Tools and Techniques

Most CPG companies interviewed are still relying on reporting and analytical techniques focused on understanding the past, commonly referred to as “descriptive” analytics and reports. These techniques have been used for the last 10 to 15 years to answer the question: “What did happen?” Two examples of descriptive analytics are post-event analysis of trade promotion activity and a basic top-line status report that compares shipment sales, consumption, forecasts, and targets.

In contrast, only a handful of CPG companies interviewed were using predictive analytics, statistical analysis, and advanced modeling capabilities to capitalize on the accumulation of historical data over the last decade. Predictive analytics answers the question: “What is likely to happen?”

One example of predictive analytics is trade promotion optimization (TPO), which leverages years of trade promotional cause data (e.g., feature, display, price, competitive set, marketing overlay) and effect data (e.g., sales, ROI, consumption) to improve promotional forecasting and optimize future trade promotion performance.  Many analytically mature CPG companies have also found predictive analytics valuable in other areas of the business: such as supply chain with demand planning, and finance with financial planning and forecasting.

This research found an immediate opportunity for many CPG companies to reduce the manual processes required to support the aforementioned “descriptive” analytics and reports.  This manual effort is often performed by analyst resources that spend their time administering and producing reports rather than analyzing the information within the reports. The more mature CPG companies interviewed have developed systems to automatically apply business rules to generate reports with limited, manual intervention. This allows analysts to shift their effort towards delivering higher-level insights.

The new era of Big Data presents new opportunities for organizations to develop their predictive analytical capabilities. Organizations that struggle to maintain their descriptive analytics capabilities, and those organizations in which analytical talent spends time manually generating reports, will find it difficult to take advantage of predictive capabilities. For those CPG companies looking to take advantage of Big Data, the subsequent sections will describe four additional areas that deserve significant attention. 

  Recommendation and Conclusion #1

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Information and Insights