Flipkart - Walmart deal: What Snapdeal lost, and lessons for entrepreneurs

The market is all buzzing with the Walmart Flipkart deal that happened last week. And why shouldn't it be, at $16bn USD, this is the biggest acquisition deal to have happened in India ever. The fact that the fortune #1 company is involved in it, investing in the e-commerce sector that it couldn't crack on its own, in an emerging economy like India, is only going to help the investor sentiment for Indian companies in general. But I am not writing this post to cover this deal - there is enough coverage out there already.

Note: Back of paper calculations follow, with lot of hindsight knowledge.

As I was reading through the news, I couldn't help wonder that Flipkart's valuation has almost doubled from $11.6bn in July 2017 to $22bn in less than an year. And how Snapdeal has missed the proverbial bus, spectacularly.

Here is a breakup of all funding raised by Snapdeal (all figures in USD):

$12M Nexus Venture, Indo-US (Kalaari) Venture Partners
$45M Bessemer and existing
$50M eBay and existing
$75M Softbank
$133M eBay, Kalaari Capital, Nexus Venture, Bessemer, Intel Capital and Saama Capital
$105M BlackRock, Temasek Holdings, Premji Invest and others
$647M Softbank
$500M Alibaba, Foxconn and SoftBank
$200M Ontario Teachers' Pension Plan
$17.5M (INR 113 crore) Nexus Venture

Total: $1.8 billion USD overall

Once valued at $6.5Bn, Snapdeal had been offered a USD $950M payout by Flipkart in June 2017. If Snapdeal had agreed, Flipkart could be having a better cumulative market share at around 45% compared to the 34% it has currently (at that time, Flipkart + Snapdeal stood at 37+14 % respectively). It would also have a wider reach amongst sellers, at least at around 300K vs the 100K it is believed to have currently. Snapdeal was a pure marketplace play, and it alone had 300K sellers. Finally, Snapdeal wouldn't have needed to sell FreeCharge, UniCommerce, and Vulcan Express to keep itself financially afloat. Without the sale at a steep discount of USD $60M from the purchase price of USD $400M, Freecharge would have been a readily available platform to complement UPI based PhonePe.

Given that Walmart's deal would have included private valuations of Myntra and Jabong as well within the final numbers, its anyone's guess how having Snapdeal in the clutch would have led the investors getting an even better valuation for the Flipkart group of companies. Even with a doubling up of valuation that already happened, Snapdeal's potential value would stand at ~ USD $1.8Bn today.

My optimistic guess is that the deal would have happened at a USD $25-26bn valuation if Snapdeal was also included, since having Snapdeal in kitty would have made Flipkart the market leader by a huge margin (compared to just the 5-7% lead it has right now over Amazon), with a stronger seller base, giving an ~2.3 multiplier.

In terms of the personal fortunes made, the founders of Snapdeal were reported to be making ~INR 250 Crore at the time of deal, having already made ~150 crore from previous stake sale. The Walmart deal would have meant that within an year of stay at Flipkart, they would be getting double the value they were initially receiving. Given Walmart is not keen on retaining non - core founders (Sachin Bansal to exit), it could have been an easy way out for Snapdeal founders as well. Employees of Snapdeal wouldn't have needed to be laid off, since Flipkart would have retained most of them.

Finally, the investors would stand to exactly recover the base investments they made in Snapdeal. Investors after all, like everyone else, want good return on their money. Snapdeal founders deciding at the last minute to kill the deal didn't really help anyone, probably except themselves. Its funny to see that a stake projected worth USD $450M is now being sold for INR 40 Crore, at almost 1/60th of the price when it could have been a very different story for the first investors who put their trust and money in you.

While Snapdeal isn't dead yet - it actually reported an increase in number of transactions - to me, there are 3 important lessons here for all entrepreneur's to remember:

1. Never underestimate the deal making abilities of a Power Investor, in this case, Softbank (PowerInvestor:Investing::10XProgrammer:Coding)
2. Good things happen to those who wait. Shortsightedness can (literally) prove costly in the startup world
3. While coming on top at the first position is best, a position at the pedestal is still worth more than being an also-ran.

PS: I'm an ex Amazon techie, but wasn't high up in the food chain to know any of the sensitive market penetration details or strategies involved. All the content is my opinion alone, and builds from publicly available information.

