Arting Chang

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On November 14, 2018, Arting Chang commented on American Express has Struck Gold with Machine Learning :

Very interesting article. I was thinking about your first question as I was going through, and I think It would be close to impossible for American Express to recommend products to customers without raising issues of privacy and authenticity. I think about how I uncomfortable feel when Facebook, Instagram or generally on Google I receive a recommendation on a product to buy that was related to something I had spoken to from a friend without actually searching for it. I think with a credit card, when I know the company would be intimately familiar with my spending, the last thing I would like to see is recommendations. I feel it’s at the same time deeply invading and highly uncomfortable.

However, to your second question, the company can and should certainly use their own data to better segment the customers. As it’s internal, and you can curate the services and marketing messages to a specific group of people, it would create the same visceral reaction as the former example I had mentioned. It do think it’s crucial for the company to strive to present its marketing message as a place of authenticity as opposed to a place where customers may feel like their privacy is invaded.

Ali – great article. You pose two interesting questions – I’ve always thought of soundcloud as a raw platform for artists to showcase their work and invite others to comment and express their thoughts on their creative products. However, spotify has basically offered a similar platform, albeit without the crowdsourcing aspect of commentary and feedback (sans the 5 star rating system). What’s interesting to me is your question on whether consumers are aware of what they want with the usage of open innovation with regards to creative content consumption. I’ve found the majority of music recommended to me by spotify to be in the ballpark of what I’d be interested in but hardly a homerun. Of all the streaming services, Pandora, surprisingly, has been the one that I’ve felt has been most able to capitalize on machine learning and crowd sourcing to curate a playlist that is more aligned with my tastes. While I think open innovation will be very relevant to the industry as a whole going forward, I’m unsure if Soundcloud is the platform that would be able to benefit from the most from that trend. The company needs to work to figure out what they’ll stand for among its competitors going forward.

Great article – very enjoyable to read. I do think that Glossier should consider investing in machine learning that would operate in a way similar to the crowdsourcing model that the firm currently employs. As Glossier continues to scale, it would be too manual for employees that Glossier to sift through all of the comments from customers, although the accuracy would be better than utilizing machine learning in the beginning. I agree with you on the idea of adopting a “stitch fix” model – finding patterns, large qualitative trends, and potentially personalizing the recommendation would be important to have as the company continues to grow – Glossier definitely should start investing in those capabilities now if the firm would like to remain competitive. However, to your other question, I wouldn’t worry about machine learning threatening Glossier’s brand image – if anything it would allow the brand to potentially focus more on developing personalized recommendations for all of its customers as opposed to devoting most of its time sifting through the data to develop product that is more “one product fits all” based on the majority of opinions. I believe that customers would react very postiviely to these changes.

On November 13, 2018, Arting Chang commented on Who Defines Beauty: Humans or Meitu? :

What an interesting article. Every time I visit Taiwan or China, I notice people emphasizing specific aspects of beauty particular to the East Asian culture. With the advent of social media, the standards seem to have converged on something that everyone strives to achieve. When you see famous “wang hong” stars in platforms such as Weibo and similar rankings, the features are all eerily similar – pale skin, narrow faces, large eyes, and “cute” gestures or expressions. We’ve long known that plastic surgery has been a route that many individuals have gone towards in order to align with what society may consider “attractive” – but we can save the philosophical debate on autonomy and authenticity for another day. I firmly believe that platforms like Meitu have a social responsibility to make sure that they do not contribute to an unhealthy standard of what is considered “attractive” on a global basis. Instead of recommended filters, the application should just provide the filters or editing tools that people can customize, without suggestions on what should be the “right” beauty. If platforms like Meitu do not utilize its influence to try and push back from potentially socially destructive behavior, issues of self-perception and self-confidence will continue soaring.

On November 13, 2018, Arting Chang commented on Burberry: Digitizing Luxury Retail with Machine Learning :

Great article Charlotte – and the question you posed regarding whether to develop ML in house is really interesting. I would argue that Burberry should invest now in developing capabilities to develop ML in-house as opposed to outsourcing. Two reasons – first, the Company would be able to react real time to data as it flows in, and developing the ML muscle will allow the company to utilize the algorithm and data collected more effectively. Second, more than ever, competitors are trying to gain an edge on collecting the right data to improve their customer segmentation and increase revenues; the possibility of outsourced ML data falling into the wrong hands is not worth the risk.

Regarding your second question, I do think quality is incredibly important, especially to a luxury retail brand. Accuracy of the data is also key, as these luxury retail houses don’t subscribe to constant change in styles and collections that are created in a Fall or Spring collection have much slower turnovers. Burberry’s margin of error that it can afford is much smaller than those of fast fashion houses as well. I would encourage Burberry to focus on increasing accuracy and quality over speed instead of finding ways to accelerate ML data collection.

Great article – as an avid user of Task Rabbit in my New York days, there were several facts in your article that I wasn’t aware of, especially the acquisition by Ikea. I do believe that Ikea could offer the capital and resources needed for Task Rabbit to further expand its offerings, but hopefully it has learned from peers both within and out of the industry that often you need to leave start-ups alone after acquisition without forcing a certain culture or infrastructure. I’m optimistic that while Ikea may be biased towards more hardline and hardware related services that Task Rabbit would continue to offer a variety of services through its contractual labor model via crowdsourcing and realize the demand may not single handedly come from areas that Ikea places focuses on.

With regards to your first question though, on whether Task Rabbit should get ahead of potential labor benefit risks, I think they definitely should. If you look at the highest rated taskers, these individuals have often put in numerous hours per week over what would be considered “part-time” or “contractual labor” employment. However, I wonder if a group of taskers decide to bring the issue to Management, would all taskers agree? Likely those that have reaped the benefits form the platform would like to keep their job as is should the Company decide to compromise their revenue stream in favor of providing benefits. It would be interesting to see how Task Rabbit confronts this issue as it continues to grow.