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Marrying science and business judgment in consumer packaged goods

Consumer goods companies now have access to a wider array of data sources to fuel their decision making, but the changes are still coming slowly. Cross-functional teams are marrying quantitative models and structured analytics with the realities of the marketplace and consumer behavior, bringing them together in new ways to make better decisions.

This should be a golden age for the world’s biggest consumer packaged goods companies. They have access to more data than ever—from retailers, consumer panels, syndicators, government agencies, online sources, shipments, and internal financial records—and the tools to mine that data become more powerful every year.

But advances are coming slowly as companies struggle to navigate the bewildering array of possibilities. They’re far behind the most sophisticated retailers, for example, in gathering and analyzing consumer data. Some retailers are willing to share their information about customer journeys, but CPG managers who have built their careers on loyalty card and point-of-sale data, for example, now wonder which other sources they should tap.

They’re struggling to meld new troves of data, especially in unstructured formats, with existing data. While they can choose from a wide array of tools to improve business intelligence and find new insights into promotion management, pricing, and other areas, they’re having trouble integrating the data from different sources and tracking performance.

In this cluttered landscape, no dominant solution has emerged. So as technologies proliferate, many CPG leaders are turning to firms that offer “solutions as a service,” from diagnosis and data-gathering to advanced analytics, capability building, execution, and ongoing support.

Most leaders have already begun to use data analytics to harvest low-hanging fruit in revenue management, pricing, and promotional effectiveness, for example. But pressure is rising as online competitors make prices instantly transparent across channels, markets, and even countries. Getting to the next level of value creation will require better tools and more talent to analyze the rising tide of data and deliver actionable insights.

The barriers to progress include talent gaps. Every CPG company needs skilled data scientists who also understand the business—“interpreters” who can explain analytic insights in ways that move senior management. But finding, recruiting, training, and retaining these people is costly, difficult, and time-consuming.

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