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Great post, Andrea! I find ways in which Quora incentivizes its users to promote quantity and quality of participation particularly interesting. Once you have a significant number of up votes on your answers, you can ask more questions and promote them to the top of relevant users’ feed. Some users, especially sales and marketing professionals or startup founders, also find the site useful to build their own personal brand or to spread awareness about their companies and products. On value capture, I wonder if there’s a potential B2B play possible by selling to companies excerpts of anonymized data on what users think about the company’s products or brands. Thoughts?

Great post! This post made me Google around a bit to better understand Zara (and other retailers’) demand prediction models. For the statisticians (or just plain nerds) out there, this link does a good job of explaining the basic premise of Zara’s regression models. https://highlyscalable.wordpress.com/2015/03/10/data-mining-problems-in-retail/

I wonder if the brand’s e-commerce channel can serve as another input to the demand prediction model. By tracking which products, colours, and designs are performing better than the others in which regions, or running A/B experiments, a retailer can successfully connect the offline with online, assuming, of course, that the preferences and behavior of the online shopper are the same as that of a retail store shopper.

Very interesting post! What is striking about many machine learning models is that the primary data that the machine seeks to “learn” from is users’ actions. Recommendation engines also find such algorithms really useful. There are primarily two different ways to think about recommendation engine algorithms: find similar items, and find similar users. The first approach seeks to assign certain properties/attributes to each item (e.g., videos in the case of Youtube, or products in the case of Amazon), and then recommends new items which have properties similar to the item being viewed/purchased. The second approach assigns properties/attributes to users to segment them into clusters, then recommends what other users in the same segment viewed/purchased. Most recommendation engines find the latter approach to be more effective and have greater “predictive” power than the former.

Great post! What I like most about Airbnb’s pricing tool is that it’s dynamic in nature and that the price recommendation changes from time to time based on market conditions – the tool buzzes the renter to raise prices during the peak season and slash them down if the nearby hotel is running a discount over the weekend to boost their room utilization levels.

I disagree with Noam here. I think Nextdoor’s key strengths over Facebook are two fold : (1) It’s content is more “local” and therefore more relevant in nature, and (2) Most of this content seems to be generated by “word of mouth” and “recommendations”, which is a more effective marketing / sales pitch for businesses than display or search ads. There’s research that shows that Word-of-mouth marketing is the fastest growing segment in the US advertising industry, because (1) it creates independent credibility, and (2) it is self-generated and spreads virally. In a media saturated world where consumers see hundreds of brands a day and thousands of TV commercials a year, word-of-mouth marketing can actually be more effective. With innovative ad-formats, Nextdoor can potentially charge much more “per click” for its ads than Facebook or Google can.

Great analysis, Rob! Although I agree with MaskOfZorro that Amazon’s ecosystem is somewhat limited to shopping and prime music / video, I do think they can make a dent with their Alexa strategy given all popular Google applications (think Gmail, Calendar, Youtube, Maps, etc.) have open APIs that are readily available to both Amazon and Alexa app developers. This essentially means Amazon and third-party developers can make Alexa apps that access content from and perform actions on users’ Google app accounts. Apple, however, has lowered it’s multi-homing factors for both users and developers by “closing” its ecosystem, making it difficult for Alexa to become useful for loyal iOS users.

Interesting post. I wonder, though, if Google is going to beat Facebook at the VR game with a strategy that’s almost antithetical to the Oculus approach to VR. In 2014, Google launched Google Cardboard — a $20 device made of cardboard that is essentially a “good enough”, low cost version of a VR headset. VR hardware is incredibly hard to get right — It has traditionally needed tonnes of R&D investment, and there is only a very few widely acclaimed high quality VR headsets on the market. Google, however, is now poised to create strong indirect network effects in the VR market rather quickly compared to Oculus headsets which haven’t been launched in the market yet. Google’s $20 headsets will acquire users and in turn attract developers way quicker than $200+ Oculus headsets.

Love your analysis in this post, Nazli! I’d happily have labeled Instacart a winner already, if it wasn’t for this summer. I interned in Seattle, where most people would just use Amazon Fresh and had never heard of Instacart before. I also visited the Bay Area for a couple of days, only to find my friends using Google shopping express almost exclusively. I think Instacart may be the hot favorite in the Boston area because other competing services don’t exist here yet or have newly launched operations here. I’ve no idea how the service is doing in NYC area — perhaps, that may be the best sample to look at given both Amazon and Google deliver groceries there.

Also, agree with CSS that lower price may emerge as a key differentiator for Amazon Fresh and Google Shopping Express, quickly wiping away the competitive advantage of being “easy and intuitive to use” that Instacart currently enjoys. I guess we’d have to wait and watch to see how this market dynamic unfolds.

Great article, Sara! Thanks for sharing these insights. Out of the various interesting value creation ideas that you share, I really like the “target the business traveler” one. I think Airbnb can enable businesses to save a ton of money by partnering with the large corporate travel agencies and integrating with the self-service travel booking portals that are frequently used in the workplace.

I’d be cognizant of ideas such as “keyless entry” which have a huge logistical component attached to their execution and don’t align well with AirBnB’s core capabilities which are in software. To evaluate such ideas, one needs to be able to understand how much time / effort / money would implementation involve vs. how much does the idea move the needle in terms of better customer experience, average booking size, and retention.

Completely agree with you, Chrisoula! Given the clunky UX and lack of innovation of Paypal’s part, their past partners have now gone ahead and implemented home-grown payments solutions. The facebook friend-to-friend payment button that’s free to use is a good example. Here’s how it looks
http://techcrunch.com/2015/03/17/facebook-pay/#.wxkvpu:Bq7o