We’ve gotten used to emphasizing the divide between digital and physical, but it’s quickly disappearing: when digital data about the physical world is comprehensive, real-time and freely available, the physical and digital augment each other.
When testing innovations, it’s risky to ask consumers to compare a new concept against an actual product that they currently purchase. This unbalances the entire evaluation by setting up an unfair comparison.
FMCG success today is now dependent on quality product images, solid SEO and prominent placement on e-tailer websites—far more so than simply having an abundant quantity or variety on the shelf at the local store.
Unbeknownst to most consumers, tremendous thought goes into developing even the most commonplace products. As a result, product development in the FMCG industry is anything but fast-moving. But what if algorithms could help streamline the process and the outcomes?
How many things can you say for certain that you're paying attention to, or even seeing, at any given moment? Our brains just aren’t good at recalling the kinds of details marketers need to evaluate their efforts in a complex world. That’s where the right neuroscience tools can help.
Companies striving for “leaner, bigger, better” innovations require realistic marketing inputs and an accurate forecast to identify their most promising initiatives. Proving that “consumers love it” without a realistic volumetric assessment simply isn’t enough.
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.
Most new product launches are “small” or “sustaining” innovations, which include the many, many brand extensions that large companies launch year after year. These launches are absolutely essential for growing existing brands and defending shelf space.
Most of the customer data companies gather about innovation is structured to show correlations rather than causations. Yet after decades of watching great companies do poorly at innovation, we’ve come to the conclusion that the focus on correlation is taking firms in the wrong direction.
Brands armed with new products have always rushed to be first to market, as first movers often establish a stronghold that can be difficult for later entrants to break into. But being “first mover” at the expense of being “best mover” can often lead brands to competitive disadvantage.
Growing a brand isn’t easy, especially for those in in crowded categories. But even the most established categories change over time, and even categories that appear stable may be one critical innovation away from awarding one brand a significant long-term advantage.
Marketers often think of “earned” media as asymmetric marketing opportunities—they’re cheap and fast, which make them quite easy for smaller brands to exploit. But the power of earned media as an asymmetric strategy is more appearance than reality.
Typically, small teams build concepts, get qualitative or quantitative feedback, refine concepts, collect another round of feedback, and so on, until they arrive at a “winning” concept. This technique works well, but it suffers from one major drawback: It often produces ideas that are good enough but not the best.
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.