Positioning Luxury Retail for a Digital Future
The world is becoming increasingly digital. Luxury retail is no exception. By 2025, nearly 20% of luxury sales will occur online, and 80% of the purchase decisions will be influenced digitally1(Exhibit 1). Tech-savvy Millennial and Gen Z consumers will account for 45% of the global luxury market then2. Research on purchasing behavior of this group suggests that their key purchase criteria include: personalization, resonance with brand value, and experience over possession3. These trends hold two important implications for the future of luxury retail. First, having a robust ecommerce presence will not be optional, but a must-have. Second, to win, brands must not only know a great deal about their consumers, but they must also translate that consumer knowledge into products and services that are personalized, responsive and relatable. This is where ML comes into play.
Machine Learning in Retail
In its simplest form, ML algorithms identify patterns in large datasets and use them to generate predictions. In the world of retail, the most common applications are personalized product recommendations and marketing campaigns. Amazon generates 35% of total sales from personalized recommendations4, which are created based on browsing and purchase histories of both the individual shoppers and those with similar shopping patterns. Similarly, digitally-native beauty brand Glossier mines fan comments on its beauty blog to inform new product development and launch decisions5. Tumi, a high-end luggage brand, uses ML to customize its outbound marketing campaigns (e.g., emails and 1-on-1 chats) based on a connected database of emails, social media activities and browsing across the web6. Across these businesses, ML creates a competitive advantage in how they acquire, retain and increase lifetime value of consumers.
Burberry: A Case Study
Whereas most luxury brands hesitate to fully embrace digital, Burberry has made deliberate decisions to invest in and integrate ML into its digital strategy.
Since 2006, the British fashion label has been offering data-driven personalized product recommendations, both online and in-store7. These programs had allegedly led to 50% increase in repeat purchases by 2015. Burberry launched Facebook chatbots during the 2016 London Fashion Week. Like Amazon’s Alexa, these “smart assistants” offered dynamic 1-on-1 interactions with patrons, with key functionalities including selling products from the latest collection and showing behind-the-scene inspirations8. Though rudimentary, the chatbot exhibited abilities to respond to user-generated phrases beyond pre-set buttons, indicating integration of natural language processing capabilities (See Screenshot in Exhibit 2).
Looking ahead, continuous improvements to the ML algorithms require large amounts of high quality training data. To elicit voluntary data sharing from its online community, Burberry has developed an advanced data platform integrated with Facebook and Twitter, to which consumers are encouraged to upload photos of themselves in Burberry products9. These data will enable the brand to further customize the products and experiences they offer. For the longer term, Burberry has announced plans to continue investing in ML across front- and back-end functions. The company’s SVP of IT discussed plans to use ML to automate supporting functions (e.g., development and operations, testing), improve scenario modeling for planning and logistics, and improve security and fraud prevention through ML applications10.
Beyond these planned initiatives, I would argue there is space for Burberry to think outside the box even more with regards to potential ML applications. Some considerations below:
- Ideation: Similar to Glossier, use user-generated data to guide and inform new product development pipeline.
- Immersive ecommerce: Combine chatbot technology with VR/AR applications to create a 100% personalized, immersive digital store, where virtual shopping assistants can replicate the in-store service experience for consumers at home.
- Full product personalization: Based on personal dimensional data and expressed historical preferences, create demos of unique, individually-designed products. Offer exclusively to high-spenders. Produce on a made-to-order basis.
- Supply chain management: Forecast demand at the SKU level to limit inventory pressure and better manage vendor relationships. Wayfair has demonstrated success in this regard11.
- Pricing: Pricing is another obvious area of ML application. However, since luxury brands generally adopt a no-discount strategy, I would deprioritize this lever.
In light of the trends and ideas suggested so far, the following questions merit more thought:
- How should luxury retail brands decide whether to develop ML capabilities in-house or outsource to a 3rd party? If outsourced, how to mitigate concerns about data privacy (both to protect consumers and to preserve brands’ competitive advantage)?
- How to accelerate the process of data collection for training and improving algorithms without compromising data quality (e.g., personal, interconnected, comprehensive, accurate)?
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1. Achille, A., Marchessou, S. and Remy, N. (2018). Luxury in the age of digital Darwinism. [online] McKinsey & Company. Available at: https://www.mckinsey.com/industries/retail/our-insights/luxury-in-the-age-of-digital-darwinism [Accessed 10 Nov. 2018].
2. D’Arpizio, C. (2018). Spring Luxury Update. [online] Bain. Available at: https://www.bain.com/about/media-center/press-releases/2017/global-personal-luxury-goods-market-expected-to-grow-by-2-4-percent/ [Accessed 10 Nov. 2018].
10. AI Business. (2018). Where are Burberry with AI? Exclusive Interview with David Harris, SVP of IT. [online] Available at: https://aibusiness.com/where-are-burberry-with-ai-exclusive-interview-with-david-harris-svp-of-it/ [Accessed 10 Nov. 2018].
3. Woo, A. (2018). Understanding The Research On Millennial Shopping Behaviors. [online] Forbes. Available at: https://www.forbes.com/sites/forbesagencycouncil/2018/06/04/understanding-the-research-on-millennial-shopping-behaviors/ [Accessed 10 Nov. 2018].
4. MacKenzie, I., Meyer, C. and Noble, S. (2018). How retailers can keep up with consumers. [online] McKinsey & Company. Available at: https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers [Accessed 10 Nov. 2018].
5. Milnes, H. (2018). How Glossier uses data to make content and commerce work. [online] Digiday. Available at: https://digiday.com/marketing/glossier-uses-data-make-content-commerce-work/ [Accessed 10 Nov. 2018].
6. Milnes, H. (2018). How Tumi is using AI in marketing campaigns, online and in stores. [online] Digiday. Available at: https://digiday.com/marketing/tumi-using-ai-marketing-campaigns-online-stores/ [Accessed 10 Nov. 2018].
7. Marr, B. (2018). The Amazing Ways Burberry Is Using Artificial Intelligence And Big Data To Drive Success. [online] Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2017/09/25/the-amazing-ways-burberry-is-using-artificial-intelligence-and-big-data-to-drive-success/ [Accessed 10 Nov. 2018].
8. Maruti Techlabs. (2018). Chatbots as your Fashion Adviser. [online] Available at: https://www.marutitech.com/chatbots-as-your-fashion-adviser/ [Accessed 10 Nov. 2018].
9. Mittal, S. (2018). How To Leverage Digital Tech To Drive Revenue Growth. [online] Forbes. Available at: https://www.forbes.com/sites/forbescommunicationscouncil/2018/10/02/how-to-leverage-digital-tech-to-drive-revenue-growth/ [Accessed 10 Nov. 2018].
11. Supply Chain 247. (2018). Machine Learning Steps Up Retail Performance. [online] Available at: https://www.supplychain247.com/paper/machine_learning_steps_up_retail_performance [Accessed 10 Nov. 2018].