No items found.
Success Case
Datahunt builds high-quality data based on the experience and understanding of professional engineers compared to other companies. In addition, a consulting team specializing in data voucher support projects helps with the A to Z of support projects, from the design stage to project selection. There are many companies that have invested in Data Hunt's vision and capabilities. Let's take a look at how R&ONE, the creator of Korea's first outdoor activity platform, Pairplay, improved user experience during the data voucher support program.
The DataVoucher Support Project is a government program that provides vouchers for data purchase and processing costs to early and mid-sized companies, small and medium-sized enterprises, and budding entrepreneurs. DataHunt has been selected as a supplier for four consecutive years since 2019, and was recognized as a best practice in 2020, garnering attention from the data processing industry.Check out some of the real-life examples of clients who have collaborated withData Hunt below.
R&One's outdoor activity platform, Pairplay, is a service that enables the entire nation to make exercise a way of life by gathering with people around them at the time they need it. It is said that the challenge of forming an exercise habit is most effective when done 'with someone', so we started Pairplay with the hope that anyone can use it to start exercising and form a habit.
Pairplay aims to be a service that allows users to find the perfect workout group. In this process, we use data and AI efficiently. To this end, we felt the need to refine data such as information entered when signing up, information on clubs opened and applied for by users, and posts and comments made in community forums.
We started looking for a data voucher provider for this task, which is a great opportunity for small and medium-sized enterprises. After we started working with DataHunt, we realized that we could improve the quality of the service smoothly.
In short, we worked on labeling TEXT data for training the recommendation model. In short,Netflix's recommendation system is composed of sophisticated algorithms.However, it is said that users are less than 50% satisfied with the recommendations, so the process of refining and processing data to improve Pairplay's recommendation model started from the user's point of view.
The engineers at Datahunt, who collaborated with us, had a lot of experience in text data cleansing and recommendation AI models. The people in the operations team also had a good understanding of recommendation models and algorithms, so they were able to easily implement the model that R&O pursued.
It's the same mountain climbing, but the difficulty level is different depending on which mountain you climb. Through the data voucher support project, we have broken down the categorization into specific categories and advanced it so that it can be linked to the time and number of people in the meeting. New users would get more granular choices, and existing users would get more sophisticated recommendations as they built up their activity history.
Building on the prior work described above, the greater variety of workouts Pairplay offered paved the way for more users. Our users are also much more satisfied, and we're excited about the growth we're seeing.
The best part of our collaboration with DataHunt is the consulting. Building a recommendation model is probably one of the most common infrastructures for platform service companies, and it is important to express goal alignment and social impact in order to meet the screening criteria. There were many internal discussions during this process, but Data Hunt's close consulting helped us to smoothly organize and successfully select the project.
Since the upgrade ofData Hunt's classification system, the number of comments and engagements generated by users has increased, and I think we can collaborate on advancing the recommendation model by utilizing additional new data.
I'm sure many demanders have a lot of trouble choosing a supplier, and while it's good to look at the company's website, I think it's important to check whether there isa good fit between the companies through active communication.
In the era of big data, there are many suppliers that can process data, but there are not many companies that understand the business vision and orientation of demand companies. DataHunt has been a very capable partner in the data voucher application process, not only because of its attitude of implementing functions together, but also because of its extensive expertise. I would like to thankDataHunt for their hard work and sincerity.
.svg)
