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Great article Amanda!

I think this phase for TAL should be the data collection phase, where the company gets access to all this data from this increase in subscription. The company should then use this data for its benefits. An example could include TAL moving towards more of competency based education to help students focus directly on their weaknesses and improve them (i.e. adapt the study plan to the speed at which the student is learning, the mistakes he/she has done, etc.). Another way they could leverage the data is to monetize it by selling to/ partnering with schools to provide them high-level performance reports and recommendations for study plans based on closely monitored performance of students.
TAL could also gamify the experience to keep the students engage for longer, beyond the COVID crisis.

Thanks for sharing Marius! Great article!!

Wayfair’s digital business model helped it survive and do well in this pandemic. In fact, it was interesting to see how they adapted their delivery model to meet the crisis (usually customers had to sign, but they stopped that for now to avoid contact). They are also continuously monitoring their delivery personnel to check for symptoms (e.g. checking their temperature).
But I agree with your recommendations for them to keep their momentum going. I also believe the company should work on reducing its customer acquisition costs (advertising is a huge part of their spending). Hopefully they will be able to leverage this deluge of data to develop better targeting algorithms.

Great article C Xu! Thanks for sharing – I agree with the recommendations you have developed.

Trust is vital for a platform such as Shopify. Multiple sites were added to the platform during this pandemic related to covid, most of which were not real. The company should be wary about letting these kind of sites and should worry about the impact it would have on its reputation. One way to deal with this is to require more information when registering to the platform to very the sites are legit (right now all you need is an email address and a card) – this could registration process less seamless but would ensure a certain level of quality.

Great article – thank you for sharing!

I agree with the challenges that you’ve mentioned.
On the data perspective, I agree that the expression of emotions varies between cultures, gender, etc. and they also vary based on the situation the person is in (stressful, personal, professional, etc.) In addition, research conducted by the Association for Psychological Science states that: “the relationship between facial expression and emotion is nebulous, convoluted and far from universal”. Another perspective lies with the ability of humans to understand their own emotions – so if most of the time we don’t know the output (e.g. am i happy? am i sad?), how can we train even if we have all the needed inputs? (I’m not sure unsupervised learning works in this context).

I also completely agree with the ethical perspective. Facial recognition technologies are said to be used in certain situations to further racial segregation and exploitation. Focusing on emotional AI, the worry would also be on the undesirable use cases that can arise from its use (e.g. retailers exploiting sadness or happiness to push products).

Great article Jona – Thank you!
I completely agree with the challenges mentioned regarding bias in the data. As you mentioned, the company should really invest in data cleansing and feature engineering. Even though removing variables associated with bias (e.g. gender, race) could help alleviate this problem, it is enough. A study showed that most algorithms will still find other variables that will lead to the same bias (ZIP codes, club participation: e.g. “women in investing”, etc.), some of which are not obvious. As you mentioned, continuously monitoring your algorithms and actively looking for bias and then adjusting accordingly will potentially help alleviate some of that problem.
Another worry I have, also related to the data, is the common effect referred to as the “LinkedIn Alter Ego”. This refers to most people over-inflating their credentials on LinkedIn which could cause some negative network effects. Could LinkedIn use AI to try to identify these people? or use some sort of other mechanisms to do so? (e.g. manager input, business card scanning)

Thank you for this article!
I’m a Duolingo user and I was not aware of the placement test. I believe I started using the app before this feature was rolled out. I always wished that Duolingo should develop this test – a form of competency based testing – given that it has the data required to do so. I will check how I can take this test – thank you 🙂
Right now, each category has 5 levels that you can test out of. I feel the next step for Duolingo would be to venture more into personalized learning plans for each individual based on their placement test, the speed at which they’ve been learning, the number of times they’ve failed certain levels, etc. This could also be complemented by a survey at the beginning that asks users what they wish to focus on (work conversations, etc.)

Great article!