AWS Summit 2018, Mumbai

Today, I attended the AWS Cloud Summit in Mumbai. Held at the massive Bombay Exhibition Center, the event would have been attended by over 3000+ participants by my estimates. Overall, there were 6 different tracks for the talks: 

• Build: Building on AWS
• Scale: Scaling your AWS
• Secure: secure your position in the cloud
• Migrate: Migrating from on-prem to Cloud
• Innovate: Innovation with the Cloud
• Impact: Cloud in Public Sector & Education for digital India

Based on my current interests, I figured that Migrate would be far fetched for me, and the talks under Secure seemed completely promotional. So, I made a list of the ones that I liked from the description, and here are the talks that I attended which avoided most conflicts:

• 11:30 – 12:10 <Scale> Optimizing Costs as You Scale on AWS
• 13:30 – 14:10 <Build> Accelerate Business Innovation Using AWS Serverless Technologies
• 14:00 – 14:30 <Impact> Addressing Risk and Compliance in Public Sector
• 14:30 – 15:00 <Impact> Cloud Procurement in Public Sector - Making It Work
• 15:00 – 15:30 <Impact> Smart Cities – The Journey Toward Greater Economic, Social & Environmental Achievement
• 16:00 – 16:40 <Innovate> Building Engaging Voice Experiences with Amazon Alexa
• 17:00 – 17:40 <Innovate> Data Driven Applications with AWS AppSync and GraphQL

You may notice that I attended a few talks from the Impact series as well. I felt that this track is also to mid and large sized organisations which can have elements of bureaucracy in their processes, and hence the content may be relevant. There was one more track on startups, that was in an open lounge setup (rather than conference setup for the others) - but I found the talks in it repetitive and plain copies of what the folks at AWS booths were anyway telling manually.

Since the AWS rep who confirmed my participation informed that registrations would begin at 7.45 AM, I left by 7 AM and managed to be there by 7.35 AM. Even though officially registrations were to start at 8, by the time I went in, the sweatshirts meant for first 1500 participants were over. Though there was more swag from various booths, and one at the end of the conf, so I guess it was ok.

This Summit had over 40 Companies partnering at different level. Some of the renowned ones included: Intel, Vmware, Arista networks, Dell EMC, Druva, Kaspersky Labs, mongoDB, SendGrid, SumoLogic, Talend, Knowlarity, Kuliza

Overall, I found the summit and it talks to be quite informative. My favorite talk, not surprisingly, was the first one: Optimizing Costs as You Scale on AWS. Having worked at multiple startups, and tried my hands at few ideas of my own, I believe AWS costs are something every one tries to optimise sooner or later.

Among the booths, I really enjoyed visiting the ones under innovations. These stalls featured startups which are working on next gen ideas, like Wattman by Zenatix for power consumption analysis, Imaginate for VR conferences, and Scapic for generating AR and VR content. Amongst the AWS booths, the one informing on the EdTech program was really helpful. EdTech is an AWS initiative which helps less than 5 year old EdTech startup get access to credits, communities and senior folks to make their product better. Its live only in the US right now, but will be launched in India soon, and is definitely something to watch out for.

Alexa meetup: Designing Multimodal Skills

Yesterday, I attended a meetup on designing multimodal skills for Alexa, and in this post I'll share some of the interesting pointers from the presentation and discussion.

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-> We are in the era of Voice UI

While terminals were the primary mode of interacting with computers when they were first invented in the 70's, systems have evolved over the years to support different types of interaction paradigms - from GUI, to Web, to Mobile. In one way, 2010's are are the era of Voice User Interface (VUI).

Voice comes naturally to us, and we have been using it for thousands of years for interacting with one another. Voice, is the next big computing platform

-> Cloud enables experiences that were not possible earlier

While sentient chat systems and bots have been imagined forever, our efforts used to lack earlier because of the limited computing available to the edge machine.

For example, designing an AI assisstant like Alexa broadly involves many complex steps, like:

• Speech Recognition
• Machine Learning based Natural Language Understanding 
• convert user's utterances to an intent
• Text to Speech

This was not possible earlier when all the processing was done by the device. Cloud computing enables AI like Alexa to flourish, by offloading all computing from the end device.

-> Multi Modal experiences are the way forward

Multi modal experiences refer to applications where there are multiple modes of experiencing the skill. For example, with an echo spot, your users can have both voice and visual experiences.

While the focus is always on voice first apps in case of Alexa, experiences can now also be augmented with the help of visual cues.

