Chinese Speech Recognition Company focused on Natural Language Processing
Unisound Information Technology Co. Ltd. also called Unisound or Yunzhisheng in Chinese is a speech recognition and artificial intelligence company based in Beijing. Current applications of its algorithms include smart medical plans, smart home solutions and intelligent car solutions (Unisound, 2019).
The company was founded in 2012 by Huang Wei, its current CEO, and made recent news by being described worth over $1 billion dollar, which makes it one of China’s unicorns (Yelin et al. 2018). The company states that its vision is to “make the future enjoyable” and it sees the technology industry moving from “device-centric” to “user-centric” and “data-centric”.
Next to its endeavors in the industrial production, medical, mobility and teaching sector; one of Unisound’s core products is “UniHome”. UniHome offers its users IoT for apartments and houses including voice-controlled devices, intelligent execution decisions and sound source localization.
To build these services and the required products, Unisound focuses most of its resources on recruiting the best talent, strategically selecting production & marketing locations and collaborating with various top-notch partners. Partners include Lenovo, Intel Qualcomm and Huawei. Next to leveraging its own resources, Unisound extensively leverages the knowledge of external developer by allowing them to create programs for its IoT platforms and devices. Unisound provides open source developer tools and guidance on its website (Unisound, 2019).
What differentiates Unisound from its competitors is its proprietary and patented voice recognition chip. It allows programs to accurately and quickly understand semantics, connect is with a user profile and synthesis text to speech.
While the company’s core product is a solution for the business-to-consumer segment, most of Unisound’s customers, if counted in groups, are business-to-business customers. Its education solutions are sold to schools, its medical solutions are sold to hospitals and its car solutions to car manufacturers and original equipment manufacturers (Unisound, 2019). While it is not explicitly stated on their website, it can be assumed that it tries to maintain strong customer relationships with large business customers and keep start-ups and scale-ups that soon might opt for expanding their solution by voice control, at arm’s length.
The company’s revenue was estimated to be 13 million Euro, i.e. 100 million yuan, in 2018 (Yelin, 2018). Costs are expected to have exceeded the 13 million Euro revenue as the company invested a lot in R&D. However, over time costs are expected to settle at around 80% of revenue (Damodoran, 2018).
While, to the best of my knowledge, Unisound is not planning to involve customers for feeding its voice recognition systems for instance, the company’s open source developer tools can be seen as crowd-sourcing customer knowledge (Olson, 2013). Moreover, data collected, showing when and how users engage with the voice recognition and AI systems can be used. While many aspects, such as the specifics of what is said to whom and when may not be used for analytics, other parts of customer data can be used to further improve the products and services.
Literature on crowd sourcing
Blohm et al. (2018), finds 4 archetypes of crowd sourcing of which Unisound can be awarded to the “open collaboration” type. It invites contributors to team up to jointly solve complex problems that require input of many contributors. By providing extensive documentation, multiple software downloads such as SDK and helping developers to manage their applications, Unisound assures quality and regulates the use of the open source platform as suggested by Blohm et al. (2018). However, neither does the company provide incentives to developers nor does it provide support through coaching, tutorials or on-boarding. Blohm et al. also recommends to market the solutions as crowd-sourced, which Unisound does.
Moreover, Nishikawa et al. (2017) find that companies which market their product as crowd-sourced will have increased market performance. Their experiment finds that the performance can go up by 20%. Unisound does emphasize their use of crowd-sourcing solutions on one of its landing pages that visitors will find even if they do not browse to the “developers” section.
Lastly, Schlaeger et al.(2018), emphasize the importance of customization. Unisound does to my knowledge not close the feedback loop from customers to developers extensively enough to help developers customize solutions. The data analytics board is kept rather simple by tracking engagement but direct communication about customer wished for changes is not facilitated by rating systems and other functions yet (Unisound, 2019)
Efficiency of the Business Model
Overall it can be concluded that Unisound has a well-functioning and thought through business model that can be assumed to create the desired results. The revenue and cost structure as well as the key activities and partners match with the value proposition and expectations of an R&D driven company. The focus on its in-house hardware improvement and out-sourced product development seems efficient and is expected to succeed. However, incentives for developers should be created to increased developer input. Moreover, Unisound could think about tracking customers attempts to engage with the systems that failed to improve it.
Blohm, I., Zogaj, S., Bretschneider, U., & Leimeister, J. M. (2018). How to manage crowdsourcing platforms effectively?. California Management Review, 60(2), 122-149.
Damodoran. 2018. Retrieved on 12.03.2019 at http://pages.stern.nyu.edu/~adamodar/
Nishikawa, H., Schreier, M., Fuchs, C., & Ogawa, S. (2017). The value of marketing crowdsourced new products as such: Evidence from two randomized field experiments. Journal of Marketing Research, 54(4), 525-539.
Schlager, T., Hildebrand, C., Häubl, G., Franke, N., & Herrmann, A. (2018). Social product-customization systems: Peer input, conformity, and consumers’ evaluation of customized products. Journal of Management Information Systems, 35(1), 319-349.
Olson. 2013. Retreived on 11.03.2019 at https://www.researchgate.net/publication/257704728_Crowdsourcing_and_open_source_software_participation
Unisound, 2019 Retrieved at 05.03.2019 on https://www.unisound.com/usc.html
Yelin. M, Zhanqu Z. and Quijan H. (2018) Retrieved at 05.03.2019 on