Predictive analytics make it possible for consumer goods companies to forecast customer behavior, potential risks, product associations and demand, and has usually been considered to be only for those that have a specialized approach to identify anomalies. To succeed in creating effective predictive analytics models that generate actionable findings, organizations need to develop a strategy that ties the technology to business operations in a more pervasive and comprehensive approach throughout the enterprise.
During a recent CGT web seminar, executives from Accenture and SAP discussed how predictive analytics could bring consumer goods companies closer to their customers.
Panelists discussed what processes can be leveraged to drive better enterprise decisions that will enhance the usage of predictive analytics; how to leverage internal data to maximize the information coming from the new customer driven channels of mobile and social; how a comprehensive assessment of analytics requirements and capabilities will enable consumer goods companies identify and anticipate best customers and keep their business; and how predictive analytics and business intelligence can be effective in segmenting a consumer goods company’s customer base and be used to influence a range of desired customer behaviors.
Craig Macdonald, managing director for Accenture, kicked off the web event by sharing new catalysts in the market that are driving new innovations in analytics for consumer goods while including detailed examples. These include new data sources driving massive innovation, the desire to make everything “smart”, the integration of analytics across disciplines and the lower cost driving democratization of analytics.
“Leveraging new data sources, several major beverage companies are changing their approach to C-store distribution,” says Macdonald, reinforcing his example of making everything smart through new data. “The goal is to drive higher profitability and reductions in working capital,” he explains, also noting that it is being tested in the United States now.
Anthony Collura, global director – Data Science Solutions & Portfolio Management for SAP, and Denis Malov, head of Science & Technology NA Performance & Insight Optimization for SAP, then explained how consumer goods companies can gain new insights from the large amount of data across businesses to predict the future. They explained how predictive analytics all begins with the data that is mainly generated from outside the enterprise. They pinpointed big data and predictive challenges, including cost, value, data governance, technology requirements and more. Collura and Malov posed the questions, “Imagine if you could…
… identify hidden revenue opportunities through predictive analytics?”
… see changes in demand or supply across your entire supply chain immediately?”
… monitor and analyze all deviations and quality issues in your production process?”
… predict how market price volatility will impact your production plans?”
They also explained what it would be like to “provide exactly the right offers and service levels to every consumer, have a continuously-updated window onto future sales, show changes in real time, understand what consumers are saying about you, and instantly predict market trends and consumer needs.”
And, of course, Collura and Malov believe that “Every company deserves a ‘data scientist’ to deliver real results with big data services.”
To listen to this web seminar in its entirety, click here.
Originally published on consumergoods
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