AI is enabling companies to better understand how consumers are shopping, why they shop and most importantly, predict what consumers will buy in the future. This is fundamentally shifting how companies explore product development cycles, pricing models and understandings of how to change the minds of fickle consumers.
At Nielsen, we have a clear view of open, one that is not ajar or a “bit more open.” To us, open means exactly that—open. We define open as the ability to use different parties and types of data, models to enrich and applications to consume and take action.
With so many DMP vendors fighting to stand out, it’s no surprise that many marketers aren’t able to truly differentiate the competing solutions. And to be fair, from an eagle’s eye view, I don’t know that there is a way to.
Digital adoption is sweeping the globe. The uptake of mobile devices and increasing access to the internet have huge ramifications for businesses in all industries. Retailers can’t afford to ignore this new reality.
There are many problems and challenges ahead of us. We also have many possibilities and options to wade through as we navigate the right way forward. It’s up to us to leverage the opportunities by adopting better strategies for using data and technology.
It’s rational that shoppers would be willing to pay more for a product that is of a higher demonstrated quality or value, but there is also a more subjective component that factors into many shoppers’ ideas of what premium means.
Data is everywhere. As our individual behaviors leave an ever-expanding data footprint, we are faced with the challenge of making sense of all of this data and extrapolating meaningful insights to drive performance.
Aligning your organization toward common goals is challenging, especially when the goals change. That’s because it’s common for marketing teams to operate in silos. Most marketing organizations are split between marketing and media, and the split is compounded by multiple layers up and down the org chart.
As marketers seek greater accountability in today’s increasingly omnichannel shopper landscape, demand for outcome-based ROI measurement has become more important than ever across the media, retail and FMCG industries.
Unconstrained by physical walls, e-commerce retailers offer a huge inventory of products in endless aisles. Unfortunately, our physical world product coding processes can’t scale to e-commerce: they’re too costly and too slow.
In the coming decades, machine learning will transform work as we know it. And unlike previous revolutions, which primarily affected blue-collar workers, the smart machine revolution has white-collar workers in its sights.
As the world collaborates on the United Nation’s 2030 Agenda for Sustainable Development, good data are critical to the world’s ability to set goals, generate plans and measure our collective progress.
In about four months, we’ll have officially made it to "the future"—at least according to the time-stamp on Doc Brown's DeLorean in the "Back to the Future" movie series. So now that we’re there, what will 2020 look like?
All established companies must address a key challenge: How to find the next disruptive innovation while reacting to the disruptive innovations of others. To use the language of this year's TIBCO conference, how can one “ride the disruption wave”? Mitch Barns explores three things he's found that can play a big role.