Super Search: http://itasoftware.com/
US:
Consolidated
Expedia.com
Travelocity.com
Orbitz.com
Airfare comparison:
Kayak.com
Sidestep.com
Mobissimo.com
International airfare:
from US to China:
UK: http://www.travel.co.uk/
Wednesday, October 31, 2007
Friday, July 06, 2007
From group buy to social commerce
A friend of mine recently bought a piano. He did it with several other families. He represented them to contact the piano dealer and negotiated a bargain price because of the volume and then get it delivered. Buy together to increase the bargaining power is a classic strategy. Yet how to use it in an innovative way is interesting to see.
Back in late 90s, some startups offer such business model of buying together completely online. There are still sites like storemob to allow local shoppers find each other via message board and get a discount price by shopping together. However, how to do this completely online is still a challenge. Somehow it does not work well though it looks like a perfectly model to be implemented on the Web. Think about it, all this model needs is group sufficient number of people together, the Web provide an unprecedented opportunity for this, now people living in different places could buy together! It is easier to find enough people compared with one can only find via local resident or circle of friends. So why there are no good website for this?
There are several possibilities that prevent such site from popular.
First, it needs a higher threshold of critical mass. For an online retailer like Amazon.com or auction site like eBay.com, users use them frequently and immediately. More important, they can easily find what they want. However, for a team buy site, even one can list a whole catalog of every product, finding enough team members to buy the same product takes a long time. How long? It is hard to see. So the success rate of buying a product with discount price was largely offset by the risk of could not find enough buyers -- in other words, wasteful waiting.
Second, the savings has to be large to justify the efforts. People will take the effort to find others and get a discount price for a piano but few will do that for a $15 pencil sharpener. The popularity of eBay and Amazon comes from the small priced items, not pianos. Such selectivity on products prevent team buy sites from popular.
There are also other challenges like coordinating the delivery though online retailers could do that (drop-shipper).
A pure team buy service might not pull up but this concept could be blended into innovative Web shopping models.
Recently, the so-called social commerce became a trend. Sites like jellyfish.com are popular among certain online shopping groups. Jellyfish is a combination of comparison-shopping, cash back rebate, and reverse auction.
On top of all these, a feature make it distinct from ordinary shopping sites, is the social elements -- it allows shoppers to see each other and know each other. Shoppers could create their profile page within jellyfish. They could also see and chat with each other in reverse auction (the smack shopping). The reverse auction is running like a TV station, a schedule for product categories in auction was released for reference. during each day, products in different categories will be auctioned at different time slots.
This site is addictive. Some users claimed that they are there all the time except working and sleeping. Will this, together with Woot.com, be a new trend?
Back in late 90s, some startups offer such business model of buying together completely online. There are still sites like storemob to allow local shoppers find each other via message board and get a discount price by shopping together. However, how to do this completely online is still a challenge. Somehow it does not work well though it looks like a perfectly model to be implemented on the Web. Think about it, all this model needs is group sufficient number of people together, the Web provide an unprecedented opportunity for this, now people living in different places could buy together! It is easier to find enough people compared with one can only find via local resident or circle of friends. So why there are no good website for this?
There are several possibilities that prevent such site from popular.
First, it needs a higher threshold of critical mass. For an online retailer like Amazon.com or auction site like eBay.com, users use them frequently and immediately. More important, they can easily find what they want. However, for a team buy site, even one can list a whole catalog of every product, finding enough team members to buy the same product takes a long time. How long? It is hard to see. So the success rate of buying a product with discount price was largely offset by the risk of could not find enough buyers -- in other words, wasteful waiting.
Second, the savings has to be large to justify the efforts. People will take the effort to find others and get a discount price for a piano but few will do that for a $15 pencil sharpener. The popularity of eBay and Amazon comes from the small priced items, not pianos. Such selectivity on products prevent team buy sites from popular.
There are also other challenges like coordinating the delivery though online retailers could do that (drop-shipper).
A pure team buy service might not pull up but this concept could be blended into innovative Web shopping models.
Recently, the so-called social commerce became a trend. Sites like jellyfish.com are popular among certain online shopping groups. Jellyfish is a combination of comparison-shopping, cash back rebate, and reverse auction.
