Summary
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 worlds 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 Nestlé, Coca-Cola, and Mondel?z, 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.
Synopsis
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?
3Vs definition of big data
Beyond the 3Vs
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: Mondel?z 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: Mondel?z 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
Appendix
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 3Vs 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
