Every time we try to predict the future, we naively try to imagine it from our point of view: with a lineal understanding of time as past-present-future. However, weve found out that sometimes our future is not within our own timeline but in someone elses, and even in the present or past of other brands and categories. Thats why we worked with both research inputs and outputs in order to move from guessing the future to designing it.
How Universal Avenue increased earnings for their B2B sales team by creating advanced predictions based on internal and external data streams.
This paper describes how the research industry can use Virtual Reality (VR) simulations to predict and produce the effectiveness of shopper marketing activations. We propose a VR pre-testing programme for companies keen to understand and hone the effectiveness of shopper activations. We set out how, by combining the latest thinking from psychology and the best-in-class VR technology, we can unlock growth for brand owners and retailers alike.
Every time we try to predict the future, we naively try to imagine it from our point of view: with a lineal understanding of time as past-present-future. However, we've found out that sometimes our future is not within our own timeline but in someone else's, and even in the present or past of other brands and categories. That's why we worked with both research inputs and outputs in order to move from guessing the future to designing it. Our objective? To stop hoping that we are going to find an oracle or magic ball that can guess the future, and instead build tools and processes that foster the kind of thinking that led Verne and Da Vinci to design the future.
The world is becoming more complex - consumers are exercising greater choice, information sources of information are fragmenting, and the regulatory and political environment is growing more sensitive. In such an environment, the asks of the Insights function are becoming more demanding - in terms of a widening sphere of influence, becoming more predictive and real time in analytics, and taking on more activities. In such an environment the Insights team is suddenly finding that they finally have a seat at the executive table with the attention of the wider organization. This paper reflects the journey of PepsiCo India to effectively build networks to drive greater impact to business.
Mobile devices are everywhere, and changing everything. Nowadays, people simply swipe or tap their screen to share their opinions with the world. Decisions are influenced by feelings, moods and our memory. As a research industry we have to adjust to this new reality if we want to stay in touch with who our customers really are. Measuring purely rational processes is no longer good enough. Instead, bridging rational and emotional drivers is key to accurately predicting how consumers behave and decide. Inspired by these developments, we are introducing Unspoken?. By combining intuitive mobile techniques with psychological theory and advanced modelling, this mobile-only implicit application opens the door to a new generation of research solutions.
Learning from masters of sociological thought, we understand the future of advertising already today. Say Goodbye to target audiences and media and Google. Say Hello to smart machines, liquid self-concepts and technological armed consumers!
Neural networks are methods widely used in tasks such as image recognition, computer vision, sequence prediction, text encoding and document classification. At Viacom's Data Science and Advanced Analytics (DSAA) we are building cutting-edge platforms that utilize a specific flavor of recurrent neural nets (Long Short-Term Memory network) that feed into broader ensemble models to predict ratings for our networks. This talk will be a blitz workshop intended for practitioners in data science with an informal account of what we've learned about the design and training of LSTMs for sequence prediction.
How Universal Avenue increased earnings for their B2B sales team by creating advanced predictions based on internal and external data streams.
As The Wall Street Journal reported last year, streaming services such as Netflix and a rise in original TV programming have impacted the once-lucrative syndication market. After taking major losses on network hits, cable executives now have to scrutinise the value of rerunning a successful show before investing. Viacom has built an accessible, visually appealing app that uses statistical and machine-learning techniques, such as clustering, predictive modeling, and collaborative filtering, to help the media industry make quick decisions that will benefit brands and their audiences. By "gamifying" data, we have made the app user friendly for acquisition experts, marketers, content strategists, and others outside of STEM fields who have shied away from quantitative analyses in the past.