A.I. won't replace workers who do intellectual activities. Is this still true? Check this out in our newest study.
Reading articles about market research and listening to what research buyers tell us they need, there continues to be a hunger for research to: 1. Capture authentic emotion 2. Deliver actionable diagnostic insight 3. Inspire the confidence to act with robust findings. MM-Eye has created a hybrid qual-quant research technique that does all 3: 1. Collecting emotionally rich qual data at scale 2. Converting it into quant data 3. Using a next-gen AI-powered text analytic approach we have custom designed for market research. It is called ThoughtScape and we would like to share our approach and the learnings we have gained along the way. ThoughtScape is theoretically robust, being grounded in the latest neuroscience understanding of how the brain works. It is also backed up by a decade of use, having been the core of the global brand research we undertake for Jaguar Land Rover. And to illustrate the unique insight ThoughtScape delivers we've conducted a demonstration study. The topic we have chosen is airline brands and the study directly contrasts two matched samples; one using traditional survey approaches, the other including ThoughtScape. And we'd like to share these results to demonstrate the proof of concept.
This work consists of a meta-analysis compiling learnings from different advertising sales experiments that ran in Latin America between 2017 and 2018. By contrasting results of measurements performed with advertising campaigns, we aim to elucidate how different marketing practices contribute to generate brand sales. Measurement methodologies consist of different approaches, for which we provide detail. Our conclusions are towards (1) validating effectiveness of different advertising practices based on evidence produced by experiments and (2) the adoption of a test-and-learn mindset, where brands continuously generate evidence of how their advertising practices work to produce results, is fundamental to growth in a rapidly changing environment.
This work consists of a meta-analysis compiling learnings from different advertising sales experiments that ran in Latin America between 2017 and 2018. By contrasting results of measurements performed with advertising campaigns, we aim to elucidate how different marketing practices contribute to generate brand sales. Measurement methodologies consist of different approaches, for which we provide detail. Our conclusions are towards (1) validating effectiveness of different advertising practices based on evidence produced by experiments and (2) the adoption of a test-and-learn mindset, where brands continuously generate evidence of how their advertising practices work to produce results, is fundamental to growth in a rapidly changing environment.
Our goal was to conduct a study on a research project using big data and to compare the outcome of the analyses with traditional survey research. Online shopper ratings and review data in social media is an exciting and on trend data source and was compared to traditional survey data. The survey data included product tests, i.e. products were placed in-home and consumers evaluated the products using a standardized questionnaire. The objective was to derive substantive insights about the core drivers for a five-star-rating of consumer reviews in online shops or platform ratings for pet care products and compare these insights with drivers of liking from traditional research on pet care products already existing in the market. We validated the hierarchy of drivers for the overall product rating by conducting a meta analyses on previous product tests and assessed the drivers of overall liking.
Our goal was to conduct a study on a research project using big data and to compare the outcome of the analyses with traditional survey research. Online shopper ratings and review data in social media is an exciting and on trend data source and was compared to traditional survey data. The survey data included product tests, i.e. products were placed in-home and consumers evaluated the products using a standardized questionnaire. The objective was to derive substantive insights about the core drivers for a five-star-rating of consumer reviews in online shops or platform ratings for pet care products and compare these insights with drivers of liking from traditional research on pet care products already existing in the market. We validated the hierarchy of drivers for the overall product rating by conducting a meta analyses on previous product tests and assessed the drivers of overall liking.
Whereas Market Intelligence is a well-known term, the expansion into Augmented Market Intelligence might need some explaining. The logic here is the fusion of it with Augmented Intelligence, a term coming from the Artificial Intelligence / Machine Learning framework. In that context, what is augmented is originally meant to be the human intelligence, via automated analytics that are able to learn from training data. In our discussion here the scope is actually bidirectional. Certainly we are talking about some of the means that can be used to enhance and make more scalable human analytics and judgement, but it can also be viewed as a way to improve the quality of Artificial Intelligence. So in this talk we will be talking about the synergies and interaction of both human and automated analytics in the specific domain of Market Intelligence.
Whereas Market Intelligence is a well-known term, the expansion into Augmented Market Intelligence might need some explaining. The logic here is the fusion of it with Augmented Intelligence, a term coming from the Artificial Intelligence / Machine Learning framework. In that context, what is augmented is originally meant to be the human intelligence, via automated analytics that are able to learn from training data. In our discussion here the scope is actually bidirectional. Certainly we are talking about some of the means that can be used to enhance and make more scalable human analytics and judgement, but it can also be viewed as a way to improve the quality of Artificial Intelligence. So in this talk we will be talking about the synergies and interaction of both human and automated analytics in the specific domain of Market Intelligence.
This presentation will look at our learnings that have come out of mapping dozens of brands and categories on Twitter. Among other things, we will characterise the different shapes of category conversations that can emerge around brands; we will look at the different patterns of communications that brands can adopt and the efficacy of each approach; and, we will look at the different strategies that brands can employ to knit together communities and to encourage the sharing of their communications & activations. We will also explore the link between the insights from social data and insights from traditional surveys to see where they align (or don't) in order to bridge the gap between traditional survey-based research and newer data science-informed approaches.
Exploring the creation of data-driven buyer personas combining psychographic and demographic data to know who speaks about them.