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Great post, Noam! Love the points you bring up in context of LinkedIn. I completely agree that LinkedIn can, in fact, decide to attack Glassdoor’s core business. If you think about it, LinkedIn already has – for 300+ million professionals in the world – most of the information that Glasssdoor seeks, e.g., users’ current and previous jobs, titles, etc., and has existing properties such as Groups which they can use to engage in salary and interview questions related information. However, the real question is whether users would want to engage in such conversations on a platform such as LinkedIn. I, personally, have only shared such information on platforms such as Glassdoor on an anonymous basis. As long as most users would want to only anonymously share such sensitive information, I think Glassdoor is well protected from competition from LinkedIn.

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On December 14, 2015, S commented on WHY YELP ISN’T RELEVANT + WHAT CAN BE DONE :

Excellent points here, YSH! I loved how you narrated in the first part of the post. I agree with most of the concerns you have raised here. One of the biggest factors that negatively impacts the utility of Yelp scores is the herding effect you allude to. If 99% ratings lie between 3.5 and 4.5, that’s just not an efficient and useful scoring scale. I wonder if Yelp can provide the user with guidelines and best practices when they’re entering reviews to get them to give more divergent ratings to restaurants. On fraudulent reviews, adopting the approach that Gmail takes with the “Report Spam” feature is a great way to use crowd-sourced information to detect and act on spam.

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On December 13, 2015, S commented on Quora: Crowdsourced Answers :

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?

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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.

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On November 22, 2015, Shashank Chaudhary commented on Gmail: ensuring a spam-free inbox with Machine Learning :

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.

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On November 22, 2015, Shashank Chaudhary commented on Airbnb Leverages Machine Learning to Maximize Host and Company Revenue :

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.

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On October 5, 2015, Shashank Chaudhary commented on Is Nextdoor the next big social network? :

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.

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On October 5, 2015, Shashank Chaudhary commented on Alexa, Amazon and the Beauty of a Platform that Makes No Money :

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.

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On October 5, 2015, Shashank Chaudhary commented on Zuckerberg’s Big Bet on a New Set of Network Benefits :

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.

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On September 14, 2015, Shashank Chaudhary commented on Instacart: Shopping for groceries has never been more fun :

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.

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On September 14, 2015, Shashank Chaudhary commented on Airbnb: Stop Locking Guests Out! :

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.

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On September 14, 2015, Shashank Chaudhary commented on Pa(in)Pal: A Company In Denial :

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

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On September 14, 2015, Shashank Chaudhary commented on Digital Matchmakers Win! :

Interesting article – I had no idea that Tinder, OK Cupid, and Match are all owned by the same entity. A very smart strategy indeed of creating value by “following” the consumer through their various life stages. “Churn” is usually bad for companies, but Tinder users churn only to move to OK Cupid or match.com like portals. It all stays in the family!