On top of all these, a feature make it distinct from ordinary shopping sites, is the social elements -- it allows shoppers to see each other and know each other. Shoppers could create their profile page within jellyfish. They could also see and chat with each other in reverse auction (the smack shopping). The reverse auction is running like a TV station, a schedule for product categories in auction was released for reference. during each day, products in different categories will be auctioned at different time slots.
This site is addictive. Some users claimed that they are there all the time except working and sleeping. Will this, together with Woot.com, be a new trend?
Tuesday, June 19, 2007
A new meta-search shopbot
Today (June 19, 2007), a new meta-search shopbot (up-stream shopbot) emerged. Discountmore.com could simultaneously search all major shopbots. The rational for a meta-search shopbot was: "...the only way to find out who really has the lowest price for individual products." according to Founder-Creator and CEO Bobby Kalili.
As in a paper I have written a couple of years before, the emergence of up-stream meta-search shopbot was inevitable. However, the real issue is more complicated than the pitch in the announcement. For example, is meta-search shopbot really the "last piece to the comparison shopping puzzle" as indicated by Kalili? Certainly not the last one.
For example, choice overload is the first problem a meta-search has to handle. How to balance the needs and present the same number of choices aggregated from different shopbot? This is more complex than individual shopbot handling choices from vendors. Now the choices come not only from vendors but also from auctions, and whatever courses one could get the product.
Another issue is still the coverage. Shopbots covered more or less the same set of online vendors via data-feeding by vendor themselves. A meta-shopbot only expand that set a little bit. There are still a lot of vendors (those do not involve in comparison-shopping at all) not being covered by shopbots.
The last one is the challenge about how to incorporate factors like coupons and promotions that are not covered by shopbots. They make big differences on price. however, unless we find a way to make the information structured, we won't be able to benefit from this in a large scale.
But still, this is a good move and nice search engine. I enjoy it.
Growth, Division of labor, and BPR
Almost finished "Direct from Dell," which illustrated the first 15 years of Dell, the Texas based computer manufacturer. The business acumen of the founder, Michael Dell and the timing (aseembling PC was a high margin emerging industry in early 80s) seems to be the top 2 critical factors leading to the growth and success of the company.
Another issue impressed me is how Dell handle the growth of the business. When a business growth from millions to billions and then to tens of billions within a short period of time, the company has to constantly re-structure to meet the growth need. The major theme seems to be the division of functions. For Dell, it first realized the importance of P&L (Profit and Loss Statement), and then how to apply it to different product categories. Then they found a manager who used to manage one product category would soon be outgrown by it. Thus, what they did is whenver the product category became too large, they will separate it into two or more product categories and have additional managers for each new category (it somehow reminds me of how eBay handle product categories). For each product category, P&L would be applied and subsequently, strategic decisions were made based on data collected.
This is very similar to most enteprirse did in 50s and 60s in the US. Presciely because both Dell and those companies in 50s and 60s faced the same seller's market. PC is a new and hot product, the demand will exceed the supply so this method works.
Anyway, though BPR became popular in 90s, Dell seemed not being affected by it, which makes sense for a company that focus on growth.
Another issue impressed me is how Dell handle the growth of the business. When a business growth from millions to billions and then to tens of billions within a short period of time, the company has to constantly re-structure to meet the growth need. The major theme seems to be the division of functions. For Dell, it first realized the importance of P&L (Profit and Loss Statement), and then how to apply it to different product categories. Then they found a manager who used to manage one product category would soon be outgrown by it. Thus, what they did is whenver the product category became too large, they will separate it into two or more product categories and have additional managers for each new category (it somehow reminds me of how eBay handle product categories). For each product category, P&L would be applied and subsequently, strategic decisions were made based on data collected.
This is very similar to most enteprirse did in 50s and 60s in the US. Presciely because both Dell and those companies in 50s and 60s faced the same seller's market. PC is a new and hot product, the demand will exceed the supply so this method works.
Anyway, though BPR became popular in 90s, Dell seemed not being affected by it, which makes sense for a company that focus on growth.
Tuesday, June 12, 2007
Business Process Reengineering revisited
I am reading Hammer & Champy's "Reengineering the Corporation" these days and found more historical contexts about BPR. My first contact with BPR was during my doctoral study from those academic papers. The missing part is why BPR was needed or why corporations were not optimizing their business process from the very beginning. It turns out they did but later they failed to adapt the process to changing environment.
