The main goal of this presentation is to open a discussion around internet research (quali & quanti and panels), whether the offline audience is still different vs online, and whether you need to check the online results with ethnography.
We conducted this research in order to be able to cross analyse the results of these happiness indexes with online behaviour. Our research - in France, Germany and the UK - combined a traditional online survey, which matched the happiness question wording of the official well-being surveys with passive tracking data (i.e. web and app behaviour tracked across the participantsâ phones, tablets and PC/laptops). It was vital here to obtain real behavioural data because when it comes to Internet usage, declarative data may be biased or inaccurate (even if you are ready to face the truth, it is difficult to estimate the time you spend online each day, on every website, every app, etc.). Our research combined traditional and new âpassiveâ methods.
We conducted this research in order to be able to cross analyse the results of these happiness indexes with online behaviour. Our research - in France, Germany and the UK - combined a traditional online survey, which matched the happiness question wording of the official well-being surveys with passive tracking data (i.e. web and app behaviour tracked across the participants' phones, tablets and PC/laptops). It was vital here to obtain real behavioural data because when it comes to Internet usage, declarative data may be biased or inaccurate (even if you are ready to face the truth, it is difficult to estimate the time you spend online each day, on every website, every app, etc.). Our research combined traditional and new passive methods.
Recognising innovation opportunities based on customer needs online is labor intensive, as user-generated content grows exponentially. Machine learning takes the insight process to a new level by utilising machine's and human's individual strengths. In the following, we are sharing our leanings from a project that allowed a direct comparison between human-driven and machine-driven approaches in qualitative research: classic Netnography conducted by researchers versus machine-enabled research supported by algorithms.
As oil lead to Global Warming, data leads to Social Cooling. Comparing these two problems is not just intended as a warning. It offers hope, a blueprint for how to deal with this issue, and a deeper understanding of what it means to be human in our data-driven world.
The aim of this paper is to present and to âtrainâ attendees to a new methodology made to decipher in a qualitative manner web navigation data. This paper will discuss the opportunity of using this kind of data for qual researchers, present the methodology that we suggest, and use it on a couple of examples from various industries. One of our core illustrations will be based on the â'appiness projectâ presented at an earlier conference.
The aim of this paper is to present and to train attendees to a new methodology made to decipher in a qualitative manner web navigation data. This paper will discuss the opportunity of using this kind of data for qual researchers, present the methodology that we suggest, and use it on a couple of examples from various industries. One of our core illustrations will be based on the 'appiness project presented at an earlier conference..
In this research, we analyze the digital path to purchase for spirits shoppers' trough the Meter Netquest digital tool in a digital panel with a sample of 660 spirits consumers with the following specifications: Consumers between 18 and 65 years old, living in principal and small cities with a social level between 1 to 6. We analyze the shopping and behavior data on the different local eCommerce channels, social networks, and search platforms. Not only the type of shopping also we analyze time, moment and traffic behavior. We would be able to build a brand digital strategy to impact these consumers in all their relevant moments and with the right and relevant content with this digital research, and in this way we'll able to increase the purchases and traffic in our own eCommerce platform.
The company, Pernod Ricard, is looking to increase sales and market share, has identified that one potential channel to achieve this goal is online sales. This will increase online and offline sales. This business objective triggered the need to achieve a deeper understanding of the liquor shopper; understanding their needs, interest and shopping pathways in order to influence their decision in the right place, moment and form.
Researchers are looking for new ways to understand online consumer behaviour. Evidence already exists that survey based data collection combined with passive data collection helps to generate the full picture of online consumer behaviour. Understanding online consumer behaviour is becoming increasingly complex for many brands operating on the internet. These brands require comparable research methods that stretch beyond national borders. In order to shed light on this challenge, we have implemented a commonly used research design using survey based and behavioural data collection in two different markets. We aim to provide insights about how to combine both methods, with particular emphasis on understanding the drivers for differences and similarities between markets.