AI Data Analytic Platform provides insights for retail stores

In May, we had an interview with the Hong Kong Economic Times (HKET). The following is a summary of the story.

AI data analytic platform

Luna2, AI data analytic platform performance was invented with 7 digits investment. It provides offline retail advise for innovative product and innovative business teams by collecting and analysing the big data of retailers and consumer behaviour. 

It also provides business matching opportunities for both retailers and innovative product companies. This can create a win-win situation for then as customer experience can be enhanced and the revenue for businesses can be increased.

Innovative product and retail channel matching point

The founder of Luna2, Damien Ng, has over 15 years of financial data management experience which is highly data-driven. After working in several start-up companies, he realised there are many innovative product startups having difficulties to connect with traditional retail channels. Launching their products into the mass market becomes extremely challenging. 

He also added that although e-commerce is popular, consumers cannot get any physical product experience if the product is launched online only. This leads to a lower chance of end sale. Therefore, he decided to apply his knowledge and collect big data for AI analytics, in order to provide the most suitable retail locations for products and service strategies. These help to enhance the competitiveness of small and medium enterprises in the retail industry. 

Optimise data sets to help finding valuable opportunities

Luna2 platform was invented 2 years ago. And 6 months ago, it was officially launched in Hong Kong. Over 1000 businesses have already registered a Luna2 account, including STEM education products, travel gadgets, smart living products etc. Some of these products have been successfully connected and collaborated with over 280 offline retail channels to test their retail sales, such as hotels, cinemas, public transportation, and restaurants.  

Data has been collecting from different retail channels continuously. Optimising the updated data sets is a way to find the most valuable retail channels from the sea of data, in order to attract related customers. And the final goal for this year is to increase the number of member to 5,000 and bring more operation opportunities to small and medium enterprises. 

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