Strategies to use Big Data in FMCG: FMCG businesses utilize big data to gain a commercial advantage

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Big data is becoming huge. Across industries, companies are taking advantage of data resources and analytics capabilities to cut costs and target customers more effectively. The FMCG sector has arguably been slow to embrace the potential of big data. This will change as businesses see how big data can be used to find new visions that drive business.

Key Findings

The global market for big data is maturing: forecasts predict the spend on big data technology and services to exceed $40bn in 2018. A review of the world’s top FMCG companies reveals big data being used widely. Significant gains relate to using customer behavioral data to improve supply-chain issues, and support for specific marketing initiatives.

Manufacturers and FMCG companies are already using big data to achieve significant savings in inventory costs (in the case of Procter & Gamble, over $1bn). Others, such as Nestl, Coca-Cola, and Mondelz, are using it to develop innovative products, improve targeting and increase revenue per customer.

Most FMCG businesses do not lack data. Instead, they have historically been unable to link data on marketing activity to commercial outcomes. This has placed FMCG businesses at a significant disadvantage relative to other business models and retailers.

The FMCG sector already uses big data, often the same data as the retail sector, but greater opportunities exist. This report examines how companies can use big data to improve revenues, control costs, and even innovate more effectively

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Summarizes key uses of big data, allowing companies to capitalize without having to invest in expensive experiments.

Provides easily understood strategies that companies can use in future business planning.

Provides case studies so you can see how big data has been effectively utilized.

Outlines the framework within which businesses are considering the use of big data and some of the key issues they are likely to face.

Table of Contents
Professor Merlin Stone
Jane Fae
Executive summary
Conceptual framework
The impact of big data
Data: types, sources, implementation
Using big data
Conclusions: future recommendations
Chapter 1 Conceptual framework
What is big data?
3V’s definition of big data
Beyond the 3V’s
An alternative view

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