WRITING YOUR THESIS AT DVJ INSIGHTS
We provide you with the opportunity to write your thesis with our data. You have the opportunity to work at the DVJ office in Utrecht either for some days a week or also for a whole week and ‘work along’. There is the option to work from home, which is the default if you only write your thesis based on our data. In case of working along, you would work partly on your thesis and partly on other projects, unrelated to your thesis. You will also follow different courses from our onboarding program for new researchers that teaches you among others how we work at DVJ Insights, how to develop a questionnaire, and how to program the survey in our survey software. The internships allowance is €400 per month when you work 40 hours a week.
WHO ARE WE?
DVJ Insights is an ambitious, innovative and fast-growing marketing research agency located in the Netherlands, the UK, Germany and Sweden with the ambition to be the best global agency for brand growth. We add value by helping our clients to better understand consumer behaviour, improve their brand positioning, realise more successful product introductions, and increase the effectiveness of their media campaigns. At DVJ Insights, almost 100 passionate colleagues from 5 different locations work for clients such as Philips, Samsung, Beiersdorf, Heineken, Cloetta, Domino’s, Nestlé, ING, Rituals, and the Dutch Central Government. We are a dynamic and informal place to work with people from different cultures and backgrounds.
WHAT CAN WE OFFER?
The opportunity to write your thesis at DVJ and be able to work with our data. We are conducting internal meta-analyses on a regular basis but want to learn even more from all the data we gather. By conducting research on our databases, we can gather a lot of general learnings on why some innovations are successful, how advertising becomes more effective, why brands grow and why they don’t, and the way media deployment can be optimised.
Therefore, you get the opportunity to work with data that is interesting, very rich and practically relevant. Furthermore, you will be able to use sophisticated econometric modelling to find interesting insights, both from an academic as well as a practical perspective. For a thesis topic, we can develop a topic together, and you would be able to use our panel for data collection. Or, since we conduct so many different studies and we have a lot of data, you have the option to work on one of the following topics:
-
Shopper missions (Marketing Analytics topic). For our clients we sometimes conduct customer journey or shopper mission studies. In those studies we examine what needs are triggers to go to the store and buy. Based on those studies we can identify different shopper missions, such as the shopper on the go, or doing the large groceries. Additionally, we have purchase data from Danish supermarkets available at the individual level for about two years, which was registered via an app. Thus, it includes all purchase receipts including product information for different supermarkets in different regions. There is also information on the users, namely age and region where they live. This enables you to examine whether the shopper missions as identified, can also be distinguished in the data. Combining this again with background information of the users, leads to rich insights on shopper missions and consumers. More specifically, one can think of the following research questions: Are certain categories bought more/less often for certain shopper missions? Are certain types of products (i.e., A-brands, private label, different pack sizes) more/less popular for certain shopper missions? Are certain consumers price sensitive for certain shopper missions? Do we observe brand loyalty versus variety seeking? Etc. etc.
-
Packaging (Marketing Analytics & Management topic). In today’s market, brands whose products are sold in retail face several challenges. As shelf space increasingly comes at a premium, each product will have to compete with many others for the consumer’s attention. And, especially for fast-moving consumer goods, where consumer involvement is relatively low and decision-making is primarily based on heuristic cues, it becomes vital that, within a fraction of seconds, a product’s package is able to capture the consumer’s attention and is identified as belonging to the brand (so that the product’s main features will also be linked to the brand). Therefore, DVJ’s vision is that a proper pack test should not only force consumers to pay attention to a package in evaluating it, but should also measure whether the package is able to immediately attract (positive) attention and link its key features to the brand. This database contains evaluation, associations and attention metrics for approximately 150 packs from about 10 countries and different categories. A few example research questions can be: what determines the success of packaging? Which elements are crucial for standing out on the shelf and what determines brand recall?
-
AI based eye-tracking validation (Marketing Analytics & Management topic). Recently we have been partnering up with a party that is able to predict with an AI-based tool, where people will look at an ad. They trained their AI tool on real eye-tracking data. We would like to know more about how the AI eye-tracking metrics relate to other elements from our pretest. One can think of the following questions (but do not feel limited to those): How does the online eye-tracking correspond to our own mouse-tracking? How does the online eyetracking relate to evaluation of an ad, brand recall, and engagement? What are elements in ads that generate much attention? And which less?
-
Cross-country differences in advertising response (Marketing Analytics & Management topic). Many brands nowadays operate at least at a somewhat global scale, i.e. they sell their products, and advertise them, across multiple countries. However, we still quite often observe the exact same advertisements being used across different countries, either because of the desire to save costs, or because one assumes that the advertisement will be perceived in a similar way across different markets. However, is this actually the case? Do typical "style elements" in ads invoke the same response across countries, or are there profound differences? And if so, can these differences be conceptually linked to the attributes of these countries (e.g. their national culture, socio-economic position, et cetera)? This project would require the coding of different ads in terms of several "style elements" they may or may not be using. We have different datasets available with different types of ads, namely for TV, TikTok, Online video, or OOH. We also have one specific dataset containing only Christmas TV commercials.
-
Valuable open ended feedback (Marketing Analytics & Management topic). In marketing research, we aim to collect respondents opinions and attitudes around a plethora of topics. Much market research relies only on quantitative measures such as pre-defined statements and questions. However, the danger is that important things are missed. And therefore, it is important to start with letting respondents share their stories and associations around a certain topic, without limiting them to pre-defined statements. However, the added value of this approach all lies within the richness of the open answers. We already did some internal tests to see how we can enhance richness of the open answers, for example by using a social nudge and through AI-SmartProbing technology (using AI to ask follow-up questions). What are more ways to ensure qualitatively rich open answers in online survey research? Are there any new ways known in academic research and if so, how effective are these? And related to this: what are cost and time efficient ways to filter out AI-generated answers?
WHO ARE WE LOOKING FOR?
A master student (M/F/O) with a marketing (research) or psychology/consumer behaviour background. Do you recognise yourself in these points? We are looking for someone who:
-
Is studying econometrics, data science, computer science, marketing management, marketing research, psychology, (technical) business studies, or anything related
-
Is interested in consumer research and the why behind the what
-
Loves (marketing) data and modelling
-
Knows how to handle big data from multiple sources
-
Can identify interesting research topics based on (patterns in) the available data
-
Has excellent knowledge of different statistical software packages such as SPSS or R
-
Has excellent knowledge of MS Office
-
Is fluent in English
-
Is willing to go the extra mile
-
Has high quality standards
