Cover Image


Strategies to use Big Data in FMCG : How FMCG businesses are utilizing big data to gain a commercial advantage

出版商 GlobalData 商品編碼 344937
出版日期 內容資訊 英文 82 Pages
Back to Top
FMCG部門的巨量資料使用的策略:FMCG企業對商業性變得有利的有效利用方法 Strategies to use Big Data in FMCG : How FMCG businesses are utilizing big data to gain a commercial advantage
出版日期: 2015年10月31日 內容資訊: 英文 82 Pages




本報告提供FMCG (日常消費品) 部門的企業的巨量資料有效利用方法調查分析、概要、策略、案例研究、推動因素和問題等系統性資訊。

第1章 概念架構

  • 摘要
  • 簡介
  • 所謂巨量資料
  • FMCG部門的巨量資料的地位
  • 巨量資料相關的商務、技術上的問題
  • 報告的背景

第2章 巨量資料的影響

  • 摘要
  • 簡介
  • 主要的影響領域
  • 巨量資料的規模
  • FMCG的巨量資料

第3章 資料:種類、來源、引進

  • 摘要
  • 簡介
  • 資料的種類
  • 資料的獲得
  • 資料的課題
  • 引進主要問題
  • 商務上的問題

第4章 巨量資料的使用

  • 摘要
  • 簡介
  • 位置資料的使用
  • 數位足跡:線上資料
  • 產品使用資料:IoT
  • 標準外的資料來源

第5章 結論:未來的建議


Product Code: CS1016IS


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 and Gamble, over $1bn). Others, such as Nestle, Coca-Cola, and Mondelez, 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

Reasons To Buy

  • 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
  • Disclaimer

Executive summary

  • Conceptual framework
  • The impact of big data
  • Data: types, sources, implementation
  • Using big data
  • Conclusions: future recommendations

Chapter 1 - Conceptual framework

  • Summary
  • Introduction
  • What is big data?
    • 3V's definition of big data
    • Beyond the 3V's
    • An alternative view
  • Locating big data in the FMCG sector
  • Business and technical issues associated with big data
  • Report context
    • Aims and objectives

Chapter 2 - The impact of big data

  • Summary
  • Introduction
  • Key areas of impact
  • The scale of big data
    • The big data market: global
    • Big data satisfaction
  • Big data in FMCG
    • Spend
    • The size of the prize
    • Big data as a defensive strategy
    • FMCG intentions: 'blue skies' thinking

Chapter 3 - Data: types, sources, implementation

  • Summary
  • Introduction
  • Data types
    • Traditional data
    • New data
      • Process data
      • Location data
      • Digital footprint: online data
      • State of mind
      • Product use: the Internet of things
    • The new data landscape
      • Closing the loop
  • Obtaining data
    • Principal sources of data
    • Valuable data FMCG businesses do not know they have
  • The data challenge
    • Vision
  • Key issues in implementation
    • Background
    • Technical trends
      • Growth in data lakes
      • The spread of unstructured approaches
      • The rise of Hadoop?
      • The impact of real-time
  • Business issues
    • Social concern
    • Skill shortage

Chapter 4 - Using big data

  • Summary
  • Introduction
  • Using location data
    • Using location data to enhance the distribution chain
    • Combining location data with consumer trends
    • Using location data to serve geo-targeted advertising
    • Enhanced use of aggregate location data
    • Use of location data instore
      • Case study: Mondelez International
    • Location data: the role of mobile
      • Case study: Reckitt Benckiser field force app
  • Digital footprint: online data
    • The data management platform
    • Programmatic media
      • Case study: Reckitt Benckiser programmatic marketing
    • Customer relationship management retargeting
    • Predictive analytics: from segmentation to relevance
    • Online data
      • Case study: BeachMint
    • Digital listening
      • Case study: Heineken
    • Direct consumer relationships
      • Case study: Mondelez International
    • Collaborative use of data
      • Case study: Ahold
      • Case study: Walmart
    • Third party apps
      • Avansera
      • Heineken
  • Product use data: the Internet of things
  • Non-standard data sources

Chapter 5 - Conclusions: future recommendations

  • Summary
  • Introduction
  • Outlook
  • A note of caution


  • Methodology
    • Primary research
    • Secondary research
  • Glossary/abbreviations
  • Bibliography/references

List of Tables

  • Table 1: Areas of big data impact by function
  • Table 2: Objectives considered relevant to FMCG business, 2010
  • Table 3: Big data projects by leading FMCG companies, 2014 (part 1)
  • Table 4: Big data projects by leading FMCG companies, 2014 (part 2)
  • Table 5: Big data projects in the UK by leading FMCG companies (2014)
  • Table 6: Traditional data: types and sources (part 1)
  • Table 7: Traditional data: types and sources (part 2)
  • Table 8: New data: types and sources
  • Table 9: Principal sources of data (part 1)
  • Table 10: Principal sources of data (part 2)
  • Table 11: Key stages in data management platform usage
  • Table 12: The pros and cons of programmatic advertising

List of Figures

  • Figure 1: The 3V's view of the big data challenge
  • Figure 2: Big data market forecast ($bn), 2011-17
  • Figure 3: Breakdown of supply-chain functions by sector (%), 2009
  • Figure 4: Breakdown of logistics costs by region and source (%), 2005
  • Figure 5: Heat map of customer movements instore using Path Tracker
  • Figure 6: Individual movement around San Francisco, mapped via FourSquare
  • Figure 7: The changing data landscape
  • Figure 8: Approaches to modeling a store customer base
  • Figure 9: Top industries by share of US digital and mobile spend (%), May 2015
  • Figure 10: US digital ad spending by format ($bn), January 2015
Back to Top