Where does artificial intelligence have to get to, in order to start matching the human capacity to turn data into insights? Human analysis of social media delivers in-depth insight into people's feelings, intentions, drivers and barriers of usage - and so on. This approach turns data into valuable and actionable insights for marketing, product and proposition development. The talk will make a case to upgrade automated analysis to take advantage of the richness of social media.
The Internet of Things (IoT), the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment, has quickly become a hot topic for 21st century businesses and Market Research businesses in particular. By attending this firestarter presentation, market researchers will answer questions, such as: what is the state of the Internet of Things (IoT) and how will it evolve in the coming years? How can research companies help their clients make the most of new insights derived from smart objects? What are the best practices of incorporating IoT insights into your research programs? What are some current examples of market researchers who have harnessed the value of incorporating IoT insights to research? This paper will investigate how the Internet Of Things can, if used correctly, add an extra dimension to market research, providing a window into the needs and satisfaction of customers and participants.
Since November 2015, ING Bank in Poland has been offering a new Internet banking system, which (according to customers) is simple, intuitive and mobile friendly, and which supports the management of their daily finances. The System, which has been giving Bank access to millions of transactions is now a basis to improve its big data management as well as to launch friendly, successful communication to its 4 million plus customers in channels and ways preferred by them. My ING is built on the basis of a detailed analysis of the needs of customers. Since the beginning of the project, the Bank has been testing, on an unprecedented (even in Poland) scale, the usability of the system. ING is eager to present some of the most interesting observations/conclusions from testing Moje ING.
In the digital age, market researchers are looking for new methodologies to understand consumer behaviour. Behavioural data collection through passive metering adds value to survey based data collection, as it measures objective data better because it is not limited to human memory. Data collected in a passive way can be perceived as intrusive to the research participant's privacy and intimacy. In our presentation we intend to provide a model to empower the participants, carefully protect their personal information and at the same time preserve the utility of the data collected. This model can help researchers to effectively deal with difficulty in recruiting research participants as well as help prevent biases in the collected data.
This paper describes the use of computerised topic analysis to segment and analyse tweets by US presidential election candidates in 2016. The statistical technique used to create the topic is called Latent Dirichlet Analysis (LDA). The paper shows how LDA can be used to automatically generate topics un textual data and discusses the potential to use LDA as both a static 'batch" and real time analysis approach for textual data.
In a world of Big Data and an ever-widening variety of data types, technological tools and infrastructure, while not glamorous, are mission-critical to insights creation and building a data-driven business culture. Microsoft's Market Research team (CMR) will share an innovative solution they developed to capture on-device product feedback that, when linked with terabytes of behavioral data, has generated insights to drive supply chain, product development and customer engagement decisions. This on-device measurement is part of the CMR's larger data management strategy, which brings together telemetry, survey data, and on-device measurement, using modern technologies like Azure, R, Machine Learning, and PowerBI, to generate and share knowledge that drives decision-making across Microsoft.
In a typical segmentation study, segments are identified that differ in terms of their needs, attitudes or behaviours. However, these segments rarely differ on demographic or firmographic variables that exist on a customer's database(s). Thus, it is not possible to target the segments via data on the customer's database(s). Targetable Segmentation solves this problem by linking customer data with survey data. In other words, the technique produces segments that differ in terms of their needs, attitudes or behaviours and that are also able to be identified using internal data. Cases studies are presented that show how attitudinally different segments can be identified on databases containing hundreds of thousands of records, thus making the segmentation analysis much more actionable.
In recent years, the rise of the National Front party has been a major feature of the French political landscape. Understanding the drivers behind the electoral successes of that party is now crucial for mainstream parties if they want to counter it efficiently. This is no small task, as the vote for the National Front is difficult to estimate for pollsters. Our contribution shows how Open Data can help shed light on the question, by combining voting data with freely available administrative data sets. In particular, we address the important question of the link between turn out and the National Front vote. We show that simple correlations between the two are biased and we estimate the true link by econometric modelling, dealing with endogeneity.
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 will illustrate how AI algorithms on big data sources (social media, search, e-commerce, Internet of Things, Mobile data) can be developed and applied for transforming consumer insights and marketing programs. This transformation will be on two counts: re-invention of existing programs by making them better, granular, cheaper, faster, and development of new programs to harness the opportunities provided by the new connected world like personalisation strategy, real-time optimisation, development of early warning system and integration of NPD with digital activation. The focus will be on what kind of AI algorithm will work in marketing application context, bringing this to life with real-world cases and generate learnings for us as an industry.
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.
Where does artificial intelligence have to get to, in order to start matching the human capacity to turn data into insights? Human analysis of social media delivers in-depth insight into people's feelings, intentions, drivers and barriers of usage - and so on. This approach turns data into valuable and actionable insights for marketing, product and proposition development. The talk will make a case to upgrade automated analysis to take advantage of the richness of social media.