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Chain of Demand

How Bluebell Taiwan Used Chain of Demand’s AI Models

Marked as the “the brand behind the brands,” Bluebell Group has been helping luxury, premium, and lifestyle brands establish their names in Asia since 1954. With a massive distribution network set up for over 60+ years, they have helped over 150 brand partners, established over 600 online and brick-and-mortar stores that span across 10+ countries across the continent. 

 

Targeted for the Asian consumer, they serve as an accelerator for retailers to adapt to local markets. Jimmy Choo and Moschino are just a few of the names among their curated list of exclusive brands.

 

Regarding the former, Jimmy Choo is a global brand based out of the United Kingdom known prominently for its luxurious, high-end women’s shoes. Over the years, the company has become a fashion house, extending its product lines into handbags, accessories, and fragrances but has continued to maintain its mark as a high-end fashion staple for shoes. 

 

The latter brand, Moschino, was created in 1983 by the late Franco Moschino. Famous for their colorful, eccentric designs, the Italian high fashion brand is most prominently known for its specialty in leather accessories, shoes, and bags.

Bluebell Footwear

bluebell-jimmychoo-header

To demonstrate how our solution could help with their namesake category, footwear, we aimed to improve the bottom line for the brand’s Taiwanese market (operated by the Bluebell Group) and increase the predictive accuracy of best and worst sellers.

The recent increase in online shopping has decreased the demand for in-store shopping. Many retailers, including Jimmy Choo, have begun to notice in-store sales slowing down over the last few years. 

 

With fewer people purchasing their items in the US, and markets like China set to surpass the US in terms of retail sales, brands like Jimmy Choo have been further pushing their efforts in the Asian market. While growth in Asia has been strong over the last few years, with the market continually changing, Jimmy Choo sees opportunities to improve areas in their business, for the market.  

 

Our revenues in Taiwan, HK and Malaysia have significantly increased in the last few years, and we need to keep adapting to the market changes and upgrading our processes in order to maintain the trend in the future. Improving our merchandising is one of the areas we will be focusing on, with the help of Chain of Demand. (Alex Misseri, Head of Digital Transformation and Revenues)

Predicting footwear sales for the AW 2018 season, the pilot process took about three months to complete, where data from sales, product, competitors, product images, and social media were cleaned, collected, and analyzed.

 

Up until this point, sales forecasts mainly focused on historical sales data across multiple Excel files and depended on the company’s professional expertise. However, unlike apparel, high-fashion footwear is much more meticulous, with many factors to be considered. For this very reason, we integrated plenty of more data sources into the model:

 

External Data Sources Used Internal Data Sources Used
Weather data

Holiday data

Stock market

Unemployment rates

Social media

Competitor data

Marketing data

Product data

Historical sales data

Product Images

Business Process data

The results were based on a comparison of our predictive models against the brand’s historical sales for the Taiwanese market. Based on the pilot, we optimized our models to add value to where it was needed most. Analyzing over 780 SKUs, our final predictive models achieved: 

% Accuracy in predicting best and worst sellers 90%
% Accuracy predicting units sold (down to color & size SKUs) 81%

Upon completion of the pilot, we were able to demonstrate that the Jimmy Choo’s Taiwanese market had the potential to see the following benefits from Chain of Demand’s AI recommendations: 

bluebell-jimmy-choo-results-01
bluebell-jimmy-choo-results-02

Bluebell Fashion

bluebell-moschino-main-header

While the parent company of the brand, Aeffe Group, may have flatlined sales since 2014, the entrance of Moschino’s then-new Creative Director Jeremy Scott demonstrated his ability to keep the brand’s popularity and sales increasing over the years. With revenue growth of nearly 7%, making up close to 65% of the parent company’s total sales since 2014, this increased performance only continued on throughout 2016, with Aeffe’s net income doubling due to Moschino. 

The rising performance of Moschino has largely been attributed to its greater expansion into Asia, with the sales of this region showing an increase of 24%, especially excellent growth from Greater China (28% growth). This was a huge increase compared to the other markets that saw declines in top-line growth. To continue its growth in Asia, they knew they needed a solution that would be able to predict sales in the Asia market by looking beyond local data.

 

The way consumers shop has changed in the last decade, and will continue to evolve. What they buy has changed too, and we’re now trying, with the help of Chain of Demand, to predict what they will be interested in a few months from now. (Alex Misseri, Head of Digital Transformation and Revenues)

For the pilot, we focused on predicting sales of each SKU from the AW 2018 ready-to-wear dress collections, down to color and size, 6 months before the start of the season. The goal was to support their buyers’ decisions with data-driven recommendations for their open-to-buy.

 

Bluebell believed there was still an opportunity to further optimize inventory and improve margins for Moschino’s Taiwanese market. To cater to this, we tailor-made predictive models, which included the integration of the following data sources, into our Retaillect AI system.

 

External Data Sources Used Internal Data Sources Used
Weather data

Holiday data

Stock market

Unemployment rates

Social media

Competitor data

Product Attributes

Design

Historical sales data

Product Images

Business Process data

The results were based on the comparison of our predictive models against the brand’s historical sales for the Taiwanese market. The main goal of the pilot was to show a prediction accuracy of 80% or above in predicting best and worst sellers for their said market. After the three-month process, we were able to demonstrate the following results:

% Accuracy in predicting best and worst sellers 80%
% Accuracy predicting units sold (down to color & size SKUs) 89%

Upon completion of the pilot, we were able to demonstrate that Moschino would have the potential to see the following benefits from Chain of Demand’s AI recommendations:

bluebell-moschino-results-01
bluebell-moschino-results-02

Our Advantage: Simple & Straightforward

As more international brands continue to focus on markets like China and other Asian countries, it is important to stay up-to-date with the latest technologies and data relevant to the local market’s tendencies. 

 

To remain competitive and continue strengthening sales for years to come, companies like Bluebell can use the help of advanced predictive analytics solutions like Chain of Demand to make better buying decisions for improved forecasting in specific markets across their network of brands.

 

More and more, retailers of all types – from high-end luxury to casual wear – suffer from the changing landscape of offline to online shopping. Geopolitical and socio-economic changes also play a large part in consumer spending. Taking these data sources into consideration, our fashion-focused AI system predicts consumer demand from multiple angles, helping brands and retailers increase profit and minimize markdowns.

 

Chain of Demand’s pilot process is simple and straightforward, allowing us to move ahead quickly and see how well their fashion-focused algorithms fit into our business. (Alex Misseri, Head of Digital Transformation and Revenues)

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