Alibaba & the future of retail

Alibaba's data dominance and omni-channel strategy

The Role of Machine Learning

The role of machine learning in Alibaba’s product development process is immense. Part of the reason Alibaba’s machine learning capabilities are so powerful is the company’s ability to integrate data throughout its ecosystem, incorporating transaction data from shopping platforms as well as credit data from Alipay, and video consumption data from Youku, amongst many other datasets. This provides Alibaba with a broad set of behavioral information to power recommendations, and means the company can suggest both online and offline local products and services in real-time.

Alibaba is using machine learning for many aspects of its business in order to better serve its retail partners and customers. These include tools to personalise recommendations, handle customer enquiries, target advertising, and manage inventory. Alibaba’s B2C retail platform Tmall offers “Smart Selection”, an algorithm that helps recommend products to shoppers and communicates this to retailers to increase inventory and keep up with demand.

During the 2016 Singles Day, Alibaba achieved personalization for all of its retail marketplaces for the first time, from the Taobao and Tmall homepages, to other promotion pages and product detail pages. The result of this was a conversion rate 20% higher than that of non-personalized pages.

In an environment of fierce competition with retailer, Alibaba is seeking to innovate, make strategic acquisitions and expand its technological capabilities. Alibaba’s focus on innovation was underscored in 2017, when it announced a $15bn investment into a new research institute. The DAMO institute, dedicated to technological research, has a strong focus on Machine Intelligence, with the aim of developing innovations not only for Alibaba’s core e-commerce business, but for many other applications.

Alibaba’s “New Retail Strategy”

Alibaba’s “New Retail” strategy, exemplifies the company’s desire to cater to future consumer behavior and draw customers deeper into its ecosystem. The aim is to integrate online and offline shopping experiences, where interactions among consumers, inventory location and retail space are enhanced by leveraging mobile and big data. The goal is to reinvent retailers by increasing their catchment area online and offline and digitizing operations.

This initiative can be seen across both Alibaba-owned entities (including grocery store Hema and department store Intime), as well as third-party retailers.

For example, Alibaba’s Hema stores are used for regular shopping and in-store dining but also as warehouses for online orders. Alibaba uses online and offline transaction data to personalise recommendations, and geographic data to plan efficient delivery routes.

Similarly, Intime has benefited from Alibaba’s infrastructure to innovate in areas from membership to payments to logistics. In some store locations, Alibaba experimented with its automated fashion consultant FashionAI. A screen would scan item tags on products that customers were holding, then use machine learning to make suggestions on what to pair the item with.

Alibaba is also turning independent convenience stores digital through its platform “Ling Shou Tong” by equipping stores with technological tools. In return for providing analytics and a digital inventory system, Alibaba receives data on consumer behavior and the ability to use these stores as fulfilment centers.

By building out its omni-channel capabilities, Alibaba is becoming more ingrained in the daily lives of shoppers and retailers, gathering increasingly rich data which will help solidify its competitive positioning.

The next step

Alibaba may be able to do more with its machine learning capabilities and extensive datasets. Looking to Amazon, there may be an opportunity to capture an increasing share of consumers’ wallets by selectively verticalizing through launching private label products.

Given Alibaba’s data on consumer preferences, its ability to target and personalize, and its inventory management systems, the company has a unique place from which to launch new products. Alibaba understands better than most which products will be popular amongst shoppers and how to service them efficiently, optimising advertising and pricing.

Investing in private-label apparel may also give Alibaba the chance to experiment with new experiences and enhanced personalization. For example, Alibaba could trial “try before you buy” as Amazon did with its Prime Wardrobe offering whereby customers receive a package of clothing and only pay for what they keep. I also see potential in enhanced styling to deepen consumer attachment. Looking to StitchFix, Alibaba could also use its FashionAI to scale personalised styling, potentially with some human interaction. This may be a powerful proposition in the context of’s other efforts to increase personalisation such as its “white glove” last mile delivery service.

Nevertheless, there are clear downsides to these suggestions, including capital intensity and complexity of managing manufacturing processes and inventory. Moreover, should Alibaba pursue private-label brands more aggressively and cannibalise other retailers, there is a risk that they will erode the trust of their B2C clients who may seek to distribute elsewhere.

(781 words)




A Bridg to Nowhere?


Adding Value across the Value Chain — Additive Manufacturing at Siemens

Student comments on Alibaba & the future of retail

  1. It will be interesting to see if Alibaba can integrate their physical stores with online marketplace using AI. “Brick-to-click” customer purchasing is particularly important when consumers are shopping for expensive items where touching/feeling the product is essential. Embedding AI in physical stores where customers trial items can provide essential data to that target customers online and close the sale.

  2. Alibaba indeed has a powerful data ecosystem on consumers. Their “New Retail” strategy (e.g. investment in Hema supermarkets, similarly as Amazon acquired Whole Foods in the US) is capitalizing on that. Although these investments have been dilutive to Alibaba’s short term margins and have decelerated its short term stock price performance I believe Alibaba is strategically positioning itself for future performance!

  3. Interesting to read! I believe Alibaba has actually started to make their own private brand products, which is quite similar with what NetEase does. The products are mostly in the narrow category of home and clothes, under the brand “TaoBaoXinXuan”, and sold in a TaoBao store. I think they are intentionally low profile in private-brand launch, without using a separate platform yet. Given the price advantage of many vendors on the platform and big varieties of design brands, I think Alibaba has to carefully select the product category for private label development, for which quality should be much valued and stylish design is mot a must.

  4. This was an interesting piece on where you think Alibaba is going and how it is building out their capabilities and data analysis. I am particularly curious to see how the “New Retail Strategy” will pan out – especially since I am assuming a lot of these recommendations tie back to one of our initial discussions in class on how solid the input is with these algorithms. Do you think they will need designers or employees with a strong background in fashion to make recommendations – vs. basing recommendations on customers and their previous purchases. Maybe there is an option to assess how strong the incoming data is and how customers respond to these suggestions?

  5. Very interesting article. My passion lies in the intersection between retail and data analytics. I am particularly interested to see where this New Retail Strategy is going to head ! My only concern is: what will the role of the offline stores be in the next 15 years? Will we need offline stores? Or physical units will be used solely for inventory? I think AliBaba can use their data to improve the in-store experience, by customizing products to customers as they enter stores.
    Thank you for this, very interesting read 🙂

  6. The sheer amount of data Alibaba is able to compile on its customers through all of the channels you laid out is astounding. Machine learning and pattern recognition seems like an excellent tool for them and clearly they are investing heavily in this area to capitalize on that data. As you have suggested, they also can benefit from replicating the applications many of their American counterparts (ie Amazon, Stitchfix, etc) have already tested but with their expertise on the Chinese market and consumer. I’m very bullish on the company and think they have a unique position in the market and can use machine learning to really create a strong moat around the business.

  7. Very interesting article. Machine learning seems to have the potential to really separate Alibaba and JD from any other competitors or would-be competitors with massive moats from up-front investment, data, on top of the network effects from the platfrom. I wonder how much of Alibaba’s interest in owning inventory (or products) comes as a result of ceding some Tmall market share to JD. Seems like they are stuck between a tough decision – react and greater control over product – perhaps even doing private label – but potentially risk upsetting all your B2B partners. If Amazon the “vampire squid” has proven anything it’s that you can consistently put your own interests over retail partners and still dominate the market… you just have so much more negotiating leverage as the platform with the connection to the customers. This would suggest Alibaba should in fact more into launching private label. This would in turn allow it to better capitalize off of ML investments.

Leave a comment