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Improve new product & product assortment merchandising

  • Industry
    Retail
  • Location
    Hong Kong
  • Company Size
    120,000+

Client

AS Watson is the world’s largest international health and beauty retailer, completing 5 billion customer transactions each year across 15,700 retail stores and online brands. The company operates across 25 countries and employs more than 140,000 worldwide.

Objective

Improve new product & product assortment merchandising

Anticipating a market trend can make all the difference for retailers like AS Watson. Knowing which new products to bring to market, or how to shift existing assortments historically requires countless hours of merchandisers pouring over blogs, magazines, competitor websites, and more. As new and alternative resources like social media grow in customer relevance, new challenges and opportunities have emerged with identifying new trends.

AS Watson engaged Reboot AI to help design a data & AI powered solution that could improve their existing merchandising, thus maximizing revenues and minimizing unsold inventories.

Client Challenges

Reboot AI Engagement

Solution

Working closely with the client’s senior leaders, Reboot AI helped develop a strategy most likely to realize optimal results. While a fully automated merchandising process appealed to some, to achieve the highest quality results, and to ensure internal adoption, the solution had to focus on augmenting human merchandisers, not replacing them.

With this in mind, Reboot AI designed an approach playing to the relative strengths of algorithmic solutions and human merchandisers. First, Reboot AI’s web crawlers and scrapers aggregated raw data in quantities impossible for human employees to match. Next, multiple machine learning and AI techniques processed raw content into multiple hierarchical topics, and ranked each by size and rate of growth.

For ease of use by the end user, this output data then integrated with Tableau, where human merchandisers could explore and apply their intuition, creativity, and deep expertise to draw conclusions impossible for modern AI to match in quality.

Results

Due to Reboot AI’s explicit “human-AI collaboration” design guidance, senior leaders were able to achieve their goals, while merchandiser staff were able to focus more of their work on value add areas.

  • 3000 daily social media posts from top pages analyzed
  • 20,000 popular web pages regularly analyzed
  • 3 output hierarchical levels of topic data
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