Hammer and Champy's seminar HBR article ("Reengineering Work: Don't automate, Obliterate," which led the latter book) revealed this a little bit via a fictional example:
Later in their book, the historical context was explained in more details. According to their view, the existing business processes in American corporations in 80s were originated from 50s and 60s, a time of mass production. At that time, the market was so good that customers will buy whatever produced thus the main goal for a corporation was producing as many products as possible to grab the market share. Thus a division of labor, hierarchical style corporation management structure was scalable and preferred (need 1000 products? Add 100 more workers, 5 more managers, and 2 more human resources staff).
However, when market power shifts from producer to consumer and the choice of products are increasingly abundant, which is the situation in 80s, such style and structure was no longer effective. Even from departmental perspective, the highest efficiency could be achieved, the overall efficiency of the corporation can not be guaranteed because there was no alignment between the departmental goal and corporation goal. A good example cited in the book is the purchasing department. A company may spend $100 internal cost to buy a $3 battery! Also, in terms of customer experience, it may took six days for IBM credit to finance a customer purchase though the actual process took only 90 minutes! Most of the time was wasted in the transition of documents among departments.
The reengineering thus called the efforts to focus on business process intead of steps in the process because if the process is wrong, even each step of the process is efficient, the whole would still be inefficient. By reengineering the process, critical elements like customers and suppliers could be brought into the focus and being organically linked with business processes within the enterprise. This is in contrast with the previous view that the internal process of an enterprise is separated from customers and suppliers for mere efficiency goal.
The negative influence of division of labor on current corporation behavior was especially illustrated in the book and argued by the authors to be the main cause of current problems of American corporations.
Hammer and Champy's seminar HBR article ("Reengineering Work: Don't automate, Obliterate," which led the latter book) revealed this a little bit via a fictional example:
"... Many of our procedures were not designed at all, they just happened. The company founder one day recognized that he didn't have time to handle a chore, so he delegated it to Smith, smith improvised... business grew, Smith hired his entire clan to help him cope with the work volume. They all improvised...The hodge-podge of special cases and quick fixes was passed from one generation of workers to the next...institutionalized teh ad hoc and enshrined the temporary...Why do we send foreign accounts to the corner desk? Because 20 years ago, Mary spoke French and Mary had the corner desk. Today Mary is long gone, and we no longer do business in France, but we still send foreign accounts to the corner desk..."
However, when market power shifts from producer to consumer and the choice of products are increasingly abundant, which is the situation in 80s, such style and structure was no longer effective. Even from departmental perspective, the highest efficiency could be achieved, the overall efficiency of the corporation can not be guaranteed because there was no alignment between the departmental goal and corporation goal. A good example cited in the book is the purchasing department. A company may spend $100 internal cost to buy a $3 battery! Also, in terms of customer experience, it may took six days for IBM credit to finance a customer purchase though the actual process took only 90 minutes! Most of the time was wasted in the transition of documents among departments.
The reengineering thus called the efforts to focus on business process intead of steps in the process because if the process is wrong, even each step of the process is efficient, the whole would still be inefficient. By reengineering the process, critical elements like customers and suppliers could be brought into the focus and being organically linked with business processes within the enterprise. This is in contrast with the previous view that the internal process of an enterprise is separated from customers and suppliers for mere efficiency goal.
The negative influence of division of labor on current corporation behavior was especially illustrated in the book and argued by the authors to be the main cause of current problems of American corporations.
Thursday, May 24, 2007
Woot and others
Till now, most etailers are still a stretched version of brick-and-mortar format. Basically, a product inventory, a store-front, and a check-out and billing system. Most innovations are incremental like the collaborative filtering by Amazon.com. Some are just using the aggregation advantage of the Web like eBay.com (super-size garage sale).So is there anything revolutionary model? There are actually a few of them and with the proliferation of Web 2.0 technology, we should expect more.
Woot is one of them. It is a blog-based model where one product is promoted each day. The price for the product is low. A community is formed within Woot and one can discuss, comment, and share experience about the product. This new model aggregate the buying power of buyers and at the same time allow potential users to discuss and research on the product.
