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TEAM 41

Max Groenewegen 608812

Yuhang Ding 754458

Loran Knoop 784189

Anna Junkersdorf 786906

Currently, medical resources are scarce around the world. Take the Netherlands as an example, patients often cannot get same-day responses from doctors and lack sufficient understanding of their own health. Meanwhile, medical expenditures are increasing in many places. In 2024, healthcare spending in the US accounts for 17.16% of GDP, with over 60% of the population prioritizing health as their main concern ( Netherlands, S., 2025).

In this context, the market for health support-related products is continuously growing. Health apps that can track exercise habits and measure physiological indicators are attracting more and more users. However, current health apps in the market have relatively limited functions and still have much room for improvement. Therefore, we launched HealthMate AI.

GenAI is closely integrated into HealthMate AI’s core functions. GenAI can parse complex medical records, turning them into easy-to-understand personalized health recommendations that adjust over time as a user’s health status changes. Additionally, it has a virtual health assistant that gives real-time answers to users’ health questions, so people don’t need frequent visits to healthcare professionals just for basic inquiries. The AI provides medication reminders and analyzes drug interactions too—it identifies potential side effects and lowers medication risks. If an emergency happens, HealthMate AI will send automatic notifications to the user’s general practitioner or a nearby hospital. The real-time data from smart devices will be used to predict health risks after given permission.

Its business model centers on B2B data services and B2C value-added subscriptions. The B2B part mainly targets insurance companies, combining user health data to generate anonymous, aggregated reports. This helps insurers predict disease risks, provide decision support, and create stable enterprise-level revenue. The B2C side provides free features including health data tracking and basic advice. For additional valuable services including personalized health plans and more detailed medication suggestions, a subscription fee will be charged, which matches the payment needs of chronic disease patients and those focused on health.

Medical and health information is undoubtedly sensitive privacy. Therefore, the terms and conditions of HealthMate AI must comply with relevant laws and regulations, including GDPR of Europe and HIPAA of the United States. In terms of user satisfaction, we promptly respond to user feedback and upgrade the app’s features according to market demand. When the app has just entered the market, we will enhance exposure through cooperation with hospitals and insurance companies. HealthMate AI will offer different versionings and provide new users with a period of free trial, in order to collect market feedback and adjust the marketing strategy for the next phase.

Reference

Netherlands, S. (2025, May 27). Spending on health care up by 8.1 percent in 2024. Statistics Netherlands. https://www.cbs.nl/en-gb/news/2025/22/spending-on-health-care-up-by-8-1-percent-in-2024

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These years, a beauty test has become popular in South Korea and then in east Asia, which is called color analysis. Color analysis is a method of image management that classifies people into four seasonal types—spring, summer, autumn, and winter. At first, beauty clinic started with physical colorful clothes. They offer clothes of various clothes and make customers wear them. By comparing and analyzing the visual effect that colors combined the skin tone, hair color, and eye color, the customers will get personal style suggestions. For example, individuals with a warm-toned spring type suit bright yellows and oranges, while those with a cool-toned winter type are better suited to highly saturated blues and reds.

Such a color test in offline beauty clinic costs around 100 euro one time. However, with the help of GenAI, we can do it for free. I took a selfie under natural sunlight, without makeup or filter, then used the edit function in my phone to get the RGB value of my hair color, skin color and eye color. After that, I sent those RGB values to ChatGPT and asked it to give me advice following the format of color analysis. Finally I got a detailed report on beauty suggestions like clothes style and makeup style, saying I was the deep autumn type, which turned out to fit me very well.

Another GenAI function I use usually is generating pictures. My part time job is about managing a social media so I need to post many pictures according to the text. I tell ChatGPT to make a poster about a webinar, with captions of its theme, time and location. Unfortunately, in most cases the picture is not what I imagined, even I gave a clear instruction. I usually had to make adjustments for four or five times before getting a just-so-so pic. At first, I guessed the free version was the one to blame and maybe a premium could do better. But after a month of premium version I gave up. The picture generation is just not so satisfying as text analyzing function.

I believe if I give a descriptive instruction following the coding format, the picture it generates could be perfect. But the problem is that I use GenAI tool to save time and energy. If I have to type the programming code for a picture, why don’t I just make a new picture in Photoshop? In my opinion, GenAI tools are already quite smart about analysis and reactions to very clear quantitative orders. More improvements could be done in more abstract and more practical areas, especially under qualitative instructions. For example, when I say “zip up the character’s jacket, keeping the rest of the image unchanged”, don’t generate a picture where the person in it has two right eyes.

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When studying about the four business models, I happened to have a cup of tea by my side. The tea bag brand is Lipton, from Unilever. After some researches I started to think about the relation of the four business models and this world’s leading company.

Unilever is a multinational consumer goods company headquartered in the Netherlands and the United Kingdom. It mainly operates in the daily consumer goods sector, covering areas such as food, beverages, cleaning products, personal care, health, and nutrition and so on. Unilever has many well-known brands worldwide (such as Lipton, Ben & Jerry’s, Dove, Lux, Omo, etc.), and its products are almost universally available, making it one of the world’s leading fast-moving consumer goods companies. Unilever was chosen as the unit of analysis for its exceptional commitment to strategies and actions that contribute to society and the planet (Iglesias et al., 2022).

Unilever’s business model mainly revolves around innovation and integration across multiple sectors, with fast-moving consumer goods at its core, with strategies like sustainable development, brand strength, and global operations to achieve profitable growth.

When it comes to basic raw materials and components like sugar, packaging bottles, and cartons, Unilever operates just like any big manufacturer with a traditional supplier model.As a powerful buyer, Unilever sets clear specifications, quality standards, and pricing requirements. They pick the most cost-effective and reliable suppliers through methods like bidding.

Another newer model is that Unilever puts forward a specific challenge then they call for solutions from universities or research institutions. Once they find a suitable solution, Unilever integrates this external technology into their product development process, production lines, and global brands through licensing, co-development, or acquisition. Finally, they leverage their massive production, marketing, and distribution network to launch the final product in the global market( Laursen & Andersen, 2016). At this point, Unilever acts as the integrator.

Therefore, we can’t just say that Unilever is or isn’t a certain kind of model. Their operations are a mix of different approaches, which is pretty much the norm for big multinational companies these days: using the most efficient business models in different parts of their operations.

References:

Iglesias, O., Mingione, M., Ind, N., & Markovic, S. (2022). How to build a conscientious corporate brand together with business partners: A case study of Unilever. Industrial Marketing Management, 109, 1–13. https://doi.org/10.1016/j.indmarman.2022.12.008

Laursen, L. N., & Andersen, P. H. (2016). Supplier involvement in NPD: A quasi-experiment at Unilever. Industrial Marketing Management, 58, 162–171. https://doi.org/10.1016/j.indmarman.2016.05.023