When you have the Thunbergs of the world to the Trumps, and many in between, how do you find a common ground for sustainability to win?
When you have the Thunbergs of the world to the Trumps, and many in between, how do you find a common ground for sustainability to win?
Engaging drama have conflicts. Now many industries are in progress of such a drama to see how AI change the world, feeling some fears if AI steal jobs from us, human. Let's see the progress in MR if "Job to find insight" can be done by AI or not.
Engaging drama have conflicts. Now many industries are in progress of such a drama to see how AI change the world, feeling some fears if AI steal jobs from us, human. Let's see the progress in MR if "Job to find insight" can be done by AI or not.
Learn how to use machine learning text analytics, from scratch, for free, using open-source R.
Learn how to use machine learning text analytics, from scratch, for free, using open-source R.
Digital life in our modern world has merged with the analog lives of people. The horizon of human relations, including their deepest needs and interests, has extended to the virtual space of the different social networks that have positioned themselves as the leading contact and communication platforms of our time. However, despite the fact that each social network offers potential for new contacts, brands do not always know how to take advantage of the opportunity to participate in peoples real conversations with organic and relevant proposals, thus begging the question: how can brands engage in countless spontaneous and ever-changing conversations? Twitter Mexico, Arconte Research and Sinnia set out to answer this question by conducting a joint research project on topics trending in Mexico between January and November 2017. The purpose was to generate actionable lessons and facilitate the insertion of brands on Twitter the leading real-time platform. This paper presents the results of a journey that began with the detection and clustering of the most relevant conversations held over the course of a year in Mexico. It is followed by a cultural analysis of these conversations, centered on identifying their symbolic support and discursive rules to conclude with the creation of the framework that reflects the most recurrent conversational contexts on the platform, as well as the Rules of Engagement for any brand that aspires to engage in a live interaction in real-time, just like the conversations held on Twitter.
Digital life in our modern world has merged with the analog lives of people. The horizon of human relations, including their deepest needs and interests, has extended to the virtual space of the different social networks that have positioned themselves as the leading contact and communication platforms of our time. However, despite the fact that each social network offers potential for new contacts, brands do not always know how to take advantage of the opportunity to participate in peoples real conversations with organic and relevant proposals, thus begging the question: how can brands engage in countless spontaneous and ever-changing conversations? Twitter Mexico, Arconte Research and Sinnia set out to answer this question by conducting a joint research project on topics trending in Mexico between January and November 2017. The purpose was to generate actionable lessons and facilitate the insertion of brands on Twitter the leading real-time platform. This paper presents the results of a journey that began with the detection and clustering of the most relevant conversations held over the course of a year in Mexico. It is followed by a cultural analysis of these conversations, centered on identifying their symbolic support and discursive rules to conclude with the creation of the framework that reflects the most recurrent conversational contexts on the platform, as well as the Rules of Engagement for any brand that aspires to engage in a live interaction in real-time, just like the conversations held on Twitter.
Building the world's intelligence platform and capturing human behaviour at scale. The market research industry has a major problem: big brands don't trust its results. Companies are fed up with claimed behaviour and outdated panels and surveys. But if people around the world shared their thoughts and actions with us, and we were to use AI and machine learning to capture them, in the moment - then couldn't help the world's biggest companies to act like a local start-up and truly understand any market...?
We all know companies hold more customer data than ever before, but how can we successfully marry this to primary research? What's the best way to map psychographic data (attitudes, behaviours, responses, etc.) to the data you reliably have for all your customers or prospects? With a mature customer base and a propensity model that had outlived its usefulness, Sky's reward-based 'Introduce A Friend' referral scheme had achieved relatively low recent uptake. Sky wished to understand if there were customers for whom different propositions and contact strategies would be more successful. A 'reverse' segmentation of its customer base, mapping survey data onto existing fields, enabled Sky to tailor and target its referral scheme to specific customer groups, and thereby open up new headroom.
This paper defines the character of qualitative research and investigates the potential lack of objectivity. Reasons, why different researchers arrive at different results, are identified: one major reason is the existence of different qualitative schools. These schools tend to stress their own uniqueness but often disregard similarities to other schools. Using a case study, it is shown that single schools analyses tend to become too tunnelvisioned. Future qualitative research needs a wider perspective. Secondly, the paper discusses the tendency of existing models to reduce complexity and work with monocausal explanations. But they are similar enough to each other to share a number of assumptions. These are worth consideration for building a modern model of consumer behaviour. The authors present an action-orientated model with an emotional (world of meaning) and a cognitive (world of probability) sub-system and describe the interaction of both systems for behavioural control. Finally, the paper points to a number of methodological implications of the model including the unconscious clustering method.
This paper describes how consumer panels in Great Britain have been developed in recent years to provide a wide range of services that are of specific relevance to grocery retailers. The paper will go on to describe in more detail three new services that further enhance the retailers understanding of their consumers buying behaviour. The first of these looks at catchment areas, or the distance that consumers travel to their chosen retailer and the ways that this affects their purchasing. The second analyses the classification of a retailers stores into clusters and examines the ways that these can then be employed to maximise their sales with particular reference to Category Management. The third segments consumers by the ways in which they shop and using this to identify areas of strength and weakness.