There is similar ecommerce model in China called "Tuan Gou (团购）" where registered buyers formed an online community and will aggregate their buying power for a product and purchase together with a bargain price. www.beambuy.com.cn is one such
online community.Now think about it, when such online community is popular and the product category is richer, do one still need go to traditional etailer for popular product? Probably what one really need is a good comparison-shopping agent or bargain finder to search a product from one or a few of the communities who are selling it or team-buying it.
Wednesday, May 23, 2007
Shopping.com
shopping.com started as a client side plug-in application that could help consumers comparing price for product.
Initially the startup has a shaking beginning. However, soon the co-founders realized that they have to find professional management teams. As a result, the CTO of citibank was invited as the CEO.
In 2003, it changed its name from Dealtime.com to Shopping.com, the latter is bought from Alta Vista for $1 million. In the same year, it merged with epinion.com and acquired resellerratings.com.
In 2005, shopping.com was acquired by eBay but it still operates independently like half.com and paypal.com.
Initially the startup has a shaking beginning. However, soon the co-founders realized that they have to find professional management teams. As a result, the CTO of citibank was invited as the CEO.
In 2003, it changed its name from Dealtime.com to Shopping.com, the latter is bought from Alta Vista for $1 million. In the same year, it merged with epinion.com and acquired resellerratings.com.
In 2005, shopping.com was acquired by eBay but it still operates independently like half.com and paypal.com.
Tuesday, May 22, 2007
Google Product Search
The comparison-shopping service provided by Google is called Google Product Search, a name changed on April 18, 2007 from its previous Froogle (a pun on Frugal) and Froogle was launched by Google in beta version back in December 12, 2002. Google product search mainly focus on merchandises. For the time being, it does not offer comparison-shopping on travel, insurance, and other service categories.
Google gives vendors two ways to make their product being displayed on Google Product Search. Back in 2002, when Froogle was initially launched, according to Google spokeswoman Eileen Rodriguez , “There are two ways for merchants to be included in Froogle: their sites can be visited by the Google ‘bot,’ a software program that collects Web site information, or the merchants can give Froogle a direct data feed from their sites.”
Monday, May 21, 2007
An incomplete list of shopbots
Here is a list of useful shopbots and it will be updated from time to time:
-- General merchandise --
Google Product Search; Shopping.com; PriceGrabber.com; Shopzilla.com; Yahoo Shopping; MSN Shopping;
Books, CD, Movies: AddAll.com;
Magazines: magazinepricesearch.com;
Wine: wine-searcher.com;
-- Insurance --
Auto Insurance:
Health Insurance:
-- Travel --
Big Three for airfare, car rental and hotel as well as integrated package:
Orbitz.com; Expedia.com; Travelocity.com
More innovative shopbots that could scan other shopbots:
Kayak.com; sidestep.com;
-- Health --
-- General merchandise --
Google Product Search; Shopping.com; PriceGrabber.com; Shopzilla.com; Yahoo Shopping; MSN Shopping;
Books, CD, Movies: AddAll.com;
Magazines: magazinepricesearch.com;
Wine: wine-searcher.com;
-- Insurance --
Auto Insurance:
Health Insurance:
-- Travel --
Big Three for airfare, car rental and hotel as well as integrated package:
Orbitz.com; Expedia.com; Travelocity.com
More innovative shopbots that could scan other shopbots:
Kayak.com; sidestep.com;
-- Health --
Friday, May 18, 2007
Hurricane Katrina and Shopbots
In the aftermath of Hurricane Katrina (Monday, August 29, 2005), grass-root efforts to help scattered family members find each other seems have nothing to do with shopbots. But it turned out the process of those volunteers in setting up websites and sifting through web sources to find and aggregate people information is the exact process of what a shopbot does.
Initially, Red Cross, Craigslist, Yahoo and Google etc. established their own sites for people to search. All kinds of non-profit organizations including university are launching their own sites to help too. To increase the chance of finding each other, people post their search ads in some or all of those sites as well as well-known public forums. It soon turned out a central list is necessary to make this searching process more efficient.
On Friday, September 2, a group of tech-savvy volunteers launched www.katrinalist.net and they used "screen scraping" to capture relevant information from as many websites as they could found and apply the search algorithm. They also authored an open source data spec for organizing the missing person's information, the PeopleFinder Interchange Format.