I think the concerns you mentioned are very valid. Regarding data, I wonder if Farfetch can support brands on its website by providing them analytical reports regarding user behavior on the platform, things like click-rate on items, browsing, etc. (the brands will only be able to see behavior around their own brand, and high-level aggregate data on other generic user behaviors). By providing these reports, Farfetch would’ve created a value-add service that will make the relationship with brands stickier and that could potentially make brands prefer this platform over other (maybe reducing brand multi-homing).
Farfetch could also use data to reduce customer multi-homing. As you mentioned, they could create a loyalty program and fuel with personalized and target product recommendations and discounts. This will also help improve the customer experience and increase retention.

Thank you for this great article.
I completely agree that disintermediation is one of the biggest problem Upwork is facing. As you mentioned, they have already changed their pricing strcuture to try to reduce disintermediation (higher fees for the first transaction -20%- and then lower for other transactions – 5%). I believe this is helpful but is not enough.
I agree that they should focus more on providing value-added services and increase the stickiness of the platform. They already started doing this – a cool example i saw was allowing clients to monitor the work of freelancers in real-time through screenshots (although it got some negative reactions). Maybe they could take a page from the book of ZBJ and partner with co-working spaces like WeWork to provide space for freelancers on its platform for a subsidized fee.

Thanks Petra – Great article!

I completely agree about the need to reduce customer acquisition costs. Maybe some sort of referral program could be helpful. For example, if a woman already got married but still referred her friend to use the platforms, they would both get some sort of discounts (e.g. for the woman that already got married, maybe a discount on frames or books for wedding pictures, or a discount on the fee for a designer to create a collage of the picture)
Another idea would be to incentivize the bride to add the bridesmaids/ friends on the platform and assign tasks to them (e.g. bachelorette party planning). Once these bridesmaids engage with the platform and notice how easy and user friendly it is, they would be more likely to use it for their own plans.
Another idea would be to add the wedding registry function on the website to raise awareness about the platform (i.e. guests would know about it).

Great post! Netflix did indeed change the media landscape.

As you mentioned, Netflix has amassed huge amounts of data on its customers. It is interesting to see how they are using this data for multiple use cases such as for content development as you mentioned, as well as for content selection (e.g. green light, etc.), next-best product recommendations, customer retention, etc. I would be interested to see how they plan to push the barriers on that front.

In addition, it would be interesting to see how they plan to incorporate other digital technologies that could be impacting media such as virtual reality and augmented reality. They have already started experimenting with these (e.g. AR app for Stranger things, VR with Black Mirror) – looking forward to see how they innovate further on that front before they get disrupted.

Great post! Very informative.

I really admire how the company manages its partnerships and acquisitions.

A clear example of this relates to the Venmo acquisition that you have mentioned. Paypal made a strategic decision to keep the brand separate given how popular the brand was and how simple and easy the interface was to use. Paypal knew that by keeping it separate it would keep the “magic” of Venmo and would not impact the engagement of users, which worked. They used the Paypal expertise and scale to help grow the platform behind the curtains. Great acquisition play!

Another partnership you mentioned is related to Cybersecurity. It is interesting to see that Paypal has been leveraging AI to try to improve its risk management practices!

Great article! I completely agree with the levers you have mentioned and your assessment of each.

Potentially, a lever you had mentioned that they can expand on more is experience. Macy can potentially leverage key digital trends to allow it to improve the customer journey, increase their engagements and reduce costs. These can include digitizing the in-store shopping experience and leveraging data analytics.
For examples on digitizing the in-store shopping experience, Macy can launch shoppable digital windows (similar to Selfridge for example) or increase engagement with smart fitting rooms (similar to Ralph Lauren for example).
Macy can also double down on its data analytics efforts to improve customer acquisition, customer service and customer retention as well as improve its product offerings (e.g. predict taste of customers) and its operations (e.g. inventory management, workforce management), among other things.