-> The introduction of multi modal approaches call for new design principals

While Alexa is not yet suited for cases where there are long of list of items, or complex nesting between them, there are some general design guidelines that can be followed:

• design voice first - you just don't know if the user will have a visual feed or not
• do not nest actions within list items - it becomes poor Voice UX
• choose images that look great on all devices - while echo spot has a circular screen, an echo show has a rectangular screen
• use font overrides sparingly, and markups in meaningful ways
• a good way to design better Voice UX is to write the interactions down and read them in a roleplay - you better change it if it doesn't sound right

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The presentation was followed by a hands on Alexa development session, where attendees created a fresh alexa skill for space facts, and deployed a pre-coded lambda on the cloud from the serverless repository. This was a standard JSON - in - JSON - out kind of session, which helped familiarise participants with Alexa developer portal and lambda deployement process.

The meetup ended with a presentation by team YellowAnt, who were demoing the public beta notifications feature of Alexa. YellowAnt is a chatops startup, and the gist of the demo idea is that the Alexa now supports notifications in beta. These notifications can be leveraged to ping end devops users to notify them of system updates (downtime/deployments done etc).

However, given that Alexa is a voice first ecosystem, it was very interesting to hear the Alexa AI pronounce lengthy text and URLs character by character, and try reading multiple notifications one after the other. All this would have made sense as strings over email/chat notifications, but ended up loosing all the context when delivered via voice. To me, this re-emphasized the need to design voice first applications with Alexa.

Overall, I found the meetup very helpful in understanding the Alexa ecosystem, and learnt a lot of cool new things.

Book Review - Trump: The Art Of The Deal

While going through some news a couple of days back, I came over the news article on a US team visiting China for trade negotiations. These talks have been necessitated due to the mutual embargoes US and then China placed on trade from each other. In line with Donald Trump's many other statements, actions and policies which fly in the face of conventional procedures and wisdom, the unilateral move by US in March to impose trade sanctions on China had left most analysts dumbfounded.

This, coupled with his anti immigration policies around restricting the H1-B visas and the associated restrictions on the EAD (Employment Authorisation Document), which are supposed to hit the Indian IT workforce hard piqued my interest. A simple question arose: why is this guy, Donald Trump, who was much vilified by US media during the elections and afterwards, able to take such an unconventional decision? (It is only recently that the negative PR he receives has started going down, and he is getting mainstream credence, due to the possibility of North Korea's denuclearization).

Going through the list of books that could help me here, I zeroed down on Trump: The Art of the Deal since he is has credits for the book, and it would contain information to his business and personal lifestyle. The guys at Amazon delivered the book quickly, and as soon as I got my hands on it, I was lost in reading it. The book has 14 chapters. It begins by recounting a week in his office (~1980's), where he gives out details of business calls he has made and received, and a gist of each of those calls. Now this is a very fascinating chapter, not because its about Donald Trump, but because it offers an example of the topmost guys at the foodchain spend their time doing business. As a lay person, I've never come across anything similar - which describes how a top executive works day in day out, with much juicy details in there. From the second chapter onwards, he starts talking about his business principles, and his business dealings. Some of his observations regarding politicians and rich people are spot on. Though the book has a solid start, I found the book to become unceremonious slowly with time. There is a lot of talk about business transactions, some of which could point be thoughtful of as boastful, and bordering on bullying.
 
In any case, I found the book a good read, some of the incidents it narrated were really insightful (not about Trump, but the wealthy folks per se). I think it is  definitely something worth checking out. Overall rating: 4.5/5  

Stanford Log-linear Part-Of-Speech Tagger

Another day, another requirement. I was looking for projects, when I came across one project asking for an integration with Stanford NLP POS Tagger. So here were 4 big words, about which I obviously needed to do some search on to understand them in detail.

A google search for the exact term gave me the page to Stanford Natural Language Processing Group's site, which had this to say:

A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like 'noun-plural'.

Digging a step further, it seems this comes already pre shipped with the nltk package. From the downloads section:

Python: NLTK (2.0+) contains an interface to the Stanford POS tagger.

And this is the package called as the Stanford Log-linear Part-Of-Speech Tagger.

Why the Log-Linear? Well, from wikipedia:

A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model, which makes it possible to apply (possibly multivariate) linear regression.

I think freelancing is a good idea once in a while - it helps one come across a multitude of technologies, and even basic reading on them helps one grasp the direction industry is moving in general.