the CSP rack up achievements it once thought were impossible, successfully integrating over 100 data feeds from a variety of source systems (CRM, ERP, etc.) to present a single view of each targeted customer by product and exact location. The overlay deployment allows data transmission and ingestion in a loosely coupled model, eliminating the risks typically associated with tightly coupled integration models. All workflow is hands-free automated: data reception, tracking, ingestion, and processing are centralized in the Razorsight Predictive Analytics platform to ensure faster profiling, analysis and deployment. The cloud-based model of the application has also completely wiped out previous costs related to hardware, software and IT.