DataFurn: furniture enterprise analytics
Platform-as-a-Service data analytics for the furniture industry
Thanks to the spread of social media and on-line communities, furniture manufacturers now have access to a pool of readily available data on end-users’ preferences, opinions about brands and products, as well as to users willing to provide new ideas, assess solutions and co-design new products. Manufacturers can extract a lot of value from contact with their customers, moving away from an outdated mass manufacturing scenario towards a more individualized production. Furniture designers can now take customers’ opinions into account in the design of innovative, more customized and trending products that customers demand.
In other words, companies will produce better products for more commercial value if they have a broader and clearer view of what their customers are discussing online. However, despite the tremendous data overflow around, the furniture industry lags behind due to several inherent challenges:
- Social media tools monitor the global trends, but they do not provide actionable insights to the domain’s SMEs
- Furniture SMEs have a limited online presence and therefore the current content is biased towards larger brands;
- Trend prediction methods cannot easily distinguish between promotional and genuine content while facing significant difficulties when it comes to image recognition.
- Lack of a formalized digital strategy in the majority of furniture manufacturers
With EOSC-hub, Suite5 and AIDIMME designed, developed and deployed DataFurn – a furniture analytics platform-as-a-service. DataFurn collects, analyzes and visualizes online content (e.g. from social media platforms, blogs), detects useful product-related content, extracts relevant furniture product-service topics/features, monitors brand influence and customer interactions and forecasts furniture trends for the upcoming seasons.
How they used EOSC-hub services
DataFurn used EGI Cloud Compute to deploy its architecture using ten virtual machines, 80 vCPUs and 160 GBs RAM. Until June 2019, the platform consumed approximately 170 thousand virtual CPU hours and about 340 million RAM hours.
The value proposal of DataFurn
DataFurn stands out in relation to the generic social media analytics platforms by providing intuitive dashboards that have been already created and curated by furniture domain experts. In this way, DataFurn reduces the necessary effort and time to entry (for setting up, understanding and maintaining the relevant reports of interest).
Through a pay-as-you-go business model, SMEs can benefit from a dashboard that leverages untapped information, transforms it into actionable knowledge through intuitive and user-friendly (not requiring any technical background) interfaces and acts as a decision support system (e.g. by allowing for different comparisons in time and in content, better understanding how the discussions and weak signals from other neighbouring domains influence the furniture domain).
How EOSC-hub helped
EOSC-hub has provided to DataFurn the necessary infrastructures in order to quickly and efficiently test and deploy its various platform releases. Through the computing power of the EOSC-hub services, DataFurn had the opportunity to experiment on different resource-intensive algorithms for analyzing and indexing the related content that has been curated for manufacturing SMEs.
Fifteen companies are currently in the process of testing the DataFurn platform. Each company has received a personalized email to access the platform. The AIDIMME market experts have prepared a dedicated dashboard to present to the companies the numerous benefits that the DataFurn platform offers them. Future plans also include to attract more companies in the sector, and possibly expand to other domains.