I feel fortunate to be part of this time and age when data and technology abound in our lives. I am also privileged to have seen the evolution in the consumer goods and retail industry at close hand for more than 20 years. Volume, velocity, and variety in a volatile, uncertain, complex, and ambiguous environment have never been as well managed through tools and techniques, which would have been unimaginable until a few years ago.
Most consumer product organizations have always had data—whether it was internal ERP data, Point-of-Sale data, consumer speak data (through surveys, interviews, panel discussions, test market studies, research, etc.), competitor data from the market; media data, loyalty data, data from emails, chats, phone conversations, etc. Business decisions were made by slicing and dicing this data using basic spreadsheets, an individual’s analytical mindset, and, of course, whatever additional information could be made available. There were no “data/decision scientists” until a few years ago.
Mobility and high-speed connectivity have added volume, variety, and velocity through social media channels to this data and actually bring to life the N=1, R=G (meet individualized customer needs through finding the best resources globally) expostulated by CK Prahalad many years back.
The other journey that most Consumer Packaged Goods (CPG) companies have been on in the last few years is globalization, centralization, or one way of working pan-organization. That means integrating all types of data across geographies for a “single version of truth.” Although many large CPG companies are still on the sidelines and struggling to get the basics right on even core global master data management, with newer technologies for data capture and analysis, many are leveraging this data internally to drive revenues or costs and externally with an ecosystem of partners to create new revenue streams for monetizing this data.
We at Genpact have helped many large CPG and syndicated data companies monetize this complex web of data, from its lowest level for a single version of truth in near real time at extremely low costs. This requires deep understanding of the data, data quality, the markets, and a business model and strategy for integrating and creatively monetizing. We will talk about several case studies and how to do it in the next blog post.
Those who get it right using these zettabytes of data will be clearly ahead of the curve and in the leaders quadrant in the battle for market and cost leadership.