Relevance & Research Question:

$With the outbreak of the Corona pandemic in Switzerland, the authorities took measures and issued recommendations to severely restrict mobility behaviour. The questions arose as to whether the population will adhere to the measures and recommendations and what influence this will generally have on mobility behaviour in Switzerland?

Methods & Data:

On behalf of the Statistical Office of the Canton of Zurich, the Federal Statistical Office and the COVID-19 Science Task Force, the research institute intervista launched the Mobility Monitoring COVID-19 in March 2020. A geolocation tracking panel with 3,000 participants serves as the basis, and their locations are continuously recorded via a smartphone app. With the data, the distances travelled, the means of transport used, the purpose of mobility and the proportion of commuters are analysed in detail on a daily basis. Since the panel was already set up before the outbreak of the pandemic, data was analysed retrospectively since 1.1.2020. The project is still running and new results are published on an ongoing basis.

Results:

With this study, which provides almost live data, the current developments in mobility behaviour can be clearly traced. It showed that after the lockdown in March 2020, average daily distances fell from around 40 km to less than 15 km. It could be shown that older and also younger people cut back considerably. Commuting shares decreased and public transport has been used significantly less since the outbreak of the pandemic. At times, the use of public transport even dropped by about 80% compared to the time before the pandemic.

Added Value:

This study is of considerable value to the authorities as a tool for managing the pandemic. With the results, the effectiveness of the measures taken and recommendations made could be directly monitored. By disseminating the results in the media, the population received immediate feedback on how the social norm regarding mobility was changing, which may have additionally strengthened the effect of the measures taken. The monitoring also provides important planning data for the economy and serves as a basis for various scientific research projects.

Relevance & Research Question: How does COVID-19 change the consumer needs of various types of Germany based tourists and how can a travel insurer provide maximum assistance in meeting these needs (of tourists, but also of tour operators & travel agencies)?

Methods & Data: Starting May 2020 analysis of 14 million discussions of German speaking travel forums (User Generated Content) using Artificial Intelligence, Natural Language Processing and Machine Learning.

Results: Awareness of (mostly) uncontrollable risks before and during a trip has increased significantly among all travelers due to the Corona pandemic. The desire to take out insurance for possible, unforeseeable reasons for cancellation as well as for advice and support in disputes with tour operators, portals, e.g. has become clearly larger. Above all, the need for information when planning a trip has shifted noticeably, at least in the short term. Although Covid-19 only really started in Germany at the end of March / beginning of April 2020, results were already available by the end of May 2020. The derivation of measures therefore did start as early as June 2020.

Added Value: Important contribution to the innovation challenge "ReStart Reise 2021" of VKB to identify and prioritize product and service elements for the travel insurance. The most relevant results have been introduced to the market by VKB, such as a medical concierge service (https://www.vkb.de/content/services/reiseservices/) and acoverage of COVID related risks https://www.urv.de/content/privatkunden/reiseversicherungen/covidschutz/), which are intended to positively support the travel / booking behavior of customers.

Relevance & Research Question: SV Group is challenged to develop and adapt current, but also new catering concepts, especially against the backdrop of the Corona pandemic and the emergence of new trends, such as increased home office work or changing office behavior. The aim of the empirical study was to analyze the fit and sharpen the positioning of different concepts and brands in order to not only remain the number one caterer in Switzerland, but also to continue to grow by developing new innovative catering concepts.

Methods & Data: First, an AI-based neurosemiotic Big Data web technique was used to uncover associations on the topic of "lunch and snacks at work" as initial input for the b2b and b2c online survey. For the survey itself, implicit association test and MaxDiff method were used. Universal structural modeling (USM) with Bayesian neural networks was applied to identify the most salient implicit associations. In addition, TURF analyses using MaxDiff scores uncovered the top feature combinations that resonate with the most consumers. The USM and MaxDiff-TURF results were in turn used as input for further neurosemiotic Big Data web analysis to create an extended association network. A total of n=250 b2c participants and n=248 b2b participants were surveyed in November 2020.

Results: The results showed that most, but not all, of the catering concepts and only one brand offered sufficient activation potential. Considering the extended association network, four specific clusters were identified, which in turn were used as communicative input for the roll-out of the respective concepts. This four-cluster network enabled highly targeted and evidence-based positioning, especially when it comes to triggering the right associations at each touchpoint (website, app, etc.) in consumer’s mind.

Added Value: The combination of methods used created an innovative augmented insights loop from the beginning of the research start to the end and beyond to create an evidence-based management foundation. The AI-based neurosemiotic Big Data web insights analysis was the initial starting point, but also the means to further refine the uncovered insights from the other advanced methods. In addition, it can now be used on a daily basis to review and, if necessary, optimize human-generated content (e.g., claims, product descriptions) in light of the identified salient association network without further surveying consumers. This approach ensures both substance and speed for better management with evidence.