As a platform connecting both ends, Win-win, on the one hand, provides IT capabilities (data collection & analysis, IT infrastructure and systems, etc.) to the fragmented retail stores, including convenience stores, gas station retail stores, neighborhood mom & pop shops, baby shops, and supermarkets, and, on the other hand, provides the data and insights it generates from retail stores to large FMCG brands.
By consolidating the fragmented data on multiple retail ends, Win-win is able to draw a holistic view of the retail industry dynamics across China, generating insights such as which brands or types of soft drinks are more popular in the coastal regions in the first week of July, or a sales discount to what extent can generate the highest ROI for soda water, which all feed back into the supply chain optimization operations and sales & marketing campaigns for the FMCG brands. For small retail stores, small business owners can access high-quality digital services (ERP and BI dashboards on mobile devices) to empower their business decisions (inventory mgmt., sales & promotions, etc.) without large out-of-pocket expenses. As the name of the business suggests, Win-win creates value for both ends of players in the retail sector, a win-win.
Furthermore, as Win-win serves more retail stores, it’s able to achieve accelerated learning from the accumulation of industry data (i.e., large training samples) and algorithms thanks to the feedback loops of AI; for example, the firm can run fewer experiments to reach the right conclusion by generating better hypotheses and have a better sense of what additional data to collect and include to understand the correlation. Win-win can also reach economies of scale more easily as the marginal costs of generating additional insights for a new FMCG client based on a relatively similar dataset collected from the retail stores are minimal, and Win-win can easily pool the IT development and maintenance costs across multiple clients. This provides Win-win with a better stand to play in this field than FMCG brands building tracking databases themselves as the latter faces a costly set-up process in terms of time, effort, and money.
Other competitors of Win-win include traditional consulting service providers such as Nielsen and large tech giants. With large first-hand data collected through a more automatic process, Win-win can cover a far larger range of retail stores and brands from the beginning; whereas Nielsen would usually start from very few large FMCG brands by collecting data from a set of sample regions through a manual process. With regards to tech giants, who amass huge sets of data from their online sales already, Win-win differentiates itself by avoiding direct competition with tech giants in E-commerce, but rather collects data from fragmented, offline channels and chooses product categories with relatively low online sales penetration, such as soft drinks and quick snacks. In that sense, Win-win outcompetes its competitors on scalability and sustainability.
Given the purchasing power and level of consolidation, Win-win captures values by charging the player with a deeper pocket. Win-win provides data services to retail stores for free and charges FMCG brands for insights and analysis on a project or subscription basis. But it’s never a simple buyer-seller relationship between Win-win and FMCG brands or retail stores, but a process of mutual learning and benefit in which the coordination makes everyone better off.