Early September speakers from all over the world presented their work and thinking at the 2019 ESOMAR Congress in Edinburgh. The three-day conference was bursting with speeches, panel discussions and keynotes on Transformation, the 2019 congress theme.We have selected 3 of the best-rated presentations, which we will rerun in a webinar bringing you the ESOMAR content and experience at the comfort of your own desk, office or home.Our Best-of-ESOMAR selection:The state of MRX by Ray PointerA recap of the key findings about the state of market research around the globe from the ESOMAR Global Market Research Report. Learn what is going up, who is going down, what the key changes, and where the key opportunities are.Hello, I'm Alexa. I'm conducting a survey by Ennio ArmatoNot wanting to accept that the evolution of the market is strongly oriented to the Personal Assistants means you're missing a great opportunity. This presentation highlights the role of PA in market research.Using Technology to Drive Commercial Opportunities at F1 Races? by Matt RobertsInsight from sensor technology used at F1 to measure fan engagement and commercial opportunities
Research departments are under pressure. They are expected to deliver faster, cheaper and more impactful insights than ever before. Instead of doing more and faster research, insight departments are also able to revisit existing data sources. Often, companies have plenty of valuable data sources at their disposable, without being aware of their full potential. Moreover, relevant databases are often publicly available via APIs or sold via data brokers. Yet, the biggest hurdle lies in making sense of this abundance of data. Companies are struggling to connect different sources due to different structures, missing values and other complexities. In the last years enormous advancements have been made in machine learning and data science. Although demystifying is needed in order to better understand this discipline in context. With a creative and pragmatic mind-set, the problem can be solved by borrowing techniques from this field of data science. We show a case in the beverage industry where we exploited existing data sources to uncover a hidden layer of insights.
Research departments are under pressure. They are expected to deliver faster, cheaper and more impactful insights than ever before. Instead of doing more and faster research, insight departments are also able to revisit existing data sources. Often, companies have plenty of valuable data sources at their disposable, without being aware of their full potential. Moreover, relevant databases are often publicly available via APIs or sold via data brokers. Yet, the biggest hurdle lies in making sense of this abundance of data. Companies are struggling to connect different sources due to different structures, missing values and other complexities. In the last years enormous advancements have been made in machine learning and data science. Although demystifying is needed in order to better understand this discipline in context. With a creative and pragmatic mind-set, the problem can be solved by borrowing techniques from this field of data science. We show a case in the beverage industry where we exploited existing data sources to uncover a hidden layer of insights.
When beer beats wine in the restaurant! The quest fora perfect beer and food combination by using big data, algorithms and contextual consumer product testing (VR). In this paper we will therefore first introduce the concept of beer-food pairing based on molecular and machine learning information and its potential relevance for consumer delight. Subsequently, we will discuss the impact of context on the evaluation of such beer-food pairings by illustrating the current state of art in sensory and consumer context research.
When beer beats wine in the restaurant! The quest fora perfect beer and food combination by using big data, algorithms and contextual consumer product testing (VR). In this paper we will therefore first introduce the concept of beer-food pairing based on molecular and machine learning information and its potential relevance for consumer delight. Subsequently, we will discuss the impact of context on the evaluation of such beer-food pairings by illustrating the current state of art in sensory and consumer context research.
For Toyota Motor Europe, the biggest challenge is capturing reliable consumers feedback in the beginning of the car development process before prototypes are build. As the first moments of exposure to a new car in the showroom are very impactful on the purchase decision, new car makes have to feel right for consumers. The Gestalt of the new car has to fit and has to be relevant for consumers. Hence, the question is: how can you develop a new car that emotionally engages consumers and excites them to take it for a test drive and eventually buy it? And this before the real prototyping starts.
Rogil's innovative Sens-Pack model combines eye tracking with verbal quantitative and qualitative research techniques which enables the prediction of the success of your packaging or category management. In this paper we will prove that the added value of this multi-mode research approach goes beyond conventional research techniques.
For years we tried to get a hold on the shopper decision tree by traditional quantitative and qualitative research techniques using verbal measures (U&A, diary method, in shop observations, etc.). Nevertheless, the proliferation of SKU's turns the shop into a real jungle of visual stimuli where consumers, more than ever, take decisions intuitively driven by emotions and their memory based equity in a split second. The only way to get hold on this unconscious process is using objective measures in combination with quantitative and qualitative research. In this paper, the authors will guide the audience through the sense pack model in an interactive way based on case studies: Sara Lee, Douwe Egberts, Heinz, Henkel and Alpro Soya.