A global retailer with 400+ stores across China faced pressure from online and smaller-format stores. Traffic was in a multi-year decline because customers found the retailer’s stores inconvenient and unappealing. Furthermore, the retailer lacked a holistic digital membership program and therefore had limited ability to assess the reasons for customer churn or to re-engage lapsed customers.

Mother and child looking at items in store
Woman grocery shopping


Tomorrow conducted shop-alongs, focus groups, and in-depth customer interviews to identify the friction points causing customers to reduce trips. Our research showed that customers struggled to find merchandise, to identify relevant promotions and — most important – to check out quickly. Customers pinpointed the wait times as a key reason to shop elsewhere.

To alleviate these pain points, Tomorrow proposed the creation of digital platform. The platform would help customers find products, provide personalized recommendations on promotions, and streamline checkout through a scan-and-pay system.

By using an agile development approach, Tomorrow launched the platform within 10 weeks. The rollout began with a proof of concept in one store and subsequently expanded to over 400 stores. While Tomorrow was leading this initiative, we were also analyzing data collected from users of the platform. Our analysis informed the development of another platform feature: an engine based on machine learning that enhances the personalization of item recommendations and promotions.

Woman employee looking at stock in online shopping app


Customers immediately saw the benefit of the digital platform, and adoption moved quickly from 7% in POC to nearly 50% in the best stores, all within 12 months.

Meanwhile, the personalization engine has rapidly emerged as a key competitive advantage for our client, and it has attracted additional brand investment from suppliers who are seeking this type of efficient, 1-to-1 channel to grow their sales.