Convinced that huge levels of COVID-driven FMCG growth in many countries were masking a larger, fundamental change, a team of Nielsen data scientists dug into the data to understand the nuances underneath the broader retail data. And they were right.
Pandemic-led shifts to further online adoption and an increased focus on neighborhood and small-format stores have become an ongoing normal in China's rebound from COVID-19.
The stores we shopped in yesterday are not the stores we are shopping in today, and unlikely to be those we shop in tomorrow. There is no longer a need to squint … our data scientists have brought this phenomenon into plain sight.
Months of working from home, reduced levels of commuting and high unemployment numbers are all adding up to a very different outlook for the U.S. consumer packaged goods industry.
Some of the highest revenue-generating grocery stores in the world are facing sweeping changes to their customer bases and their ability to deliver value to brands as people change where they shop—a change that, for some, may be permanent.
As the manifestation of technology that uses prior observed data to train computers to predict future outcomes, machine learning is often framed as the end-game, putting traditional statistical modeling in the shade. But that’s not where it belongs.
Truth in measurement has never been more important than it is today. Therefore, truth is our only agenda. But arriving at that truth has never been more complicated. While many view big data as a panacea for measurement in a digitally rich world, we know it’s not that simple.
For much of the big data era, businesses have held the power. With immense grassroots advocacy and legislation such as the EU’s GDPR and the California Consumer Privacy Act, the pendulum of power has swung toward consumers.
While some may equate data science as pulling rabbits from hats, this thinking is misguided. But this is largely because the vast majority of people don’t understand the workings of data science.
It’s not practical, feasible or necessarily a good idea to try measuring consumer behaviors by engaging with as many people as possible. That’s where sampling comes in.