Skip to main content

E-Commerce is booming in India

The first ball in the Indian e-commerce game has been bowled. But was it a no ball? While believers celebrate each new round of investment poured into e-commerce startups, cynics are crying hoarse that the boom will pretty soon go bust.
In a free-wheeling chat with ET, Rajan Anandan, head of Internet giant Google's India operations -- quite obviously a believer -- makes a spirited argument endorsing the view that e-commerce is booming in India. Once again. To build his argument, Anandan takes a trip down memory lane and points out that the world's largest online retailer Amazon.com went public in 1997 when there were merely 50 million internet users worldwide.
India crossed 100 million users last year despite poor internet penetration and sluggish internet speed. It is a big number, says Anandan because it makes India the third largest internet market in the world by number of users. "Our estimates are that on an average, these 100 million spend about 16 hours a week online.
This is again a big number because in the case of television, with 500 million-plus users, the average viewer spends 13 to 14 hours a week. With 100 million users, we are just getting started," says Anandan. He believes that if there exists an interesting value proposition, Indians will figure out how to get to it.
"Because everything we do, we do it the hard way," Anandan said. Of the four mega trends in the Indian internet space, the first is monetisation of the first 100 million users through e-commerce. In other words, the first big trend will be all about companies figuring out how to make money out of the 100 million users who are online. "Every tech game is a test match.
The first ball of the over has been bowled in the match for ecommerce in India," says Anandan and pointed out that last year, all of e-commerce was about $5 billion in India. Industry estimate is that it will grow to $40 billion in by 2015.
He says "we think we will get to that much before that and by 2015, we will be much bigger." He also brushes the skeptics aside, saying, "In a country, where we have had like some 17 internet startups some of which went bust in 2000, when you see 10 companies get funded, you see everyone go "oh wow!" Ten companies get funded every three hours in Silicon Valley. Think about it."
But are the valuations justified? Valuations depend on your view of growth, says Anandan. Would every single e-commerce company be valued above $100 million? "The answer is probably not," he says.
But if one believes that nontravel e-commerce is going become a $30 billion industry in four to five years, can the leaders who are reasonable well positioned be valued at above $100 million? "I think the answer is yes," says Anandan.



Labels:

Comments

Post a Comment

Popular posts from this blog

Advantages and Disadvantages of EIS Advantages of EIS Easy for upper-level executives to use, extensive computer experience is not required in operations Provides timely delivery of company summary information Information that is provided is better understood Filters data for management Improves to tracking information Offers efficiency to decision makers Disadvantages of EIS System dependent Limited functionality, by design Information overload for some managers Benefits hard to quantify High implementation costs System may become slow, large, and hard to manage Need good internal processes for data management May lead to less reliable and less secure data
1 comment
Read more

Inter-Organizational Value Chain

The value chain of   a company is part of over all value chain. The over all competitive advantage of an organization is not just dependent on the quality and efficiency of the company and quality of products but also upon the that of its suppliers and wholesalers and retailers it may use. The analysis of overall supply chain is called the value system. Different parts of the value chain 1.  Supplier     2.  Firm       3.   Channel 4 .   Buyer
Post a Comment
Read more

Big-M Method and Two-Phase Method

Big-M Method The Big-M method of handling instances with artificial  variables is the “commonsense approach”. Essentially, the notion is to make the artificial variables, through their coefficients in the objective function, so costly or unprofitable that any feasible solution to the real problem would be preferred, unless the original instance possessed no feasible solutions at all. But this means that we need to assign, in the objective function, coefficients to the artificial variables that are either very small (maximization problem) or very large (minimization problem); whatever this value,let us call it Big M . In fact, this notion is an old trick in optimization in general; we  simply associate a penalty value with variables that we do not want to be part of an ultimate solution(unless such an outcome is unavoidable). Indeed, the penalty is so costly that unless any of the  respective variables' inclusion is warranted algorithmically, such variables will ...
Post a Comment
Read more