This step resembled the initial stage of shopbots development, when automated shopbots like BargainFinder were designed and released to automatically search and aggregate product informations from the Web.
However, they soon realized the are still a lot of information can not be processed automatically because such information were not structured at all. So in the next step on Saturday morning, they recruited more volunteers to manually data-coding the unstructured message on various sites and sift through all the missing person posts. To facilitate the process, they built a wiki site to dole out chucks of data to be parsed. As a result, by the end of the day, thousands were volunteering, and in total some three thousand are though to be contributed. Salesforce.com contributed a more robust back end server because the makeshift database was overloaded.
By Monday evening, 50,000 entries had been processed. the number eventually reached 650,000. People could enter a name, a zip code, or an address into a search tool to get an instant list of names matching their query. Over one million such searched were conducted in the immediate aftermath of the hurricane.
The next step of katrinalist.net team efforts resembled what shopbots are currently doing to collect product data - they ask online vendors to contribute the data (data-feeding), which are combined with their own efforts of screen-scraping. Actually, the majority of the data being collected by shopbots were now via data-feeding.
One of the constant theme before more than 60% of the Web pages possess semantic like structure will always be how to retrieve data efficiently. The heroic efforts of katrinalist.net team gives us a perfect example of how people could collaborate and using existing technologies for such a technical challenge.
Reference: Katrinalist.net and the Peoplefinder Project
Initially, Red Cross, Craigslist, Yahoo and Google etc. established their own sites for people to search. All kinds of non-profit organizations including university are launching their own sites to help too. To increase the chance of finding each other, people post their search ads in some or all of those sites as well as well-known public forums. It soon turned out a central list is necessary to make this searching process more efficient.
On Friday, September 2, a group of tech-savvy volunteers launched www.katrinalist.net and they used "screen scraping" to capture relevant information from as many websites as they could found and apply the search algorithm. They also authored an open source data spec for organizing the missing person's information, the PeopleFinder Interchange Format.
This step resembled the initial stage of shopbots development, when automated shopbots like BargainFinder were designed and released to automatically search and aggregate product informations from the Web.
However, they soon realized the are still a lot of information can not be processed automatically because such information were not structured at all. So in the next step on Saturday morning, they recruited more volunteers to manually data-coding the unstructured message on various sites and sift through all the missing person posts. To facilitate the process, they built a wiki site to dole out chucks of data to be parsed. As a result, by the end of the day, thousands were volunteering, and in total some three thousand are though to be contributed. Salesforce.com contributed a more robust back end server because the makeshift database was overloaded.
By Monday evening, 50,000 entries had been processed. the number eventually reached 650,000. People could enter a name, a zip code, or an address into a search tool to get an instant list of names matching their query. Over one million such searched were conducted in the immediate aftermath of the hurricane.
The next step of katrinalist.net team efforts resembled what shopbots are currently doing to collect product data - they ask online vendors to contribute the data (data-feeding), which are combined with their own efforts of screen-scraping. Actually, the majority of the data being collected by shopbots were now via data-feeding.
One of the constant theme before more than 60% of the Web pages possess semantic like structure will always be how to retrieve data efficiently. The heroic efforts of katrinalist.net team gives us a perfect example of how people could collaborate and using existing technologies for such a technical challenge.
Reference: Katrinalist.net and the Peoplefinder Project
Comparison-Shopping in China
Here are a short list of shopbots in China:
Topgou.com:
Bjia.com
Tejiawang.com
3jpk.com
smarter.com.cn
...
To get some statistics about ecommerce in China:
China Internet Network Information Center
This is one of the earliest official organization systematically collecting data on ecommerce and other Internet information in China.
Chinese ecommerce association
This is the largest ecommerce organization consortium in China.
Topgou.com:
Bjia.com
Tejiawang.com
3jpk.com
smarter.com.cn
...
To get some statistics about ecommerce in China:
China Internet Network Information Center
This is one of the earliest official organization systematically collecting data on ecommerce and other Internet information in China.
Chinese ecommerce association
This is the largest ecommerce organization consortium in China.
Hello World!
After several trials, I eventually decided to use this as my official reseach notes blog.
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