Great read – thank you!
Robo-advisor itself seems to be a fairly powerful idea given much of the fanfares surrounding the likes of Betterment and Wealthfront. I wonder whether embedding the core robo-advising capabilities in digital assistants (i.e., conversational agents or bots) would make robo-advisors even more powerful? With the red-hot growth of digital assistant adoption as exemplified by Amazon Alexa, Google Home, and Microsoft Cortana, robo-advising seems poised to be the next “killer skill” within a popular digital assistant to achieve personalization at scale.
Andrew Ng, one of the harbingers of machine learning and deep learning, has identified the following 4 attributes that tend to enable a company to survive and thrive as an AI-first (as opposed to product-first or platform-first) company:
(1) Unified data warehouse within the entire company
(2) Strategic data asset acquisition
(3) Pervasive automation
(4) AI-compatible job descriptions and HR practices
Some of the attributes may prove useful for Nielsen to sustain itself as the market leader in the digital era. Please find the video below for your reference (Andrew’s comment on AI company starts at the 17 minute mark):
“Ultimately, ATVI’s continued success will depend on whether it can formulate the right strategy and assemble the right organization to take advantage of these digital megatrends.”
There is one extremely positive tailwind for ATVI to assemble the “right organization” to achieve a winning position in the digital megatrends: the executive team of ATVI has very low turnover (relative to a typical tech company) and Bobby Kotick is the single longest-serving CEO among S&P500 companies. We should not discount the organizational congruency and time-honored execution power that promise to enable a successful pivot for ATIV.
Amazing post. Diehard Warcraft III and Hearthstone fan here 🙂
Amazing blogpost. As a Spotify user, I can certainly attest to the “creepiness” (in a good way) of Spotify knowing me so intimately. To diversify their existing subscription-based business model, I am wondering if Spotify can expose their state-of-the-art recommendation system as an enterprise cloud offering (e.g., in the form of a RESTful API) that might potentially generalize to another non-music domains and verticals as well?
Thank you so much again for the wonderful contribution!
Thanks for your thoughtful and valuable contribution, Sean! Your description of Netflix’s recommendation engine reminds me of the perennial dilemma on the “echo chamber” effect of information retrieval and aggregation systems. Recommending similar digital contents may indeed drive engagement metrics in the short run, but may also shield the users from discovering thrilling new content like they’ve never seen before. Going forward, how can Netflix leverage its data analytics capability to strike the fine balance between content relevance vs. content discovery?
What an amazing post, Austin. Kudos from a fellow Section Geeer.
Your blogpost reminds me of Jeff Bezos’ 2016 letter to the shareholders, where he underscored the importance of decision-making velocity in addition to decision-making quality. From your description, I got a sense that McDonald has echoed Bezos’ philosophy of maximizing decision-making velocity by aggressively shortening the cycle time of experimentation and learning through a variety of lean methods.
Fascinating read. Thank you.
Totally agree with @Juan. Hopefully IKEA can turn the one-off bootcamp into a recurring “fountain of creativity” for its various business verticals with a sustainable feedback loop for iterative learning. Wonderful post!
Great post! I am wondering how the decision makers at Glassdoor think about the defensibility of their crowd-sourcing platform? It does not yet have an effective way to “lock in” users, hence a high multi-homing propensity. The opportunity for further product differentiation also seems quite limited.
Fascinating read! Coming from the tech industry, I know many companies use Wikipedia content as training data for their machine learning and natural language processing (NLP) algorithms, and to build out knowledge graphs for various use cases. Wikipedia is not only a gift to humanity, but also a blessing to the domain of AI and ML.
As a frequent user of StackOverflow (the software engineer’s version of Quora), I am wondering if Quora could serve targeted “job posting” ads and evolve into a talent acquisition platform similar to StackOverflow? For potential employers, Quora is a compelling platform for them to identify high-potential talents. For potential job seekers, they could readily use Quora’s reputation system to boost their professional credentials, while being incentivized to contribute even more high-quality content.
In Public Entrepreneurship last semester, a guest speaker from Waze told the class that Google, in acquiring Waze, had promised to keep Waze as a standalone brand and business entity. On one hand, I am fascinated by the crowdsourcing model of Waze as described compellingly in your blogpost. On the other hand, I am a little curious as to why Google hasn’t been very proactive in integrating such an amazing business model innovation (especially the crowd elements) into Google map as well?
Great point on Spotify being a “content discovery platform” instead of a pure-play music retail service via streaming. The recommendation algorithm behind the content discovery feature in turn benefits from the “learning effect” as covered in the Qihu case: as Spotify logs and analyzes a larger and larger sample of user preferences and listening patterns, its recommendation algorithm becomes better and better, forming a competitive moat against new entrants and potential complementor-turned-competitors in the digital content streaming industry.
Fascinating read – thank you!
The fact that Opendoor is an early mover/incumbent in this space makes the business even more attractive. By nature, homeowners cannot “multi-home” (no pun intended) because they simply cannot sell their properties to another competitive after they are sold to Opendoor. Extremely high multi-homing cost + strong cross-side network effects = a winner-takes-all/winner-takes-most market where Opendoor has established the crucial early moving advantage.
Thank you for your thorough research and thoughtful analysis! Wonderful read.
Following up on Juan’s comment, your account of Spotify’s story reminds me of some of the incumbents in the Chinese market, namely Xiami Music and QQ Music. Although the sample size is quite small, it seems that music streaming platforms have a higher likelihood to flourish when they position themselves to work with “ecosystem partners” who might subsidize part of their (intrinsically unattractive) business model and serve as organic user acquisition channels. In this regard, Spotify is yet to find her Mr. Right, the Western equivalence of Tencent/QQ, NetEast, etc. to share her growing pains and joys.
Umm… perhaps the jury is still out as to whether Wealthfront and other robo-advisors are “winners” yet? From the perspective of the technology adoption “S” curve, the likes of Weathfront have been quite successful in acquiring early adopters who tend to be more tech-savvy and have higher risk tolerance. How would Weathfront “cross the chasm” to attract mainstream users who may not yet have the “cultural fit” to turn their wealth portfolio to the hand of a tech startup?
Our case and class discussion on Ant Financial got me to revisit your blogpost on Venmo. Both Venmo and Ant Financial started as a pure-play peer-to-peer payment solution that aimed to disrupt incumbents through affordability, mobile-friendliness, and convenience. Fast forward to today, and Venmo is still “product” company while Ant Financial has evolved into a platform business with multiple growth opportunities and valuable data assets. To me, the key difference was the presence of an “ecosystem partner” to sustain the growth and competitive advantage of the product of interest. Without a channel partner (e.g., Alibaba for Ant Financial), Venmo may find it difficult to differentiate and grow in the increasingly commoditized peer-to-peer payment market.